MIT researchers proposed working with deep learning address the challenges of accurately understanding and modeling the planetary boundary layer (PBL) to improve weather forecasting and climate projections and address issues such as droughts. Current technology struggles to resolve important features of the PBL, such as its height, which significantly affects the weather and climate near the Earth's surface. Therefore, there is an urgent need to develop better methods for imaging and analyzing PBL to improve our understanding of atmospheric processes.
Current operational algorithms for analyzing the atmosphere, including PBL, use shallow neural networks to retrieve temperature and humidity data from measurements from satellite instruments. These methods work to some extent, but they cannot solve very complicated ABP structures. To address this, researchers at Lincoln Laboratory want to use deep learning techniques, treating the atmosphere over a region of interest as a 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 the PBL. According to the researchers, they can better understand the complicated dynamics of ABP by using newer deep learning and artificial intelligence (ai) techniques.
He Proposed methodology It involves creating a dataset comprising a combination of real and simulated data to train deep learning models to image PBL. In collaboration with NASA, the researchers demonstrate that these newer deep learning-based retrieval algorithms can improve PBL detail, including more accurate determination of PBL height compared to previous methods. Furthermore, the deep learning approach holds promise for improving drought prediction, a critical application that requires an understanding of PBL dynamics. By combining operational work with NASA's Jet Propulsion Laboratory and focusing on neural network techniques, researchers aim to further refine drought prediction models in the continental United States.
In conclusion, the paper attempts to respond to the critical need for improved methods to image and analyze the planetary boundary layer (PBL) to improve weather forecasting, climate projections, and drought prediction. The proposed approach, which leverages deep learning techniques, shows promise in overcoming current limitations and providing more accurate and detailed information on ABP dynamics. By incorporating a combination of real and simulated data and collaborating with NASA, the researchers demonstrate 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 B.tech from the Indian Institute of technology (IIT), Kharagpur. She is a technology enthusiast and has a keen interest in the scope of data science software and applications. She is always reading about the advancements in different fields of ai and ML.
