Characteristic features of winter precipitation and its variability over northwest India

Northwestern parts of India receive considerable amount of precipitation during the winter months of December-March. Although, it is only about 15 of the annual precipitation, the precipitation is very important for rabi crops and to maintain the glaciers extend in the Himalaya, which melt and supply water to the rivers during other seasons. The precipitation is mainly associated with the sequence of synoptic systems known as 'western disturbances'. The precipitation has considerable spatial and temporal variability, with maximum precipitation occurring particularly over northern hilly regions, with decreasing influence southwards. The spatially coherent winter precipitation series has been prepared for the largest possible area comprising nine meteorological subdivisions of northwest India, which constitute about 32 of the total area of the country, having similar precipitation characteristics. The precipitation series has been statistically analysed to understand its characteristics and variability. The seasonal precipitation series is found to be homogeneous, Gaussian (normal) distributed and free from persistence. The precipitation variability has increased during the most recent three decades with more excess and deficient years

Characteristic features of winter precipitation and its variability over northwest India

Northwestern parts of India receive considerable amount of precipitation during the winter months of December–March. Although, it is only about 15% of the annual precipitation, the precipitation is very important for rabi crops and to maintain the glaciers extend in the Himalaya, which melt and supply water to the rivers during other seasons. The precipitation is mainly associated with the sequence of synoptic systems known as ‘western disturbances’. The precipitation has considerable spatial and temporal variability, with maximum precipitation occurring particularly over northern hilly regions, with decreasing influence southwards. The spatially coherent winter precipitation series has been prepared for the largest possible area comprising nine meteorological subdivisions of northwest India, which constitute about 32% of the total area of the country, having similar precipitation characteristics. The precipitation series has been statistically analysed to understand its characteristics and variability. The seasonal precipitation series is found to be homogeneous, Gaussian (normal) distributed and free from persistence. The precipitation variability has increased during the most recent three decades with more excess and deficient years

Vegetation Change Detection in Mullaitivu District by using Remote Sensing and GIS Techniques

Landuse and land cover change over time in the world due to uncontrollable rate of population growth and improper resource management which change the natural environment profoundly. Several studies were carried out on the land uses and land covers changes in Sri Lanka. However, little information available on land use and vegetation change in northern parts of the country. Therefore, the objective of the study is to detect the vegetation change in Mullaitivu district of Sri Lanka using Remote Sensing (RS) and Geographic Information System (GIS). In this study, multispectral remotely sensed data of Landsat was used to prepare the land use and land covermap on fourconsecutive years of 2013, 2014, 2016 and 2017. The vegetation change detection was assessed by applying ArcGIS 10.2 through unsupervised classification. Normalised Difference Vegetation Index (NDVI) was used to develop the land use map of the district. The results of this study revealed that areas under vegetation land use such as agriculture, sparse and plantation forest and dense forest were decreased from 2013 to 2017. Changed area of agriculture land use, sparse and plantation forest, and dense forest was 2.3%, 3.6% and 5.2%, respectively and these results showed that about 11% of deduction was observed by vegetation change. However, areas under buildup area was increased by 14% from 2013 to 2017. Area under dense forest was highly decreased followed by open and plantation forest and at the same time area under buildup was increased during same period. However, higher percentage of areas was negatively changed by dense and open-plantation forest. Similarly, higher percentage of areas was positively changed by buildup. These results were clearly indicated that areas under vegetation change was negatively correlated with areas under buildup change and vegetation was highly decreased due to the buildup activities. Therefore, existing policies and legislation should be strictly implemented and amended to conserve the forest in the study areas.Keywords: GIS, Mullaitivu, NDVI, Remotes sensing, Vegetation chang

Development of a high resolution land surface dataset for the South Asian monsoon region

