Articles | Volume 15, issue 6
https://doi.org/10.5194/essd-15-2329-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-2329-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
East Asia Reanalysis System (EARS)
Jinfang Yin
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Xudong Liang
CORRESPONDING AUTHOR
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Yanxin Xie
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Feng Li
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Kaixi Hu
National Meteorological Information Center, China Meteorological Administration, Beijing 100081, China
Lijuan Cao
National Meteorological Information Center, China Meteorological Administration, Beijing 100081, China
Feng Chen
Zhejiang Institute of Meteorological Sciences, Hangzhou 310008, China
Haibo Zou
Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education, Jiangxi Normal University, Nanchang 330022, China
Feng Zhu
Inner Mongolia Meteorological Observatory, Hohhot 010051, China
Xin Sun
Inner Mongolia Meteorological Observatory, Hohhot 010051, China
Jianjun Xu
College of Oceanography and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China
Geli Wang
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Ying Zhao
School of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Juanjuan Liu
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
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Nat. Hazards Earth Syst. Sci., 25, 1719–1735, https://doi.org/10.5194/nhess-25-1719-2025, https://doi.org/10.5194/nhess-25-1719-2025, 2025
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A persistent severe rainfall event occurred over North China in July 2023, which was regarded as one of the most extreme episodes globally during that year. The extreme rainfall was significantly underestimated by forecasters at that time. Flooding from this event affected 1.3 million people, causing severe human casualties and economic losses. We examined the convective initiation and subsequent persistent heavy rainfall based on simulations with the Weather Research and Forecasting model.
Yasir Latif, Kaiyu Fan, Geli Wang, and Milan Paluš
Earth Syst. Dynam., 15, 1509–1526, https://doi.org/10.5194/esd-15-1509-2024, https://doi.org/10.5194/esd-15-1509-2024, 2024
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The El Niño–Southern Oscillation (ENSO) is a gigantic natural orchestra playing with the temperature of Pacific waters and influencing air temperature and rainfall worldwide. Naturally, the “loudness” or amplitude of ENSO has effects on climate; however, consonance of its various tones, or phases of different ENSO oscillatory components, can exert causal effects on rainfall in some areas in China. In different regions, different aspects of ENSO dynamics can predict rainfall amounts.
Jinfang Yin, Xudong Liang, Hong Wang, and Haile Xue
Geosci. Model Dev., 15, 771–786, https://doi.org/10.5194/gmd-15-771-2022, https://doi.org/10.5194/gmd-15-771-2022, 2022
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An ensemble (EN) approach was designed to improve autoconversion (ATC) from cloud water to rainwater in cloud microphysics schemes. One unique feature of the EN approach is that the ATC rate is a mean value based on the calculations from several widely used ATC schemes. The ensemble approach proposed herein appears to help improve the representation of cloud and precipitation processes in weather and climate models.
Chao Sun, Li Liu, Ruizhe Li, Xinzhu Yu, Hao Yu, Biao Zhao, Guansuo Wang, Juanjuan Liu, Fangli Qiao, and Bin Wang
Geosci. Model Dev., 14, 2635–2657, https://doi.org/10.5194/gmd-14-2635-2021, https://doi.org/10.5194/gmd-14-2635-2021, 2021
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Data assimilation (DA) provides better initial states of model runs by combining observations and models. This work focuses on the technical challenges in developing a coupled ensemble-based DA system and proposes a new DA framework DAFCC1 based on C-Coupler2. DAFCC1 enables users to conveniently integrate a DA method into a model with automatic and efficient data exchanges. A sample DA system that combines GSI/EnKF and FIO-AOW demonstrates the effectiveness of DAFCC1.
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Short summary
A collection of regional reanalysis datasets has been produced. However, little attention has been paid to East Asia, and there are no long-term, physically consistent regional reanalysis data available. The East Asia Reanalysis System was developed using the WRF model and GSI data assimilation system. A 39-year (1980–2018) reanalysis dataset is available for the East Asia region, at a high temporal (of 3 h) and spatial resolution (of 12 km), for mesoscale weather and regional climate studies.
A collection of regional reanalysis datasets has been produced. However, little attention has...
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