Validation of PERSIANN Precipitation Product Using TAWN Rain Gauge Network Over Different Agro-climatic Zones in Tamil Nadu

Abstract

Currently, several satellite-precipitation products were developed using multiple algorithms to estimate rainfall. This study carried out using Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product over seven agro-climatic zones of Tamil Nadu during the northeast monsoon (NEM) season of October to December for 2015-2017 (three years) against 118 rain-gauges data of Tamil Nadu Agricultural Weather Network (TAWN). The performance compares aggregated seasonal scale of rainfall using continuous (CC, RMSE, and NRMSE) statistical approaches. It was observed that PERSIANN is accurate in 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. The highest RMSE value was measured for northeast zone (NEZ) during 2015 (222.17 mm), and the lowest was determined for 22.63 in the High-altitude hilly zone (HAHZ) during 2016 and NRMSE had less errors during all three seasons. The study concluded that the PERSIANN data set could be useful substitute for rain-gauge precipitation data.