Makarand A Kulkarni
Articles written in Journal of Earth System Science
Volume 117 Issue 4 August 2008 pp 457-463
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.
Volume 120 Issue 5 October 2011 pp 795-805
The northeast (NE) monsoon season (October, November and December) is the major period of rainfall activity over south peninsular India. This study is mainly focused on the prediction of northeast monsoon rainfall using lead-1 products (forecasts for the season issued in beginning of September) of seven general circulation models (GCMs). An examination of the performances of these GCMs during hindcast runs (1982–2008) indicates that these models are not able to simulate the observed interannual variability of rainfall. Inaccurate response of the models to sea surface temperatures may be one of the probable reasons for the poor performance of these models to predict seasonal mean rainfall anomalies over the study domain. An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes,
Volume 128 | Issue 8
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