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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]

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]

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]

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]