Exploring the Existence of Atmospheric Blocking’s Precursor Patterns with Physics-Informed Explainable AI
Keywords: Physics-Informed Machine Learning; Explainable AI; Earth and Atmospheric Science; Atmospheric Blocking
TL;DR: We propose the integration of Two-Layer QG Model into Explainable AI framework, forming a new hypothesis regarding the existence of atmospheric blocking's precursor patterns.
Abstract: Atmospheric blocking is an atmospheric flow pattern that is quasi-stationary, self-sustaining, and long-lasting that effectively blocks the prevailing westerly atmospheric flows. This blocking is directly linked to large-scale extreme events such as heat waves, yet there is no confirmed study on the precursor patterns that signal atmospheric blocking’s evolution. In this paper, we investigate the combination of physics, Convolutional Neural Network (CNN), and eXplainable Artificial Intelligence (XAI) to form a scientific hypothesis: precursor patterns of atmospheric blocking do exist. To investigate the predictability and search for signals of the existence of precursor blocking patterns, we integrated the Two-Layer Quasi Geostrophic (QG) Model, an idealized model of atmospheric evolution, into the training process of CNN and predict atmospheric blocking, reaching the prediction accuracy of 95%, 88%, and 72% at 1, 5, and 12 lead days, respectively. Next, we employ XAI to highlight spatial patterns that guide CNN’s prediction. The resulting composite patterns highlighted by XAI algorithms are physically consistent with the composite ground truth observations at different lead days. This work hypothesizes the existence of atmospheric blocking’s precursor patterns, motivating future fundamental research directions focusing specifically on these precursor patterns.
Submission Number: 43
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