Radar-rainfall products provide valuable information for hydro-ecological modeling and ecosystem applications, especially over coastal regions that lack adequate in-situ rainfall observations. This study evaluates two radar-based rainfall products, the Multi-Sensor Stage IV and the Multi-Radar Multi-Sensor (MRMS), over the Louisiana coastal region in the United States. Surface reference rainfall observations from two independent rain gage networks were used in the analysis. The evaluation included distribution-based comparisons between radar and gage observations at different time scales (hourly to monthly), bias decomposition to quantify the contribution of different error sources, and conditional evaluation of systematic and random components of the estimation errors. Both products report large levels of random errors at the hourly scale; however, the performance of the radar-rainfall products improves significantly with the increase in time scales. After decomposing the total bias, the results show that the largest contributor to the overall bias in radar-rainfall products is false rainfall detection, followed by missed rainfall. The results also reveal that the Stage IV product experienced a significant improvement over the area in the past few years (post 2015) compared to earlier periods. The results have implications for ongoing and future coastal ecosystem modeling and planning studies.

In the spring of 2020, many countries enacted strict lockdowns to contain the spread of the 2019 coronavirus disease (COVID-19), resulting in a sharp observed decrease in regional atmospheric pollutant concentrations, such as NOx and aerosols in early 2020. Atmospheric composition can influence cloud properties and might have a significant effect on the initiation of precipitation. This study investigated changes in precipitation patterns during COVID-19 lockdowns and compared them to patterns observed during the previous 19 years (2001 through 2019) across two regions of interest, the Hubei province in China and Northern Italy using a satellite-based precipitation dataset. Results indicated that overall rainfall averages were higher in the spring of 2020 with respect to their corresponding climatological means, with higher standard deviations especially in the more urbanized regions like Wuhan, China and Milan, Italy. Precipitation rates observed during the Spring of 2020 tend to fall outside of the climatological 25–75th percentile bounds. Similarly, the number of rainy pixels was in several cases in Spring 2020 higher than the climatological 75th percentile and sometimes even higher than the 95th one. These anomalies may be due to natural variations and may not be caused directly by the reduction in atmospheric pollutant concentrations. Nevertheless, our analysis proved that precipitation patterns during the lockdowns were on the extreme tails of the precipitation climatological distributions for both regions of interest. Lastly, decorrelation lags and distances in Northern Italy remained similar to their corresponding climatological values, whereas in the Hubei province some differences were observed, with the Spring 2020 spatial correlation variogram almost overlapping the climatological 5th percentile and with a decorrelation distance shorter than the climatological value.