Analysis methods for numerical weather prediction

Quarterly Journal of the Royal Meteorological Society - Tập 112 Số 474 - Trang 1177-1194 - 1986
Andrew C. Lorenc1
1Meteorological Office, Bracknell

Tóm tắt

AbstractBayesian probabilistic arguments are used to derive idealized equations for finding the best analysis for numerical weather prediction. These equations are compared with those from other published methods in the light of the physical characteristics of the NWP analysis problem; namely the predetermined nature of the basis for the analysis, the need for approximation because of large‐order systems, the underdeterminacy of the problem when using observations alone, and the availability of prior relationships to resolve the underdeterminacy.Prior relationships result from (1) knowledge of the time evolution of the model (which together with the use of a time distribution of observations constitutes four‐dimensional data assimilation); (2) knowledge that the atmosphere varies slowly (leading to balance relationships); (3) other nonlinear relationships coupling parameters and scales in the atmosphere.Methods discussed include variational techniques, smoothing splines, Kriging, optimal interpolation, successive corrections, constrained initialization, the Kalman‐Bucy filter, and adjoint model data assimilation. They are all shown to relate to the idealized analysis, and hence to each other. Opinions are given on when particular methods might be more appropriate. By comparison with the idealized method some insight is gained into appropriate choices of parameters in the practical methods.

Từ khóa


Tài liệu tham khảo

Barnes S. L.1973‘Mesoscale objective analysis using weighted time‐series observations’. NOAA Tech. Memorandum ERL NSSL‐62

Bengtsson L., 1975, 4‐dimensional assimilation of meteorological observations

10.1007/978-1-4612-5970-1

Bergthorsson P., 1955, Numerical weather map analysis, Tellus, 7, 329, 10.3402/tellusa.v7i3.8902

10.1111/j.1600-0870.1986.tb00476.x

10.1175/1520-0469(1969)026<1160:UOIHDT>2.0.CO;2

Cressman G. P., 1959, An operational objective analysis scheme, Mon. Wea. Rev., 87, 367, 10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2

Errico R., 1986, Predictability experiments using a high‐resolution limited‐area model, Mon. Wea. Rev., 114

Franke R.andGordon W. J.1983‘The structure of optimum interpolation functions’. Naval Postgraduate School Monterey Cal. Report NPS‐53‐83‐0005

Gandin L. S.1963‘Objective Analysis of Meteorological Fields’. Gidrometeorologicheskoe Izdatel'stro Leningrad. Translated from Russian Israeli Program for Scientific Translations Jerusalem 1965

10.1007/978-1-4612-5970-1_5

Hollingsworth A., 1986, The statistical structure of short‐range forecast errors as determined from radiosonde data. Part I: The wind field. Part II: the covariance of height and wind error, Tellus, 38, 111, 10.3402/tellusa.v38i2.11707

10.2467/mripapers.35.71

10.2467/mripapers.35.81

10.1007/BF02067214

10.1111/j.1600-0870.1986.tb00459.x

10.1111/j.1600-0870.1985.tb00430.x

10.1175/1520-0493(1981)109<0701:AGTDMS>2.0.CO;2

Lorenc A. C.1984‘Analysis methods for the quality control of observations’ in proceedings of ECMWF workshop on The use and quality control of meteorological observations for numerical weather prediction 6–9 Nov. 1984

10.1016/B978-0-12-208440-9.50035-1

Lorenc A. C. Adams W.andEyre J.1985‘The analysis of high‐resolution satellite data in the Meteorological Office’ in Proceedings of ECMWF workshop on high‐resolution analysis 24–26 June 1985

10.1175/1520-0485(1985)015<0330:DTOCAI>2.0.CO;2

Matheron G., 1963, Traité de géostatistique appliquée

Menke W., 1984, Geophysical data analysis: Discrete inverse theory

10.1016/0021-9991(85)90121-4

Parrish D. E.andCohn S. E.1985‘A Kalman filter for a two‐dimensional shallow‐water model’. Proc. Seventh Conf. Numerical Weather Prediction June 17–20 1985 Montreal Canada. American Meteorological Society

10.1175/1520-0493(1982)110<1329:OTCOMV>2.0.CO;2

10.1111/j.1600-0870.1986.tb00418.x

Purser R. J.1984‘A new approach to the optimal assimilation of meteorological data by iterative Bayesian analysis’. Pp.102–105in preprints 10th conference on weather forecasting and analysis. American Meteorological Society

10.1007/BF02162161

10.1029/RG014i004p00609

10.1175/1520-0493(1970)098<0875:SBFINV>2.3.CO;2

10.1175/1520-0493(1981)109<0110:AAOOTT>2.0.CO;2

Seaman R. S., 1985, Comparative real data tests of some objective analysis methods by withholding observations, Aust. Met. Mag., 33, 37

Wahba G., 1978, Improper priors, spline smoothing and the problem of guarding against model errors in regression, J. R. Statist. Soc., 364

Wahba G., 1982, The interaction between objective analysis and initialization: Proc. 14th Stansted seminar, 178

Wahba G.1985‘Variational methods for multidimensional inverse problems’. Pp.385–410inAdvances in remote sensing retrieval methods. Eds. H. E. Fleming and M. T. Chanine A. Deepak.

10.1175/1520-0493(1980)108<1122:SNMMFV>2.0.CO;2

Williamson D., 1982, The interaction between objective analysis and initialization

Thông tin
Thông tin xuất bản

Quarterly Journal of the Royal Meteorological Society

Tập 112 Số 474

1177-1194

Thông tin tác giả