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Volume 5 Issue 1

Jan.  1988

Article Contents

A HIGH-RESOLUTION ANALYSIS METHOD OF INSTABILITY ENERGY


doi: 10.1007/BF02657348

  • This paper describes a geopotential thickness difference method for computing instability energy E. EP1P2 =g0 ( HsP1P2-HP1P2 , where HP1P2 is the geopotential thickness of P1-P2 level; HsP1P2 is called adia-batic geopotential thickness, based on which a computational method for high resolution of instability energy is proposed. E(x,y)g0(A(x,y) - B(x,y)), where A is interpolating polynomial of HSP2P2 and it is afunction ofe, of surface observing stations (x, y); B(x,y) is the thickness over corresponding stations (x, y) obtained using surface fitting method. Therefore, data of METAR can be used by computer to produce hourly horizontal distribution chart of E of surface observing station density. With the result that the temporal and spatial resolution of stability analysis has been improved. Practical use has shown that this method is an effective tool for very short range forecast of severe convective storms.
  • [1] Xun LI, Noel E. DAVIDSON, Yihong DUAN, Kevin J. TORY, Zhian SUN, Qinbo CAI, 2020: Analysis of an Ensemble of High-Resolution WRF Simulations for the Rapid Intensification of Super Typhoon Rammasun (2014), ADVANCES IN ATMOSPHERIC SCIENCES, 37, 187-210.  doi: 10.1007/s00376-019-8274-z
    [2] Xinghai ZHANG, Yihong DUAN, Yuqing WANG, Na WEI, Hao HU, 2017: A High-resolution Simulation of Supertyphoon Rammasun (2014) —— Part I: Model Verification and Surface Energetics Analysis, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 757-770.  doi: 10.1007/s00376-017-6255-7
    [3] FU Gang, GUO Jingtian, ZHANG Meigen, 2004: High-Resolution Simulation and Analysis of the Mature Structure of a Polar Low over the Sea of Japan on 21 January 1997, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 597-608.  doi: 10.1007/BF02915727
    [4] Qiyang LIU, Fengxue QIAO, Yongqiang YU, Yiting ZHU, Shuwen ZHAO, Yujia LIU, Fulin JIANG, Xinyu HU, 2023: Bias Analysis in the Simulation of the Western North Pacific Tropical Cyclone Characteristics by Two High-Resolution Global Atmospheric Models, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 634-652.  doi: 10.1007/s00376-022-2159-2
    [5] LI Rui, ZHANG Zuowei, WU Lixin, 2014: High-Resolution Modeling Study of the Kuroshio Path Variations South of Japan, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 1233-1244.  doi: 10.1007/s00376-014-3230-4
    [6] Eric P. CHASSIGNET, Xiaobiao XU, 2021: On the Importance of High-Resolution in Large-Scale Ocean Models, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1621-1634.  doi: 10.1007/s00376-021-0385-7
    [7] Yuan QIU, Jinming FENG, Zhongwei YAN, Jun WANG, 2022: High-resolution Projection Dataset of Agroclimatic Indicators over Central Asia, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1734-1745.  doi: 10.1007/s00376-022-2008-3
    [8] ZHAI Fangguo, WANG Qingye, WANG Fujun, Hu Dunxin, 2014: Variation of the North Equatorial Current, Mindanao Current, and Kuroshio Current in a High-Resolution Data Assimilation during 20082012, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 1445-1459.  doi: 10.1007/s00376-014-3241-1
    [9] Mingkui LI, Shaoqing ZHANG, Lixin WU, Xiaopei LIN, Ping CHANG, Gohkan DANABASOGLU, Zhiqiang WEI, Xiaolin YU, Huiqin HU, Xiaohui MA, Weiwei MA, Haoran ZHAO, Dongning JIA, Xin LIU, Kai MAO, Youwei MA, Yingjing JIANG, Xue WANG, Guangliang LIU, Yuhu CHEN, 2020: An Examination of the Predictability of Tropical Cyclone Genesis in High-Resolution Coupled Models with Dynamically Downscaled Coupled Data Assimilation Initialization, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 939-950.  doi: 10.1007/s00376-020-9220-9
    [10] YU Entao, WANG Tao, GAO Yongqi, and XIANG Weiling, 2014: Precipitation Pattern of the Mid-Holocene Simulated by a High-Resolution Regional Climate Model, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 962-971.  doi: 10.1007/s00376-013-3178-9
