Estimation of surface temperature from MONTBLEX data

It is observed that the daily mean temperature of the soil is linear with depth and the variation of the temperature is sinusoidal with a period of a day. Based on these observations the one-dimensional heat conduction equation for the soil can be solved which gives the amplitude and phase variation of the temperature wave with depth. Given the temperature data at three levels below the surface, the amplitude and phase variation and hence the surface temperature variation over the day are estimated. The daily mean temperature of the surface is estimated from linear extrapolation of the daily means at the three levels below the surface. Estimated values of soil thermal diffusivity show a subtantial change after sudden and heavy rains.

Interannual Variations of Sea Surface Temperature over the Arabian Sea and the Indian Monsoon: A New Perspective

The interannual variation of surface fields over the Arabian Sea and Bay of Bengal are studied using data between 1900 and 1979. It is emphasized that the monthly mean sea surface temperature (SST) over the north Indian Ocean and monsoon rainfall are significantly affected by synoptic systems and other intraseasonal variations. To highlight the interannual signals it is important to remove the large-amplitude high-frequency noise and very low frequency long-term trends, if any. By suitable spatial and temporal averaging of the SST and the rainfall data and by removing the long-term trend from the SST data, we have been able to show that there exists a homogeneous region in the southeastern Arabian Sea over which the March�April (MA) SST anomalies are significantly correlated with the seasonal (June�September) rainfall over India. A potential of this premonsoon signal for predicting the seasonal rainfall over India is indicated. It is shown that the correlation between the SST and the seasonal monsoon rainfall goes through a change of sign from significantly positive with premonsoon SST to very small values with SST during the monsoon season and to significantly negative with SST during the post-monsoon months. For the first time, we have demonstrated that heavy or deficient rainfall years are associated with large-scale coherent changes in the SST (although perhaps of small amplitude) over the north Indian 0cean. We also indicate possible reasons for the apparent lack of persistence of the premonsoon SST anomalies.

Role of predictors in downscaling surface temperature to river basin in India for IPCC SRES scenarios using support vector machine

In this paper, downscaling models are developed using a support vector machine (SVM) for obtaining projections of monthly mean maximum and minimum temperatures (T-max and T-min) to river-basin scale. The effectiveness of the model is demonstrated through application to downscale the predictands for the catchment of the Malaprabha reservoir in India, which is considered to be a climatically sensitive region. The probable predictor variables are extracted from (1) the National Centers for Environmental Prediction (NCEP) reanalysis dataset for the period 1978-2000, and (2) the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 1978-2100. The predictor variables are classified into three groups, namely A, B and C. Large-scale atmospheric variables Such as air temperature, zonal and meridional wind velocities at 925 nib which are often used for downscaling temperature are considered as predictors in Group A. Surface flux variables such as latent heat (LH), sensible heat, shortwave radiation and longwave radiation fluxes, which control temperature of the Earth's surface are tried as plausible predictors in Group B. Group C comprises of all the predictor variables in both the Groups A and B. The scatter plots and cross-correlations are used for verifying the reliability of the simulation of the predictor variables by the CGCM3 and to Study the predictor-predictand relationships. The impact of trend in predictor variables on downscaled temperature was studied. The predictor, air temperature at 925 mb showed an increasing trend, while the rest of the predictors showed no trend. The performance of the SVM models that are developed, one for each combination of predictor group, predictand, calibration period and location-based stratification (land, land and ocean) of climate variables, was evaluated. In general, the models which use predictor variables pertaining to land surface improved the performance of SVM models for downscaling T-max and T-min

Interplay of spin and lattice degrees of freedom in the frustrated antiferromagnet CdCr2O4: High-field and temperature-induced anomalies of the elastic constants

Temperature and magnetic field studies of the elastic constants of the chromium spinel CdCr2O4 show pronounced anomalies related to strong spin-phonon coupling in this frustrated antiferromagnet. A detailed comparison of the longitudinal acoustic mode propagating along the 111] direction with a theory based on an exchange-striction mechanism leads to an estimate of the strength of the magnetoelastic interaction. The derived spin-phonon coupling constant is in good agreement with previous determinations based on infrared absorption. Further insight is gained from intermediate and high magnetic field experiments in the field regime of the magnetization plateau. The role of the antisymmetric Dzyaloshinskii-Moriya interaction is discussed.

