Morphological classification pertaining to validate the climatology and category of thunderstorms over Kolkata,India

As the world's highest and largest plateau, the Qinghai–Xizang Plateau has experienced a greater warming than the Northern Hemisphere and global averages. This warming has been reported to exhibit an elevation-dependent pattern. However, the finding involved plenty of uncertainties caused by the spatially limited datasets and complex topography. Here, we explored an approach integrating satellite-derived LST data and ground records to generate a spatially continuous air temperature dataset for the plateau grasslands from 2003 to 2012, and then examined influences of elevation/topography on temperature change trends. The derived temperature dataset was validated to be closely correlated with field-station records. Based on the derived spatially continuous temperature datasets, we found an opposite change trend of annually average temperature between Qinghai and Xizang Province. The contrasted trend was obvious in daytime and more so in summer season. By analyzing the temperature trend in relation to elevation, we found an enhanced temperature change trend in higher elevation than in lower elevation for autumn nights and winter temperatures, while the temperature change trends for other seasons were more evident in lower elevation areas. The varying temperature change trends as regulated by elevation implies that temperate grasslands have experienced a more rapid temperature change than alpine grasslands during the past decade.     

Role of interaction between dynamics,thermodynamics and cloud microphysics on summer monsoon precipitating clouds over the Myanmar Coast and the Western Ghats

Indian summer monsoon circulation can be characterized by mean tropospheric temperature (TT) gradient between ocean and land. Two major heat sources, one near the Myanmar Coast and the other near the Western Ghats play seminal role in defining this TT gradient. While both regions are characterized by very similar orographic features, there are significant differences in frequency of occurrence of precipitating clouds and their characteristics even when the amount of rain in June–July months is almost same in the two regions. Deeper (shallower) clouds appear more frequently over the Myanmar Coast (the Western Ghats). There is a sharp decrease in amount of rainfall from June–July to August–September in both the areas. Rather counter intuitively, during the June–July–August–September season, low and moderate rains contribute more to the total rain in the Myanmar Coast while heavy rains contribute more to the total rain in the Western Ghats. Western Ghats also gets more intense rains but less frequently. With significant differences in moisture availability, updraft, amount and characteristics of cloud condensate in the two regions, this study proposes that the nontrivial differences in features between them could be explained by linkages between cloud microphysics and large scale dynamics. Presence of more cloud liquid water and the role of giant cloud condensation nuclei reveals dominance of warm rain process in the Western Ghats whereas more cloud ice, snow and graupel formation in the Myanmar Coast indicates stronger possibility of cold rain coming from mixed phase processes. Stronger heating caused by mixed phase process in the mid and upper troposphere in the Myanmar Coast and its feedback on buoyancy of air parcel explains the appearance of deeper clouds. Thus, our study highlights importance of mixed phase processes, a major cause of uncertainty in GCMs.     

Evaluation of mean and intraseasonal variability of Indian summer monsoon simulation in ECHAM5: identification of possible source of bias

The performance of ECHAM5 atmospheric general circulation model (AGCM) is evaluated to simulate the seasonal mean and intraseasonal variability of Indian summer monsoon (ISM). The model is simulated at two different vertical resolutions, with 19 and 31 levels (L19 and L31, respectively), using observed monthly mean sea surface temperature and compared with the observation. The analyses examine the biases present in the internal dynamics of the model in simulating the mean monsoon and the evolution of the boreal summer intraseasonal oscillation (BSISO) and attempts to unveil the reason behind them. The model reasonably simulates the seasonal mean-state of the atmosphere during ISM. However, some notable discrepancies are found in the simulated summer mean moisture and rainfall distribution. Both the vertical resolutions, overestimate the seasonal mean precipitation over the oceanic regions, but underestimate the precipitation over the Indian landmass. The performance of the model improves with the increment of the vertical resolution. The AGCM reasonably simulates some salient features of BSISO, but fails to show the eastward propagation of the convection across the Maritime Continent in L19 simulation. The propagation across the Maritime Continent and tilted rainband structure improve as one moves from L19 to L31. The model unlikely shows prominent westward propagation that originates over the tropical western Pacific region. L31 also produces some of the observed characteristics of the northward propagating BSISOs. However, the northward propagating convection becomes stationary in phase 5–7. The simulation of shallow diabatic heating structure and the heavy rainfall activity over the Bay of Bengal indicate the abundance of the premature convection-generated precipitation events in the model. It is found that the moist physics is responsible for the poor simulation of the northward propagating convection anomalies.     

Geochemistry and petrogenesis of pyrophyllite deposit of Madrangjodi,Keonjhar district,Orissa

Pyrophyllite deposit at Madrangjodi is a large lensoidal massif overlain unconformably by Dhanjori quartzite and underlain by the parent Singhbhum granite (Phase — II). Pyrophyllite and quartz are the major minerals with minor to trace amounts of muscovite, chloritoid opaques and tourmaline. It is broadly divisible into lamellar, granular and schistose varieties. SiO₂ (66.90–74.36%) and Al₂O₃ (20.80–27.54%) are the major oxides. The major elements data indicate its derivation from Singhbhum granite with depletion of SiO₂ and increment of Al₂O₃. Trace and REE data are discussed to corroborate its genesis.     