In this study, we report the development of a high resolution land surface dataset for the South Asian monsoon region for studies on land surface processes, and land and atmosphere coupling. The high resolu- tion land data assimilation system was used to develop the land surface dataset utilizing TRMM rainfall and ECMWF atmospheric variables as forcing parameters. The dataset was developed at a spatial resolution of 0.5° and temporal resolution of 1 h and spans a period of 6 years, i.e. 1 January 2005 to 31 December 2010. The major highlights in the development of the present dataset are higher spatial and temporal resolution of land surface parameters, use of sub-daily forcing parameters including rainfall, use of MODIS land-use data in lieu of USGS land-use data and weekly varying vegetation fraction instead of monthly vegetation climatology. A comparison of soil moisture and soil temperature with limited surface observations of the IMD suggests reasonable reliability of the land surface data. The model sensible heat flux data are compared with in situ measurements at Ranchi and MEERA reanalysis data. The sensitivity analysis shows that the land surface data are sensitive to rainfall and green vegetation cover data used as the forcing parameters. The dataset has been used to discuss the variations of land surface processes associated with active and break spells and a severe heat wave observed in 2009. The present dataset will be useful for many applications, including initializing numerical models for weather prediction. This high resolution land surface dataset is available for research on request

Improved prediction of severe thunderstorms over the Indian Monsoon region using high-resolution soil moisture and temperature initialization

The hypothesis that realistic land conditions such as soil moisture/soil temperature (SM/ST) can significantly improve the modeling of mesoscale deep convection is tested over the Indian monsoon region (IMR). A high resolution (3 km foot print) SM/ST dataset prepared from a land data assimilation system, as part of a national monsoon mission project, showed close agreement with observations. Experiments are conducted with (LDAS) and without (CNTL) initialization of SM/ST dataset. Results highlight the significance of realistic land surface conditions on numerical prediction of initiation, movement and timing of severe thunderstorms as compared to that currently being initialized by climatological fields in CNTL run. Realistic land conditions improved mass flux, convective updrafts and diabatic heating in the boundary layer that contributed to low level positive potential vorticity. The LDAS run reproduced reflectivity echoes and associated rainfall bands more efficiently. Improper representation of surface conditions in CNTL run limit the evolution boundary layer processes and thereby failed to simulate convection at right time and place. These findings thus provide strong support to the role land conditions play in impacting the deep convection over the IMR. These findings also have direct implications for improving heavy rain forecasting over the IMR, by developing realistic land conditions

16S rRNA gene taxonomic profiling of endophytic bacteria associated with phylaenopsis roots

Orchids are one of the main groups of ornamental plants commercially exploited. In the present study, we analyzed the diversity of bacterial community in Phalaenopsis root using metagenomic approach. The diversity of bacterial taxonomic category was assessed at different Operational Taxonomic Unit (OTU) levels using Ribosomal Database Project (RDP) pipeline and MG-RAST. At phylum level, Proteobacteria (61.34%) was the most dominant group followed by unclassified derived from bacteria (24.74%) and Actinobacteria (12.52%). Genus level analysis revealed the abundance of Rubrobacter, Pseudomonas and Acinetobacter. The study revealed that of the total species detected 50.83 per cent were unclassified, stressing the importance of metagenomics to assess the diversity of endophytes associated with orchid roots

Modulation of surface meteorological parameters by extratropical planetary-scale Rossby waves

This study examines the link between upper-tropospheric planetary-scale Rossby waves and surface meteorological parameters based on the observations made in association with the Ganges Valley Aerosol Experiment (GVAX) campaign at an extratropical site at Aryabhatta Research Institute of Observational Sciences, Nainital (29.45° N, 79.5° E) during November–December 2011. The spectral analysis of the tropospheric wind field from radiosonde measurements indicates a predominance power of around 8 days in the upper troposphere during the observational period. An analysis of the 200 hPa meridional wind (v200 hPa) anomalies from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis shows distinct Rossby-wave-like structures over a high-altitude site in the central Himalayan region. Furthermore, the spectral analysis of global v200 hPa anomalies indicates the Rossby waves are characterized by zonal wave number 6. The amplification of the Rossby wave packets over the site leads to persistent subtropical jet stream (STJ) patterns, which further affects the surface weather conditions. The propagating Rossby waves in the upper troposphere along with the undulations in the STJ create convergence and divergence regions in the mid-troposphere. Therefore, the surface meteorological parameters such as the relative humidity, wind speeds, and temperature are synchronized with the phase of the propagating Rossby waves. Moreover, the present study finds important implications for medium-range forecasting through the upper-level Rossby waves over the study region

