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A new comprehensive surface temperature data set for India is used to document changes in Indian temperature over seven decades, in order to examine the patterns and possible effects of global warming. The data set is subdivided into pre-monsoon, monsoon, and post-monsoon categories in order to study the temperature patterns in each of these periods. When the decade means in maximum, minimum and daily mean temperature for the 2000s are compared to those of the 1950s, a consistent pattern of warming is found over northwestern and southern India, and a pattern of cooling is seen in a broad zone anchored over northeastern India and extending southwestward across central India. These patterns are explained by the presence of a large region of anthropogenic brown haze over India and adjacent ocean regions. These aerosols absorb solar radiation, leading to warming of the haze layer over northeastern and central India and to cooling of the surface air beneath. The heated air rises and then sinks to the north and south of the haze region over northwestern and southern India, warming the air by compression as it sinks in those regions. The possible impact of these temperature patterns on Indian agriculture is considered.

Vertical profiles of carbon monoxide (CO) and ozone retrieved from Tropospheric Emission Spectrometer have been analyzed during two super cyclone systems Mala and Sidr. Super cyclones Mala and Sidr traversed the Bay of Bengal (BOB) region on April 24-29, 2006 and November 12-16, 2007 respectively. The CO and ozone plume is observed as a strong enhancement of these pollutants in the upper troposphere over the BOB, indicating deep convective transport. Longitude-height cross-section of these pollutants shows vertical transport to the upper troposphere. CO mixing ratio ~90 ppb is observed near the 146-mb level during the cyclone Mala and near 316 mb during the cyclone Sidr. Ozone mixing ratio ~60-100 ppb is observed near the 316-mb level during both the cyclones. Analysis of National Centers for Environmental Prediction (NCEP) reanalysis vertical winds (omega) confirms vertical transport in the BOB.

A method for improving weather and climate forecast skill has been developed. It is called a superensemble, and it arose from a study of the statistical properties of a low-order spectral model. Multiple regression was used to determine coefficients from multimodel forecasts and observations. The coefficients were then used in the superensemble technique. The superensemble was shown to outperform all model forecasts for multiseasonal, medium-range weather and hurricane forecasts. In addition, the superensemble was shown to have higher skill than forecasts based solely on ensemble averaging.