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To detect landslides by object-based image analysis using criteria based on shape, color, texture, and, in particular, contextual information and process knowledge, candidate segments must be delineated properly. This has proved challenging in the past, since segments are mainly created using spectral and size criteria that are not consistent for landslides. This paper presents an approach to select objectively parameters for a region growing segmentation technique to outline landslides as individual segments and also addresses the scale dependence of landslides and false positives occurring in a natural landscape. Multiple scale parameters were determined using a plateau objective function derived from the spatial autocorrelation and intrasegment variance analysis, allowing for differently sized features to be identified. While a high-resolution Resourcesat-1 Linear Imaging and Self Scanning Sensor IV (5.8 m) multispectral image was used to create segments for landslide recognition, terrain curvature derived from a digital terrain model based on Cartosat-1 (2.5 m) data was used to create segments for subsequent landslide classification. Here, optimal segments were used in a knowledge-based classification approach with the thresholds of diagnostic parameters derived from If-means cluster analysis, to detect landslides of five different types, with an overall recognition accuracy of 76.9%. The approach, when tested in a geomorphologically dissimilar area, recognized landslides with an overall accuracy of 77.7%, without modification to the methodology. The multiscale classification-based segment optimization procedure was also able to reduce the error of commission significantly in comparison to a single-optimal-scale approach. [ABSTRACT FROM AUTHOR]

This article describes a unique field experiment on Severe Thunderstorm Observations and Regional Modeling (STORM) jointly undertaken by eight South Asian countries. Several pilot field experiments have been conducted so far, and the results are analyzed. The field experiments will continue through 2016. The STORM program was originally conceived for understanding the severe thunderstorms known as nor'westers that affect West Bengal and the northeastern parts of India during the pre-monsoon season. The nor'westers cause loss of human lives and damage to properties worth millions of dollars annually. Since the neighboring South Asian countries are also affected by thunderstorms, the STORM program is expanded to cover the South Asian countries under the South Asian Association for Regional Cooperation (SAARC). It covers all the SAARC countries (Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka) in three phases. Some of the science plans (monitoring the life cycle of nor'westers/severe thunderstorms and their three-dimensional structure) designed to understand the interrelationship among dynamics, cloud microphysics, and electrical properties in the thunderstorm environment are new to severe weather research. This paper describes the general setting of the field experiment and discusses preliminary results based on the pilot field data. Typical lengths and the intensity of squall lines, the speed of movements, and cloud-top temperatures and their heights are discussed based on the pilot field data. The SAARC STORM program will complement the Severe Weather Forecast Demonstration Project (SWFDP) of the WMO. It should also generate large-scale interest for fueling research among the scientific community and broaden the perspectives of operational meteorologists and researchers. [ABSTRACT FROM AUTHOR]

In this paper, the three-dimensional variational data assimilation scheme (3DVAR) in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5) is used to study the impact of assimilating Atmospheric Infrared Sounder (AIRS) retrieved temperature and moisture profiles on board Aqua, a satellite that is part of NASA’s Earth Observing System. A record-breaking heavy rain event that occurred over Mumbai, India, on 26 July 2005 with 24-h rainfall exceeding 94 cm was used for the simulation. By analyzing the data from the NCEP–NCAR reanalysis, possible causes of this heavy rainfall event were investigated. The temporal evolution of meteorological fields clearly indicates the formation of midtropospheric mesoscale vortices over Mumbai that exactly coincides with the duration of the intense rainfall. Analysis also indicated the midlevel dryness with higher temperature and moisture in the lower levels. This midlevel dryness with high temperature and moisture in the lower levels increases the conditional instability, which was conducive for the development of very severe local thunderstorms. The midtropospheric mesoscale vortices existed over Mumbai together with lower-level instability and the active monsoon conditions over the west coast resulted in intense rainfall, on the order of 94 cm in 24 h. Numerical experiments were conducted, with two nested domains (45- and 15-km grid spacing). The assimilation of the AIRS-retrieved temperature and moisture profiles produced significant impacts on the location and intensity of the simulated rainfall. It is seen from the numerical experiments that the assimilation of AIRS data could produce the structure of mesoscale vortices, and lower-level thermodynamics and convergence much more realistically compared with the control simulation. The spatial distribution of the rainfall from the simulation using AIRS data was more realistic than that without AIRS data. To make the quantitative comparison of the predicted rainfall with the observed one, the equitable threat score and bias were calculated for different threshold values of rainfall. Inclusion of AIRS data significantly improved the precipitation as indicated by the equitable threat scores and biases for almost all of the threshold rainfall categories. [ABSTRACT FROM AUTHOR]