A three-level common-mode voltage eliminated inverter with single dc supply using flying capacitor inverter and cascaded H-bridge

A three-level common-mode voltage eliminated in- verter with single dc supply using flying capacitor inverter and cascaded H-bridge has been proposed in this paper. The three phase space vector polygon formed by this configuration and the polygon formed by the common-mode eliminated states have been discussed. The entire system is simulated in Simulink and the re- sults are experimentally verified. This system has an advantage that if one of devices in the H-bridge fails, the system can still be oper- ated as a normal three-level inverter at full power. This inverter has many other advantages like use of single dc supply, making it pos- sible for a back-to-back grid-tied converter application, improved reliability, etc

Semantic Web-Based Integration of Cancer Pathways and Allele Frequency Data

We demonstrate the use of Semantic Web technology to integrate the ALFRED allele frequency database and the Starpath pathway resource. The linking of population-specific genotype data with cancer-related pathway data is potentially useful given the growing interest in personalized medicine and the exploitation of pathway knowledge for cancer drug discovery. We model our data using the Web Ontology Language (OWL), drawing upon ideas from existing standard formats BioPAX for pathway data and PML for allele frequency data. We store our data within an Oracle database, using Oracle Semantic Technologies. We then query the data using Oracle’s rule-based inference engine and SPARQL-like RDF query language. The ability to perform queries across the domains of population genetics and pathways offers the potential to answer a number of cancer-related research questions. Among the possibilities is the ability to identify genetic variants which are associated with cancer pathways and whose frequency varies significantly between ethnic groups. This sort of information could be useful for designing clinical studies and for providing background data in personalized medicine. It could also assist with the interpretation of genetic analysis results such as those from genome-wide association studies

Evaluating a subset of ancestry informative SNPs for discriminating among Southwest Asian and circum-Mediterranean populations

AbstractMany different published sets of single nucleotide polymorphisms (SNPs) and/or insertion-deletion polymorphisms (InDels) can serve as ancestry informative markers (AIMs) to distinguish among continental regions of the world. For a focus on Southwest Asian ancestry we chose to start with the Kidd Lab panel of 55 ancestry-informative SNPs (AISNPs) because it already provided good global reference data (FROG-kb: frog.med.yale.edu) in a set of 73 population samples distinguishing at least 8 biogeographic clusters of populations. This panel serves as a good first tier ancestry panel. We are now interested in identifying region-specific second tier panels for more refined distinction among populations within each of the global regions. We have begun studying the global region centered on Southwest Asia and the region encompassing the Mediterranean Sea. We have incorporated 10 populations from North Africa, Turkey and Iran and included 31 of the original 73 populations and eleven 1000 Genomes Phase3 populations for a total of 3129 individuals from 52 populations, all typed for the 55 AISNPs. We have then identified the subset of the 55 AISNPs that are most informative for this region of the world using Heatmap, Fst, and Informativeness analyses to eliminate those SNPs essentially redundant or providing no information among populations in this region, reducing the number of SNPs to 32. STRUCTURE and PCA analyses show the remaining 32 SNPs identify the North African cluster and appropriately include the Turkish and Iranian samples with the Southwest Asian cluster. These markers provide the basis for building an improved, optimized panel of AISNPs that provides additional information on differences among populations in this part of the world. The data have also allowed an examination of the accuracy of the ancestry inference based on 32 SNPs for the newly studied populations from this region. The likelihood ratio approach to ancestry inference embodied in FROG-kb provides highly significant population assignments within one order of magnitude for each individual in the Turkish, Iranian, and Tunisian populations