Search

We use new survey data from 1,203 households in rural Eastern India to estimate cross-sectional models of overall energy use and embedded emissions. Findings indicate that the primary driver of household energy use is household size and affluence. This is unsurprising and consistent with findings from the engineering literature on energy demand. However, there is also a substantial and significant moderating influence of skills on energy use. For emissions, we observe a bifurcated relationship in line with educational attainment. For those that have completed secondary education or more, skills are negatively associated with energy use and emissions, whereas for those with lesser qualifications more skills are associated with more energy use and emissions. The results are consistent with the environmental Kuznets curve, which implies that a critical level of affluence is required before environmental impacts start lessening. The results also echo a sociological critique of human capital theory − that individual abilities are not productive in and of themselves, but rather in relation to socially determined opportunity structures. Our findings show that this could also hold for greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

Although the Dudhganga watershed is the primary water and food resource of the Kashmir Valley, it has undergone significant changes in food resources and strategies due to rampant urbanization in the area over the past 20 years. This urbanization has had a profound impact on the watershed and has also affected land use and land cover (LULC) patterns and environmental changes. The objective of this study is to investigate the effects of urban development on food security parameters in the Dudhganga watershed area, India, from 2000 to 2020, by evaluating LULC changes. Additionally, the study aims to examine the relationship between climate changes and LULC indices, such as the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI). The results indicate a 21.66% increase in barren areas, at the expense of snow-covered lands, during the 2000–2020 period. The primary land cover transition observed is towards barren areas. The predictions for LULC in 2030 highlight the need for careful management of land use and climate changes in the study area. This study can assist local government officials in reassessing food strategies by identifying areas where urban expansion should be controlled and climate impacts minimized, to prevent local hunger and ecological degradation. Therefore, the development of systematic urban planning approaches and mitigation of climate change sources are crucial. Furthermore, the adoption of advanced agricultural technology should be considered to mitigate the impact of urban expansion. [ABSTRACT FROM AUTHOR]

