Mesoscale objective analysis of daily rainfall with satellite and conventional data over Indian summer monsoon region

A mesoscale objective analysis scheme for producing daily rainfall analysis on a regular latitude/longitude grid over the Indian monsoon region is described. The Barnes scheme is applied to interpolate irregularly distributed daily rainfall data on to a regular grid. The spatial resolution of the interpolated arrays is 0.25 degrees of latitude by 0.25 degrees of longitude. Daily rainfall derived from INSAT IR radiances and raingauge observations are combined to produce this analysis. Some objectively determined constraints are employed in this study: (i) weights are determined as a function of data spacing, (ii) in order to achieve convergence of the analysed values three passes through the data are considered and there is automatic elimination of wavelengths smaller than twice the average data spacing. The case of a typical westward moving monsoon depression during the 1994 monsoon season is selected to represent the characteristics of the analysed rainfall. Objective analyses of six days (16 to 21 August 1994) have been carried out using Barnes three pass scheme. The weighting function scale length parameter (c, denominator in the exponential Gaussian weight function) is varied from over a range of values and the root mean square (rms) errors are computed to select the appropriate value of c. The value of c depends on the number of correction passes being performed and on the density of the observations. The characteristics of the output field from this analysis system have been examined by comparing the analysed rainfall with the observed values. The heavy rainfall over the Western Ghat of India has been clearly brought out in this analysis

Determination of forecasts errors arising from different components of model physics and dynamics

This paper addresses a procedure to extract error estimates for the physical and dynamical components of a forecast model. This is a two-step process in which contributions to the forecast tendencies from individual terms of the model equations are first determined using an elaborate bookkeeping of the forecast. The second step regresses these estimates of tendencies from individual terms of the model equations against the observed total tendencies. This process is executed separately for the entire horizontal and vertical transform grid points of a global model. The summary of results based on the corrections to the physics and dynamics provided by the regression coefficients highlights the component errors of the model arising from its formulation. This study provides information on geographical and vertical distribution of forecast errors contributed by features such as nonlinear advective dynamics, the rest of the dynamics, deep cumulus convection, large-scale condensation physics, radiative processes, and the rest of physics. Several future possibilities from this work are also discussed in this paper

Seasonal climate forecasts of the South Asian monsoon using multiple coupled models

This study addresses seasonal climate forecasts using coupled atmosphere-ocean multimodels. Using as many as 67 different seasonal-forecast runs per season from a variety of coupled (atmosphere-ocean) models consensus seasonal forecasts have been prepared from about 4500 experiments. These include the European Center's DEMETER (Development of a European Multi-Model Ensemble System for Seasonal to Inter-Annual Prediction) database and a suite of Florida State University (FSU) models (based on different combinations of physical parametrizations). This is one of the largest databases on coupled models. The monsoon region was selected to examine the predictability issue. The methodology involves construction of seasonal anomalies of all model forecasts for a number of variables including precipitation, 850 hPa winds, 2-m/surface temperatures, and sea surface temperatures. This study explores the skills of the ensemble mean and the FSU multimodel superensemble. The metrics for forecast evaluation include computation of hindcast and verification anomalies from model/ observed climatology, time-series of specific climate indices, and standard deterministic ensemble mean scores such as anomaly correlation coefficient and root mean square error. The results were deliberately prepared to match the metrics used by European DEMETER models. Invariably in all modes of evaluation, the results from the FSU multimodel superensemble demonstrate greater skill for most of the variables tested here than those obtained in earlier studies. The specific inquiry of this study was on this question: is it going to be wetter or drier, warmer or colder than the long-term recent climatology of the monsoon; and where and when during the next season?These results are most encouraging, and they suggest that this vast database and the superensemble methodology are able to provide some useful answers to the seasonal monsoon forecast issue compared to the use of single climate models or from the conventional ensemble averaging

A multi-model superensemble algorithm for seasonal climate prediction using DEMETER forecasts

