Solar Resource Mapping in Malawi ANNUAL SOLAR RESOURCE REPORT July 2017 This report was prepared by Solargis, under contract to The World Bank. Resource Mapping and Geospatial Planning Malawi [Project ID: P151289]. This activity is funded and supported by the Energy Sector Management Assistance Program (ESMAP), a multi-donor trust fund administered by The World Bank, under a global initiative on Renewable Energy Resource Mapping. Further details on the initiative can be obtained from the ESMAP website. This report is an interim output from the above-mentioned project. Users are strongly advised to exercise caution when utilizing the information and data contained, as this has not been subject to validation using ground measurement data or peer review. The final output from this project will be a validated Malawi Solar Atlas, which will be published once the project is completed. To obtain additional maps and information on solar resources globally, please visit: http://globalsolaratlas.info Copyright © 2017 THE WORLD BANK Washington DC 20433 Telephone: +1-202-473-1000 Internet: www.worldbank.org The World Bank, comprising the International Bank for Reconstruction and Development (IBRD) and the International Development Association (IDA), is the commissioning agent and copyright holder for this publication. However, this work is a product of the consultants listed, and not of World Bank staff. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work and accept no responsibility for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for non-commercial purposes as long as full attribution to this work is given. Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: +1-202-522-2625; e-mail: pubrights@worldbank.org. Furthermore, the ESMAP Program Manager would appreciate receiving a copy of the publication that uses this publication for its source sent in care of the address above, or to esmap@worldbank.org. Annual Solar Resource Report for solar meteorological stations after completion of 12 months of measurements Republic of Malawi Reference No. 141-06/2017 Date: 14 July 2017 Customer Consultant World Bank Solargis s.r.o. Energy Sector Management Assistance Program Contact: Mr. Marcel Suri Contact: Mr. Oliver J. Knight Mytna 48, 811 07 Bratislava, Slovakia 1818 H St NW, Washington DC, 20433, USA Phone +421 2 4319 1708 Phone: +1-202-473-3159 E-mail: marcel.suri@solargis.com E-mail: mailto:oknight@worldbank.org http://solargis.com http://www.esmap.org/RE_Mapping Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 TABLE OF CONTENTS Table of contents ............................................................................................................................................... 4 Acronyms ........................................................................................................................................................... 6 Glossary ............................................................................................................................................................. 7 1 Introduction ............................................................................................................................................... 8 1.1 Background ................................................................................................................................................... 8 1.2 Information included in this report .............................................................................................................. 8 2 Position of solar meteorological sites ........................................................................................................ 9 3 Ground measurements in Malawi ............................................................................................................. 11 3.1 Instruments and measured parameters ................................................................................................... 11 3.2 Station operation and calibration of instruments .................................................................................... 12 3.3 Quality control of measured solar resource data ..................................................................................... 13 3.3.1 Chileka .......................................................................................................................................... 13 3.3.2 Kasungu ........................................................................................................................................ 15 3.3.3 Mzuzu ........................................................................................................................................... 18 3.4 Recommendations on the operation and maintenance of the sites ....................................................... 21 4 Solar resource model data ....................................................................................................................... 22 4.1 Solar model ................................................................................................................................................. 22 4.2 Site adaptation of the solar model − method ........................................................................................... 23 4.3 Results of the model adaptation at three sites ........................................................................................ 25 5 Meteorological model data ....................................................................................................................... 31 5.1 Meteorological model ................................................................................................................................ 31 5.2 Validation of meteorological data ............................................................................................................. 31 5.2.1 Air temperature at 2 meters ........................................................................................................ 32 5.2.2 Relative humidity .......................................................................................................................... 34 5.2.3 Wind speed and wind direction at 10 meters ............................................................................. 36 5.3 Uncertainty of meteorological model data ............................................................................................... 38 6 Solar resource: uncertainty of long-term estimates .................................................................................. 39 6.1 Uncertainty of solar resource yearly estimate .......................................................................................... 39 6.2 Uncertainty due to interannual variability of solar radiation .................................................................... 40 6.3 Combined uncertainty ................................................................................................................................ 41 7 Time series and Typical Meteorological Year data ................................................................................... 44 7.1 Delivered data sets ..................................................................................................................................... 44 7.2 TMY method ............................................................................................................................................... 45 7.3 Results ........................................................................................................................................................ 45 8 Conclusions ............................................................................................................................................. 49 Annex 1: Site related data statistics.................................................................................................................. 50 Yearly summaries of solar and meteorological model outputs .......................................................................... 50 Monthly summaries of solar and meteorological model outputs ....................................................................... 52 Frequency of occurrence of GHI and DNI daily model values for a period 1994 to 2016 ................................. 54 Frequency of occurrence of GHI and DNI 15-minute model values for a period 1994 to 2016 ........................ 57 Frequency of occurrence of GHI and DNI measured and model values representing 12 months.................... 60 © 2017 Solargis page 4 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Frequency of occurrence of GHI and DNI ramps ................................................................................................. 62 List of figures ................................................................................................................................................... 65 List of tables .................................................................................................................................................... 67 References ....................................................................................................................................................... 68 Support information ......................................................................................................................................... 70 Background on Solargis ......................................................................................................................................... 70 Legal information ................................................................................................................................................... 70 © 2017 Solargis page 5 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 ACRONYMS AP Atmospheric Pressure CFSR Climate Forecast System Reanalysis. The meteorological model operated by the US service NOAA (National Oceanic and Atmospheric Administration) CFS v2 Climate Forecast System Version 2 CFSv2 model is the operational extension of the CFSR (NOAA, NCEP) DIF Diffuse Horizontal Irradiation, if integrated solar energy is assumed. Diffuse Horizontal Irradiance, if solar power values are discussed DNI Direct Normal Irradiation, if integrated solar energy is assumed. Direct Normal Irradiance, if solar power values are discussed. GFS Global Forecast System. The meteorological model operated by the US service NOAA (National Oceanic and Atmospheric Administration) GHI Global Horizontal Irradiation, if integrated solar energy is assumed. Global Horizontal Irradiance, if solar power values are discussed. MACC Monitoring Atmospheric Composition and Climate – meteorological model operated by the European service ECMWF (European Centre for Medium-Range Weather Forecasts) Meteosat MFG Meteosat satellite operated by EUMETSAT organization. MSG: Meteosat Second and MSG Generation; MFG: Meteosat First Generation PWAT Precipitable water (water vapour) RH Relative Humidity at 2 metres TEMP Air Temperature at 2 metres WD Wind Direction at 10 metres WS Wind Speed at 10 metres © 2017 Solargis page 6 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 GLOSSARY Aerosols Small solid or liquid particles suspended in air, for example desert sand or soil particles, sea salts, burning biomass, pollen, industrial and traffic pollution. All-sky irradiance The amount of solar radiation reaching the Earth's surface is mainly determined by Earth-Sun geometry (the position of a point on the Earth's surface relative to the Sun which is determined by latitude, the time of year and the time of day) and the atmospheric conditions (the level of cloud cover and the optical transparency of atmosphere). All-sky irradiance is computed with all factors taken into account Bias Represents systematic deviation (over- or underestimation) and it is determined by systematic or seasonal issues in cloud identification algorithms, coarse resolution and regional imperfections of atmospheric data (aerosols, water vapour), terrain, sun position, satellite viewing angle, microclimate effects, high mountains, etc. Clear-sky irradiance The clear sky irradiance is calculated similarly to all-sky irradiance, but without considering the impact of cloud cover. Long-term average Average value of selected parameter (GHI, DNI, etc.) based on multiyear historical time series. Long-term averages provide a basic overview of solar resource availability and its seasonal variability. P50 value Best estimate or median value represents 50% probability of exceedance. For annual and monthly solar irradiation summaries it is close to average, since multiyear distribution of solar radiation resembles normal distribution. P90 value Conservative estimate, assuming 90% probability of exceedance (with the 90% probability the value should be exceeded). When assuming normal distribution, the P90 value is also a lower boundary of the 80% probability of occurrence. P90 value can be calculated by subtracting uncertainty from the P50 value. In this report, we apply a simplified assumption of normal distribution of yearly values. Root Mean Square Represents spread of deviations given by random discrepancies between measured Deviation (RMSD) and modelled data and is calculated according to this formula: 3 ' 2 '45 ' ()*+,-). − (0.)1). = On the modelling side, this could be low accuracy of cloud estimate (e.g. intermediate clouds), under/over estimation of atmospheric input data, terrain, microclimate and other effects, which are not captured by the model. Part of this discrepancy is natural - as satellite monitors large area (of approx. 3.3 x 3.4 km for MSG satellite), while sensor sees only micro area of approx. 1 sq. centimetre. On the measurement side, the discrepancy may be determined by accuracy/quality and errors of the instrument, pollution of the detector, misalignment, data loggers, insufficient quality control, etc. 2 Solar irradiance Solar power (instantaneous energy) falling on a unit area per unit time [W/m ]. Solar resource or solar radiation is used when considering both irradiance and irradiation. 