Defining a framework for integration of geospatial technologies for emergency management

Geospatial technologies are emerging as one of the most promising environments for development of emergency management framework. Geospatial technologies such as Remote Sensing, Geographic Information Systems and Global Positioning Systems integrated with valuable near/real-time field information can provide a comprehensive platform for emergency management. Satellite imagery, vector database, Location Based Services and ancillary data having the relevant attribute information with access and query tools helps the disaster manager to analyse the required information for relief operations. However, the conceptual design of the database, integration procedures, development of decision support functions and dissemination of information integrating real-time information from the field are very important issues in development framework for emergency management. There are some technological challenges in the emergency response for deriving the required outputs such as damage assessment, evacuation planning, situation monitoring, information dissemination, and coordination of resource and logistic planning. This article provides a framework for the integration of various multi-disciplinary technologies for addressing emergency management.     

Structure and dynamics of undercurrents in the western boundary current of the Bay of Bengal

Ocean Dynamics - The structure and variability of undercurrents in the East India Coastal Current (EICC), which is the western boundary current system in the Bay of Bengal (BoB), and the mechanisms...     

Forced oscillation measurements of seismic attenuation in fluid saturated sandstone

Adopting the method of forced oscillation, attenuation was studied in Fontainebleau sandstone (porosity 10%, permeability 10 mD) at seismic frequencies (1–100 Hz). Confining pressures of 5, 10, and 15 MPa were chosen to simulate reservoir conditions. First, the strain effect on attenuation was investigated in the dry sample for 11 different strains across the range 1 × 10^?6–8 × 10^?6, at the confining pressure of 5 MPa. The comparison showed that a strain of at least 5 × 10^?6 is necessary to obtain a good signal to noise ratio. These results also indicate that nonlinear effects are absent for strains up to 8 × 10^?6. For all the confining pressures, attenuation in the dry rock was low, while partial (90%) and full (100%) saturation with water yielded a higher magnitude and frequency dependence of attenuation. The observed high and frequency dependent attenuation was interpreted as being caused by squirt flow.     

Flare Prediction Using Photospheric and Coronal Image Data

The precise physical process that triggers solar flares is not currently understood. Here we attempt to capture the signature of this mechanism in solar-image data of various wavelengths and use these signatures to predict flaring activity. We do this by developing an algorithm that i) automatically generates features in 5.5 TB of image data taken by the Solar Dynamics Observatory of the solar photosphere, chromosphere, transition region, and corona during the time period between May 2010 and May 2014, ii) combines these features with other features based on flaring history and a physical understanding of putative flaring processes, and iii) classifies these features to predict whether a solar active region will flare within a time period of \(T\) hours, where \(T = 2 \mbox{ and }24\). Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We find that when optimizing for the True Skill Score (TSS), photospheric vector-magnetic-field data combined with flaring history yields the best performance, and when optimizing for the area under the precision–recall curve, all of the data are helpful. Our model performance yields a TSS of \(0.84 \pm0.03\) and \(0.81 \pm0.03\) in the \(T = 2\)- and 24-hour cases, respectively, and a value of \(0.13 \pm0.07\) and \(0.43 \pm0.08\) for the area under the precision–recall curve in the \(T=2\)- and 24-hour cases, respectively. These relatively high scores are competitive with previous attempts at solar prediction, but our different methodology and extreme care in task design and experimental setup provide an independent confirmation of these results. Given the similar values of algorithm performance across various types of models reported in the literature, we conclude that we can expect a certain baseline predictive capacity using these data. We believe that this is the first attempt to predict solar flares using photospheric vector-magnetic field data as well as multiple wavelengths of image data from the chromosphere, transition region, and corona, and it points the way towards greater data integration across diverse sources in future work.     