In this paper, a multi-model ensemble approach with statistical correction for seasonal precipitation forecasts using a coupled DEMETER model data set is presented. Despite the continuous improvement of coupled models, they have serious systematic errors in terms of the mean, the annual cycle and the interannual variability; consequently, the predictive skill of extended forecasts remains quite low. One of the approaches to the improvement of seasonal prediction is the empirical weighted multi-model ensemble, or superensemble, combination. In the superensemble approach, the different model forecasts are statistically combined during the training phase using multiple linear regression, with the skill of each ensemble member implicitly factored into the superensemble forecast. The skill of a superensemble relies strongly on the past performance of the individual member models used in its construction. The algorithm proposed here involves empirical orthogonal function (EOF) filtering of the actual data set prior to the construction of a multi-model ensemble or superensemble as an alternative solution for seasonal prediction. This algorithm generates a new data set from the input multi-model data set by finding a consistent spatial pattern between the observed analysis and the individual model forecast. This procedure is a multiple linear regression problem in the EOF space. The newly generated EOF-filtered data set is then used as an input data set for the construction of a multi-model ensemble and superensemble. The skill of forecast anomalies is assessed using statistics of categorical forecast, spatial anomaly correlation and root mean square (RMS) errors. The various verifications show that the unbiased multi-model ensemble of DEMETER forecasts improves the prediction of spatial patterns (i.e. the anomaly correlation), but it shows poor skill in categorical forecast. Due to the removal of seasonal mean biases of the different models, the forecast errors of the bias-corrected multi-model ensemble and superensemble are already quite small. Based on the anomaly correlation and RMS measures, the forecasts produced by the proposed method slightly outperform the other conventional forecasts

Experimental real-time multi-model ensemble (MME) prediction of rainfall during monsoon 2008: Large-scale medium-range aspects

Realistic simulation/prediction of the Asian summer monsoon rainfall on various space-time scales is a challenging scientific task. Compared to mid-latitudes, a proportional skill improvement in the prediction of monsoon rainfall in the medium range has not happened in recent years. Global models and data assimilation techniques are being improved for monsoon/tropics. However, multimodel ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and handling model uncertainties. As major centers are exchanging model output in near real-time, MME is a viable inexpensive way of enhancing the forecasting skill and information content. During monsoon 2008, on an experimental basis, an MME forecasting of large-scale monsoon precipitation in the medium range was carried out in real-time at National Centre for Medium Range Weather Forecasting (NCMRWF), India. Simple ensemble mean (EMN) giving equal weight to member models, biascorrected ensemble mean (BCEMn) and MME forecast, where different weights are given to member models, are the products of the algorithm tested here. In general, the aforementioned products from the multi-model ensemble forecast system have a higher skill than individual model forecasts. The skill score for the Indian domain and other sub-regions indicates that the BCEMn produces the best result, compared to EMN and MME. Giving weights to different models to obtain an MME product helps to improve individual member models only marginally. It is noted that for higher rainfall values, the skill of the global model rainfall forecast decreases rapidly beyond day-3, and hence for day-4 and day-5, the MME products could not bring much improvement over member models. However, up to day-3, the MME products were always better than individual member models

Dendritic Spine Shape Analysis: A Clustering Perspective

Functional properties of neurons are strongly coupled with their morphology. Changes in neuronal activity alter morphological characteristics of dendritic spines. First step towards understanding the structure-function relationship is to group spines into main spine classes reported in the literature. Shape analysis of dendritic spines can help neuroscientists understand the underlying relationships. Due to unavailability of reliable automated tools, this analysis is currently performed manually which is a time-intensive and subjective task. Several studies on spine shape classification have been reported in the literature, however, there is an on-going debate on whether distinct spine shape classes exist or whether spines should be modeled through a continuum of shape variations. Another challenge is the subjectivity and bias that is introduced due to the supervised nature of classification approaches. In this paper, we aim to address these issues by presenting a clustering perspective. In this context, clustering may serve both confirmation of known patterns and discovery of new ones. We perform cluster analysis on two-photon microscopic images of spines using morphological, shape, and appearance based features and gain insights into the spine shape analysis problem. We use histogram of oriented gradients (HOG), disjunctive normal shape models (DNSM), morphological features, and intensity profile based features for cluster analysis. We use x-means to perform cluster analysis that selects the number of clusters automatically using the Bayesian information criterion (BIC). For all features, this analysis produces 4 clusters and we observe the formation of at least one cluster consisting of spines which are difficult to be assigned to a known class. This observation supports the argument of intermediate shape types.Comment: Accepted for BioImageComputing workshop at ECCV 201

Comparison of a novel combination of bio-organic fertilizers vis-à-vis a chemical fertilizer