Understanding the LULC change is essential to adopt the new development activities in the particular study area. The study reveals the LULC change for four different decades (1990, 2000, 2011, and 2022) of the Sulur-Singanallur region of the Coimbatore district. In this study, the Landsat satellite data of four decades were used to assess the regional LULC changes and identified the area under Built-up land increased from 6 to 25.77% and Waterbodies decreased by 5.48% (6–0.52%). Similarly, the current scenario of agriculture has decreased as Crop lands by 12.77% (30–17.23%). At the same time, the fallow land has risen and fall from starting to the ending years. Hence, the CA-ANN method has been adopted using the Modules for Land Use Change Evaluation (MOLUSCE) plugin of QGIS software. The spatial entities such as DEM, slope, distance to river, and distance to road were utilized as drivers to predict the future LULC of 2030, 2040, and 2050. The study's findings reveal that the crop land has decreased to 10.90%, 8.89% and 8.87%, respectively. Similarly, the waterbodies will be decreased by upto 1%, and the prediction findings portrays the increasing trend was observed in Built-up area and the better kappa statistics results identified as 0.81, 0.76, and 0.76 for 2030, 2040, and 2050. The observation of post classification shows that MLC is one of the best suited method and producing realistic result in the study for the change detection analysis. Overall, the outcome of the study showcased the continuous increase Built-up land were declined the natural land environment. If it continues, the agriculture ecosystem will be affected, and minimizes the cropping pattern and further anthropogenic influences will also reduce the agricultural activities in future. This modelling study results serves the benefit to land managers, stakeholders, farmers, and researchers to take possible measures immediately to manage the land sustainably. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) is linked with a wide spectrum of human health effects and has the highest contribution to total air pollution mortality. This study aims to quantify health benefits of reducing PM2.5 concentration to World Health Organization standard (annual mean = 10 µg m−3) for various health endpoints during 2011–2019 period using AirQ+ and BenMAP-CE software packages. Intraurban assessment in Vellore city, India was done by estimating health benefits at ward level. Both software packages estimated annual average all-cause, ischemic heart disease, stroke, and chronic obstructive pulmonary disease health benefits in the range of 919–945, 175–234, 70–152, and 99–175 cases at city level and 15–16, 3–4, 1–3, and 2–3 cases at ward level, respectively. Sensitivity analysis showed that relative risk had a large influence on health benefit estimates. Present study results will play a crucial role in the future air quality and public health policies of Vellore city. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Carbamazepine (CBZ), an anti-seizure drug, is widely prescribed for the management of focal seizures. At a given therapeutic dose, CBZ exhibits marked interindividual variation in the plasma CBZ levels. The aim wasto study the influence of EPHX1 c.337 T>C and UGT2B7*2 genetic polymorphisms on plasma carbamazepine (CBZ) levels in persons with epilepsy (PWE) from South India. METHODS: 115 PWE belong to South India origin who are on carbamazepine monotherapy were recruited. Genotyping of the two variants weredone using RT-PCR method. PWE who had seizure freedom for one year and their last dose which was not changed for one year duration were included and their plasma levels of CBZ and its active metabolite CBZ 10,11 epoxide were analysed by reverse phase HPLC. RESULTS: In EPHX1 c. 337 (T>C) polymorphism, the PWE carrying CC had lower plasma CBZ levels when compared to CT genotype (2.45 µg/ml vs 3.15 µg/ml. In UGT2B7*2, PWE carrying homozygous mutant TT had higher levels when compared with CT (3.09 µg/ml vs 2.74 µg/ml) genotype but found no statistical significance. Mutant genotype of EPHX1 (CC) had higher metabolic ratio compared to TT genotype (1.33 vs 1.17) but not found to be statistically significant. Mutant genotype of UGT2B7*2 (TT) was found to be having lower metabolic ratio when compared with CC genotype (1.18 vs 1.35; p value =0.08). CONCLUSION: PWE carrying EPHX1 c.337 T>C (rs1051740) and UGT2B7*2 (rs7439366) genetic polymorphisms did not affect the plasma CBZ levels and metabolic ratio of PWE of South Indian origin. However, this finding should be confirmed in a larger sample size which may help in optimization and personalized CBZ therapy in South Indians. [ABSTRACT FROM AUTHOR]

The ecosystem services in an area are quite dependent on its ambient land use and land cover (LULC) attributes. Here we assess the spatio-temporal distribution of Ecosystem Services Value (ESV) for the years 1990, 2000, 2010, 2020, based on the then existing LULC aspects of the Kolkata Urban Agglomeration in eastern India. Further, these are simulated for 2030, 2040 and 2050 to determine the future potential ESV. The respective LULC layers were extracted from Landsat images using the support vector machine method and future projections were done using Markov Chain-Cellular Automata models. Results reveal that all LULC aspects are likely to significantly decrease except built-up tracts. The available ESV shall concomitantly decline by 29.7%, especially due to wetland loss. The ESV patterns also showed strong spatial correlation/clustering, with higher ESV patches locating along rivers/wetlands. These results can better inform management of high-value EVS components for sustaining/improving the urban environmental quality. [ABSTRACT FROM AUTHOR]

Urbanization has profound influence on the changes of land use and land cover, which on the other hand exert significant impact on ecosystem services and their values, especially in the urban agglomerations. Kolkata urban agglomeration of India has been selected to determine the causes in changes of ecosystem services under present and projected land use land cover scenario. Land use land cover maps of 1990, 2000, 2010 and 2020 were prepared by support vector machine method, using LANDSAT satellite imageries and projected up-to 2040 using CA–Markov model. Built-up land was increased by 65.39% during 1990–2020 and will further increase to 76.88% by 2040. Built-up lands are mainly encroaching crop lands and wetlands. During the period of fifty years (1990–2040), the ecosystem service value will decrease from $38,486.49 to $28,060.79, with annual rate of decrement of 0.54%. During the same period, the spatial extent of very low ecosystem service value will be increased from 16.60 to 58.88%; whereas, the area coverage of very high ecosystem service value will be decreased from 2.69 to 2.35%. Water supply contributed highest ecosystem service value followed by disturbance regulation and nutrient cycling; whereas, lacking in soil formation, pollination and biological control services contributed lowest. The study will help in decision making process for sustainable management of natural resources and also provide useful guideline for the quality improvement in urban ecosystems. [ABSTRACT FROM AUTHOR]

