Identifying time stamps with missing satellite data

One of the key benefits of Solargis time series data is that the data have no gaps. However please note that sometimes there are 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 time series datasets without any gaps. Techniques used for gap filling depend on the time of day and how much data is missing.

Each irradiance data value in the Solargis time series dataset is accompanied by a quality flag (FlagR) that indicates how the cloud information was derived from the satellite data. An explanation of the irradiance quality flags used by Solargis is given below:

0: sun below the horizon - no cloud information is calculated as there is no solar radiation
1: model value - data come from the satellite model, and no data are missing
2: interpolated <=1 hour - few time slots are missing and these are interpolated from surrounding hours
3: extrapolated <=1 hour - few time slots are missing at the beginning or end of the day, cloud information is extrapolated from the closest available value
4: interpolated/extrapolated >1 hour - the same as 2 and 3, but the period of missing data is longer than 1 hour
5: long-term monthly median or persistence - big gaps in the data (e.g. whole day) are replaced by data from the previous day
6: synthetic data - the same as 5, but data are replaced by synthetically generated data

In most cases, especially in regions that use satellite data from Meteosat satellites, the occurrence of (5) and (6) is very low (for last 10 years, only 2-3 days of data are missing).