Variational Assimilation of GPS Precipitable Water Vapor and   Hourly Rainfall Observations for a Meso- Scale Heavy Precipitation Event During the 2002 Mei-Yu Season

ZHANG Meng; NI Yunqi; ZHANG Fuqing

doi:10.1007/s00376-007-0509-8

Impact Factor: 5.8

Volume 24 Issue 3

May 2007

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Article > ADVANCES IN ATMOSPHERIC SCIENCES > 2007 > 24(3): 509-526

Variational Assimilation of GPS Precipitable Water Vapor and Hourly Rainfall Observations for a Meso- Scale Heavy Precipitation Event During the 2002 Mei-Yu Season

1.
Department of Atmospheric Sciences, Nanjing University, Nanjing 210093; Department of Atmospheric Sciences, Texas A & M University, College Station, Texas 77843-3150, USA,State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081,Department of Atmospheric Sciences, Texas A & M University, College Station, Texas 77843-3150, USA

doi: 10.1007/s00376-007-0509-8

Abstract
References
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Abstract:
Recent advances in Global Positioning System (GPS) remote sensing technology allow for a direct estimation of the precipitable water vapor (PWV) from delayed signals transmitted by GPS satellites, which can be assimilated into numerical models with four-dimensional variational (4DVAR) data assimilation. A mesoscale model and its 4DVAR system are used to access the impacts of assimilating GPS-PWV and hourly rainfall observations on the short-range prediction of a heavy rainfall event on 20 June 2002. The heavy precipitation was induced by a sequence of meso-beta-scale convective systems (MCS) along the mei-yu front in China. The experiments with GPS-PWV assimilation successfully simulated the evolution of the observed MCS cluster and also eliminated the erroneous rainfall systems found in the experiment without 4DVAR assimilation. Experiments with hourly rainfall assimilation performed similarly both on the prediction of MCS initiation and the elimination of erroneous systems, however the MCS dissipated much sooner than it did in observations. It is found that the assimilation-induced moisture perturbation and mesoscale low-level jet are helpful for the MCS generation and development. It is also discovered that spurious gravity waves may post serious limitations for the current 4DVAR algorithm, which would degrade the assimilation efficiency, especially for rainfall data. Sensitivity experiments with different observations, assimilation windows and observation weightings suggest that assimilating GPS-PWV can be quite effective, even with the assimilation window as short as 1 h. On the other hand, assimilating rainfall observations requires extreme cautions on the selection of observation weightings and the control of spurious gravity waves.
- GPS,
- precipitable water vapor,
- four-dimensional variational assimilation,
- meso-beta -scale convective system
References