The aim of the study is to understand the role of insurance company reputation, performance, and positive/negative affect on health insurance policy customer retention and the moderating influence of customer inertia. A structured questionnaire was used for data collection. Covariance-based structural equation modeling was employed to assess the hypothesized relationships between the variables. The findings revealed that reputation, performance, and affect influenced customer retention in insurance sector. Positive affect had greater impact on customer retention in comparison to other constructs. Further, customer inertia was an important moderating influence on the negative affect for health insurance policy customer retention. To the best of our knowledge, the present study is the first of its kind that attempts to investigate customer inertia in the health insurance sector in an emerging market context, i.e., India. Customer inertia has not been much studied in light of company reputation, performance, and positive and negative affect in the health insurance milieu. The research findings may help health insurance companies understand the importance of reputation, performance, customer retention, and inertia while marketing insurance services. [ABSTRACT FROM AUTHOR]

Although the Dudhganga watershed is the primary water and food resource of the Kashmir Valley, it has undergone significant changes in food resources and strategies due to rampant urbanization in the area over the past 20 years. This urbanization has had a profound impact on the watershed and has also affected land use and land cover (LULC) patterns and environmental changes. The objective of this study is to investigate the effects of urban development on food security parameters in the Dudhganga watershed area, India, from 2000 to 2020, by evaluating LULC changes. Additionally, the study aims to examine the relationship between climate changes and LULC indices, such as the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI). The results indicate a 21.66% increase in barren areas, at the expense of snow-covered lands, during the 2000-2020 period. The primary land cover transition observed is towards barren areas. The predictions for LULC in 2030 highlight the need for careful management of land use and climate changes in the study area. This study can assist local government officials in reassessing food strategies by identifying areas where urban expansion should be controlled and climate impacts minimized, to prevent local hunger and ecological degradation. Therefore, the development of systematic urban planning approaches and mitigation of climate change sources are crucial. Furthermore, the adoption of advanced agricultural technology should be considered to mitigate the impact of urban expansion., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Purpose: This research aims to develop a new generic framework for estimating different maintenance costs (preventive, corrective and conditional based) and its distribution to original equipment manufacturer (OEM), customer and supply chain due to no fault found (NFF) events. The study extend the domain of NFF to military aircraft maintenance, repair and overhaul (MRO) by including broader range of cost drivers than are normally found in maintenance NFF literature. Design/methodology/approach: The research applies the soft system methodology involving 80 field surveys and five in depth semi-structured interviews with practicing experts having background in military aircraft NFF MRO. For impact analysis, authors have used an agent-based model to represent and prioritize the critical NFF cost drivers during aircraft MRO based on the cost inputs of 21 technology transfer cases. Findings: The paper provides imperial insights about how NFF cost drivers affect the OEM, customer and supply chain. It suggests that NFF cost need to be part of the commercial MRO contract, depending on its frequency pattern in different types of maintenances. Research limitations/implications: The context of the current research is military aircrafts industry and may lack generalizability to commercial aircrafts. Therefore, researchers are encouraged to test the proposed propositions further. Practical implications: The developed framework will provide invaluable help in key financial decision-making during signing of MRO contract in technology transfer cases. Originality/value: This paper proposed a new prediction model for NFF cost estimation across its shareholders and current status of NFF in military aircraft NFF MRO in India. [ABSTRACT FROM AUTHOR]