Detection of Changes in Glacier Mass Balance Using Satellite and Meteorological Data in Tirungkhad Basin Located in Western Himalaya

Himalayan glaciers and their mass balance are poorly sampled through direct mass balance measurements. Thus, based on Landsat datasets of ETM⁺ (2000), ETM⁺ (2006) and TM (2011), mass balance studies of 32 glaciers was carried out using accumulation area ratio (AAR) method in the Tirungkhad river basin, a tributary of Satluj River, located in western Himalayan region. The overall specific mass balance was negative varying from ?27 cm (2000) to ?41 cm (2011). Out of 32 glaciers, 27 glaciers (81.2 %) showed negative mean mass balance and 5 glaciers (18.7 %) showed positive mean mass balance. Mean of specific mass balance for the year 2000, 2006 and 2011 was found to be ?48 cm, ?55 cm and ?0.61 cm respectively, in case of glaciers with negative mass balance while in case of glaciers with positive mass balance, it was 0.67 cm (2000), 0.56 cm (2006) and 0.47 cm (2011). The investigations suggested a loss of ?0.034 km³ of glacial ice for 2000, 0.036 km³ for 2006 and 0.038 km³ for 2011 respectively. The negative mass balance of the glaciers since 2000 correlates well with the increasing trend of annual mean temperature and decreasing trend of precipitation (snow water equivalent (SWE) and rainfall). Based on Mann Kendall test the temperature and SWE trends were significant at 95 % confidence level, however, the rainfall trend was insignificant.     

Application of meteorological and vegetation indices for evaluation of drought impact: a case study for Rajasthan, India

Drought is a serious climatic condition that affects nearly all climatic zones worldwide, with semi-arid regions being especially susceptible to drought conditions because of their low annual precipitation and sensitivity to climate changes. Drought indices such as the standardized precipitation index (SPI) using meteorological data and vegetation indices from satellite data were developed for quantifying drought conditions. Remote sensing of semi-arid vegetation can provide vegetation indices which can be used to link drought conditions when correlated with various meteorological data based drought indices. The present study was carried out for drought monitoring for three districts namely Bhilwara, Kota and Udaipur of Rajasthan state in India using SPI, normalized difference vegetation index (NDVI), water supply vegetation index (WSVI) and vegetation condition index (VCI) derived from the Advanced Very High resolution Radiometer (AVHRR). The SPI was computed at different time scales of 1, 2, 3, 6, 9 and 12 months using monthly rainfall data. The NDVI and WSVI were correlated to the SPI and it was observed that for the three stations, the correlation coefficient was high for different time scales. Bhilwara district having the best correlation for the 9-month time scale shows late response while Kota district having the best correlation for 1-month shows fast response. On the basis of the SPI analysis, it was found that the area was worst affected by drought in the year 2002. This was validated on the basis of NDVI, WSVI and VCI. The study clearly shows that integrated analysis of ground measured data and satellite data has a great potential in drought monitoring.     

Pre-onset land surface processes and ��internal�� interannual variabilities of the Indian summer monsoon

It is proposed that, land?Catmosphere interaction around the time of monsoon onset could modulate the first episode of climatological intraseasonal oscillation (CISO) and may generate significant ??internal?? interannual variation in the Indian summer monsoon rainfall. The regional climate model RegCM3 is used over Indian monsoon domain for 27?years of control simulation. In order to prove the hypothesis, another two sets of experiment are performed using two different boundary conditions (El Ni?o year and non-ENSO year). In each of these experiments, a single year of boundary conditions are used repeatedly year after year to generate ??internal?? interannual monsoon variability. Simulation of monsoon climate in the control model run is found to be in reasonably good agreement with observation. However, large rainfall bias is seen over Arabian Sea and Bay of Bengal. The interannual monsoon rainfall variability are of the same order in two experiments, which suggest that the external influences may not be important on the generation of ??internal?? monsoon rainfall variability. It is shown that, a dry (wet) pre-onset land-surface condition increases (decreases) rainfall in June which in turn leads to an anomalous increase (decrease) in seasonal (JJAS) rainfall. The phase and amplitude of CISO are modulated during May?CJune and beyond that the modulation of CISO is quite negligible. Though the pre-onset rainfall is unpredictable, significant modulation of the post-onset monsoon rainfall by it can be exploited to improve predictive skill within the monsoon season.     

Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches

The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood coastal aquifer placed in the northern part of Iran. Six hydrological GALDIT parameters (i.e., G groundwater occurrence, A aquifer hydraulic conductivity, L level of groundwater above sea level, D distance from the shore, I impact of the existing status of seawater intrusion in the region, and T thickness of the aquifer) were considered as inputs for each model. In the training step, the values of GALDIT’s vulnerability index were conditioned by using the values of TDS concentration in order to obtain the conditioned vulnerability index (CVI). The CVI was considered as the target for each model. After training the models, each model was tested using a separate TDS dataset. The results indicated that the ANN and ANFIS algorithms performed better than the SVM algorithm. The values of correlation were obtained as 88, 87, and 80% for ANN, ANFIS, and SVM models, respectively. In the testing step of the SCMAI model, the values of RMSE, R², and r were obtained as 6.4, 0.95, and 97%, respectively. Overall, SCMAI model outperformed other models to spatially predicting vulnerable zones. The result of the SCMAI model confirmed that the western zones along the shoreline had the highest vulnerability to seawater intrusion; therefore, it seems critical to consider emergency protection plans for study area.

Graphic abstract