Improving short to medium range GEFS precipitation forecasts in India using post-processing techniques

Abstract

This study aims to enhance the accuracy and reliability of the Global Ensemble Forecast System’s (GEFS) precipitation forecasts over the Indian subcontinent using two post-processing techniques, namely the Analog method (AN) and Logistic Regression method (LR). The post-processing techniques and GEFS Numerical Weather Prediction Model (NWP) outputs were evaluated against the observed dataset using probabilistic and deterministic evaluation metrics. Results found that both the methods considerably improves short range to medium range (1-15 day) precipitation forecasts over India. Overall results showed that both the methods perform poorly during the monsoon seasons compared to other seasons. Basin analysis showed that both the methods underperform in the Western Ghats, while the performance is comparable and decent in other parts of India. Analysis of precipitation at different terciles showed that both the AN and LR methods underperforms at higher terciles compared to the lower ones. This is because the GEFS model itself was performing poorly in detecting the heavy precipitation events. The comparison of logistic regression and analog methods shows that the LR method outperforms the AN method in almost all the locations and lead times.