• Shivani Shah

      Articles written in Journal of Earth System Science

    • Verification of cloud cover forecast with INSAT observation over western India

      Shivani Shah B M Rao Prashant Kumar P K Pal

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      Since the beginning of the summer monsoon 2009, experimental mesoscale weather forecasts in real time are being generated using WRF model by the Meteorology and Oceanography Group at the Space Applications Centre (ISRO)and are disseminated through MOSDAC (www.mosdac.gov.in) to various users. To begin with, the 12 h, 24 h and 48 h forecasts for the western India region are made available. A study is undertaken to comprehensively assess the cloudiness prediction performance of WRF model. The evaluations have been made over the three months period during monsoon 2009. INSAT cloud imagery data has been used as a reference for these evaluations. The verification strategy includes computation of various skill scores. It is seen that probability of detection (POD)of cloud is 84% and the false alarm rate (FAR) is around 18%. It is hoped that this assessment will provide information on the use of these forecasts in various applications.

    • A simple model for post-landfall intensity changes of tropical cyclone over India, Bangladesh and Myanmar coasts

      C M Kishtawal Shivani Shah Sashmita Chaurasia Neeru Jaiswal

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      Using JTWC (Joint Typhoon Warning Center) best track analysis data for the Indian Ocean cyclones, we developed an empirical equation for prediction of maximum surface wind speed of tropical cyclones during first 6–12 hours of landfall along the coastline of Indian subcontinent. A non-linear data fitting approach, the Genetic Algorithm, has been used to develop the above empirical equation using data for 74 tropical cyclones that made landfall on the coasts of India, Bangladesh and Myanmar during the period 1978–2011. For an out of sample validation test, the mean absolute error of the prediction was found to be 5.2 kt, and a correlation of 0.97. Our analysis indicates that time-integration of land area intercepted by cyclones during the landfall is a better predictor of post-landfall intensity compared to post-landfall time span. This approach also helps to tackle the complexity of coastline geometry of Indian subcontinent area.

    • A new perspective on the infrared brightness temperature distribution of the deep convective clouds

      Rakesh Teja Konduru C M Kishtawal Shivani Shah

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      We are proposing a statistical technique to analyze the best fit of the histogram of infrared brightness temperature of convective cloud pixels. For this we have utilized the infrared brightness temperatures (IRTB) of Kalpana-1 (8 km resolution) and globally merged infrared brightness temperatures of Climate Prediction Centre NCEP/NWS (4 km resolution, merged from all the available geostationary satellites GOES-8/10, METEOSAT-7/5 and GMS), for both deep convective and non-deep convective (shallow cloud) cases. It is observed that Johnson SB function is the best continuous distribution function in explaining the histogram of infrared brightness temperatures of the convective clouds. The best fit is confirmed by Kolmogorov–Smirnov statistic. Johnson SB’s distribution of histogram of infrared brightness temperatures clearly discriminates the cloud pixels of deep convective and non-deep convective cases. It also captures the asymmetric nature in histogram of infrared brightness temperatures. We also observed that Johnson SB distribution of infrared brightness temperatures for deep convective systems is different in each of the pre-monsoon, monsoon and post-monsoon seasons. And Johnson SB parameters are observed to be best in discriminating the Johnson SB distribution of infrared brightness temperatures of deep convective systems for each season. Due to these properties of Johnson SB function, it can be utilized in the modelling of the histogram of infrared brightness temperature of deep convective and non-deep convective systems. It focuses a new perspective on the infrared brightness temperature that will be helpful in cloud detection, classification and modelling.

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