2 Solar irradiation Amount of solar energy falling on a unit area over a stated time interval [Wh/m or 2 kWh/m ]. Uncertainty Is a parameter characterizing the possible dispersion of the values attributed to an of estimate, Uest estimated irradiance/irradiation values. In this report, uncertainty assessment of the solar resource model estimate is based on a detailed understanding of the achievable accuracy of the solar radiation model and its data inputs (satellite, atmospheric and other data), which is confronted by an extensive data validation experience. The second source of uncertainty is ground measurements. Their quality depends on accuracy of instruments, their maintenance and data quality control. Third contribution to the uncertainty is from the site adaptation method where ground-measured and satellite- based data are correlated. © 2017 Solargis page 7 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 1 INTRODUCTION 1.1 Background This report is prepared within Phase 2 of the project Renewable Energy Resource Mapping for the Republic of Malawi. This project focuses on solar resource mapping and measurement services as part of a technical assistance in the renewable energy development implemented by the World Bank in Malawi. It is being undertaken in close coordination with the Ministry of Natural Resources, Energy and Mining (MoNREM) of Malawi, the World Bank’s primary country counterpart for this project, and Malawi Meteorological Services (MMS). This project is funded by the Energy Sector Management Assistance Program (ESMAP), administered by the World Bank and supported by bilateral donors. This report summarizes results of first 12 months of the measuring campaign at three solar meteorological stations installed in Malawi, as part of the World Bank’s ESMAP mission in Malawi. The report describes delivery of site-specific data, site adaptation of solar model, data uncertainty and statistical summary. This report accompanies delivery of site-specific solar resource and meteorological data for three sites, where solar meteorological stations have been operated. As a result of high-quality operation of the meteorological sites and site adaptation of the Solargis model, reliable historical time series and TMY data is computed. The delivered time series and Typical Meteorological Year data is ready to use for bankable evaluation of solar energy projects. The measurements at the ground stations are provided by GeoSUN Africa company (South Africa). The model data for the same sites and related works, together with this report are supplied by Solargis (Slovakia). 1.2 Information included in this report This report presents: • Solar resource and meteorological measurements after 12 months of operation o Review and quality check of the measured data o Calibration procedures and results o List and explanation of the occurred disturbances and failures • Solar resource and meteorological data derivedfrom the models o Description of the underlying methods o Validation of the models o Model time series and Typical Meteorological Year data products • Comparison of the measurements to the model and uncertainty analysis o Comparison of solar and meteo measurements with the model data o Site adaptation of the model data based on ground measurements o Estimate of data uncertainty • Data analysis (measured vs. modelled) o Monthly summaries of solar and meteorological parameters captured at the site o Variability of measured solar parameters o Frequency of occurrence of GHI and DNI 1-minute and 15-minute values o Frequency of occurrence of GHI and DNI 1-minute and 15-minute ramps © 2017 Solargis page 8 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 2 POSITION OF SOLAR METEOROLOGICAL SITES In Malawi, three measuring stations have been installed within the World Bank’s ESMAP Solar Resource Mapping initiative. Two of them are located at the airports (airport Chileka and the airstrip in Kasungu), and one at the University in Mzuzu, all within the premises of Malawi Meteorological Service (Figure 2.1, Table 2.1). Figure 2.1: Position of solar meteorological stations in Malawi © 2017 Solargis page 9 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Table 2.1 Overview information on the solar meteorological station locations Site location Nearest town Latitude [º] Longitude [º] Altitude [m a.s.l.] Measurement station host Chileka airport Blantyre -15.67984 34.97229 767 Malawi Meteorological Services Kasungu airport Kasungu -13.01530 33.46840 1065 Malawi Meteorological Services Uni Mzuzu Mzuzu -11.41990 33.99530 1285 Mzuzu University Besides the good geographical distribution within the territory of Malawi, the sites fit well to the population centres, where solar installations can be primarily deployed. In addition to geographical and socio-economic criteria, the sites fulfil the criteria for the operation and maintenance of the solar measuring stations: • Availability of free horizon, • Availability of GSM networks, • Availability of local work force for maintenance, • Easy to access and high level of security During the first year of the measurements, the measured data was analyzed and harmonized with the objective to acquire reference solar radiation data for reducing the uncertainty of the model (Chapter 6). The quality data from Tier-1 and Tier-2 meteorological stations are available for this assessment (Chapter 3). Position and detailed information about measurement sites is available also on Global Solar Atlas website: http://globalsolaratlas.info/?c=-12.811801,34.541016,7&e=1 © 2017 Solargis page 10 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 3 GROUND MEASUREMENTS IN MALAWI 3.1 Instruments and measured parameters Basic information about the measurements sites is in Table 3.1. The solar parameters are measured by high accuracy instruments (CMP 10 for measurements of GHI and CHP1 for measurements of DNI) and by medium accuracy instruments (RSR for GHI, DNI and DIF) (Table 3.2 and 3.3). The measurement campaign in Malawi is being undertaken by company GeoSUN Africa (South Africa). Table 3.1 Overview information on measurement stations operated in the region ID Site name Station type Installation date 1 Chileka Tier 1 18 March 2016 2 Kasungu Tier 2 11 March 2016 3 Mzuzu Tier 2 14 March 2016 Table 3.2 Instruments installed at the solar meteorological stations Site name GHI DIF DNI WS WD TEMP RH AP PWAT Chileka CMP 10 CMP 10 CHP 1 05103 05103 HMP155 HMP155 PTB110 TB4 CMP 10 - - Kasungu 014A 024A HMP155 HMP155 PTB110 TB4 RSR 2 RSR 2 RSR 2 CMP 10 - - Mzuzu 014A 024A HMP155 HMP155 PTB110 TB4 RSR 2 RSR 2 RSR 2 Table 3.3 Technical parameters and accuracy class of the instruments at Tier 1 and Tier 2 stations Parameter Instrument Type Manufacturer Uncertainty GHI Secondary standard pyranometer CMP 10 Kipp & Zonen < ±2 % (daily) DIF Secondary standard pyranometer CMP 10 Kipp & Zonen < ±2 % (daily) DNI Pyrheliometer CHP 1 Kipp & Zonen < 1 % (daily) GHI 2 Rotating Shadowband Radiometer with LI200 RSR 2 Irradiance Inc. Indicatively ±5 % DIF 2 Rotating Shadowband Radiometer with LI200 RSR 2 Irradiance Inc. Indicatively ±8 % DNI 2 Rotating Shadowband Radiometer with LI200 RSR 2 Irradiance Inc. Indicatively ±5 % WS Tier 1 station wind speed sensor (at 10 m) 05103 R.M. Young ±0.3 m/s Tier 2 station wind speed sensor (at 3 m) 014A Met One ±1.5 % WD Tier 1 station wind direction sensor (at 10 m) 05103 R.M. Young ±3 ° Tier 2 station wind direction sensor (at 3 m) 024A Met One ±5° TEMP Temperature probe (at 2 m) HMP 155 Vaisala ±0.45°C RH Relative humidity probe HMP 155 Vaisala ±1.7% RH AP Barometric pressure sensor PTB110 Vaisala ±1.5 hPa PWAT Tipping-bucket rain gauge TB4 Hydrological services ±3% ± (0.06% of reading - Data logger CR1000 Campbell Scientific + offset) © 2017 Solargis page 11 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 3.2 Station operation and calibration of instruments In this report, the solar and meteorological measurements from the first year of 2-year measurement campaign are analyzed. As the measurement stations have been installed during March 2016, the period considered for the data analysis represents more than 12 months – until 31 March 2017 for all three stations. Overview of the data availability, time step and measured parameters is shown in Tables 3.4 and 3.5. Table 3.4 Overview information on solar meteorological stations operating in the region Site name First year measurement period Primary time step Chileka 19 March 2016 – 31 March 2017 1 minute Kasungu 18 March 2016 – 31 March 2017 1 minute Mzuzu 18 March 2016 – 31 March 2017 1 minute Table 3.5 Period of measurements analyzed in this report Year, month 2015 2016 2017 Station 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Chileka Kasungu Mzuzu 1 min time step of measurements During the solar measuring campaign, local staff of Malawi Meteorological Services was trained by GeoSUN, and they executed the instruments inspection and cleaning visits, typically in 1-5 days intervals. GeoSUN Africa performed detailed visits and station maintenance after 6 and 12 months of operation. Instruments field verification, i.e. comparative measurements of solar radiation parameters and cross check with the reference instruments to proof that sensitivity (calibration constants) remained stable within the instrument specifications, was performed by GeoSUN Africa after one year of operation (Table 3.6). Table 3.6 Meteorological stations maintenance and instruments field verification Station Instruments Annual Instruments for Site name Comments and issues type cleaning interval verification visit field verification • Several events of pyrheliometer misalignment occurred during GHI – reference campaign causing inaccurate DNI CMP11 measurements DNI – reference • Several events of Solys tracker Chileka Tier 1 2 – 5 days 3 April 2017 CHP1 misalignment occurred during DIF – reference campaign causing inaccurate DIF CMP21 measurements • No cleaning performed in December 2016 • Shading of the instruments by trees GHI – reference during the early morning and late Kasungu Tier 2 1 – 5 days 6 April 2017 CMP11 evening • Shading of the instruments by trees GHI – reference during the early morning and late Mzuzu Tier 2 2 - 4 days 6 April 2017 CMP11 evening To perform a verification of the measurements, the spare instruments of the same category were used. The solar sensors (thermopile pyrheliometer and thermopile pyranometer) were side-by-side compared under ambient sky conditions and only clear-sky values were used. In case of RSR, the GHI values of the RSR were compared to the GHI of the reference thermopile pyranometer for a 12-month period to assess possible drift. Results of field instruments verification are listed in Table 3.7. During data analysis, it was found that incorrect multipliers were applied on RSR sensors, thus the calibration coefficients at all meteorological stations had been © 2017 Solargis page 12 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 updated and measured data have been post-processed to correct this issue. Detailed results and discussion is supplied in Sensor verification report delivered in July 2017. Table 3.7 Results of field instruments verification at the respective stations Measured bias Site Station GHI DNI DIF name type 2 2 2 [W/m ] [%] [W/m ] [%] [W/m ] [%] Chileka Tier 1 -0.39 -0.08 -0.21 -0.03 1.15 1.21 Kasungu Tier 2 13.95 1.43 - - - - Mzuzu Tier 2 9.22 0.89 - - - - 3.3 Quality control of measured solar resource data Prior to correlation with satellite-based solar data, the ground-measured solar radiation was quality-controlled by Solargis. Quality control (QC) was based on methods defined in SERI QC procedures and Younes et al. [1, 2] and the in-house developed tests. The ground measurements were also inspected visually, mainly for identification of shading, effect of dew and other error patterns. Figures 3.1 to 3.9 show results of quality control for individual stations. The colors in Figures 3.1, 3.3 and 3.6 indicate the following flags: • Blue: data excluded by visual inspection - mainly shading • Green: data passed all tests • Grey: sun below horizon • White and brown strips: missing data • Red and violet: GHI, DNI and DIF consistency problem or problems with physical limits The data records not passing the quality control test were flagged and excluded from the further processing. The results show relatively small amount of excluded data readings (Tables 3.10, 3.13, 3.16) except of Mzuzu station, where increased shading was identified. Some problems with insufficient cleaning were also identified in Chileka data. 3.3.1 Chileka Table 3.8 Occurrence of data readings for Chileka meteorological station Data availability DNI CHP1 GHI CMP10 Sun below horizon 315 337 49.9% 315 337 49.9% Sun above horizon 316 570 50.1% 316 570 50.1% Total data readings 631 907 100.0% 631 907 100.0% © 2017 Solargis page 13 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Table 3.9 Excluded ground measurements after quality control (Sun above horizon) in Chileka Occurrence of data samples (Sun above horizon) Type of test DNI CHP1 GHI CMP10 Physical limits test 0 0.0% 1 977 0.6% Consistency test (GHI – DNI – DIF) 13 534 4.3% 13 534 4.3% Visual test (incorrect data) 9 250 2.9% 9 026 2.9% Other (non valid data) 771 0.2% 101 0.0% Total excluded data samples 23 555 7.4% 24 638 7.8% Total samples 316 570 100.0% 316 570 100.0% Figure 3.1 Results of GHI and DNI quality control in Chileka. Green – data passing all tests; grey – sun below horizon; red – consistency issue, violet – physical limit, blue excluded by visual inspection. Top: DNI (CHP1); bottom: GHI (CMP10) Figure 3.2 Insufficient cleaning – skewed DIF and DNI profiles. Blue: DNI RSR; yellow: GHI CMP 10; red: GHI RSR; green: DIF RSR; dashed: cleaning © 2017 Solargis page 14 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Main findings: • Several periods of inconsistency between independent GHI-DNI and DIF measurements is present in the data (Figure 3.2). This might be a result of insufficient cleaning. • Early morning shading from surrounding objects Table 3.10 Quality control summary - Chileka Description Station description, Installation report available metadata Instrument accuracy 2 x Secondary standard pyranometer CMP10 (GHI, DIF) 1 x First class pyrheliometer CHP1 (DNI) Instrument calibration Instruments were calibrated Data structure Clear Cleaning and maintenance Cleaning log available information Several periods with insufficient cleaning identified Time reference Correct and clear time reference Quality control complexity Full quality control tests applied including (GHI-DNI-DIF) consistency test Quality control results Several periods with degraded measurements due to insufficient cleaning Period Almost 13 months Other issues Legend: Quality marker Very good Good Sufficient Problematic Insufficient Not specified 3.3.2 Kasungu Table 3.11 Occurrence of data readings for Kasungu meteorological station Data availability GHI CMP10 GHI, DNI RSR Sun below horizon 270 800 49.6% 273 387 49.6% Sun above horizon 275 063 50.4% 278 132 50.4% Total data readings 545 863 100.0% 551 519 100.0% Table 3.12 Excluded ground measurements after quality control (Sun above horizon) in Kasungu Occurrence of data samples (Sun above horizon) Type of test GHI CMP10 DNI RSR GHI RSR Physical limits test 2 373 0.9% 0 0.0% 1 961 0.7% Consistency test (GHI – DNI – DIF) - - 0 0.0% 0 0.0% Visual test (incorrect data) 1 164 0.4% 0 0.0% 1 0.0% Other (non valid data) 0 0.0% 2 000 0.7% 2 000 0.7% Total excluded data samples 3 537 1.3% 2 000 0.7% 3 962 1.4% Total