197-204Plant nutrients are essential for the production of healthy crops for the world’s expanding population and thus, they are a vital component of sustainable agriculture. Increased crop production depends on the type of fertilizers used to supplement essential nutrients for plants. The nutrient level fertilizers provide depends on their nature- each type of fertilizer has its advantages and disadvantages concerning crop growth and soil fertility. The management of using fertilizer must aim to ensure both an enhanced and safeguarded environment. Thus, a balanced fertilization strategy must be implemented. An experiment was conducted under field conditions to assess the effects of combinations of bio-fertilizers on agronomic and quality criteria of Brassica juncea (brown mustard), Basella alba (climbing spinach), and Amaranthus dubius (red spinach). Randomized block design with three replicas were used for the study, one set with the application of fertilizers containing Azotobacter, Rhizobium, Sesbania, Bacillus subtilis, Bacillus cereus, Bacillus megaterium, Pseudomonas fluorescens and Glomus (Mycorrhizal inoculant)- under bio-fertilizer; another with a mixture of urea, Potassium Nitrate, Super Phosphate, Potassium Sulfate, and Maple EM solution as chemical fertilizer and a control (water). Results indicated that yield and other plant criteria like chlorophyll content and gel volume were enhanced in bio-fertilizer treated plants compared to the plants grown with chemical fertilizer and control. In general, the application of bio-fertilizer significantly increased leaf length by 16-50%, the total number of leaves by 50-80%, plant size 19.15-63.15%, and gel volume by 147% (approximately) in comparison with untreated plants

Malaria and vitamin A deficiency in African children: a vicious circle?

Vitamin A deficiency and malaria are both highly prevalent health problems in Africa. Vitamin A deficiency affects over 30 million children, most of whom are in the age-group (under five years) most affected by malaria. Vitamin A deficiency increases all-cause mortality in this part of the population, and malaria is an important cause of death in children at this age. A low serum retinol concentration (a marker of vitamin A deficiency) is commonly found in children suffering from malaria, but it is not certain whether this represents pre-existing vitamin A deficiency, a contribution of malaria to vitamin A deficiency, or merely an acute effect of malaria on retinol metabolism or binding. In this paper, available evidence in support of a causal relationship in each direction between vitamin A deficiency and malaria is reviewed. If such a relationship exists, and especially if this is bidirectional, interventions against either disease may convey an amplified benefit for health

Novel insights into the cardio-protective effects of FGF21 in lean and obese rat hearts

Aims: Fibroblast growth factor 21 (FGF21) is a hepatic metabolic regulator with pleotropic actions. Its plasma concentrations are increased in obesity and diabetes; states associated with an increased incidence of cardiovascular disease. We therefore investigated the direct effect of FGF21 on cardio-protection in obese and lean hearts in response to ischemia. Methods and Results: FGF21, FGF21-receptor 1 (FGFR1) and beta-Klotho (βKlotho) were expressed in rodent, human hearts and primary rat cardiomyocytes. Cardiac FGF21 was expressed and secreted (real time RT-PCR/western blot and ELISA) in an autocrine-paracrine manner, in response to obesity and hypoxia, involving FGFR1-βKlotho components. Cardiac-FGF21 expression and secretion were increased in response to global ischemia. In contrast βKlotho was reduced in obese hearts. In isolated adult rat cardiomyocytes, FGF21 activated PI3K/Akt (phosphatidylinositol 3-kinase/Akt), ERK1/2(extracellular signal-regulated kinase) and AMPK (AMP-activated protein kinase) pathways. In Langendorff perfused rat [adult male wild-type wistar] hearts, FGF21 administration induced significant cardio-protection and restoration of function following global ischemia. Inhibition of PI3K/Akt, AMPK, ERK1/2 and ROR-α (retinoic-acid receptor alpha) pathway led to significant decrease of FGF21 induced cardio-protection and restoration of cardiac function in response to global ischemia. More importantly, this cardio-protective response induced by FGF21 was reduced in obesity, although the cardiac expression profiles and circulating FGF21 levels were increased. Conclusion: In an ex vivo Langendorff system, we show that FGF21 induced cardiac protection and restoration of cardiac function involving autocrine-paracrine pathways, with reduced effect in obesity. Collectively, our findings provide novel insights into FGF21-induced cardiac effects in obesity and ischemia

Thinking about Later Life: Insights from the Capability Approach

A major criticism of mainstream gerontological frameworks is the inability of such frameworks to appreciate and incorporate issues of diversity and difference in engaging with experiences of aging. Given the prevailing socially structured nature of inequalities, such differences matter greatly in shaping experiences, as well as social constructions, of aging. I argue that Amartya Sen’s capability approach (2009) potentially offers gerontological scholars a broad conceptual framework that places at its core consideration of human beings (their values) and centrality of human diversity. As well as identifying these key features of the capability approach, I discuss and demonstrate their relevance to thinking about old age and aging. I maintain that in the context of complex and emerging identities in later life that shape and are shaped by shifting people-place and people-people relationships, Sen’s capability approach offers significant possibilities for gerontological research