Reanalysis of western Pacific typhoons in 2004 with multi-satellite observations
A pilot tropical cyclone reanalysis project was conducted to construct a reliable, high temporal and spatial resolution tropical cyclone dataset for selected western Pacific typhoons in summer 2004, with the application of the latest satellite observations and a 4-dimensional variational data assimilation method. Primary data used for the reanalysis include SSM/I rain rate, GOES-retrieved upper-level wind, QuikSCAT surface wind, Aqua AIRS/AMSU retrieved temperature and moisture profiles, and JTWC best track data. A regular reanalysis procedure was established and up to 12 western Pacific typhoons have been reanalyzed. The reanalysis period covers the entire life cycle of a tropical cyclone, from a few days prior to its genesis to its final decay stage. A preliminary analysis shows that the reanalysis product significantly improves typhoon intensity, structure, and track, compared to the NCEP operational final analysis. The validation of the TC structure against independent observations shows that the reanalysis reproduces well the asymmetric characteristics of TC rain bands and cloud bands. A further modeling experiment with an initial condition from the reanalysis product reveals a significant improvement in typhoon intensity forecast compared to a parallel experiment with an initial condition from the NCEP final analysis, which provides a further indication of quality of the tropical cyclone reanalysis. The reanalysis product and the raw observational data will soon be posted on the data server of the IPRC Asia-Pacific Data-Research Center (http://apdrc.soest.hawaii.edu/) for public use.
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- Chen SS, Tenerelli JE, Zhao W, Foster RA, Liu WT (2004) Improving tropical cyclone prediction using scatterometer surface winds in model initialization. ENVSAT Symposium Abstract and Program Book, Salzburg, Austria, No. 669Google Scholar
- Chen Y, Snyder C (2006) Assimilating vortex position with an ensemble Kalman filter. Mon Wea Rev (in revision)Google Scholar
- Daley R, Barker E (2001) NAVDAS Source Book 2001. NRL Publication NRL/PU/7530-01-441, 163 ppGoogle Scholar
- Fujita, T 1952Pressure distribution within a typhoonGeophys Mag23437451Google Scholar
- Grell GA, Dudhia J, Stauffer DR (1995) A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCAR/TN-398+STR, 138 pp [Available from NCAR Publications Office, P.O. Box 3000, Boulder, CO 80307-3000]Google Scholar
- Hollinger JP (1991) DMSP Special Sensor Microwave/Imager calibration/validation. Final Report, Vol. II, Naval Research Laboratory, Washington, DC, 106 pp [Available from Hollinger JP, Naval Research Laboratory, Washington, DC 20375]Google Scholar
- Kain, JS, Fritsch, JM 1993Convective parameterization for mesoscale models: The Kain-Fritsch scheme. The representation of cumulus convection in numerical modelsMeteor Monogr24165170Google Scholar
- Mills GA, Gallan GM, Goodman BM (1986) A mesoscale data assimilation scheme for real time use in the McIDAS environment. CIMSS Tech. Report, 86 pp [Available from CIMSS, Space Science and Engineering Center, University of Wisconsin, 1225 West Dayton St., Madison, WI 53706]Google Scholar
- Ruggiero FH, Michalakes J, Nehrkorn T, Modica GD, Zou X (2006) Development of a new distributed-memory MM5 adjoint. J Atmos Ocean Technol 23 (DOI: 10.1175/JTECH1862.1, 424–436)Google Scholar
- Weng F, Zhu T, Yan B (2006) Use of rain-affected radiances from microwave observations for hurricane vortex analysis. J Atmos Sci (submitted)Google Scholar
- Zhang, X, Wang, B, Zhang, X 2003Four-dimensional variational assimilation using rainfall observation in heavy rain simulationProg Nat Sci1313291333Google Scholar
- Zhang X, Xiao Q, Fitzpatrick P (2006) The impact of multi-satellite data on the initialization and simulation of hurricane Lili’s (2002) Rapid weakening phase. Mon Wea Rev (accepted)Google Scholar
- Zou, X, Navon, IM, Ledimet, FX 1992Incomplete observations and control of gravity waves in variational data assimilationTellus44A273296Google Scholar