samples 275 063 100.0% 278 132 100.0% 278 132 100.0% © 2017 Solargis page 15 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Figure 3.3 Results of GHI and DNI quality control in Kasungu. Green – data passing all tests; grey – sun below horizon; red – consistency issue, violet – physical limit issue, blue excluded by visual inspection; brown – missing data. Top: DNI (RSR); middle: GHI (RSR); bottom: GHI (CMP10) Main findings: • Effect of dew on CPM10 instrument in the morning hours (Figure 3.4). • Short periods of discrepancy between GHI measured by the CMP10 and RSR. Overall difference is below 0.4% (data from CMP10 is higher, Figure 3.5), but occasionally the difference is at the level of 3-4% (excluding the effect of dew, when the effect is much higher) © 2017 Solargis page 16 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Figure 3.4 Effect of dew – degraded GHI CMP10 readings. Blue: DNI RSR; yellow: GHI CMP 10; red: GHI RSR; green: DIF RSR; dashed: cleaning Figure 3.5 Difference between GHI from CMP10 and RSR - Kasungu. Table 3.13 Quality control summary - Kasungu Description Station description, metadata Installation report available Instrument accuracy Secondary standard pyranometer CMP10 (GHI) Rotating Shadowband Radiometer RSR 2 (GHI, DIF, DNI) Instrument calibration Instruments were calibrated Data structure Clear Cleaning and maintenance Cleaning log available information Diligent cleaning Time reference Correct and clear time reference Quality control complexity RSR data, full QC CMP10 data, without (GHI-DNI-DIF) consistency test, compared to GHI from RSR Quality control results Small issues with degradation of GHI from CMP10 due to morning dew Small issues with early morning shading Period Almost 13 months Other issues Legend: Quality marker Very good Good Sufficient Problematic Insufficient Not specified © 2017 Solargis page 17 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 3.3.3 Mzuzu Table 3.14 Occurrence of data readings for Mzuzu meteorological station Data availability GHI CMP10 GHI, DNI RSR Sun below horizon 270 657 49.6% 270 657 49.6% Sun above horizon 275 090 50.4% 275 090 50.4% Total data readings 545 747 100.0% 545 747 100.0% Table 3.15 Excluded ground measurements after quality control (Sun above horizon) in Mzuzu Occurrence of data samples (Sun above horizon) Type of test GHI CMP10 DNI RSR GHI RSR Physical limits test 2 839 1.0% 0 0.0% 2 037 0.7% Consistency test (GHI – DNI – DIF) - - 0 0.0% 0 0.0% Visual test (incorrect data) 36 385 13.2% 35 425 12.9% 33 397 12.1% Other (non valid data) 98 0.0% 35 0.0% 35 0.0% Total excluded data samples 39 322 14.3% 35 460 12.9% 35 469 12.9% Total samples 275 090 100.0% 275 090 100.0% 275 090 100.0% © 2017 Solargis page 18 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Figure 3.6 Results of GHI and DNI quality control − Mzuzu. Green – data passing all tests; grey – sun below horizon; red – consistency issue, violet – physical limit issue, blue excluded by visual inspection. Top: DNI (RSR); middle: GHI (RSR); bottom: GHI (CMP10) Main findings: • Significant late afternoon and early morning shading from the surrounding objects (Figure 3.7). • Effect of dew on CPM10 instrument in morning hours (Figure 3.8). • Short periods of discrepancy between GHI measured by the CMP10 and RSR. Overall difference is negligible (approaching 0.0%, Figure 3.9), but occasionally the difference is at the level of 3-4% (excluding the effect of dew, when the effect is much higher). © 2017 Solargis page 19 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Figure 3.7 Systematic shading – drop of DNI in Mzuzu. Blue: shaded DNI; red: Unshaded DNI Figure 3.8 Effect of dew – degraded GHI CMP10 readings. Blue: DNI RSP; yellow: GHI CMP 10; red: GHI RSR; green: DIF RSR; dashed: cleaning Figure 3.9 Difference between GHI from CMP10 and RSR − Mzuzu. © 2017 Solargis page 20 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Table 3.16 Quality control summary - Mzuzu Description Station description, Installation report available metadata Instrument accuracy Secondary standard pyranometer CMP10 (GHI) Rotating Shadowband Radiometer RSR 2 (GHI, DIF, DNI) Instrument calibration Instruments were calibrated Data structure Clear Cleaning and maintenance Cleaning log available information Diligent cleaning, lower frequency in November 2016 Time reference Correct and clear time reference Quality control complexity RSR data, full QC CMP10 data, without (GHI-DNI-DIF) consistency test, compared to GHI from RSR Quality control results Higher occurrence of degradation of GHI from CMP10 due to morning dew Small issues with early morning shading Significant late afternoon and early morning shading from surrounding objects Period Almost 13 months Other issues Legend: Quality marker Very good Good Sufficient Problematic Insufficient Not specified 3.4 Recommendations on the operation and maintenance of the sites Based on the results of quality control (Tables 3.10, 3.13 and 3.16), we conclude that the solar radiation measurements come from the high (CMP10, CHP1) and medium (RSR) accuracy equipment that is professionally operated and maintained. Some issues were identified during the data quality control: • Insufficient cleaning (Chileka) resulting in several periods with degraded GHI, DNI and DIF measurements. These data were flagged and excluded from further processing. • Effect of morning dew condensation on CMP10 instrument measurements (to some extent at all sites). These data values were flagged and excluded from further processing. • Significant early morning and late afternoon shading from surrounding objects at Mzuzu site. The data were flagged and excluded from further processing. For future works, we recommend: • Improve cleaning frequency at Chileka site. • Consider installation of ventilation units to avoid morning dew issues. © 2017 Solargis page 21 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 4 SOLAR RESOURCE MODEL DATA 4.1 Solar model Solar radiation is calculated by Solargis model, which are parameterized by a set of inputs characterizing the cloud transmittance, state of the atmosphere and terrain conditions. A comprehensive overview of the Solargis model is made available in the recent book publication [3]. The methodology is also described in [4, 5]. The related uncertainty and requirements for bankability are discussed in [6, 7]. In Solargis approach, the clear-sky irradiance is calculated by the simplified SOLIS model [8]. This model allows the fast calculation of clear-sky irradiance from the set of input parameters. Sun position is a deterministic parameter, and it is described by the algorithms with satisfactory accuracy. Stochastic variability of clear-sky atmospheric conditions is determined by changing concentrations of atmospheric constituents, namely aerosols, water vapor and ozone. Global atmospheric data, representing these constituents, are routinely calculated by world atmospheric data centers: • In Solargis, the new generation aerosol data set representing Atmospheric Optical Depth (AOD) is used. The calculation accuracy is strongly determined by quality of aerosols, especially for cloudless conditions. The aerosol data implemented by MACC-II/CAMS and MERRA-2 projects are used [9, 10]. • Water vapor is also highly variable in space and time, but it has lower impact on the values of solar radiation, compared to aerosols. The GFS and CFSR databases (NOAA NCEP) are used in Solargis, and the data represent the daily variability from 1994 to the present time [11, 12, 13]. • Ozone absorbs solar radiation at wavelengths shorter than 0.3 µm, thus having negligible influence on the broadband solar radiation. The clouds are the most influencing factor, modulating clear-sky irradiance. The effect of clouds is calculated from satellite data in the form of the cloud index (cloud transmittance). The cloud index is derived by relating radiance recorded by the satellite in spectral channels and surface albedo to the cloud optical properties. In Solargis, the modified calculation scheme of Cano has been adopted to retrieve cloud optical properties from the satellite data [14]. To calculate all-sky irradiance in each time step, the clear-sky global horizontal irradiance is coupled with cloud index. Direct Normal Irradiance (DNI) is calculated from Global Horizontal Irradiance (GHI) using modified Dirindex model [15]. Diffuse irradiance for tilted surfaces, which is calculated by Perez model [16]. The calculation procedure also includes terrain disaggregation, the spatial resolution is enhanced with use of the digital terrain model to 250 meters [17]. Solargis model version 2.1 has been used. Table 4.1 summarizes technical parameters of the model inputs and of the primary data outputs. Table 4.1 Input data used in the Solargis and related GHI and DNI outputs for Malawi Inputs into the Solargis Source Time Original Approx. grid model of input data representation time step resolution Cloud index Meteosat MFG and MSG 1994 to 2004 30 minutes 2.8 x 3.3 km satellites (EUMETSAT) 2005 to date 15 minutes 3.3 x 4.0 km Atmospheric optical depth MACC-II/CAMS* (ECMWF) 2003 to date 3 hours 75 km and 125 km (aerosols)* MERRA-2 (NASA) 1994 to 2002 1 hour 50 km Water vapour CFSR/GFS (NOAA) 1994 to date 1 hour 35 and 55 km Elevation and horizon SRTM-3 (SRTM) - - 250 m Solargis primary outputs - 1994 to date 15 minutes 250 m (GHI, DNI) * Aerosol data for 2003-2012 come from the reanalysis database; the data representing years 2013-present are derived from near-real time operational model © 2017 Solargis page 22 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Figure 4.1 Solar meteorological stations in the context of global horizontal irradiation. Data source: Solargis model 4.2 Site adaptation of the solar model − method The fundamental difference between a satellite observation and a ground measurement is that the signal received by the satellite radiometer integrates a large area, while a ground station represents a pinpoint measurement. This results in a mismatch when comparing instantaneous values from these two observation instruments, mainly during intermittent cloudy weather and changing aerosol load. Nearly half of the hourly Root Mean Square Deviation (RMSD) for GHI and DNI can be attributed to this mismatch (value at sub-pixel scale), which is also known as the “nugget effect” [18]. © 2017 Solargis page 23 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 The satellite pixel is not capable describing the inter-pixel variability in complex regions, where within one pixel diverse natural conditions mix-up (e.g. fog in narrow valleys or along the coast or locations with high gradient). In addition, the coarse spatial resolution of atmospheric databases such as aerosols or water vapor is not capable to describe local patterns of the state of atmosphere. These features can be seen in the satellite DNI data by increased bias due to imperfect description of the aerosol load. Satellite data have inherent inaccuracies, which have certain degree of geographical and time variability. Especially DNI is strongly sensitive to variability of cloud information, aerosols, water vapor, and terrain shading. The relation between uncertainty of global and direct irradiance is nonlinear. Often, a negligible error in global irradiance may have high counterpart in the direct irradiance component. The solar energy projects require representative and accurate GHI and DNI time series. The satellite-derived databases are used to describe long-term solar resource for a specific site. However, their problem when compared to the high-quality ground measurements is a slightly higher bias and partial disagreement of frequency distribution functions, which may limit their potential to record the occurrence of extreme situations (e.g. very low atmospheric turbidity resulting in a high DNI and GHI). A solution is to correlate satellite-derived data with ground measurements to understand the source of discrepancy and subsequently to improve the accuracy of the resulting time series. The Solargis satellite-derived data are correlated with ground measurement data with two objectives: • Improvement of the overall bias (removal of systematic deviations) • Improvement of the fit of the frequency distribution of values. The relation between uncertainty of global and direct irradiance is nonlinear. Often, a negligible error in the global horizontal irradiance may trigger higher error in the direct irradiance component. Limited spatial and temporal resolution of the input data and simplified nature of the models results in the occurrence of systematic and random deviations of the model outputs when compared to the ground observations. The deviations in the satellite-computed data, which have systematic nature, can be reduced by site adaptation methods. The terminology related to the procedure improving accuracy of the satellite data is not harmonized, and various terms are used: • Correlation of ground measurements and satellite-based data; • Calibration of the satellite model (its inputs and parameters); • Site adaptation of satellite based data. The term site adaptation is more general and best explains the concept of adapting the satellite-based model (by correlation, calibration, fitting and recalculation) to the ground measured data. Site adaptation aims to adapt the characteristics of the satellite-based time series to the site-specific conditions described by local measurements. Three conditions are important for successful adaptation of the satellite-based model: 1. High quality DNI and GHI ground measurements for at least 12 months must be available; optimally data for 2 or 3 years should be used; 2. High quality satellite data must be used, with consistent quality over the whole period of data; 3. There must be identified a systematic difference between both data sources. Systematic difference can be measured by two characteristics: • Bias (offset) • Systematic deviation in the distribution of hourly or daily values (in the histogram) Systematic difference can be stable over the year or it can slightly change seasonally for certain meteorological conditions (e.g. typical cloud formation during a day, seasonal air pollution). The data analysis should distinguish systematic differences from those arising at occasional events, such as extreme sand storms or forest fires. The episodically-occurring differences may mislead the results of adaptation, especially if short period of ground measurements is only available. If one of the three above-mentioned conditions is not fulfilled, site adaptation will not provide the expected results. On the contrary, such an attempt may deteriorate the quality of outputs. For the quantitative assessment of the accuracy enhancement procedures, the following metrics is used: • Metrics based on the comparison of all pairs of the hourly daytime data values: Mean Bias, and Root Mean Square Deviation (RMSD), histogram, in an absolute and relative form (divided by the daytime mean DNI values); • Metrics based on the difference of the cumulative distribution functions: KSI (Kolmogorov-Smirnov test © 2017 Solargis page 24 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Integral) [19] The normalized KSI is defined as an integral of absolute differences of two cumulative distribution functions D normalized by the integral of critical value acritical: , where critical value depends on the number of the data pairs N. As the KSI value is dependent on the size of the sample, the KSI measure may be used only for the relative comparison of fit of cumulative distribution of irradiance values. For the accuracy enhancement of solar resource parameters in this study, a combination of two methods was used. First, systematic deviations due to influence of aerosols were partially removed. Afterwards, to improve the distribution of values, the fitting of cumulative frequency distribution curves of ground measurements and satellite data was used. The site-adaptation procedure first identifies the sources of discrepancies by comparing the ground-measured data with Solargis model data, for the period of the overlap between both data sets. Based on this analysis, correction coefficients to improve the fit between the measured and the model Solargis data are developed. In the second step, these coefficients are used for the adaptation of the full history (23-years) time series. The satellite data is available in 15-minute time step and the ground measurements in 1-minute time step. To partially remove the conceptual difference of point and satellite pixel measurements, prior to site adaptation, all the measures are calculated using aggregated data in the hourly time step. The site adaptation was based on measured DNI data from RSR 2 (Kasungu and Mzuzu) and CHP1 (Chileka) instruments and GHI data from the secondary standard CMP10 instrument. GHI measured by the RSR 2 was not used, but the difference to the secondary standard instrument in the measured sites is very small (Chapter 3.3). 