Prediction of synoptic-scale sea level pressure over the Indian monsoon
region using deep learning
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 models
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 (LPS), 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. It is demonstrated that the ConvLSTM
possesses better prediction skill compared to a conventional numerical
weather prediction model, indicating the usefulness of a physics guided
deep learning model in medium range weather forecasting.