India Meteorological Department (IMD) has been using a Limited Area Model (LAM) on operational basis for the forecast up to 48 hours with the first guess fields for objective analysis and lateral boundary conditions from the global spectral model (T-80) run of the National Center for Medium Range Weather Forecasting (NCMRWF), New Delhi. In this paper the model code has been modified and made more flexible, delinking it from the NCMRWF (T-80). This has allowed the use of initial and boundary conditions directly from the NCEP Global Forecast System (GFS) products available at the resolution of 1° × 1° lat./long. The main interest in this study is to improve the analysis and forecast in the short range time scale (up to 72 hours) by improving the model (LAM) resolution and using better Initial and boundary conditions from the NCEP GFS instead of the NCMRWF T-80 model. Simulation experiments are performed on wide variety of synoptic situations which occur very often over the Indian sub-continent. The performance evaluation in terms of qualitative comparison between the model simulated outputs against actual observations and the outputs of the operational model indicates that the modified version of the model is capable to provide a improved numerical guidance on the occurrence of heavy rainfall in the 48-72 hours forecast scale. [ABSTRACT FROM AUTHOR]

The aim of the present study is to analyze the occurrences of future earthquakes using forecasting techniques from past seismicity in northeast India (latitude 20°N–31°N and longitude 87°E–97°E). The present study applies two types of retrospective binary forecasting. The first one is the pattern informatics (PI) method and the other is the Relative Intensity (RI) method. These techniques quantify the spatio-temporal seismicity rate changes in the historic seismicity of the study region. For this purpose, a uniform and complete earthquake catalogue in moment magnitude (Mw > 3) is prepared. The resulting binary forecasts are evaluated with the relative operating characteristics (ROC) diagram. The ROC diagram quantifies the results in terms of a hit rate (fraction of events that are successfully forecasted) versus a false alarm rate (no event occurs in a hotspot box). Evaluation of forecasting results using ROC diagram is more protective than maximum likelihood tests. The result gives a regional seismogenic map where earthquakes are likely to occur during a specified period in the future. The recent India–Nepal border earthquake of 18 September 2011 occurred in one of the forecasted regions. These techniques have been applied for the first time to the Indian subcontinent. [ABSTRACT FROM AUTHOR]

Several aspects of real-time forecast of Indian summer monsoon ( ISM) in 3-4 pentad lead time (extended range) are discussed in this study to explore the operational capability of the Climate Forecast System model version 2 ( CFSv2) developed by National Centre for Environmental Prediction ( NCEP). 2013 summer monsoon was a near excess monsoon year in terms of seasonal mean and was a result of rich diversity of phenomena including strong intraseasonal variations and intense northward propagations over the Indian region. Eleven-member forecasts were made at every 5-day interval during the June-September monsoon season which included monsoon onset and withdrawal phases. The ensemble members were created by perturbing the initial conditions at each start time. In addition to the CFSv2 forecasts, we also carried out forecasts using the atmospheric-only component ( GFSv2) forced with CFSv2-derived sea surface temperature (SST) subjected to a bias correction based on historical observations ( GFSbc runs). Both the CFSv2 and GFSbc runs were able to predict the progression of ISM over the Indian region and the subsequent intraseasonal oscillations (active and break phases). The analysis for an extreme event (Uttarakhand flood) and monsoon revival (MR) towards the end of the season was also performed. Comparison between the two runs shows that active and break spells were predicted with good fidelity over the Indian region, though GFSbc outperforms CFSv2 on several occasions. Thus, improvement of the operational monsoon forecast over Indian region using NCEP CFSv2 requires better representation of air-sea interaction and mean states of ocean. [ABSTRACT FROM AUTHOR]

