YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • AMS
    • Artificial Intelligence for the Earth Systems
    • View Item
    •   YE&T Library
    • AMS
    • Artificial Intelligence for the Earth Systems
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Understanding Predictability of Daily Southeast U.S. Precipitation Using Explainable Machine Learning

    Source: Artificial Intelligence for the Earth Systems:;2022:;volume( 001 ):;issue: 004
    Author:
    Kathy Pegion
    ,
    Emily J. Becker
    ,
    Ben P. Kirtman
    DOI: 10.1175/AIES-D-22-0011.1
    Publisher: American Meteorological Society
    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.
    • Download: (3.553Mb)
    • Show Full MetaData Hide Full MetaData
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Understanding Predictability of Daily Southeast U.S. Precipitation Using Explainable Machine Learning

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4290395
    Collections
    • Artificial Intelligence for the Earth Systems

    Show full item record

    contributor authorKathy Pegion
    contributor authorEmily J. Becker
    contributor authorBen P. Kirtman
    date accessioned2023-04-12T18:52:28Z
    date available2023-04-12T18:52:28Z
    date copyright2022/11/29
    date issued2022
    identifier otherAIES-D-22-0011.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4290395
    description abstractWe 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.
    publisherAmerican Meteorological Society
    titleUnderstanding Predictability of Daily Southeast U.S. Precipitation Using Explainable Machine Learning
    typeJournal Paper
    journal volume1
    journal issue4
    journal titleArtificial Intelligence for the Earth Systems
    identifier doi10.1175/AIES-D-22-0011.1
    treeArtificial Intelligence for the Earth Systems:;2022:;volume( 001 ):;issue: 004
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian