Assimilation and simulation of typhoon Rusa (2002) using the WRF system
- 479 Downloads
Using the recently developed Weather Research and Forecasting (WRF) 3DVAR and the WRF model, numerical experiments are conducted for the initialization and simulation of typhoon Rusa (2002). The observational data used in the WRF 3DVAR are conventional Global Telecommunications System (GTS) data and Korean Automatic Weather Station (AWS) surface observations. The Background Error Statistics (BES) via the National Meteorological Center (NMC) method has two different resolutions, that is, a 210-km horizontal grid space from the NCEP global model and a 10-km horizontal resolution from Korean operational forecasts. To improve the performance of the WRF simulation initialized from the WRF 3DVAR analyses, the scale-lengths used in the horizontal background error covariances via recursive filter are tuned in terms of the WRF 3DVAR control variables, streamfunction, velocity potential, unbalanced pressure and specific humidity. The experiments with respect to different background error statistics and different observational data indicate that the subsequent 24-h the WRF model forecasts of typhoon Rusa’s track and precipitation are significantly impacted upon the initial fields. Assimilation of the AWS data with the tuned background error statistics obtains improved predictions of the typhoon track and its precipitation.
Key words3DVAR data assimilation background error statistics numerical simulation typhoon
Unable to display preview. Download preview PDF.
- Courtier, P., 1985: Experiments in data assimilation using the adjoint model technique. Preprints.Workshop on High-Resolution Analysis, Reading, United Kingdom, European Centre for Medium-Range Weather Forecasts, 1–20.Google Scholar
- Courtier, P., E. Anderson, W. Heckley, J. Pailleux, D. Vasiljevic, M. Hamrud, A. Hollingsworth, F. Rabier, and M. Fischer, 1998: The ECMWF implementation of three dimensional variational (3DVAR) data assimilation. Part I: Formulation.Quart. J. Roy. Meteor. Soc.,123, 1–26.Google Scholar
- Derber, J. C., 1985: The variational 4-D assimilation of analyses using filtered models as constraints. Ph.D. dissertation, University of Wisconsin-Madison, 142pp.Google Scholar
- Hayden, C. M., and R. J. Purser, 1995: Recursive filter objective analysis of meteorological fields: Applications to NESDIS operational processing.J. Appl. Meteor.,34, 3–15.Google Scholar
- Le Dimet, F. X., 1982: A general formalism of variational analysis. CIMMS Rep. 22, 1–34. [Available from Sarkeys Energy Center, Rm 1110, University of Oklahoma, Norman, OK 73019.]Google Scholar
- Le Dimet, F. X., and O. Talagrand, 1986: Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects.Tellus,38A, 97–110.Google Scholar
- Rabier, F., A. McNally, E. Anderson, P. Courtier, P. Unden, J. Eyre, A. Hollingsworth, and F. Bouttier, 1997: The ECMWF implementation of three dimensional variational (3DVar) data assimilation. Part II: Structure function.Quart. J. Roy. Meteor. Soc.,123, 27–52.Google Scholar
- Sasaki, Y. 1958: An objective analysis based on variational methods.J. Meteor. Soc. Japan,36, 77–88.Google Scholar
- Zou, X., I. M. Navon, and J. G. Sela, 1993b: Variational data assimilation with moist threshold processes using the NMC spectral model.Tellus,45A, 370–387.Google Scholar