The ARIMA method, time series analysis technique, was proposed to perform short-term ionospheric Total Electron Content (TEC) forecast and to detect TEC anomalies. The success of the method was tested in two major earthquakes that occurred in India (M 7.7 Bhuj EQ, on Jan 26, 2001) and Turkey (M 7.1 Van EQ, on Oct 23, 2011). For ARIMA analysis, we have taken 18 and 29 days of TEC data with a 2-h temporal resolution and train the model with an accuracy of 5.1 and 2.7–2.9 TECU for India and Turkey EQs, respectively. After training the model and optimizing hyper model parameters, we applied on 8 and 9 days' time-window to observe anomalies. In Bhuj EQ, the negative anomalies are recorded on Jan 19 and 22, 2001. Similarly, positive anomalies are recorded on Jan 23, 24, and 25, 2001. In Van EQ, we recorded a strong positive anomaly on Oct 21, 2011, and in the consecutive days before the earthquake, some weak negative anomalies have also observed. The results showed that ARIMA has an adequate short-term performance of the ionospheric TEC prediction and anomaly detection of the TEC time series. [ABSTRACT FROM AUTHOR]

Objectives: Carbamazepine (CBZ) is a first-line antiseizure drug used for focal onset seizures. It exhibits inter-individual variability in plasma carbamazepine levels and there are both genetic and non-genetic factors having a role in the requirement of CBZ maintenance dose. The aim was to study the influence of EPHX1 c.337 T>C and UGT2B7*2 genetic polymorphisms on CBZ maintenance dose requirement in persons with epilepsy., Methods: Persons with epilepsy (PWE) of both gender of age 15-65 years on carbamazepine monotherapy who had been taking same maintenance dose for one year were eligible. Five milliliter of venous blood was collected in 10% EDTA under aseptic precautions. After centrifugation, the cellular component was used for DNA extraction and genotyping. For three genotypes of EPHX1 c.337 T>C and UGT2B7*2 , the differences in mean carbamazepine dose were analyzed using Analysis of Variance (ANOVA). An unpaired t-test was used to draw a comparison between the genotypes and CBZ maintenance dose requirement for dominant and recessive models of EPHX1 c.337 T>C and  UGT2B7*2 . A value of p<0.05 was considered to be statistically significant., Results: For UGT2B7*2 (rs 7439366), CT required a higher dose (CT 626 mg/day and TT 523 mg/day) but not found to be significant (p-value 0.167). PWE carrying CT genotype of  EPHX1 c.337 T>C had 62 mg higher dose when compared to homozygous mutant CC (590 mg/day for CT and 528 mg/day for CC) but p-value was not found to be significant (p-value 0.835)., Conclusions: The results of our study done in 115 PWE showed there was a lack of association between SNPs of EPHX1 c.337 T>C, UGT2B7*2 and CBZ maintenance dose requirement in Southern part of India and this finding has to be confirmed in a larger sample size., (© 2023 Walter de Gruyter GmbH, Berlin/Boston.)

Prediction of the Indian summer monsoon rainfall is essential for the country because the food production depends on the rainfall during the summer monsoon season. India being an agricultural country, its economy also depends on the monsoon rainfall. Various institutes across the world give global prediction. An experimental prediction based on the numerical simulations produced by North American Multi-Model Ensemble (NMME) has been attempted for the initial conditions of May to understand the strength, variability, predictability, and associated changes in the Indian summer monsoon. This study is particularly aimed to find out the efficacy of NMME models over the Indian region during the southwest monsoon season. The analysis shows that all the nine models underestimate the climatology. A mismatch exists between the spatial patterns of model climatology as compared to the observed climatology. It is found that models underestimate the inter-annual variability of the precipitation as compared to the observed ones. This can be attributed to the overestimation of sea surface temperature-Indian summer monsoon rainfall (SST-ISMR) response. This might lead to poor performance of the model in terms of precipitation prediction. The spatial correlation shows varying correlation pattern as compared to the observed one. However, almost all models have positive correlation over the peninsular India. The basic idea of MME approach is to generate a single prediction from the predictions from different models. The MME approach shows positive correlation over the peninsular and central India. [ABSTRACT FROM AUTHOR]

