MIT researchers Proposing cooperation with deep learning Tackle the challenge of understanding and accurately modeling the planetary boundary layer (PBL) to improve weather and climate predictions and address problems such as drought. Current technologies struggle to resolve important characteristics of PBLs, such as height, which has a significant impact on near-surface weather and climate. Therefore, there is an urgent need to develop better methods to image and analyze PBLs to improve our understanding of atmospheric processes.
Current operational algorithms for analyzing the atmosphere, including PBL, utilize shallow neural networks to obtain temperature and humidity data from satellite instrument measurements. Although these methods work to some extent, they cannot resolve very complex PBL structures. To address this, researchers at Lincoln Laboratory want to use deep learning techniques to treat the atmosphere of a region of interest as his three-dimensional image. This approach aims to improve the statistical representation of 3D temperature and humidity images to provide more accurate and detailed information about PBL. Using new deep learning and artificial intelligence (AI) techniques, researchers say they can better understand the complex dynamics of PBL.
of Proposed methodology It involves creating a dataset that combines real and simulated data to train a deep learning model for imaging PBL. Researchers collaborated with NASA to demonstrate that these new search algorithms based on deep learning can enhance PBL details, including more accurate determination of PBL height compared to previous methods. Additionally, deep learning approaches show promise for improving drought prediction, an important application that requires an understanding of PBL dynamics. By combining operations work with NASA's Jet Propulsion Laboratory and focusing on neural network technology, researchers aim to further improve drought prediction models for the continental United States.
In conclusion, this paper attempts to address the critical need for improved methods for planetary boundary layer (PBL) imaging and analysis to improve weather forecasting, climate prediction, and drought prediction. The proposed approach leveraging deep learning techniques shows the potential to overcome current limitations and provide more accurate and detailed information about PBL dynamics. By combining real and simulated data and collaborating with NASA, researchers are demonstrating the potential to significantly advance our understanding of PBL and its impact on various atmospheric processes.
Pragati Jhunjhunwala is a consulting intern at MarktechPost. She is currently pursuing her bachelor's degree from Indian Institute of Technology (IIT), Kharagpur. She is a technology enthusiast and has a keen interest in software and data. She has a strong interest in a range of science applications. She is constantly reading about developments in various areas of AI and ML.
