A cyclone genesis parameter, termed the genesis potential parameter (GPP), for the Indian Sea is proposed. The parameter is defined as the product of four variables, namely vorticity at 850 hPa, middle tropospheric relative humidity, middle tropospheric instability, and the inverse of vertical wind shear. The variables are calculated using the National Centers for Environmental Prediction (NCEP), USA, reanalysis data, averaged within a circle of 2.5°radius around the centre of cyclonic system. The parameter is tested with a sample dataset of 35 nondeveloping and developing low-pressure systems that formed over the Indian Sea during the period 1995-2005. The result shows that there is a distinction between GPP values for nondeveloping and developing systems in more than 85% cases. The composite GPP value is found to be around three to five times greater for developing systems than for nondeveloping systems. The analysis of the parameter at early development stage of a cyclonic storm appears to provide a useful predictive signal for intensification of the system.
An empirical model for predicting the maximum surface wind speed associated with a tropical cyclone after crossing the east coast of India is described. The model parameters are determined from the database of 19 recent cyclones. The model is based upon the assumption that tropical cyclone winds decay exponentially after landfall. A method for correcting the forecast during subsequent observation hours is also presented. Results show that without the correction factor the absolute mean error ranges from 6.1 to 4.9 kt (1 kt ϭ 0.5144 m s Ϫ1 ) and the root-mean-square error ranges from 7.9 to 5.6 kt, with both decreasing over time. With the incorporation of the correction procedure, a significant improvement in the forecast skill is noticed for the case in which it is tested using the dependent sample. The model is expected to be very useful to operational forecasters.
A statistical model for predicting the intensity of tropical cyclones in the Bay of Bengal has been proposed. The model is developed applying multiple linear regression technique. The model parameters are determined from the database of 62 cyclones that developed over the Bay of Bengal during the period 1981-2000. The parameters selected as predictors are: initial storm intensity, intensity changes during past 12 hours, storm motion speed, initial storm latitude position, vertical wind shear averaged along the storm track, vorticity at 850 hPa, Divergence at 200 hPa and sea surface temperature (SST). When the model is tested with the dependent samples of 62 cyclones, the forecast skill of the model for forecasts up to 72 hours is found to be reasonably good. The average absolute errors (AAE) are less than 10 knots for forecasts up to 36 hours and maximum forecast error of order 14 knots occurs at 60 hours and 72 hours. When the model is tested with the independent samples of 15 cyclones (during 2000 to 2007), the AAE is found to be less than 13 knots (ranging from 5.1 to 12.5 knots) for forecast up to 72 hours. The model is found to be superior to the empirical model proposed by Roy Bhowmik et al (2007) for the Bay of Bengal.
From the consideration of thermal energy, the maximum intensity of tropical cyclones largely depends upon the Sea Surface Temperature (SST). In this paper an empirical relationship between SST and Maximum Potential Intensity (MPI) of tropical cyclones over the Bay of Bengal has been developed using a sample of 60 cyclones from 20 years data (1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000). The relationship between SST and MPI is found to be linear. The MPI of each storm is computed using this empirical relationship and compared with observed intensity to examine how close the cyclones come to reaching their MPI. The result shows that about 18% of cyclones reach more than 80% of their MPI and about 38% of cyclones reach more than 50% of their MPI at their peak intensity. In general, cyclones attain about 51% of their MPI. The inter-seasonal variability shows cyclones in the pre-monsoon and the post-monsoon seasons tend to reach a higher percentage of their MPI than in the monsoon season. The inter-annual variability suggests there is appreciable variation in the yearly average of the ratio of observed maximum intensity to the MPI. The MPI could provide useful information to a forecaster about the possible extreme intensity of tropical cyclones, which has direct relevance to disaster management preparedness.
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