## 5 Conclusions and Future Improvements  
A new data fusion technique based on NNs was presented. It uses both visible/infrared data from GEO satellite and passive microwave data from LEO satellite to avoid their single limitations and to take advantage of both types of sensors. It provides, for a given geographical area and a time interval, a reconstruction of the surface rain field with the  
Figure 4: OPERA Radars quality index. 5 June 2015 17:30.  
Figure 5: BT @ 10.8 μm—5 June 2015 17:30.  
MSG-SEVIRI spatial and temporal resolution, e.g., 3 km at the sub satellite point and 5 km at mid-latitudes, every 15 min.  
The present method differs from previous NNs based works because it presents a significantly greater number of input using all the information available from VIS/IR data, two different NNs for day and night scenario, and the possibility to use all LEO PMW data. Moreover, these preliminary results, even if over a limited number of cases not covering the huge variability of the European climate, has shown its capacity over different climatological scenarios.  
The results that were obtained in the identification of rainy clouds in the various test areas, compared with the benchmark precipitation product, PR-OBS3A, show an improvement ranging from 59% up to 109% in terms of probability of rainy areas detection and of from 39% to 50% in terms of false alarm rate. The use of two different NNs for day and night has shown the impact of the visible channels in the estimation of the cloud water path. During the night, when the information at the visible bands is missing, the measure of such a parameter is more difficult and POD, FAR, and CSI have worse results, but always better than PR-OBS3A that uses a single infrared channel.  
As far as the retrieval of the amount of rainfall is concerned, compared with the HSAF product, in the considered test cases our algorithm increases the accuracy from 21% up to 65%. Nevertheless, a global underestimation is present, during summer/spring case studies. This behavior seems to be common to precipitation products based on visible and infrared data; however, the quality of radar data maybe an additional issue which should be more investigated.  
A possible future improvement might be the inclusion of a continuous updating and re-training of the NNs as new measurements are available for the area of interest. In this way, the technique might be more accurate and also considered for tracking or even prediction purposes although this would involve the availability of suitable processing capabilities. This would also permit to make the product operational and then to validate it in many different climatological scenes, possibly covering the European weather variability.  
Conceptualization, M.S.; methodology, M.S. and F.D.F.; software, M.S.; validation, M.S., F.D.F. and G.S.; formal analysis, M.S., F.D.F. and G.S.; investigation, M.S.; resources, M.S., F.D.F. and G.S.; data curation, M.S.; writing--original draft preparation, M.S. and F.D.F.; writing--review and editing, F.D.F. and G.S.; visualization, F.D.F.; supervision, F.D.F. and G.S.; project administration, F.D.F. and G.S.; funding acquisition, F.D.F. and G.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.