Four-Dimensional Variational Data Assimilation Experiments for a Heavy Rain Case During the 2002 IOP in China

ZHANG Lin; NI Yunqi

doi:10.1007/BF02918519

Impact Factor: 5.8

Volume 22 Issue 2

Mar. 2005

Article Contents

Article > ADVANCES IN ATMOSPHERIC SCIENCES > 2005 > 22(2): 300-312

Four-Dimensional Variational Data Assimilation Experiments for a Heavy Rain Case During the 2002 IOP in China

ZHANG Lin,
NI Yunqi

1.
Department of Atmospheric Sciences, Nanjing University, Nanjing 210093,State Key Laboratory of Sever Weather, Chinese Academy of Meteorological Sciences, Beijing 100081

ZHANG Lin,
NI Yunqi

doi: 10.1007/BF02918519

Abstract
References
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Abstract:
A heavy rainfall event along the mei-yu front during 22-23 June 2002 was chosen for this study. To assess the impact of the routine and additional IOP (intensive observation period) radiosonde observations on the mesoscale heavy rainfall forecast, a series of four-dimensional variational (4DVAR) data assimilation and model simulation experiments was conducted using nonhydrostatic mesoscale model MM5 and the MM5 4DVAR system. The effects of the intensive observations in the different areas on the heavy rainfall forecast were also investigated. The results showed that improvement of the forecast skill for mesoscale heavy rainfall intensity was possible from the assimilation of the IOP radiosonde observations. However,the impact of the IOP observations on the forecast of the rainfall pattern was not significant. Initial conditions obtained through the 4DVAR experiments with a 12-h assimilation window were capable of improving the 24-h forecast. The simulated results after the assimilation showed that it would be best to perform the intensive radiosonde observations in the upstream of the rainfall area and in the moisture passageway area at the same time. Initial conditions created by the 4DVAR led to the low-level moisture convergence over the rainfall area, enhanced frontogenesis and upward motion within the mei-yu front,and intensified middle- and high-level unstable stratification in front of the mei-yu front. Consequently,the heavy rainfall forecast was improved.
- intensive radiosonde observations,
- four-dimensional variational assimilation
References