Land Surface Temperature with Land Cover Dynamics:Multi-Resolution, Spatio-Temporal Data Analysis of Greater Bangalore

Bangalore is experiencing unprecedented urbanisation in recent times due to concentrated developmental activities with impetus on IT (Information Technology) and BT (Biotechnology) sectors. The concentrated developmental activities has resulted in the increase in population and consequent pressure on infrastructure, natural resources, ultimately giving rise to a plethora of serious challenges such as urban flooding, climate change, etc. One of the perceived impact at local levels is the increase in sensible heat flux from the land surface to the atmosphere, which is also referred as heat island effect. In this communication, we report the changes in land surface temperature (LST) with respect to land cover changes during 1973 to 2007. A novel technique combining the information from sub-pixel class proportions with information from classified image (using signatures of the respective classes collected from the ground) has been used to achieve more reliable classification. The analysis showed positive correlation with the increase in paved surfaces and LST. 466% increase in paved surfaces (buildings, roads, etc.) has lead to the increase in LST by about 2 ºC during the last 2 decades, confirming urban heat island phenomenon. LSTs’ were relatively lower (~ 4 to 7 ºC) at land uses such as vegetation (parks/forests) and water bodies which act as heat sinks.

Dynamic and thermodynamic anomalies of water at low temperatures: from bulk water to reverse micelles and DNA hydration layer

Liquid water is known to exhibit remarkable thermodynamic and dynamic anomalies, ranging from solvation properties in supercritical state to an apparent divergence of the linear response functions at a low temperature. Anomalies in various dynamic properties of water have also been observed in the hydration layer of proteins, DNA grooves and inside the nanocavity, such as reverse micelles and nanotubes. Here we report studies on the molecular origin of these anomalies in supercooled water, in the grooves of DNA double helix and reverse micelles. The anomalies have been discussed in terms of growing correlation length and intermittent population fluctuation of 4- and 5-coordinated species. We establish correlation between thermodynamic response functions and mean squared species number fluctuation. Lifetime analysis of 4- and 5-coordinated species reveals interesting differences between the role of the two species in supercooled and constrained water. The nature and manifestations of the apparent and much discussed liquid-liquid transition under confinement are found to be markedly different from that in the bulk. We find an interesting `faster than bulk' relaxation in reverse micelles which we attribute to frustration effects created by competition between the correlations imposed by surface interactions and that imposed by hydrogen bond network of water.

Mechanisms of summer intraseasonal sea surface temperature oscillations in the Bay of Bengal

Intraseasonal variations (ISV) of sea surface temperature (SST) in the Bay of Bengal (BoB) is highest in its northwestern part. An Indian Ocean model forced by QuikSCAT winds and climatological river discharge (QR run) reproduces ISV of SST, albeit with weaker magnitude. Air-sea fluxes, in the presence of a shallow mixed layer, efficiently effect intraseasonal SST fluctuations. Warming during intraseasonal events is smaller (<1°C) for June - July period and larger (1.5° to 2°C) during September, the latter due to a thinner mixed layer. To examine the effect of salinity on ISV, the model was run by artificially increasing the salinity (NORR run) and by decreasing it (MAHA10 run). In NORR, both rainfall and river discharge were switched off and in MAHA10 the discharge by river Mahanadi was increased tenfold. The spatial pattern of ISV as well as its periodicity was similar in QR, NORR and MAHA10. The ISV was stronger in NORR and weaker in MAHA10, compared to QR. In NORR, both intraseasonal warming and cooling were higher than in QR, the former due to reduced air-sea heat loss as the mean SST was lower, and the latter due to enhanced subsurface processes resulting from weaker stratification. In MAHA10, both warming and cooling were lower than in QR, the former due to higher air-sea heat loss owing to higher mean SST, and the latter due to weak subsurface processes resulting from stronger stratification. These model experiments suggest that salinity effects are crucial in determining amplitudes of intraseasonal SST variations in the BoB.

