# Automated Rain Detection by Dual-Polarization Sentinel-1 Data
## 2 Dataset
### Match-Up Database with Sentinel-1 and Weather Radar Data  
A match-up step is necessary for the following analysis because both data have different temporal and geographic coverage. Additionally, because rain can have a strong variability, assessing its impact on high-resolution products such as a SAR image requires severe criteria in both space and time. First, Sentinel-1 data are matched up with weather radar coverage. Secondly, for each Sentinel-1 case, we select the nearest weather radar observations in time. The maximum temporal difference between the satellite and weather radar is set to 5 min. Finally, the rain data at 1 km resolution are mapped on the same grid as Sentinel-1 products by searching the nearest neighbor in weather radar map. Please note that the Sentinel-1 products in Section 3 is degraded to 1 km. But in Section 4, the original resolution (5 m by 20 m) in IW mode was reduced to 200 m, 400 m, 800 m as applying the dual-polarization filter.  
Note, although we benefit here from high-resolution rain measurements and high frequency sampling (with respect to any low-orbit satellite mission), this collocation strategy is not perfect. As pointed out by [PERSON] et al. [23], there is a time difference between the time when the rain is observed by the ground-based radar and the time when the raindrops hit the sea surface. This difference depends on the distance of the observed rain event, the altitude and height of the water column as well as on the rainfall speed. For instance, for a rainfall speed of 6 to 8 m/s observed at a height of 4 km, scattering effect due to raindrops impinging the surface are expected to be observed at least 10 min later. However, as rain fall speed and sizes are not known here, for each SAR scene we simply select the weather radar observation in the shortest time lag. In addition, [PERSON] et al. [23]  
Figure 1: The locations of NEXRAD and JMA coastal stations. (**a**) The NEXRAD coastal stations over contiguous United States, Hawaii, Guam island and one Japanese island. (**b**) The locations of JMA radars equipped in the Japanese archipelago.  
pointed out that wave damping effect due to rain-generated turbulence may occur after the rainfall has stopped, inducing another time lag, which can contribute to collocation errors, but is very difficult to quantify. The spatial extent of the rain signature between the two sensors is also expected to be different for storm cells because the downdraft area is not visible on the ground-based rain radar. Finally, from our experience and as illustrated hereinafter, the correlation between the two nearest measurements in time is remarkable. After collocation, we restrict the match-up data pairs to rainy situations. We finally obtained a total of 2393 and 4311 S-1 images co-located with NEXRAD and JMA observations, respectively.  
Additionally, considering that ocean surface winds have a direct influence on sea surface roughness and consequently on the SAR backscattered signal, we also collocated the measurements with hourly forecast wind products provided by European Centre for Medium Range Weather Forecasts (ECMWF). They are available on a spatial grid of 0.125\({}^{\circ}\) limiting the time difference with the observations to 30 min.