Impact of satellite observed microwave SST on the simulation of tropical cyclones

The impact of realistic representation of sea surface temperature (SST) on the numerical simulation of track and intensity of tropical cyclones formed over the north Indian Ocean is studied using the Weather Research and Forecast (WRF) model. We have selected two intense tropical cyclones formed over the Bay of Bengal for studying the SST impact. Two different sets of SSTs were used in this study: one from TRMM Microwave Imager (TMI) satellite and other is the weekly averaged Reynold's SST analysis from National Center for Environmental Prediction (NCEP). WRF simulations were conducted using the Reynold's and TMI SST as model boundary condition for the two cyclone cases selected. The TMI SST which has a better temporal and spatial resolution showed sharper gradient when compared to the Reynold's SST. The use of TMI SST improved the WRF cyclone intensity prediction when compared to that using Reynold's SST for both the cases studied. The improvements in intensity were mainly due to the improved prediction of surface latent and sensible heat fluxes. The use of TMI SST in place of Reynold's SST improved cyclone track prediction for Orissa super cyclone but slightly degraded track prediction for cyclone Mala. The present modeling study supports the well established notion that the horizontal SST gradient is one of the major driving forces for the intensification and movement of tropical cyclones over the Indian Ocean.

Effect of January 15, 2010 annular solar eclipse on meteorological parameters over Goa, India

Atmospheric perturbations due to the annular solar eclipse were monitored to understand its influence on the meteorological parameters from surface to the lower stratosphere. A strong inversion at 13 km and an abnormal warming in the upper troposphere were noticed on the eclipse day. A decrease in tropopause height associated with increase in temperature caused anomalous warming. Considerable attenuation of incoming solar radiation resulted in abrupt increase of air temperature during the next 24 h followed by sharp decrease in relative humidity. The time lag is attributed to the distance from the totality and the response time between tropopause and surface layer. (C) 2011 Elsevier Ltd. All rights reserved.

Influences of the springtime northern Indian biomass burning over the central Himalayas

The influences of the springtime northern Indian biomass burning are shown for the first time over the central Himalayas by using three years (2007-2009) of surface and space based observations along with a radiative transfer model. Near-surface ozone, black carbon (BC), spectral aerosol optical depths (AODs) and the meteorological parameters are measured at a high altitude site Nainital (29.37 degrees N, 79.45 degrees E, 1958 m amsl) located in the central Himalayas. The satellite observations include the MODIS derived fire counts and AOD (0.55 mu m), and OMI derived tropospheric column NO(2), ultraviolet aerosol index and single scattering albedo. MODIS fire counts and BC observations are used to identify the fire-impacted periods (372 h during 2007-2009) and hence the induced enhancements in surface BC, AOD (0.5 mu m) and ozone are estimated to be 1802 ng m(-3) (similar to 145%), 0.3 (similar to 150%) and 19 ppbv (similar to 34%) respectively. Large enhancements (53-100%) are also seen in the satellite derived parameters over a 2 degrees x 2 degrees region around Nainital. The present analysis highlights the northern Indian biomass burning induced cooling at the surface (-27 W m(-2)) and top of the atmosphere (-8 W m(-2)) in the lesser polluted high altitude regions of the central Himalayas. This cooling leads to an additional atmospheric warming of 19 W m(-2) and increases the lower atmospheric heating rate by 0.8 K day(-1). These biomass burning induced changes over the central Himalayan atmosphere during spring may also lead to enhanced short-wave absorption above clouds and might have an impact on the monsoonal rainfall.

Multi-spectral satellite image classification using Glowworm Swarm Optimization

This paper investigates a new Glowworm Swarm Optimization (GSO) clustering algorithm for hierarchical splitting and merging of automatic multi-spectral satellite image classification (land cover mapping problem). Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to classify all the basic land cover classes of an urban region in a satisfactory manner. In unsupervised classification methods, the automatic generation of clusters to classify a huge database is not exploited to their full potential. The proposed methodology searches for the best possible number of clusters and its center using Glowworm Swarm Optimization (GSO). Using these clusters, we classify by merging based on parametric method (k-means technique). The performance of the proposed unsupervised classification technique is evaluated for Landsat 7 thematic mapper image. Results are evaluated in terms of the classification efficiency - individual, average and overall.

Monitoring Himalayan cryosphere using remote sensing techniques

In the Himalayas, large area is covered by glaciers, seasonal snow and changes in its extent can influence availability of water in the Himalayan Rivers. In this paper, changes in glacial extent, glacial mass balance and seasonal snow cover have been discussed. Field and satellite based investigations suggest, most of the Himalayan glaciers are retreating though the rate of retreat is varying from glacier to glacier, ranging from few meters to almost 50 meters per year, depending upon the numerous glacial, terrain and meteorological parameters. Retreat was estimated for 1868 glaciers in eleven basins distributed across the Indian Himalaya since 1962 to 2001/02. Estimates show an overall reduction in glacier area from 6332 to 5329 sq km, an overall deglaciation of 16 percent.Snow line at the end of ablation season on the Chhota Shigri glacier suggests a change in altitude from 4900 to 5200 m from late 1970’s to the present. Seasonal snow cover monitoring of the Himalaya has shown large amounts of snow cover depletion in early part of winter, i.e. from October to December. For many basins located in lower altitude and in south of Pir Panjal range, snow ablation was observed through out the winter season. In addition, average stream runoff of the Baspa basin during the month of December shows an increase by 75 per cent. This combination of glacial retreat, negative mass balance, early melting of seasonal snow cover and winter time increase in stream runoff suggest an influence of climate change on the Himalayan cryosphere.

Northeast monsoon over India: variability and prediction

South peninsular India experiences a large portion of the annual rainfall during the northeast monsoon season (October to December). In this study, the facets of diurnal, intra-seasonal and inter-annual variability of the northeast monsoon rainfall (the NEMR) over India have been examined. The analysis of satellite derived hourly rainfall reveals that there are distinct features of diurnal variation over the land and oceans during the season. Over the land, rainfall peaks during the late afternoon/evening, while over the oceans an early morning peak is observed. The harmonic analysis of hourly data reveals that the amplitude and variance are the largest over south peninsular India. The NEMR also exhibits significant intra-seasonal variability on a 20-40 day time scale. Analysis also shows significant northward propagation of the maximum cloud zone from south of equator to the south peninsula during the season. The NEMR exhibits large inter-annual variability with the co-efficient of variation (CV) of 25%. The positive phases of ENSO and the Indian Ocean Dipole (IOD) are conducive for normal to above normal rainfall activity during the northeast monsoon. There are multi-decadal variations in the statistical relationship between ENSO and the NEMR. During the period 2001-2010 the statistical relationship between ENSO and the NEMR has significantly weakened. The analysis of seasonal rainfall hindcasts for the period 1960-2005 produced by the state-of-the-art coupled climate models, ENSEMBLES, reveals that the coupled models have very poor skill in predicting the inter-annual variability of the NEMR. This is mainly due to the inability of the ENSEMBLES models to simulate the positive relationship between ENSO and the NEMR correctly. Copyright (C) 2012 Royal Meteorological Society