Prediction of wind speed in the atmospheric boundary layer is important for wind energy assessment, 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.

Numerical simulations are used to examine the dependences of the percolation transport exponents on the distribution of bond strengths g in two-dimensional models. We use the probability density function p(g) = g, a case that arises naturally in percolation of continuum systems. Our results are consistent with earlier predictions that for 0 & n (1 the exponent t differs from its counterpart t in the standard discrete lattice percolation networks by (tt) =a/(1n), while for a (0, the exponents t and t are equal.We present in this note numerical results confirming that the transport critical exponent can be nonuniversal for percolating networks with a distribution

[1] Sonic layer depth (SLD), an important parameter in underwater acoustics, is the near surface depth of first maxima of the sound speed in the ocean. The lack of direct observations of vertical profiles of velocimeters or temperature and salinity, from which sound speed and SLD can be calculated, hampers the investigation of SLD. In this study, we demonstrate SLD estimation using artificial neural network (ANN) from surface measurements that can be replaced with satellite observations later. Surface and subsurface measurements from a central Arabian Sea mooring are used for this purpose. The estimated SLD had a root mean square error (correlation coefficient) of 11.83 m (0.84). Approximately 76% (91%) of estimations lie within ±10 m (±20 m). SLD has also been estimated from surface parameters using multiple regression technique (MRT). ANN proved its superiority over MRT in estimating SLD from surface parameters. Citation: Jain, S., M. M. Ali, and P. N. Sen (2007), Estimation of sonic layer depth from surface parameters, Geophys. Res. Lett., 34, L17602,

In this study, an attempt has been made to bring out the observational aspects of vertical wind shear in thunderstorms over Minicoy. Case studies of thunderstorm events have been examined to find out the effect of vertical wind shear and instability on strength and longevity of thunderstorms. Role of vertical wind shear in thunderstorms and its mechanism has been explored in this study. Results reveal that for prolonged thunderstorms high and low instability along with moderate to high vertical wind shear (moderate: 0.003 S −1 ≤ vertical wind shear ≤ 0.005 S −1 and high: > 0.005 S −1 ) play a significant role in longevity and strength of thunderstorms. The mechanism of vertical wind shear in thunderstorms was investigated in a few cases of thunderstorm events where the duration of thunderstorm was covered by the radiosonde/rawin ascent observation taken at Minicoy. Empirical model has been developed to classify thunderstorm type and to determine the strength and longevity of thunderstorms. Model validation has been carried out for selected cases. Model could classify thunderstorm type for most of the cases of thunderstorm events over island and coastal stations.

An investigation has been carried out to identify the trends in maximum, minimum and mean temperatures and temperature range over the Indian land mass during the winter (January, and February), premonsoon (March-May), southwest monsoon (June-September) and post-monsoon (October-December) seasons by using high resolution daily gridded data set prepared by India Meteorological Department for the period of 1969-2005. It has been observed that the maximum temperatures over the west coast of India show rising trend in winter, southwest monsoon and post-monsoon seasons but the maximum temperatures do not show any significant trend over the other parts of the country. Minimum temperatures show increasing trend over the North Indian states in all seasons and they show an increasing trend over the west coast of India in winter and southwest monsoon seasons. Mean temperature shows an increasing trend over the west coast of India during winter and southwest monsoon seasons. Decreasing trend is observed in the temperature range over North India in all seasons due to increasing trend in minimum temperature.