IIT-Bhubaneswar pioneers new rain predictions technology

Bhubaneswar, Aug 13: The Indian Institute of Technology (IIT)-Bhubaneswar has made a significant breakthrough in weather forecasting by integrating the Weather Research and Forecasting (WRF) model with a deep learning (DL) model. This hybrid technology aims to improve the accuracy of predictions, particularly focusing on forecasting heavy rainfall events with adequate lead time. This advancement highlights the potential of artificial intelligence in enhancing real-time weather forecasts, especially in the diverse terrains of the Indian region, according to official sources on Monday.

The research was conducted over the complex landscape of Assam, a region highly prone to severe flooding, in June 2023, and over Odisha, where heavy rainfall events are influenced by the arrival of multiple intense monsoon low-pressure systems. "In Assam, the hybrid model's prediction accuracy at the district level is almost double that of traditional ensemble models, with a lead time of up to 96 hours, demonstrating its exceptional performance. These innovative studies were based on retrospective cases," sources revealed.

In another groundbreaking study, researchers from IIT-Bhubaneswar have significantly advanced the accurate prediction of heavy rainfall events over the region in real time using deep learning techniques. The study showcased the robustness of the new hybrid technology in real-time scenarios over Assam's complex terrain. "The study, titled 'Minimization of Forecast Error Using Deep Learning for Real-Time Heavy Rainfall Events Over Assam,' published in IEEE Xplore, revealed that integrating DL with the traditional WRF model substantially enhances forecast accuracy for heavy rainfall events in real time, a critical development for Assam, a flood-prone mountainous region," added sources.

Between 13th and 17th June 2023, Assam experienced severe flooding due to heavy rainfall. The DL model accurately predicted the spatial distribution and intensity of rainfall at the district scale. The research utilised the WRF model to generate initial weather forecasts in real-time, which were then refined using the DL model. Experts employing this new method can now perform a more detailed analysis of rainfall patterns, incorporating a spatio-attention module to capture intricate spatial dependencies in the data.

The model was trained using data from past heavy rainfall events, multiple ensemble outputs, and observations from the India Meteorological Department (IMD) to improve its accuracy. "This advancement is crucial for mitigating the impacts of natural disasters and ensuring public safety. Moreover, these pioneering works will guide the development of similar hybrid models for other complex topographical terrains, such as the Western Himalayas and the Western Ghats regions of India," official sources said.