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The study reports diversity in nitrifying microbial enrichments from low (0·5-5‰) and high (18-35‰) saline ecosystems. Microbial community profiling of ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) enrichments was analysed by sequencing 16S rRNA and was processed using Mothur pipeline. The α-diversity indices showed the richness of nitrifying bacterial consortia from the high saline environment and were clustering based on the source of the sample. AOB and NOB enrichments from both the environments showed diverse lineages of phyla distributed in both groups with 38 and 34 phyla from low saline and 53 and 40 phyla in high saline sources, respectively. At class level, α- and γ-proteobacteria were found to be more dominant in both the enrichments. AOBs and NOBs in enrichments from low saline environments were dominated by Nitrosomonadaceae, Gallionellaceae (Nitrotoga sp.) and Ectothiorhodospiraceae and Nitrospira, respectively. Though Chromatiaceae were present in both AOB and NOB enrichments, Nitrosoglobus and Nitrosococcus dominated the AOBs while NOBs were dominated by uncultured genera, whereas Rhizobiales were found in both the enrichments. AOBs and NOBs in enrichments from high saline environments were dominated by Nitrospira-like AOBs, Nitrosomonas and Nitrosococcus genera, whereas ammonia-oxidizing archaea (AOA) group included Nitrosopumilus and Nitrososphaera genera comprising and Nitrospirae, respectively. The majority of the genera obtained in both the salinities were found to be either uncultured or unclassified groups. Results of the study suggest that the AOB and NOB consortia have unique and diverse microbes in each of the enrichments, capable of functioning in aquaculture systems practised at different salinities (0-60 ppt).

Splitting of the genus Penaeus sensu lato into six new genera based on morphological features alone has been controversial in penaeid shrimp taxonomy. Several studies focused on building phylogenetic relations among the genera of Penaeus sensu lato. However, they lack in utilizing full mitochondrial DNA genome of shrimp representing all the six controversial genera. For the first time, the present study targeted the testing of all the six genera of Penaeus sensu lato for phylogenetic relations utilizing complete mitochondrial genome sequence. In addition, the study reports for the first time about the complete mitochondrial DNA genome sequence of Fenneropenaeus indicus, an important candidate species in aquaculture and fisheries, and utilized it for phylogenomics. The maximum likelihood and Bayesian approaches were deployed to generate and comprehend the phylogenetic relationship among the shrimp in the suborder, Dendrobranchiata. The phylogenetic relations established with limited taxon sampling considered in the study pointed to the monophyly of Penaeus sensu lato and suggested collapsing of the new genera to a single genus. Further, trends in mitogenome‐wide estimates of average amino acid identity in the order Decapoda and the genus Penaeus sensu lato supported restoration of the old genus, Penaeus, rather promoting the creation of new genera., The maximum likelihood and Bayesian phylogenetic inferences based on complete mitochondrial DNA genome sequences supported monophyly of the genus, Penaeus sensu lato. The mitogenome‐based similarity metrics estimated for between‐genera within a family and also for between‐species within a genus in the order Decapoda corroborated phylogenetic inferences drawn for Penaeus sensu lato.

Maximum numerical weather prediction models have their own inherent biases and these biases have high impact on accuracy of weather forecast. Hence, bias correction is an essential part of any study for any model output datasets. The current study uses a weather research and forecasting (WRF) model, simulated daily precipitation of winter season (December to February: DJF) for the period of 2010–2011 to 2016–2017 (7 years) for the bias correction and validated against observed precipitation of Snow and Avalanche Study Establishment (SASE), India. For the first time, three different methods, i.e., empirical quantile mapping (QM), linear scaling (LS), and regression (REG) have been studied for the bias correction over the Northwest Himalaya region. In order to identify the best method out of these three, four statistical measurements, i.e., skill score (SS) and its decompositions, bias in percentage, root mean square errors (RMSE), and percentile values have been examined. Based on the analysis of SS and RMSE, it is worth to note that the QM method is found to be most suitable method for the December and February forecast of WRF model, whereas the LS approach is most suitable for the January forecast. Comparison based on Taylor’s diagram and percentiles via boxplot shows that the quantile mapping approach is most advisable for bias correction to the model simulated precipitation dataset over Northwest Himalaya region.