    [11] ZHANG Junke, WANG Yuesi, HUANG Xiaojuan, LIU Zirui, JI Dongsheng, SUN Yang, 2015: Characterization of Organic Aerosols in Beijing Using an Aerodyne High-Resolution Aerosol Mass Spectrometer, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 877-888.  doi: 10.1007/s00376-014-4153-9
    [12] Zuohao CAO, Huaqing CAI, 2016: Identification of Forcing Mechanisms of Convective Initiation over Mountains through High-Resolution Numerical Simulations, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 1104-1105.  doi: 10.1007/s00376-016-6198-4
    [13] Marcus JOHNSON, Youngsun JUNG, Daniel DAWSON, Timothy SUPINIE, Ming XUE, Jongsook PARK, Yong-Hee LEE, 2018: Evaluation of Unified Model Microphysics in High-resolution NWP Simulations Using Polarimetric Radar Observations, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 771-784.  doi: 10.1007/s00376-017-7177-0
    [14] Xia LIU, Qiang WANG, Mu MU, 2018: Optimal Initial Error Growth in the Prediction of the Kuroshio Large Meander Based on a High-resolution Regional Ocean Model, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1362-1371.  doi: 10.1007/s00376-018-8003-z
    [15] Leilei KOU, Yinfeng JIANG, Aijun CHEN, Zhenhui WANG, 2020: Statistical Modeling with a Hidden Markov Tree and High-resolution Interpolation for Spaceborne Radar Reflectivity in the Wavelet Domain, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1359-1374.  doi: 10.1007/s00376-020-0035-5
    [16] Bo AN, Yongqiang YU, Qing BAO, Bian HE, Jinxiao LI, Yihua LUAN, Kangjun CHEN, Weipeng ZHENG, 2022: CAS FGOALS-f3-H Dataset for the High-Resolution Model Intercomparison Project (HighResMIP) Tier 2, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1873-1884.  doi: 10.1007/s00376-022-2030-5
    [17] LI Qingqing, DUAN Yihong, YU Hui, FU Gang, 2010: Finescale Spiral Rainbands Modeled in a High-Resolution Simulation of Typhoon Rananim (2004), ADVANCES IN ATMOSPHERIC SCIENCES, 27, 685-704.  doi: 10.1007/s00376-009-9127-y
    [18] Seung-Woo LEE, Dong-Kyou LEE, 2011: Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 758-774.  doi: 10.1007/s00376-010-0145-6
    [19] FENG Lei, ZHOU Tianjun, WU Bo, Tim LI, Jing-Jia LUO, 2011: Projection of Future Precipitation Change over China with a High-Resolution Global Atmospheric Model, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 464-476.  doi: 10.1007/s00376-010-0016-1
    [20] FU Weiwei, ZHU Jiang, ZHOU Guangqing, WANG Huijun, 2005: A Comparison Study of Tropical Pacific Ocean State Estimation: Low-Resolution Assimilation vs. High-Resolution Simulation, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 212-219.  doi: 10.1007/BF02918510

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Manuscript History

Manuscript received: 10 January 1988
Manuscript revised: 10 January 1988
通讯作者: 陈斌, bchen63@163.com
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A HIGH-RESOLUTION ANALYSIS METHOD OF INSTABILITY ENERGY

  • 1. Air Force Meteorological Research Institute,Air Force Meteorological Research Institute,Air Force Meteorological Research Institute

Abstract: This paper describes a geopotential thickness difference method for computing instability energy E. EP1P2 =g0 ( HsP1P2-HP1P2 , where HP1P2 is the geopotential thickness of P1-P2 level; HsP1P2 is called adia-batic geopotential thickness, based on which a computational method for high resolution of instability energy is proposed. E(x,y)g0(A(x,y) - B(x,y)), where A is interpolating polynomial of HSP2P2 and it is afunction ofe, of surface observing stations (x, y); B(x,y) is the thickness over corresponding stations (x, y) obtained using surface fitting method. Therefore, data of METAR can be used by computer to produce hourly horizontal distribution chart of E of surface observing station density. With the result that the temporal and spatial resolution of stability analysis has been improved. Practical use has shown that this method is an effective tool for very short range forecast of severe convective storms.

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