Ground-based microwave radiometers are getting great attention in recent years due to their capability to profile the temperature and humidity at high temporal and vertical resolution in the lower troposphere. The process of retrieving these parameters from the measurements of radiometric brightness temperature ($T_B$) includes the inversion algorithm, which uses the background information from a forward model. In the present study, an algorithm development and evaluation of this forward model for a ground-based microwave radiometer, being developed by Society for Applied Microwave Electronics Engineering and Research (SAMEER) of India, is presented. Initially, the analysis of absorption coefficient and weighting function at different frequencies was made to select the channels. Further the range of variation of $T_B$ for these selected channels for the year 2011, over the two stations Mumbai and Delhi is discussed. Finally the comparison between forward-model simulated $T_B$s and radiometer measured $T_B$s at Mahabaleshwar (73.66°E and 17.93°N) is done to evaluate the model. There is good agreement between model simulations and radiometer observations, which suggests that these forward model simulations can be used as background for inversion models for retrieving the temperature and humidity profiles.

In order to examine the spatial variability of the aerosol characteristics across the Brahmaputra valley, a land campaign was conducted during late winter (February 3–March 2) 2011. Measurements of particulate matter (PM, PM₁₀, PM_2.5) and black carbon (BC) concentrations were made onboard an interior redesigned vehicle. The length of the campaign trail stretched about 700 km, covering the longitude belt of 89.97°–95.55°E and latitude belt of 26.1°–27.6°N, comprising 13 measurement locations. The valley is divided into three sectors longitudinally: western sector (R1: 89.97°–91.75°E), middle sector (R2: 92.5°–94.01°E) and eastern sector (R3: 94.63°–95.55°E). Spatial heterogeneity in aerosol distribution has been observed with higher PM₁₀ and PM_2.5 concentrations at the western and middle sectors compared to the eastern sector. The locations in the western sector are found to be rich in BC compared to the other two sectors and there is a gradual decrease in BC concentrations from west to east of the Brahmaputra valley. Two hotspots within the western and middle sectors with high PM and BC concentrations have been identified. The associated physico-optical parameters of PM reveal abundance of $PM_{2.5}$ aerosols along the entire valley. High population density in the western and middle sectors, together with the contribution of remote aerosols, leads to higher anthropogenic aerosols over those regions. Spectral Radiation-Transport Model for Aerosol Species (SPRINTARS) slightly underestimates the measured PM₁₀ and PM_2.5 at the eastern sector while the model overestimates the measurements at a number of locations in the western sector. In general, BC is underestimated by the model. The variation of BC within the campaign trail has not been adequately captured by the model leading to higher variance in the western locations as compared to the middle and eastern locations.

In this study, a Multivariate Adaptive Regression Spline (MARS) based lead seven days minimum and maximum surface air temperature prediction system is modelled for station Chennai, India. To emphasize the effectiveness of the proposed system, comparison is made with the models created using statistical learning technique Support Vector Machine Regression (SVMr). The analysis highlights that prediction accuracy of MARS models for minimum temperature forecast are promising for short-term forecast (lead days 1 to 3) with mean absolute error (MAE) less than 1°C and the prediction efficiency and skill degrades in medium term forecast (lead days 4 to 7) with slightly above 1°C. The MAE of maximum temperature is little higher than minimum temperature forecast varying from 0.87°C for day-one to 1.27°C for lag day-seven with MARS approach. The statistical error analysis emphasizes that MARS models perform well with an average 0.2°C of reduction in MAE over SVMr models for all ahead seven days and provide significant guidance for the prediction of temperature event. The study also suggests that the correlation between the atmospheric parameters used as predictors and the temperature event decreases as the lag increases with both approaches.

