Prediction of Synoptic-Scale Sea Level Pressure Over the Indian Monsoon Region Using Deep Learning

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...

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Veröffentlicht in:IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Hauptverfasser: Sinha, Aryaman, Gupta, Mayuna, Srujan, K. S. S. Sai, Kodamana, Hariprasad, Sandeep, S.
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Sprache:eng
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Zusammenfassung: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.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2021.3100899