GSTDTAP  > 气候变化
DOI10.1029/2019GL083662
Improving Atmospheric River Forecasts With Machine Learning
Chapman, W. E.1; Subramanian, A. C.2; Delle Monache, L.1; Xie, S. P.1; Ralph, F. M.1
2019-09-06
发表期刊GEOPHYSICAL RESEARCH LETTERS
ISSN0094-8276
EISSN1944-8007
出版年2019
文章类型Article;Early Access
语种英语
国家USA
英文摘要

This study tests the utility of convolutional neural networks as a postprocessing framework for improving the National Center for Environmental Prediction's Global Forecast System's integrated vapor transport forecast field in the Eastern Pacific and western United States. Integrated vapor transport is the characteristic field of atmospheric rivers, which provide over 65% of yearly precipitation at some western U.S. locations. The method reduces full-field root-mean-square error (RMSE) at forecast leads from 3 hr to seven days (9-17% reduction), while increasing correlation between observations and predictions (0.5-12% increase). This represents an approximately one- to two-day lead time improvement in RMSE. Decomposing RMSE shows that random error and conditional biases are predominantly reduced. Systematic error is reduced up to five-day forecast lead, but accounts for a smaller portion of RMSE. This work demonstrates convolutional neural networks potential to improve forecast skill out to seven days for precipitation events affecting the western United States.


英文关键词atmospheric river machine learning convolutional neural network postprocess forecasting
领域气候变化
收录类别SCI-E
WOS记录号WOS:000485374700001
WOS关键词NUMERICAL WEATHER FORECASTS ; EXTREME PRECIPITATION ; NEURAL-NETWORKS ; TEMPERATURE ; ALGORITHM ; PREDICTIONS ; SATELLITE ; SCALE
WOS类目Geosciences, Multidisciplinary
WOS研究方向Geology
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/186952
专题气候变化
作者单位1.Univ Calif San Diego, Scripps Inst Oceanog, La Jolla, CA 92093 USA;
2.Univ Colorado, Atmospher & Ocean Sci, Boulder, CO 80309 USA
推荐引用方式
GB/T 7714
Chapman, W. E.,Subramanian, A. C.,Delle Monache, L.,et al. Improving Atmospheric River Forecasts With Machine Learning[J]. GEOPHYSICAL RESEARCH LETTERS,2019.
APA Chapman, W. E.,Subramanian, A. C.,Delle Monache, L.,Xie, S. P.,&Ralph, F. M..(2019).Improving Atmospheric River Forecasts With Machine Learning.GEOPHYSICAL RESEARCH LETTERS.
MLA Chapman, W. E.,et al."Improving Atmospheric River Forecasts With Machine Learning".GEOPHYSICAL RESEARCH LETTERS (2019).
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Chapman, W. E.]的文章
[Subramanian, A. C.]的文章
[Delle Monache, L.]的文章
百度学术
百度学术中相似的文章
[Chapman, W. E.]的文章
[Subramanian, A. C.]的文章
[Delle Monache, L.]的文章
必应学术
必应学术中相似的文章
[Chapman, W. E.]的文章
[Subramanian, A. C.]的文章
[Delle Monache, L.]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。