About Solar system estimator Malawi
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6 FAQs about [Solar system estimator Malawi]
What is the purpose of ground measurements in Malawi?
3 Ground measurements in Malawi 3.1 Solar meteorological stations: specifications and data Data from the measuring stations in Malawi was collected and harmonized with the objective of acquiring reference solar radiation data for reducing the uncertainty of the solar models.
Does Solargis model work in Malawi?
Solargis model is based on the use of the best available algorithms and input data, and it has been calibrated and validated for all geographies. Therefore, the model has robust and uniform behaviour in all conditions. Validation sites in Malawi show consistent bias within the expected range, except for the Mzuzu station.
What data will be incorporated into the Global Solar Atlas?
Any improved or validated solar resource data will be incorporated into the Global Solar Atlas. Copyright © 2018 THE WORLD BANK Washington DC 20433 Telephone: +1-202-473-1000 Internet: 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.
Does spatial interpolation extend the validity of solar meteorological correction factors?
Applying the spatial interpolation, we extended the validity of the correction factors identified at the solar meteorological stations to the entire territory of Malawi and neighbouring areas. To maintain the stability of model in a wider regional context, also effect of corrections from neighbouring regions (Zambia) was introduced.
What is the typical uncertainty of the Solargis model estimate?
The typical uncertainty of the Solargis model estimate has been reduced from the original range of ±12.0% (exceptionally for specific regions above 20%) for DNI yearly values to the range of ±5.0% to ±7.0% (exceptionally ±10.0%) for accuracy enhanced values.
Are Solargis data overestimated?
The original Solargis data show a regional pattern of overestimation, compared to the ground measurements, for both GHI and DNI. The highest difference is seen atMzuzu station, where the systematic deviation between the ground measurements and the satellite data exceeds 12% and 20%, for yearly values GHI and DNI respectively.
