## 2 Datasets
### MSG SEVIRI Cloud Mask  
The MSG SEVIRI cloud mask (CMa) product permits the identification of cloud free pixels in a satellite scene with a high confidence [24]. Indeed, cloudy pixels can be often identified, because they appear colder (at 10.5 \(\upmu\)m) and/or brighter (at 0.6 or 0.8 \(\upmu\)m) than cloud free areas. A fine analysis of their respective spectral behavior is nevertheless needed to perform a full cloud detection. For example, low clouds identification at night-time relies on their low emissivity at 3.8 \(\upmu\)m, whereas thin cirrus clouds can be identified due to their different emissivity at 10.5 \(\upmu\)m and 12 \(\upmu\)m. Cloud free areas covered by snow or ice are identified at daytime with their very low reflectivity at 3.7 \(\upmu\)m and high reflectivity at 0.6 \(\upmu\)m, whereas oceanic cloud free areas affected by sun glint are identified with their very high reflectivity at 3.8 \(\upmu\)m. The CMa identifies pixels that are contaminated by either clouds, dust, or snow/sea ice. The problem to be solved is to automatically predict, with sufficient accuracy, brightness temperatures and reflectance of cloud free areas, so that any discrep  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SATELLITE** & **SENSOR** & **LIFE SPAN** \\ \hline
**Meteorological Operational Satellites-B** & **Microwave Humidity Sounder** & **2012–2017** \\ \hline Meteorological Operational Satellites-C & Microwave Humidity Sounder & 2016–beyond 2021 \\ \hline
**National Oceanic and Atmospheric** & **Microwave Humidity Sounder** & **2011–2017** \\
**Administration-18** & **Microwave Humidity Sounder** & **2011–2018** \\ \hline
**National Oceanic and Atmospheric** & **Microwave Humidity Sounder** & **2011–2018** \\
**Administration-19** & **Advanced Technology Microwave Sounder** & **2012–2019** \\ \hline Joint Polar Satellite System-1 & Advanced Technology Microwave Sounder & 2017–beyond 2021 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
ancy between the measured and predicted values can be used to detect contaminated pixels. Therefore, the algorithm is based on multispectral threshold technique applied to each pixel of the image. A first process allows the identification of pixels contaminated by clouds or snow/ice. This is complemented by an analysis of the temporal variation (between individual image repeat cycles) of some spectral combination of channels (to detect rapidly moving clouds) and a specific treatment combining temporal coherency analysis and region growing technique (to improve the detection of low clouds). This first process is carried out to determine the cloud cover category of each pixel (cloud-free, cloud contaminated, cloud filled, snow/ice contaminated, or undefined/non-processed) and compute a quality flag on the processing itself. A second process, allowing the identification of dust clouds and volcanic ash clouds, is applied to all pixels (even already classified as cloud-free or contaminated by clouds). The result is stored in the dust cloud and volcanic ash cloud flags. Within the encoding of the product the pixels are identified as one of the following:  
* Clear sky over water;
* Clear sky over land;
* Cloud;
* No data (if none of the classifications above is possible).  
This product, as provided by EUMETSAT [25], will be used to select, at the SEVIRI space resolution, for both the NN detecting rainy pixels and the NN providing rainfall estimation, only cloudy pixels.