Direct massively parallel sequencing of SARS-CoV-2 genome was undertaken from nasopharyngeal and oropharyngeal swab samples of infected individuals in Eastern India. Seven of the isolates belonged to the A2a clade, while one belonged to the B4 clade. Specific mutations, characteristic of the A2a clade, were also detected, which included the P323L in RNA-dependent RNA polymerase and D614G in the Spike glycoprotein. Further, our data revealed emergence of novel subclones harbouring nonsynonymous mutations, viz. G1124V in Spike (S) protein, R203K, and G204R in the nucleocapsid (N) protein. The N protein mutations reside in the SR-rich region involved in viral capsid formation and the S protein mutation is in the S 2 domain, which is involved in triggering viral fusion with the host cell membrane. Interesting correlation was observed between these mutations and travel or contact history of COVID-19 positive cases. Consequent alterations of miRNA binding and structure were also predicted for these mutations. More importantly, the possible implications of mutation D614G (in S D domain) and G1124V (in S 2 subunit) on the structural stability of S protein have also been discussed. Results report for the first time a bird's eye view on the accumulation of mutations in SARS-CoV-2 genome in Eastern India.
This review provides a summary of work in the area of ensemble forecasts for weather, climate, oceans, and hurricanes. This includes a combination of multiple forecast model results that does not dwell on the ensemble mean but uses a unique collective bias reduction procedure. A theoretical framework for this procedure is provided, utilizing a suite of models that is constructed from the well-known Lorenz low-order nonlinear system. A tutorial that includes a walk-through table and illustrates the inner workings of the multimodel superensemble's principle is provided. Systematic errors in a single deterministic model arise from a host of features that range from the model's initial state (data assimilation), resolution, representation of physics, dynamics, and ocean processes, local aspects of orography, water bodies, and details of the land surface. Models, in their diversity of representation of such features, end up leaving unique signatures of systematic errors. The multimodel superensemble utilizes as many as 10 million weights to take into account the bias errors arising from these diverse features of multimodels. The design of a single deterministic forecast models that utilizes multiple features from the use of the large volume of weights is provided here. This has led to a better understanding of the error growths and the collective bias reductions for several of the physical parameterizations within diverse models, such as cumulus convection, planetary boundary layer physics, and radiative transfer. A number of examples for weather, seasonal climate, hurricanes and sub surface oceanic forecast skills of member models, the ensemble mean, and the superensemble are provided.A suite of models, each of which carry somewhat different representation of the above processes, can be combined to reduce the collective local biases in space, time, and for different variables from the different models. That is the theme of the multimodel superensemble. The multimodel superensemble (which is not an ensemble mean) utilizes as many as ten million weights toward the reduction of such systematic errors. This figure of 10 million comes from the products of the three-dimensional grid points, the number of dependent variables, the number of models, and the number of forecast intervals in time.The notion of the multimodel superensemble was first described in Krishnamurti et al. [1999]. This utilizes a training and a forecast phase. The training phase learns from the recent past performances of models and is used to determine statistical weights from a least square minimization via a simple multiple regression. That regression is carried out with respect to analyzed (assimilated) values. Given a number of grid locations, base variables, forecast intervals, and a suite of models, the number of statistical weights can be as high as 10 7 . That many coefficients are needed because of different responses to physical parameterizations of local features such as water bodies, local mountain features, and land surface details within d...
Forecasting tropical storm intensities is a very challenging issue. In recent years, dynamical models have improved considerably. However, for intensity forecasts more improvement is necessary. Dynamical models have different kinds of biases. Considering a multimodel consensus could eliminate some of the biases resulting in improved intensity forecasts as compared to the individual models. Apart from the ensemble mean, the construction of multimodel consensuses has always contributed to somewhat improved forecasts. The Florida State University (FSU) multimodel superensemble is one that, over the years, has systematically provided improved forecasts for hurricanes, numerical weather prediction, and seasonal climate forecasts. The present study considers an artificial neural network (ANN), based on biological principles, for the construction of a multimodel ensemble. ANN has been used for constructing multimodel consensus forecasts for tropical cyclone intensities. This study uses the generalized regression neural network (GRNN) method for the construction of consensus intensity forecasts for the Atlantic basin. Hurricane seasons 2012–16 are considered. Results show that with only five input models improved guidance for tropical storm intensities may be obtained. The consensus using GRNN mostly outperforms all the models included in the study and the ensemble mean. Forecast errors at the longer forecast leads are considerably less for this multimodel superensemble based on the generalized regression neural network. The skill and correlations of different models along with the developed consensus are provided in our analysis. Results suggest that this consensus forecast may be used for operational guidance and for planning and emergency evacuation management. Possibilities for future improvements of the consensus based on new advances in statistical algorithms are also indicated.
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