GenAI-assisted Intensity and Track Forecasting of Tropical Cyclones in Bay of Bengal using a hybrid Deep Learning architecture

Tropical cyclone (TC) is one of the most hazardous and extreme weather events that permanently affect lives of all forms with increased severity over densely populated coastal regions. For decades, numerical weather prediction (NWP) models that solve complex mathematical equations to predict TC properties such as genesis, intensity and track, have been used with good effect. Due to climate change, TCs are set to become more frequent and intense, greatly endangering human lives and affecting biodiversity along the coastal regions. Thereby, multi-modal forecasts along with NWP predictions and strategic dissemination of information amongst the masses is required. Deep Learning (DL) models are yielding very good results across multiple domains on unstructured data including time series. Consequently, DL techniques are being developed to forecast various aspects of TCs too. In the current work, INSAT-3D satellite imagery in thermal infrared band TIR1 from Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC), Government of India, and best track data from India Meteorological Department (IMD) of 64 TCs that occurred over Bay of Bengal (BoB) from 2013 to 2023 are used to model the intensity and track. Intensity of TCs is represented using estimated central pressure (ECP) and maximum sustained surface wind speed (MSW) and tracks of TCs are represented using latitude (LAT) and longitude (LON) of the centre of the TCs. These data are collected from IMD annual reports. Since the INSAT-3D data represent satellite image time series, traditional Convolution Neural Network (CNN) alone would not suffice. A two-branch DL architecture based on Long Short-Term Memory (LSTM) (for processing intensity and track) and Convolution LSTM (ConvLSTM) (for processing the time series of satellite images) algorithms is modelled on the available data to obtain simultaneous short-term forecasting of both intensity and track of TCs. The best model predicts intensity with an error of 4.68±1.95 knots and 3.45±0.38 hPa and track with an error of 169.58±48.02 km for a lead time of six hours. However, the INSAT-3D data contains missing images for a large number of timestamps. A sub-field of DL known as generative artificial intelligence (GenAI) has excelled in generating new data from existing data. The fractured MOSDAC dataset is repaired to a large extent using a hybrid ConvLSTM-CNN architecture by generating images at the timestamps where satellite observations are unavailable. All gaps of 1-3 images are filled using this technique. The images are generated with an average structural similarity index measure (SSIM) of 0.96 and an average peak signal to noise ratio of 30.42 dB. The new augmented dataset is modelled for forecasting the intensity and track of TCs using the earlier architecture. The results improved significantly to give intensity with an error of 2.86±2.00 knots and 3.03±1.84 hPa and track with an error of 31.01±11.35 km for a lead time of six hours. Additionally, experiments for longer lead times also could be conducted. Thus, given a high-quality dataset, TC intensity and track can be forecast with good levels of accuracy and can be used to supplement the forecasts of traditional numerical techniques.