An investigation has been carried out to identify the trends in maximum, minimum and mean temperatures and temperature range over the Indian land mass during the winter (January, and February), premonsoon (March–May), southwest monsoon (June–September) and post-monsoon (October–December) seasons by using high resolution daily gridded data set prepared by India Meteorological Department for the period of 1969–2005. It has been observed that the maximum temperatures over the west coast of India show rising trend in winter, southwest monsoon and post-monsoon seasons but the maximum temperatures do not show any significant trend over the other parts of the country. Minimum temperatures show increasing trend over the North Indian states in all seasons and they show an increasing trend over the west coast of India in winter and southwest monsoon seasons. Mean temperature shows an increasing trend over the west coast of India during winter and southwest monsoon seasons. Decreasing trend is observed in the temperature range over North India in all seasons due to increasing trend in minimum temperature.

The upper air RS/RW (Radio Sonde/Radio Wind) observations at Kolkata (22.65N, 88.45E) during premonsoon season March–May, 2005–2012 is used to compute some important dynamic/thermodynamic parameters and are analysed in relation to the precipitation associated with the thunderstorms over Kolkata, India. For this purpose, the pre-monsoon thunderstorms are classified as light precipitation (LP), moderate precipitation (MP) and heavy precipitation (HP) thunderstorms based on the magnitude of associated precipitation. Richardson number in non-uniformly saturated ($R_i$*) and saturated atmosphere ($R_i$); vertical shear of horizontal wind in 0–3, 0–6 and 3–7 km atmospheric layers; energy-helicity index (EHI) and vorticity generation parameter (VGP) are considered for the analysis. The instability measured in terms of Richardson number in non-uniformly saturated atmosphere ($R_i$∗) well indicate the occurrence of thunderstorms about 2 hours in advance. Moderate vertical wind shear in lower troposphere (0–3 km) and weak shear in middle troposphere (3–7 km) leads to heavy precipitation thunderstorms. The wind shear in 3–7 km atmospheric layers, EHI and VGP are good predictors of precipitation associated with thunderstorm. Lower tropospheric wind shear and Richardson number is a poor discriminator of the three classified thunderstorms.

Snowmelt-runoff modelling in a mountainous basin is perceived as difficult due to the complexity of simulation. Theoretically, the snowmelt process should be influenced by temperature changes. It is still controversial as how to incorporate the temperature changes into the snowmelt-runoff model in a mountainous basin. This paper presents the results of a study in the North Fork American River basin where the snowmelt-runoff mechanism is modelled by relating the temperature changes to the elevation band in the basin. In this study, a distributed hydrologic model is used to explore the orographic effects on the snowmelt-runoff using the snowfall-snowmelt routine in Soil and Water Assessment Tool (SWAT). Three parameters, namely maximum snowmelt factor, minimum snowmelt factor, and snowpack temperature lag were analysed during the simulation. The model was validated using streamflow data from October 1, 1991 to September 30, 1994, with and without considering the elevation band. The result of this study suggests that the snowmelt-runoff model associated with the elevation band better represents the snowmelt-runoff mechanism in terms of Nash–Sutcliffe coefficient (E_NS), ${R}^{2}$, and Root Mean Square Error (RMSE).

A study on the geochemistry of groundwater was carried out in a river basin of Andhra Pradesh to probe into the spatial controlling processes of groundwater contamination, using principal component analysis (PCA). The PCA transforms the chemical variables, pH, EC, Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO$^{−}_{3}$, Cl⁻, SO$^{2−}_{4}$, NO$^{−}_{3}$ and F⁻, into two orthogonal principal components (PC1 and PC2), accounting for 75% of the total variance of the data matrix. PC1 has high positive loadings of EC, Na⁺, Cl⁻, SO$^{2−}_{4}$, Mg²⁺ and Ca²⁺, representing a salinity controlled process of geogenic (mineral dissolution, ion exchange, and evaporation), anthropogenic (agricultural activities and domestic wastewaters), and marine (marine clay) origin. The PC2 loadings are highly positive for HCO$^{−}_{3}$, F⁻, pH and NO$^{−}_{3}$, attributing to the alkalinity and pollution controlled processes of geogenic and anthropogenic origins. The PC scores reflect the change of groundwater quality of geogenic origin from upstream to downstream area with an increase in concentration of chemical variables, which is due to anthropogenic and marine origins with varying topography, soil type, depth of water levels, and water usage. Thus, the groundwater quality shows a variation of chemical facies from Na⁺ > Ca²⁺ > Mg²⁺ > K⁺: HCO$^{−}_{3}$ > Cl⁻ > SO$^{2−}_{4}$ > NO$^{−}_{3}$ > F⁻ at high topography to Na⁺ > Mg²⁺ > Ca²⁺ > K⁺: Cl⁻ > HCO$^{−}_{3}$ > SO$^{2−}_{4}$ > NO$^{−}_{3}$ > F⁻ at low topography. With PCA, an effective tool for the spatial controlling processes of groundwater contamination, a subset of explored wells is indexed for continuous monitoring to optimize the expensive effort.