4.3 Results of the model adaptation at three sites The original Solargis data show a regional pattern of overestimation, compared to the ground measurements – for both GHI and DNI. The biggest difference was found at Mzuzu station where the mismatch between ground measurements and satellite data exceeded 12% and 18% for GHI and DNI respectively. Such discrepancy is beyond usual uncertainty of satellite data for this region (6-8% for GHI and 12-15% for DNI). The detailed inspection of the ground measurements and satellite data indicates three possible sources (or their combination) of this mismatch: • Specific geographical conditions of the site, where climate is influenced by the distance to the Lake Malawi and orographic features determining strongly changing microclimates. The gradient of solar radiation in the West-East direction at the distance of 20 km is 9% and 21% for GHI and DNI, respectively. Considering size of the satellite pixel of ca 3.3x4.0 km, the strong microclimatic gradient can be only partially recorded in the satellite data. The limited resolution of the satellite data is one source of its mismatch with pinpoint ground measurements. • Quality of ground measurements for Mzuzu site also indicates some issues, mostly related to the local shading. The high frequency variability (small scattered clouds) makes it difficult to distinguish the shading from surrounding objects from drop of irradiance due to clouds. Moreover, the permanent high shading from surrounding objects (sometimes up to 17 degrees) may partly reduce the diffuse irradiance component, thus influencing both measurements of GHI and calculated DNI. • Performance of satellite models is in general lower for the conditions with high occurrence of scattered clouds. The model adaptation allowed removing a large part of the mismatch between the satellite-based data and the ground measurements. Tables 4.2 to 4.5 summarize validation of the site-adaptation results for all solar measuring stations. © 2017 Solargis page 25 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Table 4.2 Direct Normal Irradiance: bias and KSI before and after model site-adaptation Meteo station Original DNI data DNI after regional adaptation Bias Bias KSI Bias Bias KSI 2 2 [kWh/m ] [%] [-] [kWh/m ] [%] [-] Chileka 49 12.9 157 0 0.0 80 Kasungu 31 7.2 105 0 -0.1 97 Mzuzu 71 18.5 235 0 0.1 82 Mean 50 12.9 166 0 0.0 86 Standard deviation 20.0 5.7 0.0 0.1 Table 4.3 Global Horizontal Irradiance: bias and KSI before and after model site-adaptation Meteo station Original GHI data GHI after regional adaptation Bias Bias KSI Bias Bias KSI 2 2 [kWh/m ] [%] [-] [kWh/m ] [%] [-] Chileka 43 9.5 137 0 0.0 31 Kasungu 26 5.3 87 0 0.0 26 Mzuzu 60 12.9 197 1 0.2 37 Mean 43 9.2 140 0 0.1 31 Standard deviation 17.0 3.8 0.6 0.1 Table 4.4 Direct Normal Irradiance: RMSD before and after model site-adaptation Meteo station RMSD of original DNI data RMSD of DNI after regional adaptation Hourly Daily Monthly Hourly Daily Monthly [%] [%] [%] [%] [%] [%] Chileka 42.8 23.9 16.6 40.5 20.7 10.8 Kasungu 35.6 18.3 11.1 35.2 17.9 10.2 Mzuzu 45.1 27.3 21.2 41.4 20.8 12.1 Mean 41.2 23.2 16.3 39.0 19.8 11.0 Table 4.5 Global Horizontal Irradiance: RMSD before and after model site-adaptation Meteo station RMSD of original GHI data RMSD of GHI after regional adaptation Hourly Daily Monthly Hourly Daily Monthly [%] [%] [%] [%] [%] [%] Chileka 26.2 14.8 11.3 23.5 11.0 6.0 Kasungu 20.2 10.0 6.5 19.2 8.3 3.8 Mzuzu 26.8 16.5 13.9 22.6 10.1 4.7 Mean 24.4 13.8 10.6 21.8 9.8 4.8 As a result of the site adaptation at the level of individual measurement sites in Malawi, the mean bias of the adapted values was reduced to zero. The values of RMSD and KSI accuracy parameters are also reduced, for both GHI and DNI. Figures 4.1 to 4.3 present the results of the site adaptation for all sites. © 2017 Solargis page 26 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Chileka: Original DNI Chileka: DNI after adaptation Chileka: Original GHI Chileka: GHI after adaptation Figure 4.1: Correction of DNI and GHI hourly values for Chileka. Left: original Solargis data, right: site-adapted Solargis data. The X-axis represents the measured data and the Y-axis represents the satellite-derived data. © 2017 Solargis page 27 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Kasungu: Original DNI Kasungu: DNI after adaptation Kasungu: Original DNI Kasungu: DNI after adaptation Figure 4.2: Correction of DNI and GHI hourly values for Kasungu Left: original Solargis data, right: site-adapted Solargis data. The X-axis represents the measured data and the Y-axis represents the satellite-derived data. © 2017 Solargis page 28 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Mzuzu: Original DNI Mzuzu: DNI after adaptation Mzuzu: Original DNI Mzuzu: DNI after adaptation Figure 4.3: Correction of DNI and GHI hourly values for Mzuzu. Left: original Solargis data, right: site-adapted Solargis data. The X-axis represents the measured data and the Y-axis represents the satellite-derived data. The change of model GHI and DNI after adaptation is presented on an example of Kasungu (Figure 4.4). The change for GHI is negligible, the adapted DNI is slightly lower than original one. The other sites are very similar (Table 4.6). The site-adapted model values better represent the geographical variability of DNI and GHI solar resource and they also improve the distribution and match of hourly values. The measurements show that the model performs well in the region, and these results improve the confidence about the reliability of the measured and modelled solar resource data for Malawi. © 2017 Solargis page 29 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Figure 4.4: Comparison of Solargis original and site-adapted data for Kasungu site. Left: DNI; Right: GHI; Data represent years 2005 to 2016. Table 4.6 Comparison of long-term average of yearly summaries of original and site-adapted values Meteo station DNI annual values GHI annual values Original Site-adapted Difference Original Site-adapted Difference 2 2 2 2 [kWh/m ] [kWh/m ] [%] [kWh/m ] [kWh/m ] [%] Chileka 1778 1576 -11.4 2005 1832 -8.6 Kasungu 1860 1737 -6.6 2104 2000 -4.9 Mzuzu 1782 1478 -17.1 2060 1822 -11.6 © 2017 Solargis page 30 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 5 METEOROLOGICAL MODEL DATA 5.1 Meteorological model For the territory of Malawi, the last 23 years of the Solargis model-based meteorological data is derived from the regional models. The meteorological data in Solargis database is derived from the two data sources: CFSR and CFSv2, with original characteristics specified in Table 5.1. Table 5.1 Original source of Solargis meteorological data: models CFSR and CFSv2. Climate Forecast System Reanalysis Climate Forecast System (CFSR) (CFSv2) Time period 1994 to 2010 2011 to the present time Original spatial resolution 30 x 35 km 19 x 22 km Original time resolution 1 hour 1 hour Table 5.2 shows meteorological parameters available in Solargis, their specifications, and it also indicates, which of them have been delivered within this study. In general, the original spatial resolution of the models is enhanced to 1 km for air temperature by spatial disaggregation and use of the Digital Elevation Model SRTM-3. The spatial resolution of other parameters is unchanged. Table 5.2 Solargis meteorological parameters delivered within this project Time Spatial Data Data Meteorological parameter Acronym Unit resolution representation delivered validated Air temperature at 2 metres TEMP °C 60 minute 1 km Yes Yes (dry bulb temperature) Relative humidity at 2 metres RH % 60 minute 1 km Yes Yes 2 Wind speed at 10 metres WS m/s 60 minute Original model Yes Yes Wind direction at 10 metres WD ° 60 minute Original model Yes Yes Atmospheric pressure AP hPa 60 minute Original model Yes Yes Precipitable water PWAT 60 minute Original model Yes No Important note: the meteorological parameters are derived from the numerical weather model outputs and they have lower spatial and temporal resolution. Thus, they do not represent the same accuracy as the solar resource data. Especially wind speed data has higher uncertainty, and it provides only overview information for solar energy projects. Thus, the local microclimate of the site may deviate from the values derived from the Solargis global database. 5.2 Validation of meteorological data The validation procedure was carried out to compare the modelled data with ground-measurements from the 3 meteorological stations installed within the ESMAP project: Chileka, Kasungu and Mzuzu. In general, the data from the meteorological models represent larger area, and it is not capable to represent accurately the local microclimate on Malawi measurement sites. © 2017 Solargis page 31 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 5.2.1 Air temperature at 2 meters Air temperature is derived from the model outputs of both CSFR and CSFv2 meteorological models and recalculated at the spatial resolution of 1 km (Table 5.3 and Figures 5.1 to 5.3). Considering spatial and time interpolation, the deviation of the modelled values to the ground observations for hourly values can occasionally reach several degrees of Celsius. Figures 5.1 to 5.3 show graphical representation of the model values accuracy at the meteorological stations. In general, the model matches the ground measurements quite well. The main issue identified is underestimation of temperature by the model yet the day-time temperature is represented with higher accuracy than high-time. In order to improve the data match between model and ground measurements site adaptation was applied to reduce small systematic negative bias and also improve night-time temperature. Table 5.3 Air temperature at 2 m: accuracy indicators of the model outputs [ºC]. CFSv2 model Meteorological station Bias Bias Bias Bias Bias RMSD RMSD RMSD mean min max nigh-time day-time hourly daily monthly Chileka -1.7 -2.5 0.3 -2.7 -0.7 3.1 2.2 1.9 Chileka adapted 0.0 0.0 1.0 -0.6 0.6 1.9 0.9 0.0 Mzuzu -1.4 -1.3 -0.9 -1.9 -0.9 2.3 1.7 1.5 Mzuzu adapted 0.0 0.1 0.5 -0.5 0.5 1.8 0.9 0.0 Kasungu -1.0 -1.3 -0.1 -1.5 -0.5 1.9 1.3 1.1 Kasungu adapted 0.0 0.0 0.6 -0.3 0.3 1.5 0.7 0.0 Figure 5.1: Scatterplots of air temperature at 2 m at Chileka meteorological station. Measured values (horizontal axis) and meteorological model values (vertical axis) Blue: day-time, Black: night-time measurements, Red: model data before site adaptation © 2017 Solargis page 32 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Figure 5.2: Scatterplots of air temperature at 2 m at Mzuzu meteorological station. Measured values (horizontal axis) and meteorological model values (vertical axis) Blue: day-time, Black: night-time measurements, Red: model data before site adaptation Figure 5.3: Scatterplots of air temperature at 2 m at Kasungu meteorological station. Measured values (horizontal axis) and meteorological model values (vertical axis) Blue: day-time, Black: night-time measurements, Red: model data before site adaptation © 2017 Solargis page 33 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 5.2.2 Relative humidity Relative humidity is calculated from the specific humidity, air pressure and the site-adapted air temperature. The comparison of the model values with ground measurements at all 3 meteorological stations is shown in Table 5.4 and Figures 5.4 to 5.6. In general, the model matches the ground measurements quite well, representing both daily and yearly profiles. Table 5.4 Relative humidity: accuracy indicators of the model outputs [%]. CFSv2 model Meteorological station Bias Bias Bias Bias Bias RMSD RMSD RMSD mean min max nigh-time day-time hourly daily monthly Chileka 7 2 6 10 4 15 12 10 Mzuzu 3 2 1 4 2 9 5 3 Kasungu 4 1 5 6 2 10 7 5 Figure 5.4: Scatterplots of relative humidity at 2 m at Chileka meteorological station. Measured values (horizontal axis) and meteorological model values (vertical axis). Blue: day-time, black: night-time measurements. © 2017 Solargis page 34 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Figure 5.5: Scatterplots of relative humidity at 2 m at Mzuzu meteorological station. Measured values (horizontal axis) and meteorological model values (vertical axis). Blue: day-time, black: night-time measurements. Figure 5.6: Scatterplots of relative humidity at 2 m at Kasungu meteorological station. Measured values (horizontal axis) and meteorological model values (vertical axis). Blue: day-time, black: night-time measurements. © 2017 Solargis page 35 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 5.2.3 Wind speed and wind direction at 10 meters Wind speed and direction values delivered within Solargis data represent the height at 10 