Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019GL083662 |
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
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ISSN | 0094-8276 |
EISSN | 1944-8007 |
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。 |
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