Deep learning based short-range forecasting of Indian summer monsoon rainfall using earth observation and ground station datasets. Issue 27 (13th December 2022)

• Record Type: Journal Article
• Authors: Kumar, Bipin; Abhishek, Namit; Chattopadhyay, Rajib; George, Sandeep; Singh, Bhupendra Bahadur; Samanta, Arya; Patnaik, B.S.V.; Gill, Sukhpal Singh; Nanjundiah, Ravi S.; Singh, Manmeet
• Is Part Of: Geocarto international. Volume 37:Issue 27(2023)
• Page Start: 17994
• Page End: 18021
• Publication Date: 2022-12-13
• Subjects: Short Range Forecasting -- remote sensing -- TRMM data -- station data -- Indian summer monsoon -- ConvLSTM model -- custom loss function; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2022.2136262
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: We develop a deep learning model (DL) for Indian Summer Monsoon (ISM) short-range precipitation forecasting using a ConvLSTM network. The model is built using daily precipitation records from both ground-based observations and remote sensing. Precipitation datasets from the Tropical Rainfall Measuring Mission and the India Meteorological Department are used for training, testing, forecasting, and comparison. For lead days 1 and 2, the correlation coefficient (CC), which was determined using predicted data from the previous five years and corresponding observational records (from both in-situ and remote sensing products), yielded values of 0.67 and 0.42, respectively. Interestingly, the CCs are even higher over the Western Ghats and Monsoon trough region. The model performance evaluated based on skill scores, Normalized Root Mean Squared Error (NRMSE), Mean absolute percentage error (MAPE) and ROC curves show a reasonable skill in short-range precipitation forecasting. Incorporating multivariable-based DL has the potential to match or even better the forecasts made by the state-of-the-art numerical weather prediction models.