meters and they are calculated from the CFSR and CFSv2 models, from 10 m wind u- and v- components. The spatial resolution is kept as in the original data. Wind measurements take place at the height of 3 m for Mzuzu and Kasungu stations and 10 m for Chileka. Different height of ground measuremtns and model data can be the important source of systematic difference. Comparison of the modelled wind speed with ground measurements is shown in Table 5.5 and Figures 5.7 to 5.9. The model values underestimate the wind conditions measured at Chileka and Kasungu meteorological stations. For Mzuzu, ground-measured wind speed values are lower than predicted by the model. This can be attributed to different height of ground measurements and the model. Similarly to relative humidity, the data representation for wind speed and wind direction strongly depends on the local conditions; therefore the model values are only indicative and better characterize a larger region rather than the local microclimate. Table 5.5 Wind speed: accuracy indicators of the model outputs [m/s]. CFSR and CFSv2 models Meteorological station Bias Bias Bias Bias Bias RMSD RMSD RMSD mean min max nigh-time day-time hourly daily monthly Chileka -1.9 -0.9 -3.1 -1.8 -2.0 2.3 2.0 1.9 Mzuzu 1.3 0.8 1.5 1.1 1.4 1.4 1.3 1.3 Kasungu 0.2 0.8 -0.4 0.5 -0.1 1.0 0.6 0.4 Figure 5.7: Scatterplots of wind speed at Chileka meteorological station. Measured values (horizontal axis) and meteorological model values (vertical axis). Blue: day-time, black: night-time. (observations and model data both at 10 m heigth) © 2017 Solargis page 36 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Figure 5.8: Scatterplots of wind speed at Mzuzu meteorological station. Measured values (horizontal axis) and meteorological model values (vertical axis). Blue: day-time, black: night-time. (observations at 3 m heigth and model data at 10 m) Figure 5.9: Scatterplots of wind speed at Kasungu meteorological station. Measured values (horizontal axis) and meteorological model values (vertical axis). Blue: day-time, black: night-time. (observations at 3 m heigth and model data at 10 m) © 2017 Solargis page 37 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 5.3 Uncertainty of meteorological model data The meteorological parameters are derived from two very similar numerical meteorological models covering periods from 1994 to 2010 (CFSR model) and 2011 to 2017 (CFSv2). Considering the comparison results, the uncertainty of the estimate for the main meteorological parameters is summarized in Table 5.6. It was found that the modelled air temperature fits reasonably well the measured data though (logically) due to the spatial resolution there are some issues like underestimation of night-time temperature and systematic small negative bias. Site adaptation was applied to partially mitigate these small discrepancies for air temperature. Similarly to air temperature, relative humidity from the model fits well measured data representing both daily and yearly amplitude. Wind speed data, obtained from the meteorological model, represents an area of larger region and this data is smoothed, in comparison to the point measurements collected at the meteorological sites. For Kasungu and Mzuzu, there is a discrepancy between the measuring heights at the meteorological station and the model. Table 5.6 Expected uncertainty of modelled meteorological parameters at the project sites. Unit Annual Monthly Hourly Air temperature at 2 m °C ±1.5 ±1.5 ±2.5 Relative Humidity at 2 m % ±10 ±10 ±20 Average wind speed at 10 m m/s ±2.5 ±2.5 ±3.0 © 2017 Solargis page 38 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 6 SOLAR RESOURCE: UNCERTAINTY OF LONG-TERM ESTIMATES 6.1 Uncertainty of solar resource yearly estimate The uncertainty of site-adapted satellite-based GHI and DNI is determined by the uncertainty of the model and of the ground measurements [7], more specifically it depends on: 1. Parameterization and adaptation of numerical models integrated in Solargis for the given data inputs and their ability to generate accurate results for various geographical and time-variable conditions: • Data inputs into Solargis model (accuracy of satellite data, aerosols and water vapour and Digital Terrain Model). • Clear-sky model and its capability to properly characterize various states of the atmosphere • Simulation accuracy of the satellite model and cloud transmittance algorithms, being able to properly distinguish different types of desert surface, clouds, fog, but also snow and ice. • Diffuse and direct decomposition models • Site adaptation methods. 2. Uncertainty of the ground-measurements, which is determined by: • Accuracy of the instruments • Maintenance practices, including sensor cleaning, calibration • Data post-processing and quality control procedures. The statistics, such as bias and RMSD (Chapter 4.3) characterize accuracy of Solargis model in the given validation points, relative to the ground measurements. The validation statistics is affected by local geography and by quality and reliability of the ground-measured data. Therefore the validation statistics only indicates performance of the model in the region. Solargis model uncertainty is compared to the data measured by the solar meteorological instruments. Representativeness of such data comparison (satellite and ground-measured) is determined by the precision of the measuring instruments, the maintenance and operational practices, and by quality control of the measured data – in other words, by the measurement accuracy achieved at each meteorological station. From the user’s perspective, the information about the model uncertainty has probabilistic nature. It generalizes the validation accuracy and it has to be considered at different confidence levels. The expert estimate of the calculation uncertainty in this report assumes 80% probability of occurrence of values. The solar model uncertainty is discussed in Chapters 4 and 6.1. The main findings are summarized in Table 6.1. The site-adaptation procedure reduced uncertainty of estimate of all parameters. Chapter 6.3 evaluates combined uncertainty, in which interannual variability is included as well (Chapter 6.2). The physical reduction of the model uncertainty is markable (Table 6.1), and in addition the site adaptation increases confidence in the model data values. Table 6.1 Uncertainty of the model estimates for original and site-adapted annual long-term values (Considers 80% probability of occurrence; in the neighborhood of the meteorological sites) Uncertainty of long-term Acronym Uncertainty of the original Uncertainty of the Solargis annual values Solargis model model after site adaptation Global Horizontal Irradiation GHI ±9.0% (13.0%*) ±4.5% Direct Normal Irradiance DNI ±12.0% (20.0%*) ±6.0% * in specific microclimatic conditions with strong spatial gradient of weather patterns © 2017 Solargis page 39 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 6.2 Uncertainty due to interannual variability of solar radiation Weather changes in cycles and also has stochastic nature. Therefore annual solar radiation in each year can deviate from the long-term average in the range of few percent. The estimation of the interannual variability below shows the magnitude of this change. The uncertainty of GHI and DNI prediction is the highest if only one single year is considered, but when averaged for a longer period, weather oscillations even out and approximate to the long-term average. In this report, the interannual variability is calculated from the unbiased standard deviation stdev of GHI and DNI over 23 years, considering the long-term, the normal distribution of the annual sums for n years, where xi is any particular year and is long-term yearly average. Due to the limited number of years of available data, for the calculation we apply simplified assumption of normal distribution of yearly values: 5 3 = ?45(? − )2 3=5 Tables 6.2 and 6.3 show GHI and DNI values that are to be exceeded at P90 for a consecutive number of years. The variability (varn) for a number of years (n) is calculated from the unbiased standard deviation (stdev): +C.)D 3 = 3 The uncertainty, which characterises 80% probability of occurrence (Uvar), is calculated from the variability (varn), multiplying it with 1.28155: = 1.28155 The lower boundary (negative value) of uncertainty represents 90% probability of exceedance, and it is used for calculating the P90 value. Table 6.2 Annual GHI that should be exceeded with 90% probability in the period of 1 to 10 (25) years Chileka | Years 1 2 3 4 5 6 7 8 9 10 25 Variability [±%] 4.1 2.9 2.4 2.0 1.8 1.7 1.5 1.4 1.4 1.3 0.8 Uncertainty P90 [±%] 5.2 3.7 3.0 2.6 2.3 2.1 2.0 1.9 1.7 1.7 1.0 Minimum GHI P90 1736 1764 1776 1784 1789 1793 1796 1798 1800 1802 1813 Kasungu | Years 1 2 3 4 5 6 7 8 9 10 25 Variability [±%] 3.6 2.5 2.1 1.8 1.6 1.5 1.4 1.3 1.2 1.1 0.7 Uncertainty P90 [±%] 4.6 3.2 2.6 2.3 2.0 1.9 1.7 1.6 1.5 1.4 0.9 Minimum GHI P90 1909 1936 1947 1955 1959 1963 1966 1968 1970 1971 1982 Mzuzu | Years 1 2 3 4 5 6 7 8 9 10 25 Variability [±%] 3.9 2.8 2.3 2.0 1.8 1.6 1.5 1.4 1.3 1.2 0.8 Uncertainty P90 [±%] 5.0 3.6 2.9 2.5 2.3 2.1 1.9 1.8 1.7 1.6 1.0 Minimum GHI P90 1730 1757 1769 1776 1781 1785 1787 1790 1791 1793 1804 © 2017 Solargis page 40 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Table 6.3 Annual DNI that should be exceeded with 90% probability in the period of 1 to 10 (25) years. Chileka | Years 1 2 3 4 5 6 7 8 9 10 25 Variability [±%] 9.0 6.3 5.2 4.5 4.0 3.7 3.4 3.2 3.0 2.8 1.8 Uncertainty P90 [±%] 11.5 8.1 6.6 5.7 5.1 4.7 4.3 4.1 3.8 3.6 2.3 Minimum DNI P90 1395 1448 1471 1485 1495 1502 1508 1512 1516 1519 1540 Kasungu | Years 1 2 3 4 5 6 7 8 9 10 25 Variability [±%] 8.0 5.6 4.6 4.0 3.6 3.3 3.0 2.8 2.7 2.5 1.6 Uncertainty P90 [±%] 10.2 7.2 5.9 5.1 4.6 4.2 3.9 3.6 3.4 3.2 2.0 Minimum DNI P90 1559 1611 1634 1648 1657 1664 1670 1674 1678 1681 1701 Mzuzu | Years 1 2 3 4 5 6 7 8 9 10 25 Variability [±%] 8.7 6.2 5.0 4.4 3.9 3.6 3.3 3.1 2.9 2.8 1.7 Uncertainty P90 [±%] 11.2 7.9 6.5 5.6 5.0 4.6 4.2 4.0 3.7 3.5 2.2 Minimum DNI P90 1313 1361 1383 1396 1404 1411 1416 1420 1423 1426 1445 One can interpret the above Tables 6.2 and 6.3 on the example of Chileka site as follows: i. GHI interannual variability at P90 of 5.2% has to be considered for any single year in Chileka. In other words, 2 assuming that the long-term average is 1832 kWh/m , it is expected (with 90% probability) that annual GHI 2 exceeds, at any single year, the value of 1736 kWh/m . ii. Within a period of three consecutive years, it is expected at P90 that annual average of GHI exceeds value 2 of 1776 kWh/m ; iii. For a period of 25 years, it is expected at 90% probability that due to interannual variability the estimate of the long-term annual DNI average will deviate within the range of ±2.3% in Chileka. Thus assuming that the 2 estimate of the long-term average is 1576 kWh/m , it can be expected at P90 that due to variability of 2 weather, it should be at least 1540 kWh/m . It is to be underlined that prediction of the future irradiation is based on the analysis of the recent historical data (period 1994 to 2016). Future weather changes may include man-induced climate change or natural events such as volcano eruptions, which may have impact on this prediction. Based on the existing scientific knowledge [20, 21], an effect of extreme volcano eruptions, with an emission of large amount of stratospheric aerosols, can be estimated on the example of Pinatubo event in 1991 (the second th largest volcano eruption in 20 century). It can be expected that in such a case, the annual DNI in the affected year can decrease by approx. 16% or more, compared to the long-term average, still influencing another two consecutive years. In the same way, the volcano eruption of the comparable size may reduce long-term average estimate of DNI by about 4%. The decrease of GHI is much lower; the annual value in the particular year of eruption could be reduced by about 2% compared to the long-term average. 6.3 Combined uncertainty In this Chapter, the combined uncertainty of the annual GHI and DNI values is quantified. Taking into account uncertainties of both types of data (satellite and ground measured), the combined effect of two components of the uncertainty of the site-adapted GHI and DNI values has to be considered. 1. Uncertainty of the estimate (Uest) of the annual solar resource values, which is ±4.5% for GHI and ±6.0% for DNI (Chapter 6.1); 2. Interannual variability (Uvar) in any particular year, due to changing weather. At three Malawian sites, it varies from ±4.6% to ±5.2% for GHI and from ±10.2% to ±11.5% for DNI. The uncertainty due to weather variability decreases over the time with square root of the number of years (Chapter 6.2). The two above-mentioned uncertainties combine in Uc (see Glossary), which represents a conservative expectation of the minimum GHI and DNI assuming various number of years N (Tables 6.4 and 6.5). Considering a simplified assumption of normal distribution of the annual values, probability of exceedance can be calculated © 2017 Solargis page 41 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 at different confidence levels. GHI and DNI minimum annual values expected for combined uncertainty in any single year are shown on Figure 6.1 and 6.2. Table 6.4 Combined probability of exceedance of annual GHI for uncertainty of the estimate ±4.5%. Nr. of Uncertainty Interanual Combined Expected minimum | Chileka 2 years of estimate variability uncertainty [kWh/m ] N [±%] N years [±%] P90 [±%] P01 P05 P10 P25 P50 P75 P90 P95 P99 1 4.5 5.2 6.9 2062 1994 1959 1899 1832 1765 1705 1669 1602 5 4.5 2.3 5.1 2001 1951 1925 1881 1832 1783 1739 1713 1663 10 4.5 1.7 4.8 1991 1945 1920 1878 1832 1786 1744 1719 1672 25 4.5 1.0 4.6 1986 1941 1917 1876 1832 1787 1747 1723 1678 Nr. of Uncertainty Interanual Combined Expected minimum | Kasungu 2 years of estimate variability uncertainty [kWh/m ] N [±%] N years [±%] P90 [±%] P01 P05 P10 P25 P50 P75 P90 P95 P99 1 4.5 4.6 6.4 2233 2165 2129 2068 2000 1933 1872 1835 1767 5 4.5 2.0 4.9 2180 2127 2099 2052 2000 1948 1901 1873 1821 10 4.5 1.4 4.7 2172 2122 2095 2050 2000 1951 1906 1879 1829 25 4.5 0.9 4.6 2167 2118 2092 2049 2000 1952 1908 1882 1834 Nr. of Uncertainty Interanual Combined Expected minimum | Mzuzu 2 years of estimate variability uncertainty [kWh/m ] N [±%] N years [±%] P90 [±%] P01 P05 P10 P25 P50 P75 P90 P95 P99 1 4.5 5.0 6.8 2046 1980 1945 1887 1822 1757 1699 1664 1599 5 4.5 2.3 5.0 1989 1940 1914 1870 1822 1774 1730 1704 1656 10 4.5 1.6 4.8 1980 1934 1909 1868 1822 1776 1735 1710 1664 25 4.5 1.0 4.6 1975 1930 1906 1866 1822 1778 1738 1714 1670 Table 6.5 Combined probability of exceedance of annual DNI for uncertainty of the estimate ±6.0%. Nr. of Uncertainty Interanual Combined Expected minimum | Chileka 2 years of estimate variability uncertainty [kWh/m ] N [±%] N years [±%] P90 [±%] P01 P05 P10 P25 P50 P75 P90 P95 P99 1 6.0 11.5 13.0 1947 1838 1780 1684 1576 1468 1372 1314 1205 5 6.0 5.1 7.9 1802 1736 1701 1642 1576 1511 1452 1416 1350 10 6.0 3.6 7.0 1777 1718 1687 1634 1576 1518 1465 1434 1375 25 6.0 2.3 6.4 1760 1706 1677 1629 1576 1523 1475 1446 1392 Nr. of Uncertainty Interanual Combined Expected minimum | Kasungu 2 years of estimate variability uncertainty [kWh/m ] N [±%] N years [±%] P90 [±%] P01 P05 P10 P25 P50 P75 P90 P95 P99 1 6.0 10.2 11.9 2111 2001 1943 1845 1737 1629 1531 1473 1363 5 6.0 4.6 7.5 1975 1905 1868 1806 1737 1668 1606 1569 1499 10 6.0 3.2 6.8 1952 1889 1855 1799 1737 1675 1619 1585 1522 25 6.0 2.0 6.3 1937 1878 1847 1795 1737 1679 1627 1596 1537 Nr. of Uncertainty Interanual Combined Expected minimum | Mzuzu 2 years of estimate variability uncertainty [kWh/m ] N [±%] N years [±%] P90 [±%] P01 P05 P10 P25 P50 P75 P90 P95 P99 1 6.0 11.2 12.7 1819 1719 1666 1577 1478 1380 1291 1237 1138 5 6.0 5.0 7.8 1688 1627 1594 1539 1478 1418 1363 1330 1269 10 6.0 3.5 7.0 1665 1611 1581 1533 1478 1424 1375 1346 1291 25 6.0 2.2 6.4 1650 1600 1573 1528 1478 1429 1384 1357 1307 © 2017 Solargis page 42 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 This analysis is based on the data representing a history of years 1994 to 2016, and on the expert extrapolation of the related weather variability. This report may not reflect possible man-induced climate change or occurrence of extreme events such as large volcano eruptions in the future (see the last paragraph in Chapter 6.2). Graphical visualisation of Tables 6.4 and 6.5 on the example of Chileka is shown in Figures 6.1 and 6.2, where the expected probabilities of exceedance (different Pxx scenarios) are drawn on the cumulative distribution curve showing yearly GHI amd DNI values. 