Advanced Search
Article Contents

Estimation of Winds at Different Isobaric Levels Based on the Observed Winds at 850 hPa Level Using Double Fourier Series


doi: 10.1007/BF02658152

  • A technique based on the double Fourier series is developed to estimate the winds at different isobaric levels for the limited area domain, 35oE to 140oE and 30oS to 40oN, using the observed winds at 850 hPa level for the month of June. For this purpose the wind field at a level under consideration is taken in the ratio form with that of 850 hPa level and the coefficients of the double Fourier series are computed. These coefficients are subsequently used to compute the winds which are compared with the actual winds. The results of the double Fourier series technique are compared with those of the polynomial surface fitting method developed by Bavadekar and Khaladkar (1992). The technique is also applied for the daily wind data of 11, June, 1979 and the validation of the technique is tested for a few radiosonde stations of India. The computed winds for these radiosonde stations are quite close to observed winds.
  • [1] XUE Hai-Le, SHEN Xue-Shun, CHOU Ji-Fan, 2013: A Forecast Error Correction Method in Numerical Weather Prediction by Using Recent Multiple-time Evolution Data, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1249-1259.  doi: 10.1007/s00376-013-2274-1
    [2] S.K. Sinha, S. Rajamani, 1995: Multivariate Objective Analysis of Wind and Height Fields in the Tropics, ADVANCES IN ATMOSPHERIC SCIENCES, 12, 233-244.  doi: 10.1007/BF02656836
    [3] ZHAO Jun, SONG Junqiang, LI Zhenjun, 2003: Distributed Parallelization of a Global Atmospheric Data Objective Analysis System, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 159-163.  doi: 10.1007/BF03342060
    [4] Feifei SHEN, Aiqing SHU, Zhiquan LIU, Hong LI, Lipeng JIANG, Tao ZHANG, Dongmei XU, 2024: Assimilating FY-4A AGRI Radiances with a Channel-Sensitive Cloud Detection Scheme for the Analysis and Forecasting of Multiple Typhoons, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 937-958.  doi: 10.1007/s00376-023-3072-z
    [5] Jianhua LU, 2021: Chen-Chao Koo and the Early Numerical Weather Prediction Experiments in China, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 707-716.  doi: 10.1007/s00376-021-0268-y
    [6] XUE Jishan, 2004: Progresses of Researches on Numerical Weather Prediction in China: 1999-2002, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 467-474.  doi: 10.1007/BF02915573
    [7] Guifu ZHANG, Vivek N. MAHALE, Bryan J. PUTNAM, Youcun QI, Qing CAO, Andrew D. BYRD, Petar BUKOVCIC, Dusan S. ZRNIC, Jidong GAO, Ming XUE, Youngsun JUNG, Heather D. REEVES, Pamela L. HEINSELMAN, Alexander RYZHKOV, Robert D. PALMER, Pengfei ZHANG, Mark WEBER, Greg M. MCFARQUHAR, Berrien MOORE III, Yan ZHANG, Jian ZHANG, J. VIVEKANANDAN, Yasser AL-RASHID, Richard L. ICE, Daniel S. BERKOWITZ, Chong-chi TONG, Caleb FULTON, Richard J. DOVIAK, 2019: Current Status and Future Challenges of Weather Radar Polarimetry: Bridging the Gap between Radar Meteorology/Hydrology/Engineering and Numerical Weather Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 571-588.  doi: 10.1007/s00376-019-8172-4
    [8] XUE Jishan, LIU Yan, 2007: Numerical Weather Prediction in China in the New Century ---Progress, Problems and Prospects, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 1099-1108.  doi: 10.1007/s00376-007-1099-1
    [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] Guifu ZHANG, Jidong GAO, Muyun DU, 2021: Parameterized Forward Operators for Simulation and Assimilation of Polarimetric Radar Data with Numerical Weather Predictions, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 737-754.  doi: 10.1007/s00376-021-0289-6
    [11] Lei HAN, Mingxuan CHEN, Kangkai CHEN, Haonan CHEN, Yanbiao ZHANG, Bing LU, Linye SONG, Rui QIN, 2021: A Deep Learning Method for Bias Correction of ECMWF 24–240 h Forecasts, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1444-1459.  doi: 10.1007/s00376-021-0215-y
    [12] Man-Yau CHAN, Xingchao CHEN, 2022: Improving the Analyses and Forecasts of a Tropical Squall Line Using Upper Tropospheric Infrared Satellite Observations, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 733-746.  doi: 10.1007/s00376-021-0449-8
    [13] 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
    [14] Chungu Lu, Paul Schultz, Gerald L.Browning, 2002: Scaling the Microphysics Equations and Analyzing the Variability of Hydrometeor Production Rates in a Controlled Parameter Space, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 619-650.  doi: 10.1007/s00376-002-0004-1
    [15] 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
    [16] Fabien CARMINATI, Stefano MIGLIORINI, 2021: All-sky Data Assimilation of MWTS-2 and MWHS-2 in the Met Office Global NWP System., ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1682-1694.  doi: 10.1007/s00376-021-1071-5
    [17] Wanchen WU, Wei HUANG, Baode CHEN, 2022: A Comparison of Two Bulk Microphysics Parameterizations for the Study of Aerosol Impacts on an Idealized Supercell, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 97-116.  doi: 10.1007/s00376-021-1187-7
    [18] Luyao QIN, Yaodeng CHEN, Gang MA, Fuzhong WENG, Deming MENG, Peng ZHANG, 2023: Assimilation of FY-3D MWTS-II Radiance with 3D Precipitation Detection and the Impacts on Typhoon Forecasts, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 900-919.  doi: 10.1007/s00376-022-1252-x
    [19] Kanghui ZHOU, Jisong SUN, Yongguang ZHENG, Yutao ZHANG, 2022: Quantitative Precipitation Forecast Experiment Based on Basic NWP Variables Using Deep Learning, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1472-1486.  doi: 10.1007/s00376-021-1207-7
    [20] Sen YANG, Deqin LI, Liqiang CHEN, Zhiquan LIU, Xiang-Yu HUANG, Xiao PAN, 2023: The Regularized WSM6 Microphysical Scheme and Its Validation in WRF 4D-Var, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 483-500.  doi: 10.1007/s00376-022-2058-6

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 10 July 1994
Manuscript revised: 10 July 1994
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

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Estimation of Winds at Different Isobaric Levels Based on the Observed Winds at 850 hPa Level Using Double Fourier Series

  • 1. Indian Institute of Tropical Meteorology, Pune-411008, India,Indian Institute of Tropical Meteorology, Pune-411008, India

Abstract: A technique based on the double Fourier series is developed to estimate the winds at different isobaric levels for the limited area domain, 35oE to 140oE and 30oS to 40oN, using the observed winds at 850 hPa level for the month of June. For this purpose the wind field at a level under consideration is taken in the ratio form with that of 850 hPa level and the coefficients of the double Fourier series are computed. These coefficients are subsequently used to compute the winds which are compared with the actual winds. The results of the double Fourier series technique are compared with those of the polynomial surface fitting method developed by Bavadekar and Khaladkar (1992). The technique is also applied for the daily wind data of 11, June, 1979 and the validation of the technique is tested for a few radiosonde stations of India. The computed winds for these radiosonde stations are quite close to observed winds.

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return