Processes of 30-90 days sea surface temperature variability in the northern Indian Ocean during boreal summer

During summer, the northern Indian Ocean exhibits significant atmospheric intraseasonal variability associated with active and break phases of the monsoon in the 30-90 days band. In this paper, we investigate mechanisms of the Sea Surface Temperature (SST) signature of this atmospheric variability, using a combination of observational datasets and Ocean General Circulation Model sensitivity experiments. In addition to the previously-reported intraseasonal SST signature in the Bay of Bengal, observations show clear SST signals in the Arabian Sea related to the active/break cycle of the monsoon. As the atmospheric intraseasonal oscillation moves northward, SST variations appear first at the southern tip of India (day 0), then in the Somali upwelling region (day 10), northern Bay of Bengal (day 19) and finally in the Oman upwelling region (day 23). The Bay of Bengal and Oman signals are most clearly associated with the monsoon active/break index, whereas the relationship with signals near Somali upwelling and the southern tip of India is weaker. In agreement with previous studies, we find that heat flux variations drive most of the intraseasonal SST variability in the Bay of Bengal, both in our model (regression coefficient, 0.9, against similar to 0.25 for wind stress) and in observations (0.8 regression coefficient); similar to 60% of the heat flux variation is due do shortwave radiation and similar to 40% due to latent heat flux. On the other hand, both observations and model results indicate a prominent role of dynamical oceanic processes in the Arabian Sea. Wind-stress variations force about 70-100% of SST intraseasonal variations in the Arabian Sea, through modulation of oceanic processes (entrainment, mixing, Ekman pumping, lateral advection). Our similar to 100 km resolution model suggests that internal oceanic variability (i.e. eddies) contributes substantially to intraseasonal variability at small-scale in the Somali upwelling region, but does not contribute to large-scale intraseasonal SST variability due to its small spatial scale and random phase relation to the active-break monsoon cycle. The effect of oceanic eddies; however, remains to be explored at a higher spatial resolution.

A simple model for spatial disaggregation of evaporative fraction: Comparative study with thermal sharpened land surface temperature data over India

[1] Evaporative fraction (EF) is a measure of the amount of available energy at the earth surface that is partitioned into latent heat flux. The currently operational thermal sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) on satellite platforms provide data only at 1000 m, which constraints the spatial resolution of EF estimates. A simple model (disaggregation of evaporative fraction (DEFrac)) based on the observed relationship between EF and the normalized difference vegetation index is proposed to spatially disaggregate EF. The DEFrac model was tested with EF estimated from the triangle method using 113 clear sky data sets from the MODIS sensor aboard Terra and Aqua satellites. Validation was done using the data at four micrometeorological tower sites across varied agro-climatic zones possessing different land cover conditions in India using Bowen ratio energy balance method. The root-mean-square error (RMSE) of EF estimated at 1000 m resolution using the triangle method was 0.09 for all the four sites put together. The RMSE of DEFrac disaggregated EF was 0.09 for 250 m resolution. Two models of input disaggregation were also tried with thermal data sharpened using two thermal sharpening models DisTrad and TsHARP. The RMSE of disaggregated EF was 0.14 for both the input disaggregation models for 250 m resolution. Moreover, spatial analysis of disaggregation was performed using Landsat-7 (Enhanced Thematic Mapper) ETM+ data over four grids in India for contrasted seasons. It was observed that the DEFrac model performed better than the input disaggregation models under cropped conditions while they were marginally similar under non-cropped conditions.

Assessment of land surface temperature and heat fluxes over Delhi using remote sensing data

Surface energy processes has an essential role in urban weather, climate and hydrosphere cycles, as well in urban heat redistribution. The research was undertaken to analyze the potential of Landsat and MODIS data in retrieving biophysical parameters in estimating land surface temperature & heat fluxes diurnally in summer and winter seasons of years 2000 and 2010 and understanding its effect on anthropogenic heat disturbance over Delhi and surrounding region. Results show that during years 2000-2010, settlement and industrial area increased from 5.66 to 11.74% and 4.92 to 11.87% respectively which in turn has direct effect on land surface temperature (LST) and heat fluxes including anthropogenic heat flux. Based on the energy balance model for land surface, a method to estimate the increase in anthropogenic heat flux (Has) has been proposed. The settlement and industrial areas has higher amounts of energy consumed and has high values of Has in all seasons. The comparison of satellite derived LST with that of field measured values show that Landsat estimated values are in close agreement within error of 2 degrees C than MODIS with an error of 3 degrees C. It was observed that, during 2000 and 2010, the average change in surface temperature using Landsat over settlement & industrial areas of both seasons is 1.4 degrees C & for MODIS data is 3.7 degrees C. The seasonal average change in anthropogenic heat flux (Has) estimated using Landsat & MODIS is up by around 38 W/m(2) and 62 W/m(2) respectively while higher change is observed over settlement and concrete structures. The study reveals that the dynamic range of Has values has increased in the 10 year period due to the strong anthropogenic influence over the area. The study showed that anthropogenic heat flux is an indicator of the strength of urban heat island effect, and can be used to quantify the magnitude of the urban heat island effect. (C) 2013 Elsevier Ltd. All rights reserved.

