The synoptic scale variability of the Indian summer monsoon (ISM) is contributed by the weak cyclonic vortices known as low-pressure systems (LPSs). LPSs are the primary mechanism by which central Indian plains receive rainfall. Traditionally, synoptic variability is considered to have a low predictability. In the present study, we developed a framework, namely, LPS Neural Operator (LPSNO), using the neural operator-based deep learning to predict the spatial structure of daily mean sea level pressure anomalies over the Bay of Bengal at a resolution of 1°x1°. The proposed neural operator extends the Fourier neural operator framework by employing convolutional LSTMs in the operator backbone. Further, the mean sea level pressure is reconstructed using the predicted anomaly and the climatology, which is then used to track the LPSs using a Lagrangian tracking algorithm. The median pattern correlation between the predicted and actual mean sea-level pressure anomalies over the BoB is about 88%, 60%, and 50% for 24, 48, and 72-hour forecasts, respectively. The proposed model improves the accuracy of predictions compared with the earlier ConvLSTM models. The pattern correlation between the observed and predicted synoptic activity index (SAI) is 0.94, 0.9, and 0.87 for 1, 2, and 3-day ahead predictions, respectively. A well-trained model of LPSNO takes only ~3.2 s to generate a one-day forecast on a single GPU node of Nvidia V100, which is computationally extremely cheap compared to the conventional numerical weather prediction models. The proposed LPSNO can advance operational weather forecasting substantially.

Tropical cyclones (TCs) are the most destructive weather systems that form over the tropical oceans, with 90 storms forming globally every year. The timely detection and tracking of TCs are important for advanced warning to the affected regions. As these storms form over the open oceans far from the continents, remote sensing plays a crucial role in detecting them. Here we present an automated TC detection from satellite images based on a novel deep learning technique. In this study, we propose a multi-staged deep learning framework for the detection of TCs, including, (i) a detector - Mask Region-Convolutional Neural Network (R-CNN), (ii) a wind speed filter, and (iii) a classifier - CNN. The hyperparameters of the entire pipeline is optimized to showcase the best performance using Bayesian optimization. Results indicate that the proposed approach yields high precision (97.10%), specificity (97.59%), and accuracy (86.55%) for test images.

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.

The relatively weak tropical storms, known as low-pressure systems (LPSs), contribute as much as 60% of the seasonal precipitation over the hugely populated central India. More than a third of LPS are formed by the downstream amplification of the westward propagating disturbances from the Pacific. Here, we show that the downstream LPS genesis are associated with the tropical cyclones (TCs) in the Western North Pacific (WNP). Four major clusters of landfalling TCs that have a relatively lesser degree of recurvature account for about 83% of the downstream LPS genesis. Causality in the fluctuations of the sea level pressure over the Bay of Bengal (BoB) prior to the initiation of LPS is attributed to the Rossby wave activity over WNP through transfer entropy analysis. Our results suggest a potential for the prediction of the downstream synoptic activity over the BoB at least seven days ahead.

The effect of polar sea ice melt on low latitude climate is little known. In order to understand the response of Indian summer monsoon (ISM) to the sea ice melt, we have run a suite of coupled and uncoupled climate model simulations. In one set of simulations, the albedo of sea ice is changed in such a way that it would melt as a result of increased absorption of solar radiation. We find a substantial weakening of ISM in sea ice melt experiments. Further, the genesis frequency of monsoon low-pressure systems (LPS) declines by about 40% in the sea ice melt simulations. A weakening and equatorward shift of ITCZ causes the decline in LPS genesis. Overall, the response of ISM to the sea ice melt resembles the response to greenhouse gas induced warming.