48 resultados para clouds


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Sheet-like clouds are common in turbulent gas and perhaps form via collisions between turbulent gas flows. Having examined the evolution of an isothermal shocked slab in an earlier contribution, in this work we follow the evolution of a sheet-like cloud confined by (thermal) pressure and gas in it is allowed to cool. The extant purpose of this endeavour is to study the early phases of core-formation. The observed evolution of this cloud supports the conjecture that molecular clouds themselves are three-phase media (comprising viz. a stable cold and warm medium, and a third thermally unstable medium), though it appears, clouds may evolve in this manner irrespective of whether they are gravitationally bound. We report, this sheet fragments initially due to the growth of the thermal instability (TI) and some fragments are elongated, filament-like. Subsequently, relatively large fragments become gravitationally unstable and sub-fragment into smaller cores. The formation of cores appears to be a three stage process: first, growth of the TI leads to rapid fragmentation of the slab; second, relatively small fragments acquire mass via gas-accretion and/or merger and third, sufficiently massive fragments become susceptible to the gravitational instability and sub-fragment to form smaller cores. We investigate typical properties of clumps (and smaller cores) resulting from this fragmentation process. Findings of this work support the suggestion that the weak velocity field usually observed in dense clumps and smaller cores is likely seeded by the growth of dynamic instabilities. Simulations were performed using the smooth particle hydrodynamics algorithm.

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Land surface temperature (LST) is an important variable in climate, hydrologic, ecological, biophysical and biochemical studies (Mildrexler et al., 2011). The most effective way to obtain LST measurements is through satellites. Presently, LST from moderate resolution imaging spectroradiometer (MODIS) sensor is applied in various fields due to its high spatial and temporal availability over the globe, but quite difficult to provide observations in cloudy conditions. This study evolves of prediction of LST under clear and cloudy conditions using microwave vegetation indices (MVIs), elevation, latitude, longitude and Julian day as inputs employing an artificial neural network (ANN) model. MVIs can be obtained even under cloudy condition, since microwave radiation has an ability to penetrate through clouds. In this study LST and MVIs data of the year 2010 for the Cauvery basin on a daily basis were obtained from MODIS and advanced microwave scanning radiometer (AMSR-E) sensors of aqua satellite respectively. Separate ANN models were trained and tested for the grid cells for which both LST and MVI were available. The performance of the models was evaluated based on standard evaluation measures. The best performing model was used to predict LST where MVIs were available. Results revealed that predictions of LST using ANN are in good agreement with the observed values. The ANN approach presented in this study promises to be useful for predicting LST using satellite observations even in cloudy conditions. (C) 2015 The Authors. Published by Elsevier B.V.

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Recent studies, over regions influenced by biomass burning aerosol, have shown that it is possible to define a critical cloud fraction' (CCF) at which the aerosol direct radiative forcing switch from a cooling to a warming effect. Using 4 years of multi-satellite data analysis, we show that CCF varies with aerosol composition and changed from 0.28 to 0.13 from postmonsoon to winter as a result of shift from less absorbing to moderately absorbing aerosol. Our results indicate that we can estimate aerosol absorption from space using independently measured top of the atmosphere (TOA) fluxes Cloud Aerosol Lidar with Orthogonal Polarization-Moderate resolution Imaging Spectroradiometer-Clouds and the Earth's Radiant Energy System (CALIPSO-MODIS-CERES)] combined algorithms for example.