The present investigation was made to characterize spatial and temporal variations in soil properties and to evaluate possible differences that could be dependent on the tannery effluent discharges, municipal sewage discharges, vegetation cover, soil settlement rate, crop rotation, etc. Soil total organic matter (TOM), cations like, Sodium (Na), Ammonium (NH₄), Potassium (K), Calcium (Ca) and Magnesium (Mg) contents in the bank soils and bottom sediments were recorded from seven different characteristic sites in East Kolkata wetland ecosystem, a Ramsar site (Ramsar site No. 1208). The profile maps were constructed by geostatistical methods to describe the spatial distribution as well as temporal variations of all the factors to identify the influences of composite wastewaters. The work was initiated to identify causes and consequences of the waste dumping in the concerned region for the past hundred years and thereby to suggest necessary precautionary measures to prevent further loss of soil quality.

Soil heat flux is an important input component of surface energy balance. Estimates of soil heat flux were made in the year 2008 using soil temperature data at Astronomical Observatory, Thiruvananthapuram, south Kerala. Hourly values of soil heat flux from 00 to 24 LST are presented for selected days typical of the winter, pre-monsoon, SW monsoon and NE monsoon seasons. The diurnal variation is characterized by a cross-over from negative to positive values at 0700 h, occurrence of maximum around noon and return to negative values in the late evening. The energy storage term for the soil layer 0–0.05 m is calculated and the ground heat flux 𝐺∗ is estimated in all seasons. Daytime surface energy balance at the surface on wet and dry seasons is investigated. The average Bowen’s ratio during the wet and dry seasons were 0.541 and 0.515, respectively indicating that considerable evaporation takes place at the surface. The separate energy balance components were examined and the mean surface energy balance closure was found to be 0.742 and 0.795 for wet and dry seasons, respectively. When a new method that accounts for both soil thermal conduction and soil thermal convection was adopted to calculate the surface heat flux, the energy balance closure was found to be improved. Thus on the land surface under study, the soil vertical water movement is significant.

Diurnal, seasonal and annual foF2 variability and the response of the F2-layer height over Jicamarca (11.9°S, 76.8°W, 1°N dip) during periods of low (LSA), moderate (MSA) and high (HSA) solar activities was investigated. The relative standard deviation ($V_R$) was used for the analysis. The F2-layer critical frequency pre-noon peak increases by a factor of 2 more than the post-noon peak as the solar activity increases. The variability coefficient ($V_R$) is lowest during the day (7–16%) for the three solar epochs; increases during nighttime (20–26%, 14–26%, and 10–20%, respectively for the LSA, MSA and HAS years); and attained highest magnitude during sunrise (21–27%, 24–27%, and 19–30%, respectively in similar order). Two major peaks were observed in $V_R$ – the pre-sunrise peak, which is higher, and the post-sunset peak. Generally, the variability increases as the solar activity decreases. Annually, $V_R$ peaks within 23–24%, 19–24% and 15–24% for the LSA, MSA, and HSA periods, respectively. The ionospheric F2-layer height rises to the higher level with increasing solar activity. The foF2 comparison results revealed that Jicamarca is well represented on the IRI-2012 model, with an improvement on the URSI option. The importance of vertical plasma drift and photochemistry in the F2-layer was emphasized.

