Toward an improved ensemble of multi-source daily precipitation via joint machine learning classification and regression-Reference-Cited by-同舟云学术

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Toward an improved ensemble of multi-source daily precipitation via joint machine learning classification and regression

Published:2024-07 Issue: Volume:304 Page:107385
ISSN:0169-8095
Container-title:Atmospheric Research
language:en
Short-container-title:Atmospheric Research

Author:

Chen Hao,Wang Tiejun,Montzka Carsten,Gao Huiran,Guo Ning,Chen Xi,Vereecken Harry

Funder

China Postdoctoral Science Foundation

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference79 articles.

1. Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms;Ahmed;Atmos. Res.,2020

2. The newly merged satellite remotely sensed, gauge and reanalysis-based multi-source weighted-ensemble precipitation: evaluation over Australia and Africa (1981–2016);Awange;Sci. Total Environ.,2019

3. MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data;Beck;Hydrol. Earth Syst. Sci.,2017

4. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling;Beck;Hydrol. Earth Syst. Sci.,2017

5. MSWEP V2 global 3-hourly 0.1° precipitation: methodology and quantitative assessment;Beck;Bull. Am. Meteorol. Soc.,2019

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