100 P99: 1602 P95: 1669 GHI Value at Pxx P50 P90: 1705 P75 P90 90 P95 P99 80 P75: 1765 70 60 P50: 1832 Pxx 50 40 30 20 10 0 1500 1600 1700 1800 1900 2000 2100 2200 GHI [kWh/m2] Figure 6.1: Expected Pxx values for GHI at Chileka site 100 P99: 1205 P95: 1314 DNI Value at Pxx P50 P90: 1372 P75 P90 90 P95 P99 80 P75: 1468 70 60 P50: 1576 Pxx 50 40 30 20 10 0 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 DNI [kWh/m2] Figure 6.2: Expected Pxx values for DNI at Chileka site © 2017 Solargis page 43 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 7 TIME SERIES AND TYPICAL METEOROLOGICAL YEAR DATA 7.1 Delivered data sets This report is accompanied by data sets delivered individually for position of each of three solar meteorlogical stations in Malawi. The data include (Tables 7.1 and 7.2): • Solar and meteorological measurements, after second level quality assessment (first level was delivered by GeoSUN Africa) representing first 12 months of the measuring campaign; • Time series, representing last 23+ years; • Typical Meteorological Year data, representing last 23 years. The data is delivered in formats ready to use in energy simulation software. This report provides detailed insight of the methodologies and results. Table 7.1 Delivered data characterstics Feature Time coverage Primary time step Delivered files Ground measurements Mar 2016 to Mar 2017 1 minute Quality controlled measurements – 1- minute (GeoSUN Africa) Analytical tasks consider only a period from Apr 2016 to Mar 2017 Model data – original Jan 1994 to March 2017 15 minutes Time series – hourly (Solargis) Time series – monthly Time series – yearly Model data – site adapted Jan 1994 to March 2017 15 minutes Time series – hourly (Solargis) Time series – monthly Time series – yearly Model data – site adapted Jan 1994 to Dec 2016 hourly Typical Meteorological Year P50 – hourly (Solargis) Typical Meteorological Year P90 – hourly Table 7.2 Parameters in the delivered site-adapted time series and TMY data (hourly time step) Parameter Acronym Unit TS TMY P50 TMY P90 2 Global horizontal irradiance GHI W/m X X X 2 Direct normal irradiance DNI W/m X X X 2 Diffuse horizontal irradiance DIF W/m X X X 2 Global tilted irradiance (at optimum angle) GTI W/m X - - Solar azimuth SA ° X X X Solar elevation SE ° X X X Air temperature at 2 metres TEMP °C X X X Wind speed at 10 metres WS m/s X X X Wind direction at 10 metres WD ° X X X Relative humidity RH % X X X Air Pressure AP hPa X X X 2 Precipitable Water PWAT kg/m X X X © 2017 Solargis page 44 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 7.2 TMY method The Typical Meteorological Year (TMY) data sets are delivered, together with Solargis time series data and this report. TMY contains hourly data derived from the time series covering complete 23 years (1994 to 2016). The data history of 23 years is compressed into one year (Figure 7.1 to 7.3) following two criteria: • Minimum difference between statistical characteristics (annual average, monthly averages) of TMY and long-term time series. This criterion is given about 80% weighting. • Maximum similarity of monthly Cumulative Distribution Functions (CDF) of TMY and full-time series, so that occurrence of typical hourly values is well represented for each month. This criterion is given about 20% weighting. TMY P50 data set is constructed on the monthly basis. For each month the long-term average monthly value and cumulative distribution for each parameters is calculated: Direct Normal Irradiance (DNI), Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DIF) and Air Temperature (TEMP). Following the monthly data for each individual year from the set of 23 years are compared to the long-term parameters. The monthly data from the year, which resembles the long-term parameters more closely, is selected. The procedure is repeated for all 12 months, and the TMY is constructed by concatenating the selected months into one artificial (but representative) year. The method for calculation P90 data set is based on the TMY P50 method. It has been modified in a way of how a candidate month is selected. The search for set of twelve candidates is repeated in iteration until a condition of minimization of difference between annual P90 value and annual average of new TMY is reached (instead of minimization of differences in monthly means and CDFs, as applied in P50 case). Once the selection converges to minimum difference, the TMY is created by concatenation of selected months. The P90 annual values are calculated for each confidence limit − from the combined uncertainty of estimate and inter-annual variability, which can occur in any year (Chapter 6.3). To derive TMY that fits specific needs of the selected energy application the different weights are given to individual parameters – thus highlighting important properties. In solar energy applications, the higher importance is given to GHI and DNI. In assembling TMY P50, the values of DNI, GHI, DIF and TEMP are only considered, where the weights are set as follows: 0.9 is given to DNI, 0.3 to GHI, 0.02 to diffuse horizontal irradiance, and 0.07 to air temperature (divided by the total of 1.29). To derive solar resource parameters with an hourly time step, the original satellite data with time resolution of 15-minutes were aggregated by time integration. The meteorological parameters are available in the original 1-hourly time step. The TMY datasets were constructed from solar radiation and meteorological data (Chapters 4 and 5). Time zone was adjusted to Central Africa Time CAT (UTC +02:00). More about the Solargis TMY method in [22]. 7.3 Results Two data sets are derived from the Solargis historical time series for the three sites: P50 and P90. In graphs and tables below we show the values for Chileka meteorological site, in order to present the methodology of TMY data calculation. Important note: Due to the inherent features of the underlying methods, monthly values in the TMY data sets do not fit to the values generated from full time series (Figures 7.1 to 7.3; Tables 7.2 to 7.4). Table 7.3 Monthly and yearly long-term GHI averages as calculated from time series and from TMY representing P50, and P90 cases at Chileka site Global Horizontal 2 1 2 3 4 5 6 7 8 9 10 11 12 Year Irradiation [kWh/m ] Time series (23 years) 160 150 158 143 137 116 119 146 169 184 178 173 1832 TMY for P50 case 159 148 159 144 136 115 118 149 173 179 180 172 1832 TMY for P90 case 141 110 144 136 134 110 114 138 163 172 175 165 1705 © 2017 Solargis page 45 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Table 7.4 Monthly and yearly long-term DNI averages as calculated from time series and from TMY representing P50, and P90 cases at Chileka site Direct Normal 2 1 2 3 4 5 6 7 8 9 10 11 12 Year Irradiation [kWh/m ] Time series (23 years) 106 109 125 137 155 132 121 138 140 145 141 126 1576 TMY for P50 case 106 108 127 134 155 133 124 140 139 142 141 125 1576 TMY for P90 case 80 50 107 128 154 118 116 126 128 130 130 105 1372 Table 7.5 Monthly and yearly long-term TEMP averages as calculated from time series and from TMY representing P50, and P90 cases at Chileka site Air temperature [°C] 1 2 3 4 5 6 7 8 9 10 11 12 Year Time series (23 years) 24.1 24.3 24.0 22.7 21.4 19.9 19.4 21.2 24.2 26.0 26.5 25.1 23.2 TMY for P50 case 23.1 24.5 23.6 22.6 20.8 19.4 20.1 21.3 23.9 25.6 25.4 23.6 22.8 TMY for P90 case 24.1 22.8 23.7 22.7 20.5 19.2 19.0 20.7 24.0 26.0 25.7 24.6 22.8 As an example of interpretation of the tables above, the TMY data sets for P50 and P90 for the Chileka site can be described as: 1. P50 TMY data set represents, for each month, the average climate conditions and the most representative cumulative distribution function, therefore extreme situations (e.g. extremely cloudy weather) are not represented in this dataset. The long-term annual summary of GHI and DNI are considered as the most 2 critical parameters to consider, and in this data set P50 GHI value is 1832 kWh/m and DNI value is 2 1576 kWh/m . 2. P90 TMY data set represents for each month the climate conditions, which after summarization GHI and DNI for the whole year results in the value equal or close to P90 derived by the analysis of uncertainty of the estimate and of the interannual variability for any single year (Chapter 6.3). Thus TMY for P90 represents 2 generally a conservative estimate, i.e. a year with the long-term value of GHI of 1705 kWh/m and DNI of 2 1372 kWh/m . 250 200 GHI [kWh/m2] 150 100 50 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time series TMY P50 TMY P90 Figure 7.1: GHI monthly values derived from time series and TMY P50 and P90 at Chileka site. © 2017 Solargis page 46 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 250 200 DNI [kWh/m2] 150 100 50 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time series TMY P50 TMY P90 Figure 7.2: DNI monthly values derived from time series and TMY P50 and P90 at Chileka site. 30.0 25.0 TEMP [°C] 20.0 15.0 10.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time series TMY P50 TMY P90 Figure 7.3: TEMP monthly values derived from time series and TMY P50 and P90 at Chileka site It is important to note that the data reduction in the TMY data set is not possible without loss of information contained in the original multiyear time series. Therefore time series data are considered as the most accurate reference suitable for the statistical analysis of solar resource and meteorological parameters of the site. Figure 7.4: Seasonal profile of GHI, DNI and DIF for Typical Meteorological Year P50 2 Chileka site: X-axis – day of the year; Y-axis – solar irradiance W/m © 2017 Solargis page 47 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Figure 7.5: Snapshot of Typical Meteorological Year for P50 for Chileka © 2017 Solargis page 48 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 8 CONCLUSIONS This report accompanies delivery of site-specific solar resource and meteorological data for three sites, where solar meteorological stations have been installed and operated during the first year of the measurement campaign − from March 2016 to March 2017. The measurement campaign is ongoing, and the same data exercise will be repeated after concluding 24 months of measurements, with expectation of further reducing the data uncertainty. The measured data used in site-adaptation of the Solargis model and for deriving historical time series and TMY data at the position of three sites where solar meteorological equipment is installed. The data is delivered in formats ready to use in standard phovotoltaic energy simulation software. It is to be noted that the delivered data represent only the yhtrr selected meteorological sites. For any other location a new specific set of data, accurately representing the local geography and microclimate has to be generated. © 2017 Solargis page 49 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 ANNEX 1: SITE RELATED DATA STATISTICS Yearly summaries of solar and meteorological model outputs Statistics for site adapted model yearly values representing 23 years (1994 to 2016). 6.0 2192 Average annual sum of Global Horizontal Irradiation [kWh/m2] Average daily sum of Global Horizontal Irradiation [kWh/m2] 5.5 2009 5.0 1826 4.5 1644 4.0 1461 3.5 1278 3.0 1096 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Year Chileka 5.2% Kasungu 4.8% Mzuzu 5.4% 2 Figure I: Interannual variability of site-adapted yearly GHI [kWh/m ]. Annual average (avg, solid line) and standard deviation (value behind the names of sites). 6.0 2192 Average annual sum of Direct Normal Irradiation [kWh/m2] Average daily sum of Direct Normal Irradiation [kWh/m2] 5.5 2009 5.0 1826 4.5 1644 4.0 1461 3.5 1278 3.0 1096 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Year Chileka 12.0% Kasungu 10.9% Mzuzu 12.0% 2 Figure II: Interannual variability of site-adapted yearly DNI [kWh/m ]. Annual average (avg, solid line) and standard deviation (value behind the names of sites). © 2017 Solargis page 50 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 27.0 25.5 24.0 Average anual air temperature [°C] 22.5 21.0 19.5 18.0 16.5 15.0 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Chileka Kasungu Mzuzu Figure III: Interannual variability of yearly TEMP [°C]. Annual average (avg, solid line). © 2017 Solargis page 51 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Monthly summaries of solar and meteorological model outputs The graphs compare (site-adapted) monthly model time series compared to long-term averages. 