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Advancing Sub-Seasonal Weather Predictability Through Machine Learning Techniques

Advancing Sub-Seasonal Weather Predictability Through Machine Learning Techniques
通过机器学习技术提高次季节天气预报的可预测性
批准号:
1822221
负责人:
Timothy Delsole
金额:
$45.97万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-09-01 至 2024-08-31

项目摘要

项目成果

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中文摘要
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英文摘要
Current operational weather forecasting systems can produce valuable predictions of day-to-day weather, but such predictions are only skillful up to a week or so in advance. On the longer subseasonal timescale, say between two and eight weeks, useful forecasts of mean conditions and the likelihood of extreme events may also be possible. But basic science questions, including the sources and mechanisms of subseasonal variability, their potential predictability, and the essential elements necessary for robust prediction, have not yet been resolved. These questions are of practical as well as scientific interest, as guidance from subseasonal forecasts could have a variety of uses including agricultural planning and emergency management.This project seeks to identify empirical predictive relationships between local climate variability of interest and large-scale patterns in relevant predictors such as sea surface temperature (SST), soil moisture, and atmospheric circulation. Prior work by the Principal Investigator (PI) and colleagues demonstrated such a relationship between heat waves in Texas, including the heat wave associated with the 2011 drought, and an SST pattern covering much of the North Pacific. A key limitation to such prediction methods is that the observed record is too short to identify statistically significant predictive relationships. Thus methods which seem successful when tested on past cases may fail when used for realtime prediction. The PI's strategy for circumventing this limitation is to identify predictive relationships using output from weather and climate models in place of observations, as many thousands of years of simulated weather and climate variability are available from a variety of modeling projects. Model output used here comes from the North American Multimodel Ensemble (NMME), the Subseasonal to Seasonal (S2S) Prediction Project, and the Coupled Model Intercomparison Project (CMIP). A further concern in developing empirical prediction methods is the need for regularization, meaning a way to eliminate spurious small-scale features in predictor patterns which arise due to the large number of data points used to represent the predictor fields. Such features are not usually consistent from model to model or between model output and observations. The PI uses a regularization scheme in which eigenvectors of the Laplacian operator serve to factor out small scales, leading to more robust predictive relationships. To further ensure robust predictions, the PI applies an innovative cross validation technique which bypasses the best statistical model identified in cross validation in favor of the simplest model which is within a standard deviation of the best model.Once robust predictive relationships are identified that hold across different models and in observations, the sources and mechanisms responsible for the relationships will be explored. If the empirical methods can reproduce hindcasts from the models in the NMME archive then the model output can be used to understand the time-evolving dynamical processes linking the predictor pattern to the predicted variability.In addition to the societal benefits of subseasonal forecasts, the project provides support and training to a postdoc, thereby promoting workforce development in this research area. The project also addresses public scientific literacy through public seminars by the PI on sub-seasonal prediction, a topic of interest to the general public.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(5)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1016/j.spl.2021.109064
发表时间: 2021-03-12
期刊: STATISTICS & PROBABILITY LETTERS
影响因子: 0.8
作者: [DelSole, Timothy, Tippett, Michael K.]
通讯作者: Tippett, Michael K.
Advancing interpretability of machine-learning prediction models
提高机器学习预测模型的可解释性
DOI: 10.1017/eds.2022.13
发表时间: 2022
期刊: Environmental Data Science
影响因子: --
作者: [Trenary, Laurie, DelSole, Timothy]
通讯作者: DelSole, Timothy
DOI: 10.1002/sta4.385
发表时间: 2021-12
期刊: Stat (International Statistical Institute)
影响因子: --
作者: [DelSole T, Tippett MK]
通讯作者: Tippett MK
DOI: 10.1017/eds.2023.2
发表时间: 2023-02
期刊: Environmental Data Science
影响因子: --
作者: [L. Trenary;T. DelSole]
通讯作者: L. Trenary;T. DelSole
Collaborative Research: Physics-Based Machine Learning for Sub-Seasonal Climate Forecasting
  • 批准号:
    1934529
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $41.48万
  • 财政年份:
    2019
  • 负责人:
    Timothy Delsole
  • 依托单位:
Improving Estimates of Anthropogenic Aerosol Cooling and Climate Sensitivity
  • 批准号:
    1622295
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $44.82万
  • 财政年份:
    2016
  • 负责人:
    Timothy Delsole
  • 依托单位:
EAGER: Collaborative Research: Learning Relations between Extreme Weather Events and Planet-Wide Environmental Trends
  • 批准号:
    1451945
  • 项目类别:
    Standard Grant
  • 资助金额:
    $9.99万
  • 财政年份:
    2014
  • 负责人:
    Timothy Delsole
  • 依托单位:
国内基金
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面向6G 通信的 Sub-9GHz 宽带功率放大器关键技术研究
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    ZCLMS26F0103
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2026
  • 负责人:
    李嘉进
  • 依托单位:
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  • 批准号:
    82304381
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    30万元
  • 批准年份:
    2023
  • 负责人:
    柴昕
  • 依托单位:
SDSS宽发射线活动星系核中基于光谱及光学准周期光变对sub-pc双黑洞系统的研究
  • 批准号:
    12373014
  • 项目类别:
    面上项目
  • 资助金额:
    55万元
  • 批准年份:
    2023
  • 负责人:
    张雪光
  • 依托单位: