• G V Rama

      Articles written in Journal of Earth System Science

    • Wind speed prediction using statistical regression and neural network

      Makarand A Kulkarni Sunil Patil G V Rama P N Sen

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      Prediction of wind speed in the atmospheric boundary layer is important for wind energy assess- ment,satellite launching and aviation,etc.There are a few techniques available for wind speed prediction,which require a minimum number of input parameters.Four different statistical techniques,viz.,curve fitting,Auto Regressive Integrated Moving Average Model (ARIMA),extrapolation with periodic function and Artificial Neural Networks (ANN)are employed to predict wind speed.These methods require wind speeds of previous hours as input.It has been found that wind speed can be predicted with a reasonable degree of accuracy using two methods,viz.,extrapolation using periodic curve fitting and ANN and the other two methods are not very useful.

    • Analysis on MM5 predictions at Sriharikota during northeast monsoon 2008

      D Gayatri Vani S Rambabu M Rajasekhar G V Rama B V Apparao A K Ghosh

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      The Indian northeast monsoon is inherently chaotic in nature as the rainfall realised in the peninsular India depends substantially on the formation and movement of low-pressure systems in central and southwest Bay of Bengal and on the convective activity which is mainly due to the moist north-easterlies from Bay of Bengal. The objective of this study is to analyse the performance of the PSU-NCAR Mesoscale Model Version 5 (MM5), for northeast monsoon 2008 that includes tropical cyclones – Rashmi, Khai-Muk and Nisha and convective events over Sriharikota region, the rocket launch centre. The impact of objective analysis system using radiosonde observations, surface observations and Kalpana-1 satellite derived Atmospheric Motion Wind Vectors (AMV) is also studied. The performance of the model is analysed by comparing the predicted parameters like mean sea level pressure (MSLP), intensity, track and rainfall with the observations. The results show that the model simulations could capture MSLP and intensity of all the cyclones reasonably well. The dependence of the movement of the system on the environmental flow is clearly observed in all the three cases. The vector displacement error and percentage of improvement is calculated to study the impact of objective data analysis on the movement and intensity of the cyclone.

    • Sensitivity of movement and intensity of severe cyclone AILA to the physical processes

      S Rambabu D Gayatri Vani S S V S Ramakrishna G V Rama B V AppaRao

      More Details Abstract Fulltext PDF

      Accurate prediction of movement and intensity of tropical cyclone is still most challenging problem in numerical weather prediction. The positive progress in this field can be achieved by providing network of observations in the storm region and best representation of atmospheric physical processes in the model. In the present study later part was attempted to investigate the sensitivity of movement and intensity of the severe cyclonic storm AILA to different physical processes in the Weather Research and Forecasting model. Three sets of experiments were conducted for convection, microphysics (MP) and planetary boundary layer (PBL) processes. Model-simulated fields like minimum central surface pressure, maximum surface wind, track and vector displacement error are considered to test the sensitivity. The results indicate that the movement of the system is more sensitive to the cumulus physics and the intensity of the cyclone is sensitive to both PBL and cumulus physics. The combination of Betts Miller Janjic (BMJ) for convection, Yonsei University (YSU) for PBL and Purdue Lin (LIN) for microphysics is found to perform better than other combination schemes. The horizontal and vertical features of the system along with its special features like complete northward movement of the system throughout the travel period and the consistent cyclonic storm intensity until 15 hrs after the landfall could be well simulated by the model.

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