[1]	ZHANG Meng, NI Yunqi, ZHANG Fuqing, 2007: Variational Assimilation of GPS Precipitable Water Vapor and Hourly Rainfall Observations for a Meso- Scale Heavy Precipitation Event During the 2002 Mei-Yu Season, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 509-526. doi: 10.1007/s00376-007-0509-8
[2]	Xingchao CHEN, Kun ZHAO, Juanzhen SUN, Bowen ZHOU, Wen-Chau LEE, 2016: Assimilating Surface Observations in a Four-Dimensional Variational Doppler Radar Data Assimilation System to Improve the Analysis and Forecast of a Squall Line Case, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 1106-1119. doi: 10.1007/s00376-016-5290-0
[3]	Fuqing ZHANG, Meng ZHANG, James A. HANSEN, 2009: Coupling Ensemble Kalman Filter with Four-dimensional Variational Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 1-8. doi: 10.1007/s00376-009-0001-8
[4]	WANG Bin, LIU Juanjuan, WANG Shudong, CHENG Wei, LIU Juan, LIU Chengsi, Qingnong XIAO, Ying-Hwa KUO, 2010: An Economical Approach to Four-dimensional Variational Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 715-727. doi: 10.1007/s00376-009-9122-3
[5]	Chuan GAO, Xinrong WU, Rong-Hua ZHANG, 2016: Testing a Four-Dimensional Variational Data Assimilation Method Using an Improved Intermediate Coupled Model for ENSO Analysis and Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 875-888. doi: 10.1007/s00376-016-5249-1
[6]	LIU Juanjuan, WANG Bin, WANG Shudong, 2010: The Structure of Background-error Covariance in a Four-dimensional Variational Data Assimilation System: Single-point Experiment, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1303-1310. doi: 10.1007/s00376-010-9067-6
[7]	LIU Juan, WANG Bin, LIU Hailong, YU Yongqiang, 2008: A New Global Four-Dimensional Variational Ocean Data Assimilation System and Its Application, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 680-691. doi: 10.1007/s00376-008-0680-6
[8]	LIU Juanjuan, WANG Bin, 2011: Rainfall Assimilation Using a New Four-Dimensional Variational Method: A Single-Point Observation Experiment, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 735-742. doi: 10.1007/s00376-010-0061-9
[9]	Hongqin ZHANG, Xiangjun TIAN, Wei CHENG, Lipeng JIANG, 2020: System of Multigrid Nonlinear Least-squares Four-dimensional Variational Data Assimilation for Numerical Weather Prediction (SNAP): System Formulation and Preliminary Evaluation, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1267-1284. doi: 10.1007/s00376-020-9252-1
[10]	Nathan SNOOK, Qinghong ZHANG, 2016: A Four-Dimensional Variational System for Skillful Operational Prediction of Convective Storms, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 1102-1103. doi: 10.1007/s00376-016-6170-3
[11]	Qizhen SUN, Timo VIHMA, Marius O. JONASSEN, Zhanhai ZHANG, 2020: Impact of Assimilation of Radiosonde and UAV Observations from the Southern Ocean in the Polar WRF Model, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 441-454. doi: 10.1007/s00376-020-9213-8
[12]	Hongrong SHI, Hongbin CHEN, Xiang'ao XIA, Xuehua FAN, Jinqiang ZHANG, Jun LI, Chao LING, 2017: Intensive Radiosonde Measurements of Summertime Convection over the Inner Mongolia Grassland in 2014: Difference between Shallow Cumulus and Other Conditions, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 783-790. doi: 10.1007/s00376-017-6284-2
[13]	Ken-ichi SHIMOSE, Ming XUE, Robert D. PALMER, Jidong GAO, Boon Leng CHEONG, David J. BODINE, 2013: Two-Dimensional Variational Analysis of Near-Surface Moisture from Simulated Radar Refractivity-Related Phase Change Observations, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 291-305. doi: 10.1007/s00376-012-2087-7
[14]	LIU Ye, YAN Changxiang, 2010: Application of a Recursive Filter to a Three-Dimensional Variational Ocean Data Assimilation System, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 293-302. doi: 10.1007/s00376-009-8112-9
[15]	ZHANG Xiaoyan, WANG Bin, JI Zhongzhen, Qingnong XIAO, ZHANG Xin, 2003: Initialization and Simulation of a Typhoon Using 4-Dimensional Variational Data Assimilation-Research on Typhoon Herb(1996), ADVANCES IN ATMOSPHERIC SCIENCES, 20, 612-622. doi: 10.1007/BF02915504
[16]	Chaoqun MA, Tijian WANG, Zengliang ZANG, Zhijin LI, 2018: Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 813-825. doi: 10.1007/s00376-017-7179-y
[17]	ZENG Zhihua, DUAN Yihong, LIANG Xudong, MA Leiming, Johnny Chung-leung CHAN, 2005: The Effect of Three-Dimensional Variational Data Assimilation of QuikSCAT Data on the Numerical Simulation of Typhoon Track and Intensity, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 534-544. doi: 10.1007/BF02918486
[18]	Kazutoshi SATO, Jun INOUE, Akira YAMAZAKI, Naohiko HIRASAWA, Konosuke SUGIURA, Kyohei YAMADA, 2020: Antarctic Radiosonde Observations Reduce Uncertainties and Errors in Reanalyses and Forecasts over the Southern Ocean: An Extreme Cyclone Case, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 431-440. doi: 10.1007/s00376-019-8231-x
[19]	WANG Yunfeng, WANG Bin, 2003: The Variational Assimilation Experiment of GPS Bending Angle, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 479-486. doi: 10.1007/BF02690806
[20]	Zhu Keyun, 2001: On the 4D Variational Data Assimilation with Constraint Conditions, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 1131-1145. doi: 10.1007/s00376-001-0028-y

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Manuscript History

Manuscript received: 10 March 2005

Manuscript revised: 10 March 2005

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Four-Dimensional Variational Data Assimilation Experiments for a Heavy Rain Case During the 2002 IOP in China

ZHANG Lin,
NI Yunqi

1. Department of Atmospheric Sciences, Nanjing University, Nanjing 210093,State Key Laboratory of Sever Weather, Chinese Academy of Meteorological Sciences, Beijing 100081

Manuscript received: 2005-03-10
Manuscript revised: 2005-03-10

Abstract: A heavy rainfall event along the mei-yu front during 22-23 June 2002 was chosen for this study. To assess the impact of the routine and additional IOP (intensive observation period) radiosonde observations on the mesoscale heavy rainfall forecast, a series of four-dimensional variational (4DVAR) data assimilation and model simulation experiments was conducted using nonhydrostatic mesoscale model MM5 and the MM5 4DVAR system. The effects of the intensive observations in the different areas on the heavy rainfall forecast were also investigated. The results showed that improvement of the forecast skill for mesoscale heavy rainfall intensity was possible from the assimilation of the IOP radiosonde observations. However,the impact of the IOP observations on the forecast of the rainfall pattern was not significant. Initial conditions obtained through the 4DVAR experiments with a 12-h assimilation window were capable of improving the 24-h forecast. The simulated results after the assimilation showed that it would be best to perform the intensive radiosonde observations in the upstream of the rainfall area and in the moisture passageway area at the same time. Initial conditions created by the 4DVAR led to the low-level moisture convergence over the rainfall area, enhanced frontogenesis and upward motion within the mei-yu front,and intensified middle- and high-level unstable stratification in front of the mei-yu front. Consequently,the heavy rainfall forecast was improved.

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