Impact of diurnal forcing on intraseasonal sea surface temperature oscillations in the Bay of Bengal

The diurnal cycle is an important mode of sea surface temperature (SST) variability in tropical oceans, influencing air-sea interaction and climate variability. Upper ocean mixing mechanisms are significant at diurnal time scales controlling the intraseasonal variability (ISV) of SST. Sensitivity experiments using an Ocean General Circulation Model (OGCM) for the summer monsoon of the year 2007 show that incorporation of diurnal cycle in the model atmospheric forcings improves the SST simulation at both intraseasonal and shorter time scales in the Bay of Bengal (BoB). The increase in SST-ISV amplitudes with diurnal forcing is approximate to 0.05 degrees C in the southern bay while it is approximate to 0.02 degrees C in the northern bay. Increased intraseasonal warming with diurnal forcing results from the increase in mixed layer heat gain from insolation, due to shoaling of the daytime mixed layer. Amplified intraseasonal cooling is dominantly controlled by the strengthening of subsurface processes owing to the nocturnal deepening of mixed layer. In the southern bay, intraseasonal variability is mainly determined by the diurnal cycle in insolation, while in the northern bay, diurnal cycle in insolation and winds have comparable contributions. Temperature inversions (TI) develop in the northern bay in the absence of diurnal variability in wind stress. In the northern bay, SST-ISV amplification is not as large as that in the southern bay due to the weaker diurnal variability of mixed layer depth (MLD) limited by salinity stratification. Diurnal variability of model MLD is not sufficient to create large modifications in mixed layer heat budget and SST-ISV in the northern bay.

Prediction of Land Surface Temperature under Cloudy Conditions using Microwave Remote Sensing and ANN

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.

Finite Element Solution Of Surface-Tension Driven Flows In Laser Surface-Melting

Lasers are very efficient in heating localized regions and hence they find a wide application in surface treatment processes. The surface of a material can be selectively modified to give superior wear and corrosion resistance. In laser surface-melting and welding problems, the high temperature gradient prevailing in the free surface induces a surface-tension gradient which is the dominant driving force for convection (known as thermo-capillary or Marangoni convection). It has been reported that the surface-tension driven convection plays a dominant role in determining the melt pool shape. In most of the earlier works on laser-melting and related problems, the finite difference method (FDM) has been used to solve the Navier Stokes equations [1]. Since the Reynolds number is quite high in these cases, upwinding has been used. Though upwinding gives physically realistic solutions even on a coarse grid, the results are inaccurate. McLay and Carey have solved the thermo-capillary flow in welding problems by an implicit finite element method [2]. They used the conventional Galerkin finite element method (FEM) which requires that the pressure be interpolated by one order lower than velocity (mixed interpolation). This restricts the choice of elements to certain higher order elements which need numerical integration for evaluation of element matrices. The implicit algorithm yields a system of nonlinear, unsymmetric equations which are not positive definite. Computations would be possible only with large mainframe computers.Sluzalec [3] has modeled the pulsed laser-melting problem by an explicit method (FEM). He has used the six-node triangular element with mixed interpolation. Since he has considered the buoyancy induced flow only, the velocity values are small. In the present work, an equal order explicit FEM is used to compute the thermo-capillary flow in the laser surface-melting problem. As this method permits equal order interpolation, there is no restriction in the choice of elements. Even linear elements such as the three-node triangular elements can be used. As the governing equations are solved in a sequential manner, the computer memory requirement is less. The finite element formulation is discussed in this paper along with typical numerical results.

XPS and XAES studies of surface segregation and oxidation of Cu-Ge alloy

Surface segregation of Ge is seen in the Cu-5at%Ge alloy with an activation enthalpy equal to 17 kJ/mol. Oxidation of the alloy in the temperature range 400 to 600 K shows the formation of Cu2O and GeO which on further heating in vacuum at 650 K converts to GeO2 with the reduction of Cu2O to Cu.