Meteorology

Land Use Land Cover (LULC) change analysis assists decision makers to ensure sustainable development and to understand the dynamics of our changing environment. During the past 15 years the study area has undergone many LULC changes due to rapid urban growth, poorly planned infrastructural development and a devastating earthquake event. This study was proposed to detect LULC changes and to investigate the major factors that have caused these changes. Steep topography, shallow soils and monsoonal climatic conditions tend to accelerate soil erosion that causes heavy sedimentation downstream. Therefore, erosion risk mapping was performed to prioritize the vulnerable areas for conservation efforts. For LULC change detection and analysis temporal Landsat satellite data captured by Thematic Mapper (TM) were employed. Maximum Likelihood (MLH) supervised classification algorithm was applied to classify the study area, whereas, Post Classification Comparison (PCC) approach was adopted to analyze the LULC changes. Soil erosion risk map was generated using four erosion controlling factors including rainfall, topography, soil erodibility and LULC. Results revealed that over a period of 11 years, a decrease has taken place in forest and low vegetation cover at a change rate of À02.70% and À02.60% respectively. On the other hand, built up and bare soil have increased at a rate of 01.00% and 04.20% respectively. The resultant erosion risk map shows that 59% of the area lies under low risk zone whereas 24%, 5% and 12% of the total area fall in medium, high and very high risk categories respectively. Ó 2014 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/3.0/).

Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysical parameters in multi-sensor remote sensing. Most state-of-the-art SR estimation methods, such as the vector version of the Second Simulation of the Satellite Signal in the Solar Spectrum (6SV) radiative transfer (RT) model, depend on accurate information on aerosol and atmospheric gases. In this study, a Simplified and Robust Surface Reflectance Estimation Method (SREM) based on the equations from 6SV RT model, without integrating information of aerosol particles and atmospheric gasses, is proposed and tested using Landsat 5 Thematic

Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysical parameters in multi-sensor remote sensing. Most state-of-the-art SR estimation methods, such as the vector version of the Second Simulation of the Satellite Signal in the Solar Spectrum (6SV) radiative transfer (RT) model, depend on accurate information on aerosol and atmospheric gases. In this study, a Simplified and Robust Surface Reflectance Estimation Method (SREM) based on the equations from 6SV RT model, without integrating information of aerosol particles and atmospheric gasses, is proposed and tested using Landsat 5 Thematic

Many modelling studies suggest that the El Niño–Southern Oscillation (ENSO), in interaction with the tropical Pacific background climate, will change with rising atmospheric greenhouse gas concentrations. Solar geoengineering (reducing the solar flux from outer space) has been proposed as a means to counteract anthropogenic climate change. However, the effectiveness of solar geoengineering concerning a variety of aspects of Earth's climate is uncertain. Robust results are particularly challenging to obtain for ENSO because existing geoengineering simulations are too short (typically ∼ 50 years) to detect statistically significant changes in the highly variable tropical Pacific background climate. We here present results from a 1000-year-long solar-geoengineering simulation, G1, carried out with the coupled atmosphere–ocean general circulation model HadCM3L. In agreement with previous studies, reducing the solar irradiance (4 %) to offset global mean surface warming in the model more than compensates the warming in the tropical Pacific that develops in the 4 × CO2 scenario. We see an overcooling of 0.3 ∘C and a 0.23 mm d−1 (5 %) reduction in mean rainfall over the tropical Pacific relative to preindustrial conditions in the G1 simulation, owing to the different latitudinal distributions of the shortwave (solar) and longwave (CO2) forcings. The location of the Intertropical Convergence Zone (ITCZ) in the tropical Pacific, which moved 7.5∘ southwards under 4 × CO2, is restored to its preindustrial position. However, other aspects of the tropical Pacific mean climate are not reset as effectively. Relative to preindustrial conditions, in G1 the time-averaged zonal wind stress, zonal sea surface temperature (SST) gradient, and meridional SST gradient are each statistically significantly reduced by around 10 %, and the Pacific Walker Circulation (PWC) is consistently weakened, resulting in conditions conducive to increased frequency of El Niño events. The overall amplitude of ENSO strengthens by 9 %–10 % in G1, but there is a 65 % reduction in the asymmetry between cold and warm events: cold events intensify more than warm events. Notably, the frequency of extreme El Niño and La Niña events increases by ca. 60 % and 30 %, respectively, while the total number of El Niño events increases by around 10 %. All of these changes are statistically significant at either 95 or 99 % confidence level. Somewhat paradoxically, while the number of total and extreme events increases, the extreme El Niño events become weaker relative to the preindustrial state, while the extreme La Niña events become even stronger. That is, such extreme El Niño events in G1 become less intense than under preindustrial conditions but also more frequent. In contrast, extreme La Niña events become stronger in G1, which is in agreement with the general overcooling of the tropical Pacific in G1 relative to preindustrial conditions.

This research study has been carried out to explore the dependence of fog formation on Radioactive aerosols. The aerosols containing radioactive nuclides are called radioactive aerosols. A large number of radioactive nuclides are present in the atmosphere among which the two most important nuclides, 7 Be and 210 Pb are considered here in this study. Results for Activity Concentrations of these radio-nuclides in air samples in clear and foggy conditions were comparatively analyzed. About 19% increase in Activity concentration for 210 Pb and about 23% increase in Activity Concentration for 7 Be was recorded during fog as compared to clear conditions. This increase in Activity Concentration during fog indicates that the presence of aerosols laden with these radio-nuclides is also one of the so many factors responsible for fog formation.