The accurate predictions of extreme precipitation/snowfall events are very helpful in identifying the severe avalanche/landslide prone hazard areas in advance over high mountainous regions. The Weather Research and Forecasting model (WRF) version 3.9 has been used to investigate the performance of Four-Dimensional Variational data assimilation (4D-Var) on Three-Dimensional Variational data assimilation (3D-Var) by considering two extreme snowfall events (23–26 January 2017 and 05–08 February 2019) over the Western Himalaya (WH). The result shows that the 4D-Var performed better than the 3D-Var for both the events by analyzing domain-averaged error and sensitivity parameter analysis. The initial state model variable’s domain-averaged error analysis revealed that 4D-Var has great potential to improve the initial conditions than the 3D-Var from lower to the upper atmosphere. Sensitivity parameter analysis also supports 4D-Var has more sensitive than the 3D-var especially in the lower and upper atmosphere by changing temperature and moisture fields along with winds circulations. From statistical skill scores analysis, 4D-Var performed well to reproduce the extreme snowfall events than the 3D-Var over WH.

Dynamical and Statistical models are operationally used by Snow and Avalanche Study Establishment (SASE) for winter precipitation forecasting over the Northwest Himalayas (NWH). In this paper, a statistical regression model developed for seasonal (December–April) precipitation forecast over Northwest Himalaya is discussed. After carrying out the analysis of various atmospheric parameters that affect the winter precipitation over the NWH two parameters are selected such as North Atlantic Oscillation (NAO) and Outgoing Long wave Radiation (OLR) over specific areas of North Atlantic Ocean for the development of statistical regression model. A set of 27 years (1990–1991 to 2016–2017) of observed precipitation data and parameters (NAO and OLR) are utilized. Out of 27 years of data, first 20 years (1990–1991 to 2009–2010) are used for the development of regression model and remaining 7 years (2010–2011 to 2016–2017) are used for the validation purpose. Precipitation over NWH mainly associated with Western Disturbances (WDs) and the results of the present study reveal that NAO during SON has negative relationship with WDs and also with the winter precipitation over same region. Quantitative validation of the multiple regression model, result shows good Skill Score and RMSE-observations standard deviation ratio (RSR) which is 0.79 and 0.45 respectively and BIAS − 0.92.

An analog ensemble system (AE system) is developed for real-time local-scale prediction of precipitation amount at ten stations in the Northwest Himalaya (NWH) utilizing surface-meteorological observations. Comparative evaluation of performance of the AE system with the other forecasting methods may allow to assess its strengths (weaknesses) and usefulness as a tool for real-time local-scale precipitation forecasting over the NWH. Performance of the AE system is evaluated and compared with the performances of the Weather Research and Forecasting Model (WRF model) and climatological forecast model (CL model). This is done by comparing dynamically downscaled precipitation at 10 km spatial resolution with the help of the WRF model, predicted quantitative precipitation amounts with the help of the AE system and CL model with the corresponding observations on precipitation amount at 10 stations in the NWH. Performances of all the three models are evaluated using measures-oriented and distributions-oriented forecast verification measures to get deeper insight into their performances. Results show that the AE system performs marginally poor as compared to the WRF model and it performs better than CL model for local-scale prediction of precipitation amount and/or weather over the NWH. These findings are interesting and encouraging, and suggest that the AE system can be a useful practical tool for real-time local prediction of precipitation amount and/or weather over the NWH. Local-scale real-time weather forecasts with the help of the AE system can be a large help for various applications directly linked to and influenced by weather over the NWH.

In recent years, owing to the advances in instrumentation, cryo-EM has emerged as the go-to tool for obtaining high-resolution structures of biomolecular systems. However, building three-dimensional atomic structures of biomolecules from these high-resolution maps remains a concern for the traditional map-guided structure-determination schemes. Recently, we developed a computational tool, Resolution Exchange Molecular Dynamics Flexible Fitting (ReMDFF) to address this problem by re-refining a search model against a series of maps of progressively higher resolutions, which ends with the original experimental resolution (Wang et al., J Struct Biol 204(2):319-328, 2018). In this chapter, we present a step-by-step outline for preparing, executing, and analyzing ReMDFF refinements of simple proteins and multimeric complexes. The structure determination of carbon monoxide dehydrogenase and Mg2+-channel CorA are employed as case studies. All scripts are provided via GitHub (Vant, Resolution exchange molecular dynamics flexible fitting (ReMDFF) all you want to know about flexible fitting, 2019, https://github.com/jvant/ReMDFF_Singharoy_Group.git ).