SARAS 2: a spectral radiometer for probing cosmic dawn and the epoch of reionization through detection of the global 21-cm signal

The global 21-cm signal from Cosmic Dawn (CD) and the Epoch of Reionization (EoR), at redshifts \(z \sim 6-30\), probes the nature of first sources of radiation as well as physics of the Inter-Galactic Medium (IGM). Given that the signal is predicted to be extremely weak, of wide fractional bandwidth, and lies in a frequency range that is dominated by Galactic and Extragalactic foregrounds as well as Radio Frequency Interference, detection of the signal is a daunting task. Critical to the experiment is the manner in which the sky signal is represented through the instrument. It is of utmost importance to design a system whose spectral bandpass and additive spurious signals can be well calibrated and any calibration residual does not mimic the signal. Shaped Antenna measurement of the background RAdio Spectrum (SARAS) is an ongoing experiment that aims to detect the global 21-cm signal. Here we present the design philosophy of the SARAS 2 system and discuss its performance and limitations based on laboratory and field measurements. Laboratory tests with the antenna replaced with a variety of terminations, including a network model for the antenna impedance, show that the gain calibration and modeling of internal additive signals leave no residuals with Fourier amplitudes exceeding 2 mK, or residual Gaussians of 25 MHz width with amplitudes exceeding 2 mK. Thus, even accounting for reflection and radiation efficiency losses in the antenna, the SARAS 2 system is capable of detection of complex 21-cm profiles at the level predicted by currently favoured models for thermal baryon evolution.     

Lagrangian Stochastic Modeling of Dispersion in the Stable Boundary Layer

Lagrangian stochastic models are well-suited for modeling dispersion in the stable boundary layer, especially in complex terrain. This note briefly describes the formulations and application of a Lagrangian stochastic model to predict dispersion of tracers released within nocturnal drainage flows.     

Signal enhancement and multiple suppression using Radon transform: an application to marine multichannel seismic data

Radon filters are often used for removal of multiple reflections from normal move-out-corrected seismic reflection data. In the conventional Radon transform, integration surfaces are hyperbolic rather than linear. This specific hyperbolic surface is equivalent to a parabola in terms of computational expense, but more accurately distinguishes multiples from primary reflections. The forward transform separates seismic arrivals by their differences in travel time move-out. Multiples can be suppressed by an inverse transform of the seismic data. Examples show that multiples are effectively attenuated in pre-stack and stacked seismograms. Based on the parabolic Radon transform, a new method is utilized for missing offset restoration, resampling and regularization of pre-stack individual common depth point (CDP) gathers. The method is also valid for resampling spatially aliased seismic data. Restoration of missing offsets and trace interpolation is an interesting and important problem in seismic data processing. Here we present an application of Radon transform on a multichannel seismic data set from the western continental margin of India (WCMI), which shows remarkable signal enhancement.     

Geothermal modeling for the base of gas hydrate stability zone and saturation of gas hydrate in the Krishna-Godavari basin,eastern Indian margin

The passive eastern Indian margin is rich in gas hydrates, as inferred from the wide-spread occurrences of bottom-simulating reflectors (BSRs) and recovery of gas hydrate samples from various sites in the Krishna Godavari (KG) and Mahanadi (MN) basins drilled by the Expedition 01 of the Indian National Gas Hydrate Program (NGHP). The BSRs are often interpreted to mark the thermally controlled base of gas hydrate stability zone (BGHSZ). Most of the BSRs exhibit moderate to typically higher amplitudes than those from other seismic reflectors. We estimate the average geothermal gradient of ∼40°C/km and heat flow varying from 23 to 62 mW/m² in the study area utilizing the BSR’s observed on seismic sections. Further we provide the BGHSZ where the BSR is not continuous or disturbed by local tectonics or hidden by sedimentation patterns parallel to the seafloor with a view to understand the nature of BSR.     

Mass-size distribution of PM10 and its characterization of ionic species in fine (PM2.5) and coarse (PM10−2.5) mode, New Delhi, India