A wide-angle seismic profile between Peigu Tso (85.5°E) and Pumoyong Tso (90.5°E) in southern Tibet, acquired under a joint Sino-French program in 1981, passes through the South Tibetan Detachment System (STDS, the eastern segment of the profile) and the central part of the Tethyan Himalaya (the western segment). We herein reinterpret this wide-angle seismic profile, which has a total length of ∼480 km, in order to improve our understanding of the crustal structure of the Tethyan Himalaya. We identify multiple P- and S-wave wide-angle reflections within the crystalline crust and from the Moho, but cannot identify any refracting waves beneath the Moho. We model the structure of P- and S-wave velocity throughout the whole crust, while acknowledging the significant uncertainties that are inherent in many places. A low-wave speed cover sequence may be identified with the Tethyan sedimentary cover ( Vp < 5.8 km s−1), from the surface to a depth of (typically) 5–10 km, which is only absent where the profile crosses the Kangmar Dome. It may be presumed that these sedimentary rocks overlay a felsic upper-crustal basement (5.8 < Vp < 6.3 km s−1), which in places extends as far down as 35 km, and which itself overlays a 5–10-km-thick mid-crustal low-velocity zone. Between a depth of 30 and 45 km at the base of the low-velocity zone and the Moho at a depth of ∼73 km, the observed velocities gradually increase from 6.2 to 6.8 km s−1. The low-velocity layer (LVL) may consist of a partial melt due to the high geothermal gradients found in the felsic rocks, or of a fluid-rich shear zone as in the ductile channel of a channel flow model. The difference in depth of the LVL between the western (central Tethyan Himalaya) and eastern segments (nearly along the STDS) at the boundary of the Dingye-Mabjia fault, leads to a north–south dip estimate of the LVL of 8.5°, which is consistent with the estimated dip of the MHT obtained from the INDEPTH-I project, and can support the channel flow model. This north–south low dip angle of subduction slab, supports that the subducting Indian lithosphere endured a transition from steep subduction to low-angle subduction, and the subduction slab flattening may be attributed to the break-off of Indian lithosphere slab beneath the Indur-Zurpo suture. [ABSTRACT FROM AUTHOR]

The present study is conducted to verify the short-range forecasts from mesoscale model version5 (MM5)/weather research and forecasting (WRF) model over the Indian region and to examine the impact of assimilation of quick scatterometer (QSCAT) near surface winds, spectral sensor microwave imager (SSM/I) wind speed and total precipitable water (TPW) on the forecasts by these models using their three-dimensional variational (3D-Var) data assimilation scheme for a 1-month period during July 2006. The control (without satellite data assimilation) as well as 3D-Var sensitivity experiments (with assimilating satellite data) using MM5/WRF were made for 48 h starting daily at 0000 UTC July 2006. The control run is analyzed for the intercomparison of MM5/WRF short-range forecasts and is also used as a baseline for assessing the MM5/WRF 3D-Var satellite data sensitivity experiments. As compared to the observation, the MM5 (WRF) control simulations strengthened (weakened) the cross equatorial flow over southern Arabian sea near peninsular India. The forecasts from MM5 and WRF showed a warm and moist bias at lower and upper levels with a cold bias at the middle level, which shows that the convective schemes of these models may be too active during the simulation. The forecast errors in predicted wind, temperature and humidity at different levels are lesser in WRF as compared to MM5, except the temperature prediction at lower level. The rainfall pattern and prediction skill from day 1 and day 2 forecasts by WRF is superior to MM5. The spatial distribution of forecast impact for wind, temperature, and humidity from 1-month assimilation experiments during July 2006 demonstrated that on average, for 24 and 48-h forecasts, the satellite data improved the MM5/WRF initial condition, so that model errors in predicted meteorological fields got reduced. Among the experiments, MM5/WRF wind speed prediction is most benefited from QSCAT surface wind and SSM/I TPW assimilation while temperature and humidity prediction is mostly improved due to latter. The largest improvement in MM5/WRF rainfall prediction is due to the assimilation of SSM/I TPW. The assimilation of SSM/I wind speed alone in MM5/WRF degraded the humidity and rainfall prediction. In summary the assimilation of satellite data showed similar impact on MM5/WRF prediction; largest improvement due to SSM/I TPW and degradation due to SSM/I wind speed. [ABSTRACT FROM AUTHOR]

The article discusses a study which used an Artificial Neural Network (ANN) methodology to predict groundwater levels in individual wells. Groundwater levels were predicted in neighbouring wells using model parameters from the best network of a well. The methodology was applied to an urban coastal aquifer in Andhra Pradesh state, India. The performance of various ANN algorithms were compared in predicting the groundwater levels with a one month lead in six observation wells independently. The study revealed that the feed-forward neural network with Lavenberg-Marquadt (LM) algorithm is a good choice for predicting groundwater levels in individual wells.