FGGE 4-dimensional data assimilation at ECMWF ( weather forecasts).

The 4-dimensional data-assimilation system used to produce the FGGE level III-b data set at the European Centre for Medium Range Weather Forecasts (ECMWF) is described. The system consists of a three-dimensional multivariate optimum interpolation, a nonlinear normal mode initialization, and associated automatic system for data checking. A 15-level model with a horizontal resolution of 1.875o is used for the dynamical assimilation. -from Author

Tropical cyclones in a T159 Resolution Global Climate Model: comparison with observations and re-analyses

Tropical cyclones have been investigated in a T159 version of the MPI ECHAM5 climate model using a novel technique to diagnose the evolution of the 3-dimensional vorticity structure of tropical cyclones, including their full life cycle from weak initial vortex to their possible extra-tropical transition. Results have been compared with reanalyses (ERA40 and JRA25) and observed tropical storms during the period 1978-1999 for the Northern Hemisphere. There is no indication of any trend in the number or intensity of tropical storms during this period in ECHAM5 or in re-analyses but there are distinct inter-annual variations. The storms simulated by ECHAM5 are realistic both in space and time, but the model and even more so the re-analyses, underestimate the intensities of the most intense storms (in terms of their maximum wind speeds). There is an indication of a response to ENSO with a smaller number of Atlantic storms during El Niño in agreement with previous studies. The global divergence circulation responds to El Niño by setting up a large-scale convergence flow, with the center over the central Pacific with enhanced subsidence over the tropical Atlantic. At the same time there is an increase in the vertical wind shear in the region of the tropical Atlantic where tropical storms normally develop. There is a good correspondence between the model and ERA40 except that the divergence circulation is somewhat stronger in the model. The model underestimates storms in the Atlantic but tends to overestimate them in the Western Pacific and in the North Indian Ocean. It is suggested that the overestimation of storms in the Pacific by the model is related to an overly strong response to the tropical Pacific SST anomalies. The overestimation in 2 the North Indian Ocean is likely to be due to an over prediction in the intensity of monsoon depressions, which are then classified as intense tropical storms. Nevertheless, overall results are encouraging and will further contribute to increased confidence in simulating intense tropical storms with high-resolution climate models

Deep sequencing of Pigeonpea sterility mosaic virus discloses five RNA segments related to emaraviruses

The sequences of five viral RNA segments of Pigeonpea sterility mosaic virus (PPSMV), the agent of sterility mosaic disease (SMD) of pigeonpea (Cajanus cajan, Fabaceae), were determined using the Deep sequencing technology. Each of the five RNAs encodes a single protein on the negative-sense strand with an open reading frame (ORF) of 6885, 1947, 927, 1086, and 1422 nts, respectively. In order, from RNA1 to RNA5, these ORFs encode the RNA-dependent RNA polymerase (p1, 267.9 kDa), a putative glycoprotein precursor (p2, 74.3 kDa), a putative nucleocapsid protein (p3, 34.6 kDa), a putative movement protein (p4, 40.8 kDa), while p5 (55 kDa) has an unknown function. All RNA segments of PPSMV showed the highest identity with orthologs of fig mosaic virus (FMV) and rose rosette virus (RRV). In phylogenetic trees constructed with the amino acid sequences of p1, p2 and p3, PPSMV clustered consistently with other emaraviruses, close to clades comprising members of other genera of the family Bunyaviridae. Based on the molecular characteristics unveiled in this study and the morphological and epidemiological features similar to other emaraviruses, PPSMV seems to be the seventh species to join the list of emaraviruses known to date and accordingly, its classification in the genus Emaravirus seems now legitimate

Deep sequencing of dsRNAs recovered from mosaic-diseased pigeonpea reveals the presence of a novel emaravirus: pigeonpea sterility mosaic virus 2

Deep-sequencing analysis of double-stranded RNA extracted from a mosaic-diseased pigeonpea plant (Cajanus cajan L., family Fabaceae) revealed the complete sequence of six emaravirus-like negative-sense RNA segments of 7009, 2229, 1335, 1491, 1833 and 1194 nucleotides in size. In the order from RNA1 to RNA6, these genomic RNAs contained ORFs coding for the RNA-dependent RNA polymerase (RdRp, p1 of 266 kDa), the glycoprotein precursor (GP, p2 of 74.5 kDa), the nucleocapsid (NC, p3 of 34.9 kDa), and the putative movement protein (MP, p4 of 40.7 kDa), while p5 (55 kDa) and p6 (27 kDa) had unknown functions. All RNA segments showed distant relationships to viruses of the genus Emaravirus, and in particular to pigeonpea sterility mosaic virus (PPSMV), with which they shared nucleotide sequence identity ranging from 48.5 % (RNA3) to 62.5 % (RNA1). In phylogenetic trees constructed from the sequences of the proteins encoded by RNA1, RNA2 and RNA3 (p1, p2 and p3), this new viral entity showed a consistent grouping with fig mosaic virus (FMV) and rose rosette virus (RRV), which formed a cluster of their own, clearly distinct from PPSMV-1. In experimental greenhouse trials, this novel virus was successfully transmitted to pigeonpea and French bean seedlings by the eriophyid mite Aceria cajani. Preliminary surveys conducted in the Hyderabad region (India) showed that the virus in question is widespread in pigeonpea plants affected by sterility mosaic disease (86.4 %) but is absent in symptomless plants. Based on molecular, biological and epidemiological features, this novel virus is the second emaravirus infecting pigeonpea, for which the provisional name pigeonpea sterility mosaic virus 2 (PPSMV-2) is proposed

