Abstract

                                Currently, several satellite-precipitation products were developed usingmultiple algorithms to estimate rainfall. This study carried out usingPrecipitation Estimation from Remotely Sensed Information using ArtificialNeural Networks (PERSIANN) product over seven agro-climatic zones ofTamil Nadu during the northeast monsoon (NEM) season of October toDecember for 2015-2017 (three years) against 118 rain-gauges data of TamilNadu Agricultural Weather Network (TAWN). The performance comparesaggregated seasonal scale of rainfall using continuous (CC, RMSE, andNRMSE) statistical approaches. It was observed that PERSIANN is accuratein the high-altitude hilly zone and the Cauvery delta zone. For 2015, 2016,and 2017, the correlation values were 0.77, 0.52, and 0.71, respectively. Thehighest RMSE value was measured for northeast zone (NEZ) during 2015(222.17 mm), and the lowest was determined for 22.63 in the High-altitudehilly zone (HAHZ) during 2016 and NRMSE had less errors during all threeseasons. The study concluded that the PERSIANN data set could be usefulsubstitute for rain-gauge precipitation data.

Key words : Validation; PERSIANN; Rain-gauge; Agro-Climatic zones; Tamil Nadu