A localized extreme precipitation event occurred over Munsiyari (Uttarakhand, India) on 2nd July 2018 causing flash floods, landslides and damage to the hydropower project. A preliminary study has been carried out by using Weather Research and Forecasting (WRF) model with three-dimensional variation data assimilation technique (3DVAR) to examine the feasibility of the model to predict the localized phenomena. Sensitivity experiments were carried out with two different microphysics in the model. Results show that P3 1-category plus double moment cloud water microphysics scheme with 3DVAR in WRF simulates the quantity of precipitation closer to the observed precipitation over Munsiyari. The vertical velocity and relative humidity were also simulated well during 3DVAR data assimilation as compared to without data assimilation over study region.

In recent past, rainfall-induced debris flow events in Ladakh–Nubra region have caused loss of lives and damages to civil infrastructures and army locations. Therefore, there is a need of high spatial and temporal monitoring of precipitation, and further to assess susceptible rainfall-induced debris flow zones in the area. We assessed the rainfall data collected at two gauge stations and observed a significant increase in the rainfall amount over the study region during summer-monsoonal period 1997–2017. Increasing trend was also observed from CRU gridded precipitation dataset. A GIS-based multi-criteria evaluation (MCE) method was performed by combining topographical, environmental and hydrological parameters for mapping of rainfall-induced susceptible zones. Suitability analysis of precipitation forecasts from WRF model at higher resolution (3 km) was also performed. A good agreement (r = 0.76) was observed between 4-day model forecast and field observed rainfall. Further, the simulated precipitation from WRF was incorporated into GIS model for assessment of debris flow susceptible zones for two cases of heavy precipitation events. The modelled high, medium, low and very low risk susceptible zones identified for the year 2015 events are validated with field survey and pre-post satellite imageries, and found in good agreement (ROC = 76.6%). The model was able to identify affected areas during the Leh cloud burst event in year 2010. In addition, a threshold value of rainfall for initiation of debris flow in the region was also reported.

The current trends in diurnal temperature range, maximum temperature, minimum temperature, mean temperature, and sun shine hours over different ranges and altitudes of Western Himalaya during winter have been studied. Analysis of 25 years of data shows an increasing trend in diurnal temperature range over all the ranges and altitudes of Western Himalaya during winter, thereby confirming regional warming of the region due to present climate change and global warming. Statistical studies show significant increasing trend in maximum temperature over all the ranges and altitudes of Western Himalaya. Minimum temperature shows significant decreasing trend over Pir Panjal and Shamshawari range and significant increasing trend over higher altitude of Western Himalaya. Similarly, sunshine hours show significant decreasing trend over Karakoram range. There exists strong positive correlation between diurnal temperature range and maximum temperature for all the ranges and altitudes of Western Himalaya. Strong negative correlation exists between diurnal temperature range and minimum temperature over Shamshawari and Great Himalaya range and lower altitude of Western Himalaya. Sunshine hours show strong positive correlation with diurnal temperature range over Pir Panjal and Great Himalaya range and lower and higher altitudes.

Prior studies of microburst precipitation have largely relied on estimates of the spatial scale and temporal duration of the microburst region in order to determine the radiation belt loss rate of relativistic electrons. These estimates have often relied on the statistical distribution of microburst events. However, few studies have directly observed the spatial and temporal evolution of a single microburst event. In this study, we combine Balloon Array for Radiation belt Relativistic Electron Losses balloon-borne X-ray measurements with Focused Investigations of Relativistic Electron Burst: Intensity, Range, and Dynamics II and AeroCube-6 CubeSat electron measurements to determine the spatial and temporal evolution of a microburst region in the morning MLT sector on 13 August 2015. The microburst region is found to extend across at least 4 h in local time in the morning sector, from 09:00 to 13:00 MLT, and from L of 5 out to 10. The microburst event lasts for nearly 9 h. Smaller scale structure is investigated using the dual AeroCube-6 CubeSats, and is found to be consistent with the spatial size of whistler mode chorus wave observations near the equatorial plane.

