Abstract
With the aid of Meteorological Information Composite and Processing System (MICAPS), satellite wind vectors derived from the Geostationary Meteorological Statellite-5 (GMS-5) and retrieved by National Satellite Meteorology Center of China (NSMC) can be obtained. Based on the nudging method built in the fifth-generation Mesoscale Model (MM5) of Pennsylvania State University and National Center for Atmospheric Research, a data preprocessor is developed to convert these satellite wind vectors to those with specified format required in MM5. To examine the data preprocessor and evaluate the impact of satellite winds from GMS-5 on MM5 simulations, a series of numerical experimental forecasts consisting of four typhoon cases in 2002 are designed and implemented. The results show that the preprocessor can process satellite winds smoothly and MM5 model runs successfully with a little extra computational load during ingesting these winds, and that assimilation of satellite winds by MM5 nudging method can obviously improve typhoon track forecast but contributes a little to typhoon intensity forecast. The impact of the satellite winds depends heavily upon whether the typhoon bogussing scheme in MM5 was turned on or not. The data preprocessor developed in this paper not only can treat GMS-5 satellite winds but also has capability with little modification to process derived winds from other geostationary satellites.
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References
Anthes, R. A., E. Y. Hsie and Y. H. Kuo, 1987. Description of the Penn State/NCAR Mesoscale Model Version 4 (MM4). NCAR Tech. Note. NCAR/TN-282+STR., 66pp.
Benjamin, S. B. and P. A. Miller, 1990. An alternative sea level pressure reduction and a statistical comparison of geostrophic winds estimated with observed surface winds.Mon. Wea. Rev. 118: 2099–2116.
Bratseth, F., 1986. Statistical interpolation by means of successive corrections.Tellus 38A: 439–447.
Chao, W. C. and L. P. Chang, 1992. Development of a four-dimensional variational analysis system using the adjoint method at GLA. Part 1: dynamics.Mon. Wea. Rev. 26: 1661–1673.
Cressman, G., 1959. An operational objective analysis system.Mon. Wea. Rev. 87: 367–374.
Daley, R., 1991. Atmospheric Data Analysis. Cambridge University Press, 457pp.
Dong, C. H., 1999. Handbook of Satellite Operational Product Usage. Meteorology Press, Beijing, 273pp. (in Chinese)
Evensen, G. and P. J. van Leeuwen, 1996. Assimilation of geosat altimeter data for the Agulhas current using the ensemble Kalman filter with a quasigeostrophic model.Mon. Wea. Rev. 124: 85–96.
Evensen, G., 1992. Using the extended Kalman filter with a multilayer quasi-geostrophic ocean model.J. Geophys. Res. 97: 905–17904.
Evensen, G., 1994. Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics.J. Geophys. Res. 99: 10143–10612.
Fast, J. D. and S. Zhong, 1996. Boundary layer evolution within a canyonland basin: Part II. Numerical simulations of nocturnal flows and heat budgets.J. Appl. Meteor. 35: 2162–2178.
Gandin, L., 1963. Objective analysis of meteorological fields. Israel Program for Scientific Translation, 232pp.
Gao, S. H., Z. M. Wu and H. Q. Xie, 2000. Developments and applications of Kalman filters in meteorological data assimilation.Advance in Earth Sciences 15: 571–575. (in Chinese)
Gelaro, R., R. H. Langland, C. A. Reynolds and G. D. Rohaly, 2000. A predictability study using geostationary satellite wind observations during NORPEX.Mon. Wea. Rev. 128: 3789–3807.
Goerss, J. S. and P. A. Phocbus, 1992. The navy's operational atmospheric analysis.Wea. Forecasting 7: 232–249.
Goerss, J. S., C. S. Velden and J. D. Hawkins, 1998. The impact of multispectral GOES-8 wind on Atlantic tropical cyclone track forecasts in 1995. Part II: NOGAPS forecasts.Mon. Wea. Rev. 126: 1219–1227.
Grell, G. A. and J. Dudhia, 1994. Description of the Fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note. NCAR/TN-398+STR., 107pp.
Hoffman, B. N., J. F. Louis and T. Nehrkorn, 1992. A Method for Implementing Adjoint Calculations in the Discrete Case. ECMWF Research Dept Tech Memo., 183pp.
Houtekamer, P. L. and H. L. Mitchell, 2001. A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation.Mon. Wea. Rev. 129: 123–137.
Jarvinen, H., J. N. Thepant and P. Courtier, 1995. Quasi-Continuous Variational Data Assimilation. ECMWF Research Dept Tech Memo., 210pp.
Kalman, R. E. and R. S. Bucy, 1961. New results in linear Itering and prediction theory.J. Basic Eng. 83: 95–108.
Kalman, R. E., 1960. A new approach to linear filtering and prediction problems.J. Basic Eng. 82: 34–45.
Klemp, J. B. and D. R. Durran, 1983. An upper boundary condition permitting internal gravity wave radiation in numerical mesoscale models.MoN. Wea. Rev. 111: 430–444.
Lipton, A. E. and G. D. Modica, 1999. Assimilation of visible-band satellite data for mesoscale forecasting in cloudy conditions.Mon. Wea. Rev. 127: 265–278.
