## 5 Conclusions and Perspectives  
In this article we presented a detailed analysis of the different precipitation products currently available (satellite and radar), through two case studies in which we quantified and analysed the differences in rainfall observations. The products have different spatiotemporal characteristics and processing methods for assessing the retrieval of ground-radar reflectivity or brightness temperature to obtain rain-rate estimations  
First, we present comparisons between radar products against the rain-gauge network over France. Both PANTHERE and OPERA show very satisfying performances over France, with PANTHERE having the lowest biases on average over France for both case studies. The main differences are observed over the complex topography of the south of France, where the highest precipitation amounts are observed. OPERA does not include the X-band radar over the Alps, leading to an underestimation over the area. The presence of the Swiss and German radars near the border influences the estimation of the precipitation around the Jura area by OPERA. Finally, OPERA estimated higher precipitation amounts than the observed values for both case studies. Nevertheless, the high spatiotemporal and very-short-latency characteristics of both products remain very interesting for nowcasting applications over France, for example. Then, we compare the behaviour of 11 satellite precipitation products against the observations made by RADOME over France during the two case studies. With the sole exception of DATABOURG, we should remind you that the satellite-based products have the benefits of covering a wide portion of the Earth's surface. In our case, these satellite products have the benefits of a near-real-time latency and showing good representation of high-intensity precipitation events, such as the June case. The main conclusions are as follows:  
* PDIR and GHE found difficulty in identifying the position of the April event but correctly detected the June event. The IR-based technique might be well adapted for these more convective conditions with high spatial (4 \(\times\) 4 km\({}^{2}\)) and temporal (15 min to 1 h) resolutions with relatively short latency (minutes to hours);
* Short-latency (real-time) MW-based products tend to produce worse representations of the precipitation cases such as CMORPH-RT and GSMaP-Now products. GSMaP-Now products had difficulty in representing the event but the quantification was coherent with the RADOME observations;
* In comparison, GSMaP-NRT performed better than GSMaP-Now in the detection of the event, especially in the identification of the June event;
* A similar statement can be made about the reprocessed CMORPH version compared to the heterogeneity in precipitation amounts' quantification by CMORPH-RT. The morphing method allowed for a better representation and quantification of both case studies;
* The gauge-adjustment effectuated for the GSMaP-Now-GC and GSMaP-NRT-GC did not show a significant improvement in the quantification of both events;
* IMERG-Early and IMERG-Late showed a trend to slightly overestimate the presence of rain, as shown by the overestimation recorded at low-rain-amount areas. They also tend to underestimate high rain-rate values but they well represented both case studies over the south regions of France;
* The reprocessing applied to IMERG-Late does not improve the estimations compared to IMERG-Early;
* CMORPH-RT showed traces of raw LEO satellite observation swaths over parts of the country. This was traduced by strong differences at the very-local scale, over the JUR area for example. CMORPH-RT showed low bias medians but CMORPH was better at the spatial representation of both events;
* Despite slightly overestimating during the June event, DATABOURG showed promising results with low biases for both cases with the highest spatiotemporal resolution (1 km\({}^{2}\) and 5 min).