Kumar, B and Abhishek, N and Chattopadhyay, R and George, S and Singh, BB and Samanta, A and Patnaik, BSV and Gill, SS and Nanjundiah, RS and Singh, M (2022) Deep learning based short-range forecasting of Indian summer monsoon rainfall using earth observation and ground station datasets. In: Geocarto International .
Full text not available from this repository.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.
| Item Type: | Journal Article |
|---|---|
| Publication: | Geocarto International |
| Publisher: | Taylor and Francis Ltd. |
| Additional Information: | The copyright for this article belongs to Taylor and Francis Ltd. |
| Keywords: | ConvLSTM model; custom loss function; Indian summer monsoon; remote sensing; Short Range Forecasting; station data; TRMM data |
| Department/Centre: | Division of Mechanical Sciences > Centre for Atmospheric & Oceanic Sciences |
| Date Deposited: | 15 Nov 2022 06:29 |
| Last Modified: | 15 Nov 2022 06:29 |
| URI: | https://eprints.iisc.ac.in/id/eprint/77921 |
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