9.0 9.0 8.0 Chileka 8.0 Kasungu Daily sums of GHI [kWh/m2] Daily sums of GHI [kWh/m2] 7.0 7.0 6.0 6.0 5.0 5.0 4.0 4.0 3.0 3.0 2.0 2.0 1.0 1.0 0.0 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec max-min LTA Last Year max-min LTA Last Year 9.0 8.0 Mzuzu Daily sums of GHI [kWh/m2] 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec max-min LTA Last Year 2 Figure IV: GHI monthly averages [kWh/m ]. Monthly average shown as solid line; min/max monthly values as as boundary lines; last 12 months in red. 9.0 9.0 8.0 Chileka 8.0 Kasungu 7.0 7.0 6.0 Daily sums of DNI [kWh/m2] 6.0 Daily sums of DNI [kWh/m2] 5.0 5.0 4.0 4.0 3.0 3.0 2.0 2.0 1.0 1.0 0.0 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec max-min LTA Last Year max-min LTA Last Year 9.0 8.0 Mzuzu 7.0 6.0 Daily sums of DNI [kWh/m2] 5.0 4.0 3.0 2.0 1.0 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec max-min LTA Last Year 2 Figure V: DNI monthly averages [kWh/m ]. Monthly average shown as solid line; min/max monthly values as boundary lines; last 12 months shown in red. © 2017 Solargis page 52 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 35.0 35.0 30.0 Chileka 30.0 Kasungu Monthly air temperature [°C] Monthly air temperature [°C] 25.0 25.0 20.0 20.0 15.0 15.0 10.0 10.0 5.0 5.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec max-min LTA Last Year max-min LTA Last Year 35.0 30.0 Mzuzu Monthly air temperature [°C] 25.0 20.0 15.0 10.0 5.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec max-min LTA Last Year Figure VI: TEMP monthly averages [°C]. Monthly average shown as solid line; min/max onthly values as boundary lines; last 12 months shown in red. © 2017 Solargis page 53 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Frequency of occurrence of GHI and DNI daily model values for a period 1994 to 2016 The histograms below show occurrence statistics of daily values derived from the satellite-based time series for GHI and DNI. The time covered in the graphs below is 23 complete calendar years (1994 to 2016). The occurrence is calculated separately for each month. 12 20 20 12 12 20 January February March Percentage of days 10 10 10 15 15 15 8 8 8 6 10 6 10 6 10 4 4 4 5 5 5 2 2 2 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 20 12 12 20 20 12 April May June Percentage of days 10 10 10 15 15 15 8 8 8 6 10 6 10 6 10 4 4 4 5 5 5 2 2 2 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 12 20 12 20 20 12 July August September Percentage of days 10 10 10 15 15 15 8 8 8 6 10 6 10 6 10 4 4 4 5 5 5 2 2 2 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 20 12 12 20 20 12 October November December Percentage of days 10 10 10 15 15 15 8 8 8 10 6 6 10 6 10 4 4 4 5 5 5 2 2 2 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Daily sum of GHI [kWh/m2] Daily sum of GHI [kWh/m2] Daily sum of GHI [kWh/m2] Figure VII: Histograms of daily summaries of Global Horizontal Irradiation in Chileka. 20 12 12 20 12 20 January February March Percentage of days 10 10 10 15 15 15 8 8 8 6 10 6 10 6 10 4 4 4 5 5 5 2 2 2 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 20 12 20 12 12 20 April May June Percentage of days 10 10 10 15 15 15 8 8 8 10 6 6 10 6 10 4 4 4 5 5 5 2 2 2 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 12 20 12 20 20 12 July August September Percentage of days 10 10 10 15 15 15 8 8 8 6 10 6 10 6 10 4 4 4 5 5 5 2 2 2 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 20 12 12 20 20 12 October November December Percentage of days 10 10 10 15 15 15 8 8 8 10 6 6 10 6 10 4 4 4 5 5 5 2 2 2 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Daily sum of GHI [kWh/m2] Daily sum of GHI [kWh/m2] Daily sum of GHI [kWh/m2] Figure VIII: Histograms of daily summaries of Global Horizontal Irradiation in Kasungu. © 2017 Solargis page 54 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 12 20 12 20 20 12 January February March Percentage of days 10 10 10 15 15 15 8 8 8 6 10 6 10 6 10 4 4 4 5 5 5 2 2 2 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 20 12 12 20 12 20 April May June Percentage of days 10 10 10 15 15 15 8 8 8 10 6 6 10 6 10 4 4 4 5 5 5 2 2 2 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 20 12 12 20 12 20 July August September Percentage of days 10 10 10 15 15 15 8 8 8 6 10 6 10 6 10 4 4 4 5 5 5 2 2 2 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 20 12 20 12 12 20 October November December Percentage of days 10 10 10 15 15 15 8 8 8 10 6 6 10 6 10 4 4 4 5 5 5 2 2 2 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Daily sum of GHI [kWh/m2] Daily sum of GHI [kWh/m2] Daily sum of GHI [kWh/m2] Figure IX: Histograms of daily summaries of Global Horizontal Irradiation in Mzuzu. Figures VII to IX show histograms of daily GHI summaries for each month as calculated from Solargis time series representing the years 1994 to 2016. The distribution of daily values is not symmetric: median is drawn by the vertical line, and percentiles P10, P25, and P75, and P90 are displayed with dark grey and light grey color bands, respectively. The percentiles P10 and P90 show 80% occurrence of daily values within each month and percentiles P25 and P75 show 50% occurrence. 10 10 10 January February March Percentage of days 7.5 7.5 7.5 5 5 5 2.5 2.5 2.5 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 10 10 10 April May June Percentage of days 7.5 7.5 7.5 5 5 5 2.5 2.5 2.5 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 10 10 10 July August September Percentage of days 7.5 7.5 7.5 5 5 5 2.5 2.5 2.5 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 10 10 10 October November December Percentage of days 7.5 7.5 7.5 5 5 5 2.5 2.5 2.5 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Daily sum of DNI [kWh/m2] Daily sum of DNI [kWh/m2] Daily sum of DNI [kWh/m2] Figure X: Histograms of daily summaries of Direct Normal Irradiation in Chileka. © 2017 Solargis page 55 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 10 10 10 January February March Percentage of days 7.5 7.5 7.5 5 5 5 2.5 2.5 2.5 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 10 10 10 April May June Percentage of days 7.5 7.5 7.5 5 5 5 2.5 2.5 2.5 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 10 10 10 July August September Percentage of days 7.5 7.5 7.5 5 5 5 2.5 2.5 2.5 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 10 10 10 October November December Percentage of days 7.5 7.5 7.5 5 5 5 2.5 2.5 2.5 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Daily sum of DNI [kWh/m2] Daily sum of DNI [kWh/m2] Daily sum of DNI [kWh/m2] Figure XI: Histograms of daily summaries of Direct Normal Irradiation in Kasungu. 10 10 10 January February March Percentage of days 7.5 7.5 7.5 5 5 5 2.5 2.5 2.5 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 10 10 10 April May June Percentage of days 7.5 7.5 7.5 5 5 5 2.5 2.5 2.5 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 10 10 10 July August September Percentage of days 7.5 7.5 7.5 5 5 5 2.5 2.5 2.5 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 10 10 10 October November December Percentage of days 7.5 7.5 7.5 5 5 5 2.5 2.5 2.5 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Daily sum of DNI [kWh/m2] Daily sum of DNI [kWh/m2] Daily sum of DNI [kWh/m2] Figure XII: Histograms of daily summaries of Direct Normal Irradiation in Mzuzu. Figures X to XII show histograms of daily DNI summaries for each month as calculated from Solargis time series representing the years 1994 to 2016. The distribution of daily values is not symmetric: median is drawn by the vertical line, and percentiles P10, P25, and P75, and P90 are displayed with dark grey and light grey color bands, respectively. The percentiles P10 and P90 show 80% occurrence of daily values within each month and percentiles P25 and P75 show 50% occurrence. © 2017 Solargis page 56 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Frequency of occurrence of GHI and DNI 15-minute model values for a period 1994 to 2016 The histograms below show occurrence statistics of 15-minute values derived from the satellite-based time series for GHI and DNI. The time covered in the graphs below is 23 complete calendar years (1994 to 2016). The occurrence is calculated separately for each month. Figure XIII: Histograms and cumulative distribution function of 15-minute GHI in Chileka Figure XIV: Histograms and cumulative distribution function of 15-minute GHI in Kasungu © 2017 Solargis page 57 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Figure XV: Histograms and cumulative distribution function of 15-minute GHI in Mzuzu Figures XIII to XV show monthly histograms (bars) and cumulative distribution (line) of 15-minute GHI values, 2 calculated from Solargis time series. The values represent the occurrence of GHI values within 50 W/m bins, 2 ranging from 0 to 1200 W/m . Figure XVI: Histograms and cumulative distribution function of 15-minute DNI in Chileka © 2017 Solargis page 58 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Figure XVII: Histograms and cumulative distribution function of 15-minute DNI in Kasungu Figure XVIII: Histograms and cumulative distribution function of 15-minute DNI in Mzuzu Figures XVI to XVIII show monthly histograms (bars) and cumulative distribution (line) of 15-minute DNI values, 2 calculated from Solargis time series. The values represent the occurrence of DNI values within 50 W/m bins, 2 ranging from 0 to 1200 W/m . © 2017 Solargis page 59 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Frequency of occurrence of GHI and DNI measured and model values representing 12 months Figures XIX to XXIV show histograms comparing the measured values with the model GHI and DNI data. The period covered in these histogram is last 12-months (one full year of data, i.e. from 1 Apr 2016 to 31 Mar 2017): • 1-minute measured vs. 15-min satellite-based model values • 15-minute measured (aggregated from 1-min) vs. 15-min satellite-based values • Daily measured (aggregated from 1-min) vs. daily satellite-based model values Aggregation process deals with the missing values in the ground measurement in three steps: 1. Only those 1-minute measured data values that passed through quality control (Chapter 3.3) is taken into account (satellite time series does not have gaps.); 2. Aggregation of 1-minute measured data values into 15-minute slots (equivalent to satellite time slots) is applied if more than 15 valid data-points is available, otherwise the 15-minute data slot is ignored in further statistical comparison; 3. Daily aggregation of measured data represents the same 15-minute time slots in a day (passing through the two steps above), as those in the satellite-based data. Incorrect data slots found in the measurements are excluded in both the measured and model data. Figure XIX: Measured vs. satellite-based GHI values in Chileka 1-minute measured vs. 15-min satellite-based values. 15-minute measured (aggregated from 1-min) vs. 15-min satellite-based values Daily measured (aggregated from 1-min) vs. daily satellite-based values Figure XX: Measured vs. satellite-based GHI values in Kasungu 1-minute measured vs. 15-min satellite-based values. 15-minute measured (aggregated from 1-min) vs. 15-min satellite-based values Daily measured (aggregated from 1-min) vs. daily satellite-based values © 2017 Solargis page 60 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Figure XXI: Measured vs. satellite-based GHI values in Mzuzu 1-minute measured vs. 15-min satellite-based values. 15-minute measured (aggregated from 1-min) vs. 15-min satellite-based values Daily measured (aggregated from 1-min) vs. daily satellite-based values Figure XXII: Measured vs. satellite-based DNI values in Chileka 1-minute measured vs. 15-min satellite-based values. 15-minute measured (aggregated from 1-min) vs. 15-min satellite-based values Daily measured (aggregated from 1-min) vs. daily satellite-based values Figure XXIII: Measured vs. satellite-based DNI values in Kasungu 1-minute measured vs. 15-min satellite-based values. 15-minute measured (aggregated from 1-min) vs. 15-min satellite-based values Daily measured (aggregated from 1-min) vs. daily satellite-based values Figure XXIV: Measured vs. satellite-based DNI values in Mzuzu 1-minute measured vs. 15-min satellite-based values. 15-minute measured (aggregated from 1-min) vs. 15-min satellite-based values Daily measured (aggregated from 1-min) vs. daily satellite-based values © 2017 Solargis page 61 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Frequency of occurrence of GHI and DNI ramps Figures XXV to XXXVIII show histograms of instantaneous changes (ramps) calculated from the measurements and compared to the instantaneous changes calculated fro the model data. Figures show both negative (-) and positive (+) changes. Two versions for GHI and DNI are shown: • Ramps calculated from 1-minute measured values compared to ramps calculated from 15-minute satellite-based data (figure on the left) • Ramps calculated from 15-minute aggregated valid measurement compared to ramps calculated from 15-minute satellite-based data (figure on the right). Occurrence of gaps in the measurements is managed in the same way as described about in this Chapter: 1. For measurements, only those 1-minute data values (measurements) that passed through quality control (Chapter 3.3) is taken into account (satellite time series does not have gaps.); 2. For measurements, the aggregation (averaging) of 1-minute measured data values into 15-minute slots (equivalent to satellite time slots) is applied if more than 15 valid data-points is available, otherwise the 15-minute data slot is ignored in further statistical comparison; Figure XXV: 1-minute and 15-minute GHI ramps (measured and satellite data) at Chileka. Figure XXVI: 1-minute and 15-minute GHI ramps (measured and satellite data) at Kasungu © 2017 Solargis page 62 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Figure XXVII: 1-minute and 15-minute GHI ramps (measured and satellite data) at Mzuzu Figure XXVIII: 1-minute and 15-minute DNI ramps (measured and satellite data) at Chileka Figure XXIX: 1-minute and 15-minute DNI ramps (measured and satellite data) at Kasungu © 2017 Solargis page 63 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Figure XXX: 1-minute and 15-minute DNI ramps (measured and satellite data) at Mzuzu © 2017 Solargis page 64 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 LIST OF FIGURES Figure 2.1: Position of solar meteorological stations in Malawi ................................................................................. 9 Figure 3.1 Results of GHI and DNI quality control in Chileka. ................................................................................... 14 Figure 3.2 Insufficient cleaning – skewed DIF and DNI profiles. .............................................................................. 14 Figure 3.3 Results of GHI and DNI quality control in Kasungu. ................................................................................. 16 Figure 3.4 Effect of dew – degraded GHI CMP10 readings. ..................................................................................... 17 Figure 3.5 Difference between GHI from CMP10 and RSR - Kasungu. ..................................................................... 17 Figure 3.6 Results of GHI and DNI quality control − Mzuzu. ..................................................................................... 