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.