This article summary is based on the research paper: 'Precipitaion Nowcasting using Deep Neural Network' and Techxplore post. All credits for this research goes to the authors of this paper. 👏 👏 👏 👏 Please don't forget to join our ML Subreddit Need help in creating ML Research content for your lab/startup? Talk to us at [email protected]
Deep learning models are incredibly successful in analyzing massive quantities of data and accurately forecasting future occurrences.
Meteorologists can now fairly accurately forecast broad weather patterns for the next two to three days. However, climate change has increased unexpected extreme extreme weather events such as thunderstorms, hailstorms, and hurricanes. Predicting these unexpected weather phenomena accurately a few hours ahead of time might help people prepare for them, perhaps reducing their effects and negative consequences.
Three deep neural networks have recently been constructed by researchers at IRT AESE Saint Exupéry and Météo-France to anticipate oncoming precipitation. These networks, first described in a study pre-published on arXiv, might help meteorologists, governments, sports event organizers, and other organizations forecast the advent of storms, hurricanes, and other extreme weather phenomena one to six hours ahead time.
The researchers noted in their report, “We suggest using three prominent deep learning models, the three being U-net, ConvLSTM, and SVG-LP, trained on two-dimensional precipitation maps for precipitation nowcasting.” “We also suggested a patch extraction approach for obtaining high-resolution precipitation maps.”