A modified ionospheric correction method and the corresponding approximate algorithm for spaceborne single-frequency Global Positioning System (GPS) users are proposed in this study. Single Layer Model (SLM) mapping function for spaceborne GPS was analyzed. SLM mapping functions at different altitudes were calculated. Ionospheric Pierce Point (IPP) trajectories of the dlft station (an IGS station located at the longitude of 4° 23′ 15.22′′E and the latitude of 51° 59′ 9.63′′N, in the TU Delft University, The Netherlands) and the GRACE satellite were computed with the corresponding single layer height of 350 and 500 km, respectively. The Klobuchar model was used to compute ionospheric delays for the dlft station, and modified Klobuchar model, together with scale factors, was used to compute the fractional ionospheric corrections above the GRACE altitudes. Calculation results were validated using dual-frequency observations. The study shows that the single layer height needs to be changed from 350 to 500 km according to the altitude of GRACE. Approximate forms of Earth angle and slant factor developed for modified Klobuchar model are applicable to GRACE, with accuracy adequate to preserve the essential elements required to compute ionospheric delays. Results show that the Klobuchar model is effective for ground GPS, and the modified Klobuchar model corrects more than 80% on average of the ionospheric delays for spaceborne single-frequency GPS.

The performances of regular support vector machines and random forests are experimentally compared for hyperspectral imaging land cover classification. Special characteristics of hyperspectral imaging dataset present diverse processing problems to be resolved under robust mathematical formalisms such as image classification. As a result, pixel purity index algorithm is used to obtain endmember spectral responses from Indiana pine hyperspectral image dataset. The generalized reduced gradient optimization algorithm is thereafter executed on the research data to estimate fractional abundances in the hyperspectral image and thereby obtain the numeric values for land cover classification. The Waikato environment for knowledge analysis (WEKA) data mining framework is selected as a tool to carry out the classification process by using support vector machines and random forests classifiers. Results show that performance of support vector machines is comparable to that of random forests. This study makes a positive contribution to the problem of land cover classification by exploring generalized reduced gradient method, support vector machines, and random forests to improve producer accuracy and overall classification accuracy. The performance comparison of these classifiers is valuable for a decision maker to consider tradeoffs in method accuracy versus method complexity.

Space geodesy era provides velocity information which results in the positioning of geodetic points by considering the time evolution. The geodetic point positions on the Earth’s surface change over time due to plate tectonics, and these changes have to be accounted for geodetic purposes. The velocity field of geodetic network is determined from GPS sessions. Velocities of the new structured geodetic points within the geodetic network are estimated from this velocity field by the interpolation methods. In this study, the utility of Artificial Neural Networks (ANN) widely applied in diverse fields of science is investigated in order to estimate the geodetic point velocities. Back Propagation Artificial Neural Network (BPANN) and Radial Basis Function Neural Network (RBFNN) are used to estimate the geodetic point velocities. In order to evaluate the performance of ANNs, the velocities are also interpolated by Kriging (KRIG) method. The results are compared in terms of the root mean square error (RMSE) over five different geodetic networks. It was concluded that the estimation of geodetic point velocity by BPANN is more effective and accurate than by KRIG when the points to be estimated are more than the points known.

The resolution of self-potential anomalies due to closely spaced multiple sheet-like bodies by the potential difference and potential gradient is studied in this paper. Self-potential anomalies due to several synthetic models were inverted through a very fast simulated annealing (VFSA) global optimization. Increase in depth to the top, polarization constant and depth extent of the body decreases resolution at a particular target separation. It has been observed that depth to the top and separation between two targets play an important role in the resolution. Vertical sheets at equal depth can be resolved in the potential difference measurement only if they are separated by at least four times their depth, while they can be resolved in the gradient method, if they are separated by twice the depth. Resolution using potential difference becomes more difficult for dipping sheets, although the potential gradient method can resolve them efficiently. Efficacy of potential gradient data in the inversion is demonstrated in the study using synthetic data as well as field measurement from South Purulia Shear Zone related with uranium investigation.

