Impact of Kalpana-1-Derived Water Vapor Winds on Indian Ocean Tropical Cyclone Forecasts

Abstract The water vapor winds from the operational geostationary Indian National Satellite (INSAT) Kalpana-1 have recently become operational at the Space Applications Centre (SAC). A series of experimental forecasts are attempted here to evaluate the impact of water vapor winds derived from Kalpana-1 for the track and intensity prediction of two Bay of Bengal tropical cyclones (TCs), Sidr and Nargis, using the Weather Research and Forecasting (WRF) modeling system. The assimilation of water vapor winds has made some impact in the initial position errors as well as track forecasts when compared with the corresponding control experiments for both TCs. However, no statistically significant improvement is noticed in the simulations of TC intensities [i.e., minimum sea level pressure (MSLP) and maximum surface winds forecasts when satellite winds are used for assimilation]. Moreover, the performance of Kalpana-1 winds is evaluated by repeating the same sets of experiments using Meteosat-7 winds derived at th...

[1]  C. M. Kishtawal,et al.  A Modified Tracer Selection and Tracking Procedure to Derive Winds Using Water Vapor Imagers , 2008 .

[2]  Kenneth Holmlund,et al.  The Utilization of Statistical Properties of Satellite-Derived Atmospheric Motion Vectors to Derive Quality Indicators , 1998 .

[3]  Jordan G. Powers,et al.  A Description of the Advanced Research WRF Version 2 , 2005 .

[4]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[5]  C. M. Kishtawal,et al.  Estimation of Atmospheric Motion Vectors from Kalpana-1 Imagers , 2009 .

[6]  Kenneth Holmlund,et al.  Half hourly wind data from satellite derived water vapour measurements , 1995 .

[7]  Yoshio Kurihara,et al.  The GFDL Hurricane Prediction System and Its Performance in the 1995 Hurricane Season , 1998 .

[8]  W. Paul Menzel,et al.  The Impact of Satellite-derived Winds on Numerical Hurricane Track Forecasting , 1992 .

[9]  R. Purser,et al.  Three-Dimensional Variational Analysis with Spatially Inhomogeneous Covariances , 2002 .

[10]  John Derber,et al.  The National Meteorological Center's spectral-statistical interpolation analysis system , 1992 .

[11]  Timothy L. Olander,et al.  The Impact of Multispectral GOES-8 Wind Information on Atlantic Tropical Cyclone Track Forecasts in 1995. Part I: Dataset Methodology, Description, and Case Analysis , 1998 .

[12]  Wei Huang,et al.  A Three-Dimensional Variational Data Assimilation System for MM5: Implementation and Initial Results , 2004 .

[13]  J. Dudhia.  Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model , 1989 .

[14]  Lance M. Leslie,et al.  Improved Hurricane Track Forecasting from the Continuous Assimilation of High Quality Satellite Wind Data , 1998 .

[15]  Christopher S. Velden,et al.  The Impact of Multispectral GOES-8 Wind Information on Atlantic Tropical Cyclone Track Forecasts in 1995. Part II: NOGAPS Forecasts , 1998 .

[16]  J. Kain,et al.  A One-Dimensional Entraining/Detraining Plume Model and Its Application in Convective Parameterization , 1990 .

[17]  Christopher S. Velden,et al.  The Impact of Satellite Winds on Experimental GFDL Hurricane Model Forecasts , 2001 .

[18]  H. Pan,et al.  Nonlocal Boundary Layer Vertical Diffusion in a Medium-Range Forecast Model , 1996 .