THE DEVELOPMENTS AND APPLICATIONS OF KALMAN FILTERS IN METEOROLOGICAL DATA ASSIMILATION
Received date: 1999-11-17
Revised date: 2000-03-06
Online published: 2000-10-01
Meteorological data assimilation techniques are motivated forward by the advance of numerical weather prediction models and the increasing rapidly observations, including the great part of uncon-ventional data obtained by remote measurement methods. There are mainly two general concepts that have been discussed repeatedly for data assimilation in meteorology. The variational (especially adjoint variational) method has been the popular and most used scheme, which, however, has a drawback that model errors (system noise) are not taken into account. Another class of methods are those described as sequential data assimilation, which are represented by Kalman filters. The introduction of Kalman filters and their developmental stages in the meteorological data assimilation field are presented in this paper, as well as that the importance and applications of Kalman filters, particularly simplified Kalman filters and ensemble Kalman filters. Due to that they have the ability to consider model errors and let assimilation results not drift away from observations, Kalman filters are paid more and more attentions, though they need
much of computational load. Compared with the current advance abroad, the developments and applications of Kalman filters in China are lagged. However, there will be a bright prospect for them with the improvements of computational conditions.
GAO Shan-hong,WU Zeng-mao,XIE Hong-qin . THE DEVELOPMENTS AND APPLICATIONS OF KALMAN FILTERS IN METEOROLOGICAL DATA ASSIMILATION[J]. Advances in Earth Science, 2000 , 15(5) : 571 -575 . DOI: 10.11867/j.issn.1001-8166.2000.05.0571
[1] Smedstad O M, O' Brien J J. Variational data assimilation and parameter estimation in an equatorial Pacific Ocean model [J].Prog Oceanog, 1992, 26:179～241.
[2] Panofsky, H. Objective weather-map analysis[J]. J Appl Meteor, 1949, 6:386～92.
[3] Gichrist B, Cressman G. An experiment in objective analysis[J]. Tellus, 1954, 6:309～318.
[4] Daley R. Atmospheric Data Analysis[M]. Cambridge: Cambridge University Press, 1991.
[5] Chao W C, Chang L P. Development of a four-dimensional variational analysis system using the adjoint method at GLA,Part 1: dynamics [J]. Mon Wea Rev, 1992, 26:1 661～1 673.
[6] Courtier P, Derber J C, Ron Errico,et al. Important literature on the use of adjoint, variational methods and the Kalman filter in meteorology [J]. Telus, 1993, 45A: 342～357.
[7] Hoffman B N, Jean-Francois Louis, Thomas Nehrkorn. A Method for Implementing Adjoint Calculations in the Discrete case [R]. ECMWF Research Dept Tech Memo 183, 1992.
[8] Jarvinen H, Jean-Noel Thepant and Phillippe Courtier. Quasi-continuous Variational Data Assimilation[R]. ECMWF Research Dept Tech Memo 210, 1995.
[9] Rabier F, Mahfouf J-F, Fisher M,et al. Recent Experimentation on 4D-Var and First Results from a Simplified Kalman Filter [R]. ECMWF Research Dept Tech Memo 240, 1997.
[10] Lorenc A C. Analysis methods for numerical weather prediction[J]. Quart J R Met Soc, 1986, 112:1 177～1 194.
[11] Ehrendorfer M. Four-dimensional data assimilation: comparison of variational and sequential algorithms[J]. Quart J R Met Soc, 1992, 118: 673～713.
[12] Evensen G. Using the extended Kalman filter with a multilayer quasi-geostrophic ocean model[J]. J Geophys Res,1992, 97:17 904～17 905.
[13] Kalman R E. A new approach to linear filtering and prediction problems[J]. Transaction of the ASME, Journal of Basic Engineering, 1960, 82D: 34～45.
[14] Kalman R E, Bucy R. New results in linear filtering and prediction[J]. Transaction of the ASME, Journal of Basic Engineering, 1961, 83D: 95～108.
[15] Jones R H. Optimal estimation of initial conditions for numerical prediction[J]. J Atmos Sci, 1965, 22:658～663.
[16] Epstein E S. Stochastic dynamic prediction[J]. Tellus,1969, 21:739～759.
[17] Petersen D P. On the concepts and implementation of se-quential analysis for linear random fields[J]. Tellus, 1968,20: 673～686.
[18] Petersen D P. Transient suppression in optimal sequential analysis[J]. J Atmos Sci, 1973, 12: 437～440.
[19] Petersen D P. A comparison of the performance of quasi-optimal and conventional objective analysis schemes[J]. J Atmos Sci, 1973, 12: 1 093～1 101.
[20] Phillips N A. The spatial statistics of random geostrophic modes and first-guess errors[J]. Tellus, 1986, 38A: 314～322.
[21] Dee D P. Simplification of the Kalman filter for meteorological data assimilation[J]. Quart J R Met Soc, 1991, 117:365～384.
[22] Cohn S E, Parrish D P. The behavior of forecast error covariances for a Kalman filter in two dimensions[J]. Mon Wea Rev, 1991, 119: 1 757～1 785.
[23] Leith C E. Theoretical skill of Monte Carlo forecasts[J].Mon Wea Rev, 1974,102: 409～418.
[24] Evensen G. Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics [J]. J Geophys Res, 1994, 99(C5):10 143～10 612.
[25] Burgers G, Van Leeuwen P J, Evensen G. Analysis scheme in the ensemble Kalman filter[J]. Mon Wea Rev, 1998,126: 1 719～1 724.
[26] Ghil M, Malanotte-Rizzoli P. Data assimilation in meteorology and oceanography[J]. Adv Geophys, 1991, 33:141～266.
[27] Anderson B D O, Moore J B. Optional Filtering[M].Cliffs: Prentice-Hall, Englewood, 1979.
[28] Houtekamer P L, Mitchell H L. Data assimilation using an ensemble Kalman filter technique[J]. Mon Wea Rev, 1998,126: 796～811.
[29] Evensen G, Van Leeuwen P J. Assimilation of geosat altimeter data for the Agulhas current using the ensemble Kalman filter with a quasigeostrophic model [J]. Mon Wea Rev,1996, 124: 85～96.
[30] Fisher M. Development of a Simplified Kalman Filter[R].ECMWF Research Dept Tech Memo 260, 1998.
[31] 路如华,何于班.卡尔曼滤波方法在天气预报中的应用[J].气象,1994,20(9):41～43.
[32] 黄嘉佑,谢庄.卡尔曼滤波方法在天气预报中的应用[J].气象,1993,19(4):3～7.
[33] 路如华,徐传玉,张玲,等.卡尔曼滤波的初值计算方法及其应用[J].应用气象学报,1997,8(1):34～42.
[34] 王莉,黄嘉佑. Kalman滤波的试验应用研究[J].应用气象学报,1999,10(3):276～282.
[35] 刘春霞,周家斌.用卡尔曼滤波预报南海热带气旋路径的试验[J].热带气象学报,1988,13(4):349～356.
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