Perfectionism And Eating Disorders: Current Status And Future Directions

The literature examining the relation between perfectionism and eating disorders was reviewed and content and methodological comparisons were made with the perfectionism literature in anxiety disorders and depressive disorders. A PsychInfo search using the key words perfectionism/ perfect/ perfectionistic, anorexia, bulimia, and eating disorders was performed and the generated list of papers was supplemented based on a review of reference lists in the papers. A total of 55 papers published between 1990 and 2005 were identified that assessed perfectionism among individuals with diagnosed eating disorders. The key research questions were distilled from these publications and empirical findings were summarized for each question, followed by a comparison with perfectionism papers in the anxiety and depressive disorder literatures. Also, key research design methodological parameters were identified and comparisons made across the three literatures: eating disorders, anxiety disorders, depressive disorders. The current review concludes with conceptual and methodological recommendations for researchers interested in perfectionism and eating disorders

Aerosol climate feedback due to decadal increases in Southern Hemisphere wind speeds

Observations indicate that the westerly jet in the Southern Hemisphere troposphere is accelerating. Using a global aerosol model we estimate that the increase in wind speed of 0.45 + /- 0.2 m s(-1) decade(-1) at 50-65 degrees S since the early 1980s caused a higher sea spray flux, resulting in an increase of cloud condensation nucleus concentrations of more than 85% in some regions, and of 22% on average between 50 and 65 degrees S. These fractional increases are similar in magnitude to the decreases over many northern hemisphere land areas due to changes in air pollution over the same period. The change in cloud drop concentrations causes an increase in cloud reflectivity and a summertime radiative forcing between at 50 and 65 degrees S comparable in magnitude but acting against that from greenhouse gas forcing over the same time period, and thus represents a substantial negative climate feedback. However, recovery of Antarctic ozone depletion in the next two decades will likely cause a fall in wind speeds, a decrease in cloud drop concentration and a correspondingly weaker cloud feedback

scite: A Smart Citation Index that Displays the Context of Citations and Classifies Their Intent Using Deep Learning

Citation indices are tools used by the academic community for research and research evaluation that aggregate scientific literature output and measure impact by collating citation counts. Citation indices help measure the interconnections between scientific papers but fall short because they fail to communicate contextual information about a citation. The use of citations in research evaluation without consideration of context can be problematic because a citation that presents contrasting evidence to a paper is treated the same as a citation that presents supporting evidence. To solve this problem, we have used machine learning, traditional document ingestion methods, and a network of researchers to develop a “smart citation index” called scite, which categorizes citations based on context. Scite shows how a citation was used by displaying the surrounding textual context from the citing paper and a classification from our deep learning model that indicates whether the statement provides supporting or contrasting evidence for a referenced work, or simply mentions it. Scite has been developed by analyzing over 25 million full-text scientific articles and currently has a database of more than 880 million classified citation statements. Here we describe how scite works and how it can be used to further research and research evaluation

Mapping Direct Seeded Rice in Raichur District of Karnataka, India

Across South Asia, the cost of rice cultivation has increased due to labor shortage. Direct seeding of rice is widely promoted in order to reduce labor demand during crop establishment stage, and to benefit poor farmers. To facilitate planning and to track farming practice changes, this study presents techniques to spatially distinguish between direct seeded and transplanted rice fields using multiple-sensor remote sensing imagery. The District of Raichur, a major region in northeast Karnataka, Central India, where irrigated rice is grown and direct seeded rice has been widely promoted since 2000, was selected as a case study. The extent of cropland was mapped using Landsat-8, Moderate Resolution Imaging Spectroradiometer (modis) 16-day normalized difference vegetation index (ndvi) time-series data and the cultivation practice delineated using risat-1 data for the year 2014. Areas grown to rice were mapped based on the length of the growing period detected using spectral characteristics and intensive field observations. The high resolution imagery of Landsat-8 was useful to classify the rice growing areas. The accuracy of land-use/landcover (lulc) classes varied from 84 percent to 98 percent. The results clearly demonstrated the usefulness of multiple-sensor imagery from mod09q1, Landsat-8, and risat-1 in mapping the rice area and practices accurately, routinely, and consistently. The low cost of imagery backed by ground survey, as demonstrated in this paper, can also be used across rice growing countries to identify different rice systems