Seasonal predictions of rainfall during the Indian monsoon from the North American Multi-Model Ensemble (NMME) have been used to compute the prediction skill. Observed and model-predicted rainfall have been put in three categories (namely below-normal, above-normal, and near-normal) based on the value of interannual standard deviation. In this study, focus is on the rainfall in below-normal rainfall category over central India. Forecast products were evaluated against the observed rainfall data for the period from 1982 to 2009. Simple model averaging and singular value decomposition (SVD) methods have been used to prepare the multi-model ensemble (MME) predictions. The root mean square error over central India is very less in the MME predictions compared to the member models. The weighted MME scheme using SVD method only marginally improves the skill over the simple MME scheme. The deterministic forecast skill is low (the correlation coefficient is non-significant) over central India even when the SVD scheme is employed. Both parametric and non-parametric methods have been applied to prepare probabilistic forecasts using multi-model ensemble for seasonal prediction. The probabilistic forecasts are reliable and usable as the hit rate is more than the false alarm rate for below-normal rainfall category. [ABSTRACT FROM AUTHOR]

The precipitation during winter (December through February) over India is highly variable in terms of time and space. Maximum precipitation occurs over the Himalaya region, which is important for water resources and agriculture sectors over the region and also for the economy of the country. Therefore, in the present global warming era, the realistic prediction of winter precipitation over India is important for planning and implementing agriculture and water management strategies. The National Centers for Environmental Prediction (NCEP) issued the operational prediction of climatic variables in monthly to seasonal scale since 2004 using their first version of fully coupled global climate model known as Climate Forecast System (CFSv1). In 2011, a new version of CFS (CFSv2) was introduced with the incorporation of significant changes in older version of CFS (CFSv1). The new version of CFS is required to compare in detail with the older version in the context of simulating the winter precipitation over India. Therefore, the current study presents a detailed analysis on the performance of CFSv2 as compared to CFSv1 for the winter precipitation over India. The hindcast runs of both CFS versions from 1982 to 2008 with November initial conditions are used and the model's precipitation is evaluated with that of India Meteorological Department (IMD). The models simulated wind and geopotential height against the National Center for Atmospheric Research (NCEP-NCAR) reanalysis-2 (NNRP2) and remote response patterns of SST against Extended Reconstructed Sea Surface Temperatures version 3b (ERSSTv3b) are examined for the same period. The analyses of winter precipitation revealed that both the models are able to replicate the patterns of observed climatology; interannual variability and coefficient of variation. However, the magnitude is lesser than IMD observation that can be attributed to the model's inability to simulate the observed remote response of sea surface temperatures to all India winter precipitation. Of the two, CFSv1 is appreciable in capturing year-to-year variations in observed winter precipitation while CFSv2 failed in simulating the same. CFSv1 has accounted for less mean bias and RMSE errors along with good correlations and index of agreements than CFSv2 for predicting winter precipitation over India. In addition, the CFSv1 is also having a high probability of detection in predicting different categories (normal, excess and deficit) of observed winter precipitation over India. [ABSTRACT FROM AUTHOR]