The mantle electrical conductivity-depth profile of the Niger delta region in Nigeria has been determined using solar quiet day ionospheric current (Sq).The magnetometer data obtained in 2010 from geomagnetic stations installed in Lagos by magnetic dataset (MAGDAS) in 2008 and data from magnetometers installed in some parts of Niger delta by Center for Basic Space Science, University of Nigeria, Nsukka, were employed in this study. Gauss spherical harmonic analysis (SHA) method was used to separate the internal and external field contributions to Sq current system. The result depicted that the conductivity profile rose steadily from about 0.032 S/m at a depth of 89 km to 0.041 S/m at 100 km and 0.09 S/m at 221 km. This high conductivity region agreed with the global seismic low velocity region, the asthenosphere. The conductivity profile continued increasing downward until it got to 0.157 S/m at a depth of about 373 km (close to the base of upper mantle), 0.201 S/m at 784 km and reached 0.243 S/m at a depth of 1179 km at the lower mantle.

Northeast India is one of the most highly seismically active regions in the world with more than seven earthquakes on an average per year of magnitude 5.0 and above. Reliable seismic hazard assessment could provide the necessary design inputs for earthquake resistant design of structures in this region. In this study, deterministic as well as probabilistic methods have been attempted for seismic hazard assessment of Tripura and Mizoram states at bedrock level condition. An updated earthquake catalogue was collected from various national and international seismological agencies for the period from 1731 to 2011. The homogenization, declustering and data completeness analysis of events have been carried out before hazard evaluation. Seismicity parameters have been estimated using G–R relationship for each source zone. Based on the seismicity, tectonic features and fault rupture mechanism, this region was divided into six major subzones. Region specific correlations were used for magnitude conversion for homogenization of earthquake size. Ground motion equations (Atkinson and Boore 2003; Gupta 2010) were validated with the observed PGA (peak ground acceleration) values before use in the hazard evaluation. In this study, the hazard is estimated using linear sources, identified in and around the study area. Results are presented in the form of PGA using both DSHA (deterministic seismic hazard analysis) and PSHA (probabilistic seismic hazard analysis) with 2 and 10% probability of exceedance in 50 years, and spectral acceleration (T = 0. 2 s, 1.0 s) for both the states (2% probability of exceedance in 50 years). The results are important to provide inputs for planning risk reduction strategies, for developing risk acceptance criteria and financial analysis for possible damages in the study area with a comprehensive analysis and higher resolution hazard mapping.

Timing and source of several Fe-mineralized mafic–ultramafic intrusions in the Damiao area are investigated here by coupling new geochronological and Hf–Fe isotopic data with previous results. Although regarded as a Late Paleoproterozoic assemblage previously, two ∼140 Ma intrusions are recognized by zircon U–Pb dating, indicating emplacement of these intrusions from Middle Devonian to Early Cretaceous times. Both Hf and Fe isotopic features lead to the conclusion that distinct mantle components contributed to their magma generation. As the first magmatic phase, the ∼395 Ma intrusions were mainly derived from the slightly-enriched SCLM that was prevalent during the Paleozoic. However, asthenospheric material was strongly involved in the formation of the ∼215 Ma Gaositai intrusion. Therefore, the initiation of lithospheric destruction in the northern NCC is inferred to have occurred in Late Triassic time, triggered by post-orogenic extension following the ∼250 Ma collision between the Siberian Craton and the NCC. The ∼140 Ma intrusions originated from a significantly-enriched mantle component probably resided in the predominant slightly-enriched SCLM. This mantle source would have melted in the Late Mesozoic, when the thin lithosphere enabled enhanced heat transfer from the asthenosphere. In summary, these distinct mantle sources of mafic–ultramafic magmatism provide a record of mantle heterogeneity and the gradual upward migration of the lithosphere–asthenosphere boundary during lithospheric destruction.