Size distribution of PM₁₀ mass aerosols and its ionic characteristics were studied for 2 years from January 2006 to December 2007 at central Delhi by employing an 8-stage Andersen Cascade Impactor sampler. The mass of fine (PM_2.5) and coarse (PM_10?2.5) mode particles were integrated from particle mass determined in different stages. Average concentrations of mass PM₁₀ and PM_2.5 were observed to be 306 ± 182 and 136 ± 84 μg m^?3, respectively, which are far in excess of annual averages stipulated by the Indian National Ambient Air Quality Standards (PM₁₀: 60 μg m^?3 and PM_2.5: 40 μg m^?3). The highest concentrations of PM_10?2.5 (coarse) and PM_2.5 (fine) were observed 505 ± 44 and 368 ± 61 μg m^?3, respectively, during summer (June 2006) period, whereas the lower concentrations of PM_10?2.5 (35 ± 9 μg m^?3) and PM_2.5 (29 ± 13 μg m^?3) were observed during monsoon (September 2007). In summer, because of frequent dust storms, coarse particles are more dominant than fine particles during study period. However, during winter, the PM_2.5 contribution became more pronounced as compared to summer probably due to enhanced emissions from anthropogenic activities, burning of biofuels/biomass and other human activities. A high ratio (0.58) of PM_2.5/PM₁₀ was observed during winter and low (0.24) during monsoon. A strong correlation between PM₁₀ and PM_2.5 (r ² = 0.93) was observed, indicating that variation in PM₁₀ mass is governed by the variation in PM_2.5. Major cations (NH₄ ⁺, Na⁺, K⁺, Ca²⁺ and Mg²⁺) and anions (F^?, Cl^?, SO₄ ^2? and NO₃ ^?) were analyzed along with pH. Average concentrations of SO₄ ^2? and NO₃ ^? were observed to be 12.93 ± 0.98 and 10.33 ± 1.10 μg m^?3, respectively. Significant correlation between SO₄ ^2? and NO₃ ^? in PM_1.0 was observed indicating the major sources of secondary aerosol which may be from thermal power plants located in the southeast and incomplete combustion by vehicular exhaust. A good correlation among secondary species (NH⁺, NO₃ ^? and SO₄ ^2?) suggests that most of NH₄ ⁺ is in the form of ammonium sulfate and ammonium nitrate in the atmosphere. During winter, the concentration of Ca²⁺ was also higher; it may be due to entrainment of roadside dust particles, traffic activities and low temperature. The molar ratio (1.39) between Cl^? and Na⁺ was observed to be close to that of seawater (1.16). The presence of higher Cl^? during winter is due to western disturbances and probably local emission of Cl^? due to fabric bleaching activity in a number of export garment factories in the proximity of the sampling site.     

Factors controlling January�CApril rainfall over southern India and Sri Lanka

Most of the annual rainfall over India occurs during the Southwest (June?CSeptember) and Northeast (October?CDecember) monsoon periods. In March 2008, however, Southern peninsular India and Sri Lanka received the largest rainfall anomaly on record since 1979, with amplitude comparable to summer-monsoon interannual anomalies. This anomalous rainfall appeared to be modulated at intraseasonal timescale by the Madden Julian Oscillation, and was synchronous with a decaying La Ni?a event in the Pacific Ocean. Was this a coincidence or indicative of a teleconnection pattern? In this paper, we explore factors controlling rainfall over southern India and Sri Lanka between January and April, i.e. outside of the southwest and northeast monsoons. This period accounts for 20% of annual precipitation over Sri Lanka and 10% over the southern Indian states of Kerala and Tamil Nadu. Interannual variability is strong (about 40% of the January?CApril climatology). Intraseasonal rainfall anomalies over southern India and Sri Lanka are significantly associated with equatorial eastward propagation, characteristic of the Madden Julian Oscillation. At the interannual timescale, we find a clear connection with El Ni?o-Southern Oscillation (ENSO); with El Ni?os being associated with decreased rainfall (correlation of ?0.46 significant at the 98% level). There is also a significant link with local SST anomalies over the Indian Ocean, and in particular with the inter-hemispheric sea surface temperature (SST) gradient over the Indian Ocean (with colder SST south of the equator being conducive to more rainfall, correlation of 0.55 significant at the 99% level). La Ni?as/cold SSTs south of the equator tend to have a larger impact than El Ni?os. We discuss two possible mechanisms that could explain these statistical relationships: (1) subsidence over southern India remotely forced by Pacific SST anomalies; (2) impact of ENSO-forced regional Indian Ocean SST anomalies on convection. However, the length of the observational record does not allow distinguishing between these two mechanisms in a statistically significant manner.