ABSTRACT This study aims to analyse the skill of state-of-the-art of five general circulation models ( GCMs) in predicting winter precipitation over northern India. The precipitation in winter season (December, January and February) is very important for Rabi crops in north India, particularly for wheat, as it supplements moisture and maintains low temperature for the development of the crops. The GCM outputs (seasonal mean forecasts issued in November) from various organizations are compared with the observed high-resolution gridded rainfall data obtained from India Meteorological Department ( IMD). Prediction skill of such GCMs is examined for the period 1982-2009. The climatology, interannual standard deviation ( ISD) and correlation coefficients have been computed for the five GCMs and compared with observation. It is found that the models are able to reproduce the climatology and ISD to varying degrees; however, skill of predictions is too low. Multi-model ensemble ( MME) approaches have been employed. It is found that the weighted MME using multiple linear regression technique improves the prediction skill of winter precipitation over northern India. The teleconnection between the sea surface temperature ( SST) and winter precipitation revealed that the SST over the Pacific Ocean affects the precipitation over north India in winter season. While this observed feature is represented well by some models with high fidelity, most models are unable to respond to SST variations in the Pacific Ocean in a realistic manner. Lagged correlations between the north India rainfall and SST over the Niño-3.4 region reveal that only two of the five GCMs get the observed simultaneous teleconnection correctly. Furthermore, only one of these two models has the observed phase lag with the strongest correlation as observed. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study, canonical correlation analysis (CCA) has been used to statistically downscale the seasonal predictions of the Indian summer monsoon rainfall (ISMR) from a global spectral model. An extensive diagnostic study of the global model products and observed data for the period 1981-2008 indicates that while the predictions of rainfall anomalies have poor skill, the mean flow patterns are brought out reasonably well by the model. The model precipitation is found to be more strongly dependent on sea surface temperature over the Nino regions in the Pacific Ocean. However, the observed precipitation has a stronger links to winds at 850 hPa near the Somali coast than is evident in the model. On the basis of correlation maps, potential model predictors (specific humidity and zonal and meridional winds over different regions at different levels) are chosen for CCA for the prediction of ISMR. Using leave-three-out cross-validation technique, canonical coefficients are computed using 25 years data (as training period) for CCA model. With this, predictions from the CCA model have also been prepared for the period of 1981-2005 to evaluate the performance. In addition to the above, predictions are made for four independent years (2006-2009). An improvement in skill of the composite forecasts (obtained using all the predictors) in terms of interannual variability is noticed over some parts of east- and northeast India as well as many parts of peninsular region especially over west coast of India. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Rainfall in the month of July in India is decided by large-scale monsoon pattern in seasonal to interannual timescales as well as intraseasonal oscillations. India receives maximum rainfall during July and August. Global dynamic models (either atmosphere only or coupled models) have varying skills in predicting the monthly rainfall over India during July. Multi-model ensemble (MME) methods have been utilized to evaluate the skills of five global model predictions for 1982-2004. The objective has been to develop a prediction system to be used in real time to derive the mean of the forecast distribution of monthly rainfall. It has been found that the weighted multi-model ensemble (MME) schemes have higher skill in predicting July rainfall compared to individual models. Through the MME methods, skill of rainfall predictions improved significantly over eastern parts of India. However, there is a region over India where none of the models or the MME scheme has any useful skill. Similarly, there are few typical years in which the mean distribution of July rainfall cannot be predicted with higher skill using the available statistical post-processing methods. A simple MME probabilistic scheme has been utilized to show that skill of probabilistic predictions improved when the representation of mean of forecast distribution has better skill. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The Canonical Correlation Analysis (CCA) method has been used in this study for improving General Circulation Model (GCM) predicted rainfall over India during the southwest monsoon season. Hindcast runs for 27 years (1982-2008) from six GCM outputs are used. This statistical technique relates the pattern of multivariate predictor field (model rainfall) to the pattern of predictand fields (observed rainfall). It is found that the CCA method improves the skill of three of the GCMs at the all-India level. A noticeable improvement is also observed in the composite prediction with CCA as compared to the simple mean of raw GCM products. The skill of the composite prediction after applying CCA is higher compared to the simple mean of raw model products in several homogeneous zones such as the hilly areas, west central area and over some parts of northwest India. The possible reason for the improvement in the skill of some of the GCMs may be the similarity between the loading patterns of model predictions and the observed rainfall. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982-2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low. [ABSTRACT FROM AUTHOR]

