Correcting and Improving the Performance of Soil Moisture-based Product (SM2RAIN-ASCAT) Over Iran at Daily and Monthly Time Scales

Estimating precipitation plays an important role in different studies, including meteorological, hydrological, flood simulation, and drought monitoring. SM2RAIN-ASCAT is the newest attempt to estimate precipitation using surface soil moisture and inverse solving the soil-water balance equation. This research addressed the performance of the SM2RAIN-ASCAT dataset over diverse climate regions of Iran at daily and monthly time scales in 54 synoptic stations located in Iran (2007-2018). In addition, improving the efficiency of this product using Quantile Mapping bias correction method is another objective of this study. Results showed that the performance of SM2RAIN-ASCAT at the monthly time scale was better than the daily time scale, and in most parts of Iran, the correlation coefficient between observed and estimated datasets was relatively high. At the monthly time step, 67% of the studied stations had a CC value higher than 0.65. Moreover, findings indicated that this product tended to overestimate in Iran's north and west parts. Based on the RMSE value at the monthly time scale, SM2RAIN-ASCAT performed well at extra-arid and arid regions compared to the Mediterranean and humid climate zones. Also, removing bias from the raw dataset increased the efficiency of SM2RAN-ASCAT in most parts of the studied areas. Based on contingency table metrics, the bias-corrected dataset's skills in detecting rainy days and false alarm ratio (FAR) improved significantly. For instance, the FAR metric averagely improved 26 and 45 % at daily and monthly time scales, respectively. Finally, results indicated that SM2RAIN-ASCAT is a valuable dataset for estimating monthly precipitation, especially in arid-desert to extra-arid climates. The accuracy of this dataset increases using bias correction in different climates.