The Pulang complex is located tectonically at the southern margin of the Yidun–Zhongdian island arc belt in Yunnan province, China, and is closely related to formation of the Pulang copper deposit, which is the largest copper deposit in Asia. The Pulang complex can be divided into three intrusion stages based on contact relationships and petrological characteristics: (1) a first stage of quartz dioritic porphyry; (2) a second stage of quartz monzonitic porphyry; and (3) a third stage of granodioritic porphyry. The crystallization ages of these intrusion stages were determined by single-zircon U–Pb dating, yielding ages of 221.0 ± 1.0, 211.8 ± 0.5, and 206.3 ± 0.7 Ma for the first, second, and third stages, respectively. These dates, integrated with previous geochronological data and field investigations, indicate that the second-stage quartz monzonitic porphyry has a close spatial and temporal relationship with the large Pulang porphyry copper deposit. These age data, geochemical and Sr–Nd isotopic results suggest that the Pulang complex formed in the Indo-Chinese epoch (257 ∼ 05 Ma) by multiphase intrusion of a mixture of mantle- and crust-derived magmas.

The newer dolerite dykes around Keonjhar within the Singbhum Granite occur in NE–SW, NW–SE and NNE–SSW trends. The mafic dykes of the present study exhibit several mineralogical changes like clouding of plagioclase feldspars, bastitisation of orthopyroxene, and development of fibrous amphibole (tremolite–actinolite) from clinopyroxene, which are all considered products of hydrothermal alterations. This alteration involves addition and subtraction of certain elements. Graphical analyses with alteration index and elemental abundances show that elements like Rb, Ba, Th, La and K have been added during the alteration process, whereas elements like Sc, Cr, Co, Ni, Si, Al, Fe, Mg and Ca have been removed. It is observed that in spite of such chemical alteration, correlation between major and trace elements, characteristic of petrogenetic process, is still preserved. This might reflect systematic alteration (addition or subtraction) of elements without disturbing the original element to element correlation. It has also been established by earlier workers that the evolution of newer dolerite had occurred in an arc-back arc setting which may also be true for newer dolerites of the present study. This is evident from plots of pyroxene composition and whole rock composition of newer dolerite samples in different tectonic discrimination diagrams using immobile elements. The newer dolerite dykes of the Keonjhar area may thus be considered to represent an example of hydrothermal activity on mafic rocks in an arc setting.

The December 2004 Indian Ocean Tsunami (IOT) had a major impact on the geomorphology and sedimentology of the east coast of India. Estimation of the magnitude of the tsunami from its deposits is a challenging topic to be developed in studies on tsunami hazard assessment. Two core sediments (C1 and C2) from Nagapattinam, southeast coast of India were subjected to textural, mineral, geochemical and rock-magnetic measurements. In both cores, three zones (zone I, II and III) have been distinguished based on mineralogical, geochemical and magnetic data. Zone II is featured by peculiar rockmagnetic, textural, mineralogical and geochemical signatures in both sediment cores that we interpret to correspond to the 2004 IOT deposit. Textural, mineralogical, geochemical and rock-magnetic investigations showed that the tsunami deposit is featured by relative enrichment in sand, quartz, feldspar, carbonate, SiO₂, TiO₂, K₂O and CaO and by a depletion in clay and iron oxides. These results point to a dilution of reworked ferromagnetic particles into a huge volume of paramagnetic materials, similar to what has been described in other nearshore tsunami deposits (Font et al. 2010). Correlation analysis elucidated the relationships among the textural, mineral, geochemical and magnetic parameters, and suggests that most of the quartz-rich coarse sediments have been transported offshore by the tsunami wave. These results agreed well with the previously published numerical model of tsunami induced sediment transport off southeast coast of India and can be used for future comparative studies on tsunami deposits.