Applying Machine Learning in Numerical Weather and Climate Modeling Systems

被引：2

作者：

Krasnopolsky, Vladimir ^{[1
]}

机构：

[1] Natl Ctr Environm Predict, Environm Modeling Ctr, College Pk, MD 20740 USA

来源：

CLIMATE | 2024年 / 12卷 / 06期

关键词：

machine learning; numerical weather modeling; numerical climate modeling; post-processing; neural networks; deep learning; NEURAL-NETWORK EMULATIONS; RADIATION; PARAMETERIZATION; APPROXIMATION; ASSIMILATION; SIMULATIONS; ACCURATE; PHYSICS; MICROPHYSICS; PREDICTION;

D O I：

10.3390/cli12060078

中图分类号：

P4 [大气科学（气象学）];

学科分类号：

0706 ; 070601 ;

摘要：

In this paper major machine learning (ML) tools and the most important applications developed elsewhere for numerical weather and climate modeling systems (NWCMS) are reviewed. NWCMSs are briefly introduced. The most important papers published in this field in recent years are reviewed. The advantages and limitations of the ML approach in applications to NWCMS are briefly discussed. Currently, this field is experiencing explosive growth. Several important papers are published every week. Thus, this paper should be considered as a simple introduction to the problem.

引用

页数：20

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