Lorenc, A. C., S. P. Ballard, R. S. Bell, N. B. Ingleby, P. L. F. Andrews, D. M. Barker, J. R. Bray, A. M. Clayton, T. Dalby, D. Li, T. J. Payne and F. W. Saunders, 2000. The Met. office global 3-Dimensional variational data assimilation scheme.Quart. J. Roy. Meteor. Soc. 126: 2991–3012.
Low-Nam, S. and C. Davis, 2001. Development of a Tropical Cyclone Bogussing Scheme for the MM5 System. Preprint, The Eleventh PSU/NCAR Mesoscale Model Users' Workshop, June 25–27, Boulder, Colorado, 130–134.
Meng, Z. Y., X. D. Xu and L. S. Chen, 2002. T BB -nudging four-dimensional data assimilation method and simulations on heavy rain process in Wuhan on 20 July, 1998.Chinese J. Atmos. Sci. 26: 663–675. (in Chinese)
Mitchell, H. L. and P. L. Houtekamer, 2000. An adaptive ensemble Kalman Filter. Mon. Wea.Rev. 128: 416–433.
Mitchell, H. L. and P. L. Houtekamer, 2002. Ensemble Size, Balance, and Model-Error Representation in an Ensemble Kalman Filter.Mon. Wea. Rev. 130: 2791–2808.
Nuss, W. A. and D. W. Titley, 1994. Use of multiquadric interpolation for meteorological objective analysis.Mon. Wea. Rev. 122: 1611–1631.
Rabier, F., J. F. Mahfouf, M. Fisher, H. Jarvinen, A. Simmons, E. Andersson, F. Bouttier, P. Courtier, H. Hamtud, J. Haseler, A. Hollingsworth, L. Isaksen, P. Klinker, S. Saarinen, C. Temperton, J. N. Thepaut, P. Unden and D. Vasiljevic, 1997. Recent Experimentation on 4D-Var and First Results from a Simplified Kalman Filter. ECMWF Research Dept Tech Memo., 240pp.
Rasmussen, R. and J. Reisner, 1994. Modeling winter clouds with the MM5 model. ECWMF Workshop, p. 317–337.
Reisner, J., R. M. Rasmussen and R. T. Bruintjes, 1998. Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model.Quart. J. Roy. Meteor. Soc. 124: 1071–1107.
Soden, B., C. S. Velden and R. E. Tuleya, 2001. The impact of satellite winds on experimental GFDL hurricane model forecasts.Mon. Wea. Rev. 129: 835–852.
Staufer, D. R. and N. L. Seaman, 1990. Use of four-dimensional data assimilation in a limited-area mesoscale model. Part I: Experiments with synoptic-scale data.Mon. Wea. Rev. 118: 1250–1277.
Staufer, D. R. and N. L. Seaman, 1991. Use of four-dimensional data assimilation in a limited-area mesoscale model. Part II: Effects of data assimilation within the planetary layer.Mon. Wea. Rev. 119: 734–754.
Thiebaux, H. J. and M. A. Pedder, 1987. Spatial Objective Analysis with Applications in Atmorspheric Science. Academic Press, London, 295pp.
Tomassini, G. Kelly and R. Saunders, 1999. Use and impact of satellite atmospheric motion winds on ECMWF analysis and forecasts.Mon. Wea. Rev. 127: 971–986.
Velden, S., T. L. Older and S. Wanzong, 1998. The impact of multispectral GOES-8 wind on Atlantic tropical cyclone track forecasts in 1995. Part I: datasets methodology, description, and case analysis.Mon. Wea. Rev. 126: 1202–1218.
Xiao, Q., X. Zou, M. Pondeca, M. Shapiro and C. Velden, 2002. Impact of GMS-5 and GOES-9 satellite-derived winds on the prediction of a NORPEX extratropical cyclone.Mon. Wea. Rev. 130: 507–528.
Yucel, I., W. J. Shuttleworth, X. Gao and S. Sorooshian, 2003. Short-term performance of MM5 with cloud-cover assimilation from satellite observations.Mon. Wea. Rev. 131: 1797–1810.
Zhang, D. L. and R. A. Anthes, 1982. A high-resolution model of the planetary layer-sensitivity test and comparisons with SESAME-79 data.J. Appl. Meteor. 21: 1594–1609.
Zhang, S. F. and S. W. Wang, 1999. Applied experiments of the prediction of Typhoon tracks by using cloud motion wind vectors.Meteorological Monthly 25(8): 22–25. (in Chinese)
Zou, X. and Q. Xiao, 2000. Studies on the initialization and simulations of a mature hurricane using a variational bogus data assimilation scheme.J. Atmos. Sci. 57: 836–860.
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Supported by Major State Basic Research Development Program of China (973 Program, No. 2005CB4223-01) and Key Technologics R & D Program of China (No. 2001BA603B-01).
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Shanhong, G., Zengmao, W. & Bo, Y. Assimilation of GMS-5 satellite winds using nudging method with MM5. Chin. J. Ocean. Limnol. 24, 215–224 (2006). https://doi.org/10.1007/BF02842620
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DOI: https://doi.org/10.1007/BF02842620