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Volume 27 Issue 4

Jul.  2010

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2010 >  27(4): 871-882

A Comparison of GPS- and NWP-derived PW Data over the Korean Peninsula

  • 1.

    School of Earth and Environmental Sciences, Seoul National University, Seoul 151--172, Korea,School of Earth and Environmental Sciences, Seoul National University, Seoul 151--172, Korea,School of Earth and Environmental Sciences, Seoul National University, Seoul 151--172, Korea


doi: 10.1007/s00376-009-9069-4

  • Precipitable Water (PW) derived from Global Positioning System (GPS) measurements and numerical weather prediction (NWP) model analysis data were compared to further evaluate the efficacy of applying GPS-derived PW to the NWP model. The spatial and temporal variations of GPS-derived PW during a rainfall event were also examined. GPS-derived PW measurements show good agreement with the behavior of water vapor at a high spatial resolution during the analysis period. Temporal anomalies of GPS-derived PW moving along with the front are successfully detected by the GPS array. Large positive anomalies of GPS-derived PW are indicated immediately before a rainfall event, and the intensity of these positive anomalies do not seem to decrease significantly as the precipitation system passes. These results indicate that the Korean GPS network may have great potential as a PW sensor over the Korean Peninsula. In contrast with GPS-derived PW, NWP-derived PW shows negative biases. These biases appear to stem mainly from the differences between modeled and actual GPS site elevations, as GPS sites were generally located at elevations lower than those employed by the NWP model. However, there still exists a discernable dry bias after a PW correction is applied to NWP-derived PW. GPS-derived PW better reflects the spatial and temporal moisture variations of precipitation systems, as compared to NWP-derived PW. These results provide entirely new information for improving the regional NWP system, since GPS-derived PW produced with data from the Korean GPS network may be incorporated into the NWP model to improve rainfall forecasts.
  • [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] 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
    [3] 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
    [4] 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
    [5] YAO Zhanyu, XU Chenhai, YUAN Jian, LI Wanbiao, ZHU Yuanjing, ZHAO Bolin, 2003: Estimations of Precipitable Water and Its Characteristics during the HUBEX/IOP 1998, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 219-226.  doi: 10.1007/s00376-003-0007-6
    [6] 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
    [7] P. Ernest Raj, P.C.S. Devara, 1997: Seasonal Variations in the Vertical Structure of Water Vapor Optical Depth in the Lower Troposphere over a Tropical Station, ADVANCES IN ATMOSPHERIC SCIENCES, 14, 425-432.  doi: 10.1007/s00376-997-0049-2
    [8] P. Ernest Raj, P.C.S. Devara, 1997: Seasonal Variations in the Vertical Structure of Water Vapor Optical Depth in the Lower Troposphere over a Tropical Station, ADVANCES IN ATMOSPHERIC SCIENCES, 14, 103-110.  doi: 10.1007/s00376-997-0062-5
    [9] BIAN Jianchun, CHEN Hongbin, Holger VOMEL, DUAN Yunjun, XUAN Yuejian, LU Daren, 2011: Intercomparison of Humidity and Temperature Sensors: GTS1, Vaisala RS80, and CFH, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 139-146.  doi: 10.1007/s00376-010-9170-8
    [10] 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
    [11] 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
    [12] 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
    [13] Li Shuyong, Wang Bin, Zhang Xin, 2001: The Parallel Computing of GPS Ray-shooting Model, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 1185-1191.  doi: 10.1007/s00376-001-0032-2
    [14] ZHANG Xin, LIU Yuewei, WANG Bin, JI Zhongzhen, 2004: Parallel Computing of a Variational Data Assimilation Model for GPS/MET Observation Using the Ray-Tracing Method, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 220-226.  doi: 10.1007/BF02915708
    [15] Youlong XIA, XU Guoqiang, 2007: Impacts of Systematic Precipitation Bias on Simulations of Water and Energy Balances in Northwest America, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 739-749.  doi: 10.1007/s00376-007-0739-9
    [16] Eun-Han KWON, Jinlong LI, B. J. SOHN, Elisabeth WEISZ, 2012: Use of Total Precipitable Water Classification of A Priori Error and Quality Control in Atmospheric Temperature and Water Vapor Sounding Retrieval, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 263-273.  doi: 10.1007/s00376-011-1119-z
    [17] Lei Xiaoen, Julius S. Chang, 1992: Numerical Study on Dry Deposition Processes in Canopy Layer, ADVANCES IN ATMOSPHERIC SCIENCES, 9, 491-500.  doi: 10.1007/BF02677082
    [18] Wei Chong, Xue Yongkang, Zhu Xiaoming, Zou Shouxiang, 1984: DETERMINATION OF ATMOSPHERIC PRECIPITABLE WATER AND HUMIDITY PROFILES BY A GROUND-BASED 1.35 cm RADIOMETER, ADVANCES IN ATMOSPHERIC SCIENCES, 1, 119-139.  doi: 10.1007/BF03187623
    [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] 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 July 2010
    Manuscript revised: 10 July 2010
    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

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    A Comparison of GPS- and NWP-derived PW Data over the Korean Peninsula

    • 1. School of Earth and Environmental Sciences, Seoul National University, Seoul 151--172, Korea,School of Earth and Environmental Sciences, Seoul National University, Seoul 151--172, Korea,School of Earth and Environmental Sciences, Seoul National University, Seoul 151--172, Korea
    • Manuscript received: 2010-07-10
    • Manuscript revised: 2010-07-10

    Abstract: Precipitable Water (PW) derived from Global Positioning System (GPS) measurements and numerical weather prediction (NWP) model analysis data were compared to further evaluate the efficacy of applying GPS-derived PW to the NWP model. The spatial and temporal variations of GPS-derived PW during a rainfall event were also examined. GPS-derived PW measurements show good agreement with the behavior of water vapor at a high spatial resolution during the analysis period. Temporal anomalies of GPS-derived PW moving along with the front are successfully detected by the GPS array. Large positive anomalies of GPS-derived PW are indicated immediately before a rainfall event, and the intensity of these positive anomalies do not seem to decrease significantly as the precipitation system passes. These results indicate that the Korean GPS network may have great potential as a PW sensor over the Korean Peninsula. In contrast with GPS-derived PW, NWP-derived PW shows negative biases. These biases appear to stem mainly from the differences between modeled and actual GPS site elevations, as GPS sites were generally located at elevations lower than those employed by the NWP model. However, there still exists a discernable dry bias after a PW correction is applied to NWP-derived PW. GPS-derived PW better reflects the spatial and temporal moisture variations of precipitation systems, as compared to NWP-derived PW. These results provide entirely new information for improving the regional NWP system, since GPS-derived PW produced with data from the Korean GPS network may be incorporated into the NWP model to improve rainfall forecasts.

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