contributor author | Kathy Pegion | |
contributor author | Emily J. Becker | |
contributor author | Ben P. Kirtman | |
date accessioned | 2023-04-12T18:52:28Z | |
date available | 2023-04-12T18:52:28Z | |
date copyright | 2022/11/29 | |
date issued | 2022 | |
identifier other | AIES-D-22-0011.1.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4290395 | |
description abstract | We investigate the predictability of the sign of daily southeastern U.S. (SEUS) precipitation anomalies associated with simultaneous predictors of large-scale climate variability using machine learning models. Models using index-based climate predictors and gridded fields of large-scale circulation as predictors are utilized. Logistic regression (LR) and fully connected neural networks using indices of climate phenomena as predictors produce neither accurate nor reliable predictions, indicating that the indices themselves are not good predictors. Using gridded fields as predictors, an LR and convolutional neural network (CNN) are more accurate than the index-based models. However, only the CNN can produce reliable predictions that can be used to identify forecasts of opportunity. Using explainable machine learning we identify which variables and grid points of the input fields are most relevant for confident and correct predictions in the CNN. Our results show that the local circulation is most important as represented by maximum relevance of 850-hPa geopotential heights and zonal winds to making skillful, high-probability predictions. Corresponding composite anomalies identify connections with El Niño–Southern Oscillation during winter and the Atlantic multidecadal oscillation and North Atlantic subtropical high during summer. | |
publisher | American Meteorological Society | |
title | Understanding Predictability of Daily Southeast U.S. Precipitation Using Explainable Machine Learning | |
type | Journal Paper | |
journal volume | 1 | |
journal issue | 4 | |
journal title | Artificial Intelligence for the Earth Systems | |
identifier doi | 10.1175/AIES-D-22-0011.1 | |
tree | Artificial Intelligence for the Earth Systems:;2022:;volume( 001 ):;issue: 004 | |
contenttype | Fulltext | |