Ciabatta, Luca; Brocca, Luca; Massari, Christian; Moramarco, Tommaso; Puca, Silvia; Rinollo, Angelo; Gabellani, Simone; Wagner, Wolfgang, 2015, Integration of Satellite Soil Moisture and Rainfall Observations over the Italian Territory, Journal of hydrometeorology (Print) 16 (2015): 1341–1355. doi_10.1175/JHM-D-14-0108.1,
URL: http://www.cnr.it/prodotto/i/332673

State-of-the-art rainfall products obtained by satellites are often the only way of measuring rainfall in remote areas of the world. However, it is well known that they may fail in properly reproducing the amount of precipitation reaching the ground, which is of paramount importance for hydrological applications. To address this issue, an integration between satellite rainfall and soil moisture SM products is proposed here by using an algorithm, SM2RAIN, which estimates rainfall from SM observations. A nudging scheme is used for integrating SM-derived and state-of-the-art rainfall products. Two satellite rainfall products are considered_ H05 provided by EUMESAT and the real-time (3B42-RT) TMPA product provided by NASA. The rainfall dataset obtained through SM2RAIN, SM2R(ASC), considers SM retrievals from the Advanced Scatterometer (ASCAT). The rainfall datasets are compared with quality-checked daily rainfall observations throughout the Italian territory in the period 2010-13. In the validation period 2012-13, the integrated products show improved performances in terms of correlation with an increase in median values, for 5-day rainfall accumulations, of 26% (18%) when SM2R(ASC) is integrated with the H05 (3B42-RT) product. Also, the median root-mean-square error of the integrated products is reduced by 18% and 17% with respect to H05 and 3B42-RT, respectively. The integration of the products is found to improve the threat score for medium-high rainfall accumulations. Since SM2R(ASC), H05, and 3B42-RT datasets are provided in near-real time, their integration might provide more reliable rainfall products for operational applications, for example, for flood and landslide early warning systems.

Data from https://intranet.cnr.it/people/