One of the key benefits of the Solargis time series data is that the data have no gaps. Yet, there might be gaps in the archive of satellite images that are used as input in the Solargis model. For time stamps with missing satellite data, we apply intelligent statistical algorithms to deliver datasets without any gaps. The gap-filling of our accurate and validated time series historical irradiance data has been even further improved, leading to a minor change in values.
Solargis offers TMY data which is information from a multi-year time series summarized into a Typical Meteorological Year (TMY), which reflects the most frequent weather conditions of a particular site, consisting of synthetic months. These synthetic months are updated and the generation of the TMY data is now further accelerated.
Now our historical TMY and time series data can be accessed faster for North America, Europe, Africa, South Asia, Middle East, Central Asia, and parts of East Asia.
New features and improvements:
Bugfixes
Updates:
Bugfixing:
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Solargis TMY data is constructed using multi-year time series (TS) by selecting the most representative months which are finally concatenated into one artificial and representative single year.
We introduced a new methodology for generation of different probability scenarios for TMY PXX (P99, P95, P90, P80, P75, P25, P20, P10, P5, P1) into the data delivery process.
The new methodology is based on increasing the number of months used to generate the TMY from TS. For example: if we have 27 years at the input, we create 729 synthesized candidates. This allows increasing the data points of constructing TMY in a simpler way but also increases the accuracy.
The calculation for the interannual variability of DNI for TMY Pxx (P90/95/99 - excluding P50) has been improved. The interannual variability affects the combined uncertainty, as such the expected DNI Pxx value is used for TMY generation. The combined uncertainty is now higher.