Evaluation and Statistical Post‐Processing of Two Precipitation Reforecast Products During Summer in the Mainland of China-Reference-Cited by-同舟云学术

Evaluation and Statistical Post‐Processing of Two Precipitation Reforecast Products During Summer in the Mainland of China

Published:2022-06-20 Issue:12 Volume:127 Page:
ISSN:2169-897X
Container-title:Journal of Geophysical Research: Atmospheres
language:en
Short-container-title:JGR Atmospheres

Author:

Li Wentao¹²^ORCID,Duan Qingyun¹²^ORCID,Wang Quan J.³^ORCID,Huang Sainan¹²,Liu Shiyuan¹²

Affiliation:

1. State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering and College of Hydrology & Water Resources Hohai University Nanjing China

2. CMA‐HHU Joint Laboratory for HydroMeteorological Studies Nanjing China

3. The University of Melbourne Melbourne VIC Australia

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

American Geophysical Union (AGU)

Subject

Space and Planetary Science,Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022JD036606

Reference51 articles.

1. The Schaake Shuffle: A Method for Reconstructing Space–Time Variability in Forecasted Precipitation and Temperature Fields

2. A Review of Quantitative Precipitation Forecasts and Their Use in Short- to Medium-Range Streamflow Forecasting

3. Handbook of Hydrometeorological Ensemble Forecasting

4. ECMWF. (2021).ECMWF ensemble re‐forecasts.Operational Archive[Dataset]. Retrieved fromhttps://www.ecmwf.int/en/forecasts/dataset/operational-archive

5. On the effect of ensemble size on the discrete and continuous ranked probability scores

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