[1]	Ling WANG, Xiuqing HU, Na XU, Lin CHEN, 2021: Water Vapor Retrievals from Near-infrared Channels of the Advanced Medium Resolution Spectral Imager Instrument onboard the Fengyun-3D Satellite, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1351-1366. doi: 10.1007/s00376-020-0174-8
[2]	WANG Xiaoying, WANG Xianliang, DAI Ziqiang, KE Fuyang, CAO Yunchang, WANG Feifei, SONG Lianchun, 2014: Tropospheric Wet Refractivity Tomography Based on the BeiDou Satellite System, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 355-362. doi: 10.1007/s00376-013-2311-0
[3]	Houaria NAMAOUI, Salem KAHLOUCHE, Ahmed Hafid BELBACHIR, Roeland Van MALDEREN, Hugues BRENOT, Eric POTTIAUX, 2017: GPS Water Vapor and Its Comparison with Radiosonde and ERA-Interim Data in Algeria, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 623-634. doi: 10.1007/s00376-016-6111-1
[4]	BI Yanmeng, MAO Jietai, LI Chengcai, 2006: Preliminary Results of 4-D Water Vapor Tomography in the Troposphere Using GPS, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 551-560. doi: 10.1007/s00376-006-0551-y
[5]	ZHANG Lin, NI Yunqi, 2005: Four-Dimensional Variational Data Assimilation Experiments for a Heavy Rain Case During the 2002 IOP in China, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 300-312. doi: 10.1007/BF02918519
[6]	Ha-Taek KWON, Eui-Hyun JUNG, Gyu-Ho LIM, 2010: A Comparison of GPS- and NWP-derived PW Data over the Korean Peninsula, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 871-882. doi: 10.1007/s00376-009-9069-4
[7]	XU Xiaohua, LUO Jia, SHI Chuang, 2009: Comparison of COSMIC Radio Occultation Refractivity Profiles with Radiosonde Measurements, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 1137-1145. doi: 10.1007/s00376-009-8066-y
[8]	CAO Yunchang, CHEN Yongqi, LI Pingwha, 2006: Wet Refractivity Tomography with an Improved Kalman-Filter Method, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 693-699. doi: 10.1007/s00376-006-0693-y
[9]	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
[10]	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
[11]	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
[12]	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
[13]	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
[14]	DING Jincai, YANG Yinming, YE Qixin, HUANG Yan, MA Xiaoxing, MA Leiming, Y. R. GUO, 2007: Moisture Analysis of a Squall Line Case Based on Precipitable Water Vapor Data from a Ground-Based GPS Network in the Yangtze River Delta, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 409-420. doi: 10.1007/s00376-007-0409-y
[15]	WANG Yunfeng, WANG Bin, 2003: The Variational Assimilation Experiment of GPS Bending Angle, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 479-486. doi: 10.1007/BF02690806
[16]	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
[17]	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
[18]	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
[19]	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
[20]	KUANG Zheng, WANG Bin, YANG Hualin, 2003: A Rapid Optimization Algorithm for GPS Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 437-441. doi: 10.1007/BF02690801

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Manuscript History

Manuscript received: 10 May 2007

Manuscript revised: 10 May 2007

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

Variational Assimilation of GPS Precipitable Water Vapor and Hourly Rainfall Observations for a Meso- Scale Heavy Precipitation Event During the 2002 Mei-Yu Season

1. Department of Atmospheric Sciences, Nanjing University, Nanjing 210093; Department of Atmospheric Sciences, Texas A & M University, College Station, Texas 77843-3150, USA,State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081,Department of Atmospheric Sciences, Texas A & M University, College Station, Texas 77843-3150, USA

Manuscript received: 2007-05-10
Manuscript revised: 2007-05-10

Abstract: Recent advances in Global Positioning System (GPS) remote sensing technology allow for a direct estimation of the precipitable water vapor (PWV) from delayed signals transmitted by GPS satellites, which can be assimilated into numerical models with four-dimensional variational (4DVAR) data assimilation. A mesoscale model and its 4DVAR system are used to access the impacts of assimilating GPS-PWV and hourly rainfall observations on the short-range prediction of a heavy rainfall event on 20 June 2002. The heavy precipitation was induced by a sequence of meso-beta-scale convective systems (MCS) along the mei-yu front in China. The experiments with GPS-PWV assimilation successfully simulated the evolution of the observed MCS cluster and also eliminated the erroneous rainfall systems found in the experiment without 4DVAR assimilation. Experiments with hourly rainfall assimilation performed similarly both on the prediction of MCS initiation and the elimination of erroneous systems, however the MCS dissipated much sooner than it did in observations. It is found that the assimilation-induced moisture perturbation and mesoscale low-level jet are helpful for the MCS generation and development. It is also discovered that spurious gravity waves may post serious limitations for the current 4DVAR algorithm, which would degrade the assimilation efficiency, especially for rainfall data. Sensitivity experiments with different observations, assimilation windows and observation weightings suggest that assimilating GPS-PWV can be quite effective, even with the assimilation window as short as 1 h. On the other hand, assimilating rainfall observations requires extreme cautions on the selection of observation weightings and the control of spurious gravity waves.

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