The northeast (NE) monsoon season (October, November and December) is the major period of rainfall activity over south peninsular India. This study is mainly focused on the prediction of northeast monsoon rainfall using lead-1 products (forecasts for the season issued in beginning of September) of seven general circulation models (GCMs). An examination of the performances of these GCMs during hindcast runs (1982-2008) indicates that these models are not able to simulate the observed interannual variability of rainfall. Inaccurate response of the models to sea surface temperatures may be one of the probable reasons for the poor performance of these models to predict seasonal mean rainfall anomalies over the study domain. An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among these three schemes, SVD based MME has more skill than other MME schemes as well as member models. [ABSTRACT FROM AUTHOR]

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, multi-model 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, bias-corrected 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. [ABSTRACT FROM AUTHOR]

Performance of seven fully coupled models in simulating Indian summer monsoon climatology as well as the inter-annual variability was assessed using multi member 1 month lead hindcasts made by several European climate groups as part of the program called Development of a European multi-model ensemble system for seasonal-to-inter-annual prediction (DEMETER). Dependency of the model simulated Indian summer monsoon rainfall and global sea surface temperatures on model formulation and initial conditions have been studied in detail using the nine ensemble member simulations of the seven different coupled ocean-atmosphere models participated in the DEMETER program. It was found that the skills of the monsoon predictions in these hindcasts are generally positive though they are very modest. Model simulations of India summer monsoon rainfall for the earlier period (1959-1979) are closer to the 'perfect model' (attainable) score but, large differences are observed between 'actual' skill and 'perfect model' skill in the recent period (1980-2001). Spread among the ensemble members are found to be large in simulations of India summer monsoon rainfall (ISMR) and Indian ocean dipole mode (IODM), indicating strong dependency of model simulated Indian summer monsoon on initial conditions. Multi-model ensemble performs better than the individual models in simulating ENSO indices, but does not perform better than the individual models in simulating ISMR and IODM. Decreased skill of multi-model ensemble over the region indicates amplification of errors due to existence of similar errors in the individual models. It appears that large biases in predicted SSTs over Indian Ocean region and the not so perfect ENSO-monsoon (IODM-monsoon) tele-connections are some of the possible reasons for such lower than expected skills in the recent period. The low skill of multi-model ensemble, large spread among the ensemble members of individual models and the not so perfect monsoon tele-connection with global SSTs points towards the importance of improving individual models for better simulation of the Indian monsoon. [ABSTRACT FROM AUTHOR]

In the last thirty years great strides have been made by large-scale operational numerical weather prediction models towards improving skills for the medium range time-scale of 7 days. This paper illustrates the use of these current forecasts towards the construction of a consensus multimodel forecast product called the superensemble. This procedure utilizes 120 of the recent-past forecasts from these models to arrive at the training phase statistics. These statistics are described by roughly 107 weights. Use of these weights provides the possibility for real-time medium range forecasts with the superensemble. We show the recent status of this procedure towards real-time forecasts for the Asian summer monsoon. The member models of our suite include ECMWF, NCEP/EMC, JMA, NOGAPS (US Navy), BMRC, RPN (Canada) and an FSU global spectral forecast model. We show in this paper the skill scores for day 1 through day 6 of forecasts from standard variables such as winds, temperature, 500 hPa geopotential height, sea level pressure and precipitation. In all cases we noted that the superensemble carries a higher skill compared to each of the member models and their ensemble mean. The skill matrices we use include the RMS errors, the anomaly correlations and equitable threat scores. For many of these forecasts the improvements of skill for the superensemble over the best model was found to be quite substantial. This real-time product is being provided to many interested research groups. The FSU multimodel superensemble, in real-time, stands out for providing the least errors among all of the operational large scale models. [ABSTRACT FROM AUTHOR]