19 Figure 3.7 Systematic shading – drop of DNI in Mzuzu. ........................................................................................... 20 Figure 3.8 Effect of dew – degraded GHI CMP10 readings. ..................................................................................... 20 Figure 3.9 Difference between GHI from CMP10 and RSR − Mzuzu. ....................................................................... 20 Figure 4.1 Solar meteorological stations in the context of global horizontal irradiation. ....................................... 23 Figure 4.1: Correction of DNI and GHI hourly values for Chileka. ............................................................................. 27 Figure 4.2: Correction of DNI and GHI hourly values for Kasungu ............................................................................ 28 Figure 4.3: Correction of DNI and GHI hourly values for Mzuzu................................................................................ 29 Figure 4.4: Comparison of Solargis original and site-adapted data for Kasungu site. ............................................ 30 Figure 5.1: Scatterplots of air temperature at 2 m at Chileka meteorological station. ........................................... 32 Figure 5.2: Scatterplots of air temperature at 2 m at Mzuzu meteorological station. ............................................. 33 Figure 5.3: Scatterplots of air temperature at 2 m at Kasungu meteorological station. ......................................... 33 Figure 5.4: Scatterplots of relative humidity at 2 m at Chileka meteorological station. .......................................... 34 Figure 5.5: Scatterplots of relative humidity at 2 m at Mzuzu meteorological station. ........................................... 35 Figure 5.6: Scatterplots of relative humidity at 2 m at Kasungu meteorological station. ....................................... 35 Figure 5.7: Scatterplots of wind speed at Chileka meteorological station. .............................................................. 36 Figure 5.8: Scatterplots of wind speed at Mzuzu meteorological station. ............................................................... 37 Figure 5.9: Scatterplots of wind speed at Kasungu meteorological station. ........................................................... 37 Figure 6.1: Expected Pxx values for GHI at Chileka site ............................................................................................ 43 Figure 6.2: Expected Pxx values for DNI at Chileka site ............................................................................................ 43 Figure 7.1: GHI monthly values derived from time series and TMY P50 and P90 ................................................... 46 Figure 7.2: DNI monthly values derived from time series and TMY P50 and P90 ................................................... 47 Figure 7.3: TEMP monthly values derived from time series and TMY P50 and P90 ............................................... 47 Figure 7.4: Seasonal profile of GHI, DNI and DIF for Typical Meteorological Year P50........................................... 47 Figure 7.5: Snapshot of Typical Meteorological Year for P50 for Chileka ................................................................ 48 Figure I: Interannual variability of site-adapted yearly GHI [kWh/m ]........................................................................ 50 2 Figure II: Interannual variability of site-adapted yearly DNI [kWh/m ]....................................................................... 50 2 Figure III: Interannual variability of yearly TEMP [°C]. ................................................................................................ 51 Figure IV: GHI monthly averages [kWh/m ]. ............................................................................................................... 52 2 Figure V: DNI monthly averages [kWh/m ].................................................................................................................. 52 2 Figure VI: TEMP monthly averages [°C]. ..................................................................................................................... 53 Figure VII: Histograms of daily summaries of Global Horizontal Irradiation in Chileka. ......................................... 54 Figure VIII: Histograms of daily summaries of Global Horizontal Irradiation in Kasungu. ...................................... 54 Figure IX: Histograms of daily summaries of Global Horizontal Irradiation in Mzuzu............................................. 55 Figure X: Histograms of daily summaries of Direct Normal Irradiation in Chileka. ................................................. 55 © 2017 Solargis page 65 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 Figure XI: Histograms of daily summaries of Direct Normal Irradiation in Kasungu. .............................................. 56 Figure XII: Histograms of daily summaries of Direct Normal Irradiation in Mzuzu. ................................................ 56 Figure XIII: Histograms and cumulative distribution function of 15-minute GHI in Chileka .................................... 57 Figure XIV: Histograms and cumulative distribution function of 15-minute GHI in Kasungu ................................. 57 Figure XV: Histograms and cumulative distribution function of 15-minute GHI in Mzuzu ...................................... 58 Figure XVI: Histograms and cumulative distribution function of 15-minute DNI in Chileka.................................... 58 Figure XVII: Histograms and cumulative distribution function of 15-minute DNI in Kasungu ................................ 59 Figure XVIII: Histograms and cumulative distribution function of 15-minute DNI in Mzuzu................................... 59 Figure XIX: Measured vs. satellite-based GHI values in Chileka ............................................................................... 60 Figure XX: Measured vs. satellite-based GHI values in Kasungu .............................................................................. 60 Figure XXI: Measured vs. satellite-based GHI values in Mzuzu ................................................................................ 61 Figure XXII: Measured vs. satellite-based DNI values in Chileka .............................................................................. 61 Figure XXIII: Measured vs. satellite-based DNI values in Kasungu ........................................................................... 61 Figure XXIV: Measured vs. satellite-based DNI values in Mzuzu .............................................................................. 61 Figure XXV: 1-minute and 15-minute GHI ramps (measured and satellite data) at Chileka. .................................. 62 Figure XXVI: 1-minute and 15-minute GHI ramps (measured and satellite data) at Kasungu ................................ 62 Figure XXVII: 1-minute and 15-minute GHI ramps (measured and satellite data) at Mzuzu .................................. 63 Figure XXVIII: 1-minute and 15-minute DNI ramps (measured and satellite data) at Chileka ................................ 63 Figure XXIX: 1-minute and 15-minute DNI ramps (measured and satellite data) at Kasungu ................................ 63 Figure XXX: 1-minute and 15-minute DNI ramps (measured and satellite data) at Mzuzu ..................................... 64 © 2017 Solargis page 66 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 LIST OF TABLES Table 2.1 Overview information on the solar meteorological station locations ............................................... 10 Table 3.1 Overview information on measurement stations operated in the region .......................................... 11 Table 3.2 Instruments installed at the solar meteorological stations ............................................................... 11 Table 3.3 Technical parameters and accuracy class of the instruments at Tier 1 and Tier 2 stations ........... 11 Table 3.4 Overview information on solar meteorological stations operating in the region ............................. 12 Table 3.5 Period of measurements analyzed in this report ................................................................................ 12 Table 3.6 Meteorological stations maintenance and instruments field verification ........................................ 12 Table 3.7 Results of field instruments verification at the respective stations .................................................. 13 Table 3.8 Occurrence of data readings for Chileka meteorological station ...................................................... 13 Table 3.9 Excluded ground measurements after quality control (Sun above horizon) in Chileka ................... 14 Table 3.10 Quality control summary - Chileka ....................................................................................................... 15 Table 3.11 Occurrence of data readings for Kasungu meteorological station ................................................... 15 Table 3.12 Excluded ground measurements after quality control (Sun above horizon) in Kasungu ................. 15 Table 3.13 Quality control summary - Kasungu..................................................................................................... 17 Table 3.14 Occurrence of data readings for Mzuzu meteorological station ....................................................... 18 Table 3.15 Excluded ground measurements after quality control (Sun above horizon) in Mzuzu .................... 18 Table 3.16 Quality control summary - Mzuzu ........................................................................................................ 21 Table 4.1 Input data used in the Solargis and related GHI and DNI outputs for Malawi .................................. 22 Table 4.2 Direct Normal Irradiance: bias and KSI before and after model site-adaptation .............................. 26 Table 4.3 Global Horizontal Irradiance: bias and KSI before and after model site-adaptation ........................ 26 Table 4.4 Direct Normal Irradiance: RMSD before and after model site-adaptation ........................................ 26 Table 4.5 Global Horizontal Irradiance: RMSD before and after model site-adaptation................................... 26 Table 4.6 Comparison of long-term average of yearly summaries of original and site-adapted values ......... 30 Table 5.1 Original source of Solargis meteorological data: models CFSR and CFSv2. .................................... 31 Table 5.2 Solargis meteorological parameters delivered within this project .................................................... 31 Table 5.3 Air temperature at 2 m: accuracy indicators of the model outputs [ºC]............................................ 32 Table 5.4 Relative humidity: accuracy indicators of the model outputs [%]. ..................................................... 34 Table 5.5 Wind speed: accuracy indicators of the model outputs [m/s]. .......................................................... 36 Table 5.6 Expected uncertainty of modelled meteorological parameters at the project sites. ....................... 38 Table 6.1 Uncertainty of the model estimates for original and site-adapted annual long-term values ........... 39 Table 6.2 Annual GHI that should be exceeded with 90% probability in the period of 1 to 10 (25) years ....... 40 Table 6.3 Annual DNI that should be exceeded with 90% probability in the period of 1 to 10 (25) years. ...... 41 Table 6.4 Combined probability of exceedance of annual GHI for uncertainty of the estimate ±4.5%. .......... 42 Table 6.5 Combined probability of exceedance of annual DNI for uncertainty of the estimate ±6.0%. .......... 42 Table 7.1 Delivered data characterstics .............................................................................................................. 44 Table 7.2 Parameters in the delivered site-adapted time series and TMY data (hourly time step) ................. 44 Table 7.3 Monthly and yearly long-term GHI averages as calculated from time series and from TMY .......... 45 Table 7.4 Monthly and yearly long-term DNI averages as calculated from time series and from TMY .......... 46 Table 7.5 Monthly and yearly long-term TEMP averages as calculated from time series and from TMY ...... 46 © 2017 Solargis page 67 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 REFERENCES [1] NREL, 1993. 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[22] Gueymard C., Solar resource assessment for CSP and CPV. Leonardo Energy webinar, 2010. http://www.leonardo-energy.org/webfm_send/4601 [23] Cebecauer T., Šúri M., 2015. Typical Meteorological Year Data: Solargis Approach. Energy Procedia, Volume 69, 1958-1969. http://dx.doi.org/10.1016/j.egypro.2015.03.195 © 2017 Solargis page 69 of 71 Annual Solar Resource Report for Solar Meteorological Stations in Malawi after completion of 12 months of measurements Solargis reference No. 141-06/2017 SUPPORT INFORMATION Background on Solargis The primary business of the company Solargis is in providing support to the site qualification, planning, financing and operation of solar energy systems. We are committed to increase efficiency and reliability of solar technology by expert consultancy and access to our databases and customer-oriented services. The company builds on experience in solar energy and photovoltaics for 17 years. We strive for development and operation of new generation high-resolution quality-assessed global databases with focus on solar resource and energy-related weather parameters. We are operating simulation, management and control tools, map products, and services for fast access to high quality information needed for system planning, performance assessment, forecasting and management of distributed power generation. ® Company Solargis operates a set of online services, integrated within Solargis information system, which includes data, maps, software, and geoinformation services for solar energy. Legal information Considering the nature of climate fluctuations, interannual and long-term changes, as well as the uncertainty of measurements and calculations, company Solargis cannot take guarantee of the accuracy of estimates. Company Solargis has done maximum possible for the assessment of climate conditions based on the best ® available data, software and knowledge. Solargis is the registered trademark of company Solargis. Other brand names and trademarks that may appear in this study are the ownership of their respective owners. © 2017 Solargis, all rights reserved Solargis is ISO 9001:2008 certified company for quality management. Authors: Marcel Suri Tomas Cebecauer Branislav Schnierer Artur Skoczek Daniel Chrkavy Nada Suriova Maps: Juraj Betak Veronika Madlenakova Project manager: Nada Suriova Approved by: Marcel Suri © 2017 Solargis page 70 of 71