Prediction of Synoptic-Scale Sea Level Pressure Over the Indian Monsoon Region Using Deep Learning
In one sentence. ConvLSTM predicts daily Indian monsoon sea level pressure anomalies at 7-day lead, with better synoptic skill than a conventional NWP model.
Abstract
The synoptic-scale (3–7 days) variability is a dominant contributor to the Indian summer monsoon (ISM) seasonal precipitation. An accurate prediction of ISM precipitation by dynamical or statistical model remains a challenge. Here, we show that the sea level pressure (SLP) can be used as a proxy to predict the active-break cycle as well as the genesis of low-pressure systems (LPSs), using a deep learning model, namely convolutional long short-term memory (ConvLSTM) networks. The deep learning model is able to reliably predict the daily SLP anomalies over central India and the Bay of Bengal at a lead time of 7 days. As the fluctuations in SLP drive the changes in the strength of the atmospheric circulation, the prediction of SLP anomalies is useful in predicting the intensity of ISM. A comparison of the ConvLSTM predicted SLP with the forecast of a conventional numerical weather prediction model indicates that the deep learning model possesses better skill in capturing the synoptic-scale SLP fluctuations over central India and Bay of Bengal.
Labels
- Topic
- Global medium-range NWP
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM, CNN / U-Net
- Time scale
- 3–15 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.1109/lgrs.2021.3100899 OpenAlex:W3157896282
- Added
- 2026-10-22
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