The role of the Himalayan orography representation in a Regional Climate Model (RegCM4) nested in NCMRWF global spectral model is examined in simulating the winter circulation and associated precipitation over the Northwest India (NWI; 23°–37.5°N and 69°–85°E) region. For this purpose, nine different set of orography representations for nine distinct precipitation years (three years each for wet, normal and dry) have been considered by increasing (decreasing) 5, 10, 15, and 20% from the mean height (CNTRL) of the Himalaya in RegCM4 model. Validation with various observations revealed a good improvement in reproducing the precipitation intensity and distribution with increased model height compared to the results obtained from CNTRL and reduced orography experiments. Further it has been found that, increase in height by 10% (P10) increases seasonal precipitation about 20%, while decrease in height by 10% (M10) results around 28% reduction in seasonal precipitation as compared to CNTRL experiment over NWI region. This improvement in precipitation simulation comes due to better representation of vertical pressure velocity and moisture transport as these factors play an important role in wintertime precipitation processes over NWI region. Furthermore, a comparison of model-simulated precipitation with observed precipitation at 17 station locations has been also carried out. Overall, the results suggest that when the orographic increment of 10% (P10) is applied on RegCM4 model, it has better skill in simulating the precipitation over the NWI region and this model is a useful tool for further regional downscaling studies.

An analog ensemble system was developed for the realisation of local-scale surface meteorological variables for independent test data (test data) at six stations over the north-west Himalaya (NWH), India. Extreme values (the maximum value and the minimum value) and the mean value in 10 analog days (the analog mean) and the climatological mean of each surface meteorological variable were compared with its corresponding observed values on the same day (d0, lead time 0 hour (h)), d1 (d0 + 1, lead time 24 h), d2 (d0 + 2, lead time 48 h) and d3 (d0 + 3, lead time 72 h) of test data. Pearson correlation coefficients (CCs), Mean Absolute Differences (MADs) and Root Mean Square Differences (RMSDs) of the extreme values in analog days, and the analog mean and climatological mean of each meteorological variable on d0 with its corresponding observed values on d0, d1, d2 and d3 of test data were computed at six stations over the NWH. CCs of extreme values in analog days and the analog mean of each meteorological variable on d0 with its observed values on d0, d1, d2 and d3 were found to be higher than the CCs of the climatological mean of each meteorological variable on d0 with its observed values on d0, d1, d2 and d3. MADs (RMSDs) of extreme values in analog days and the analog mean of each meteorological variable on d0 with its observed values on d0, d1, d2 and d3 were found to be lesser than the MADs (RMSDs) of the climatological mean of each meteorological variable on d0 with its observed values on d0, d1, d2 and d3. However, the MADs (RMSDs) of the extreme values of each meteorological variable in analog days were found to be higher than the MADs (RMSDs) of its analog mean. Results show that the analog mean of each meteorological variable holds better predictive skill than the extreme values in analog days and its climatological mean. MADs (RMSDs) of different surface meteorological variables in surface weather analogs comparable to Mean Absolute Errors (MAEs) and Root Mean Square Errors (RMSEs) for their prediction with the help of different types of weather forecast models show that the surface weather analogs hold good promise for the local-scale prediction of surface meteorological variables over the NWH.

Eastward-moving upper air troughs in the subtropical westerlies, commonly known as the western disturbances (WDs) in the Asian subcontinent, are primary sources of precipitation over the north-west Himalayan (NWH) region and the northern plains of India during winter. Many simulation case studies with the help of numerical weather prediction models and a few observational case studies have been conducted to understand the spatial structure, dynamics, energy and weather associated with the WDs over the NWH in the past. However, studies using in situ observations on the impacts of the approaching WDs on various surface meteorological variables at a local scale in the high-altitude mountainous regions of the NWH are lacking. The objectives of this study are to examine the impacts of the approaching WDs on various surface meteorological variables for 11 stations in the NWH and the associated precipitation amount in a 24-hr time interval. Changes (departures) in the values of various meteorological variables are examined on the first precipitation day of the occurrence of active WDs to study the impacts of the approaching WDs and the associated precipitation amount in the 24-hr time interval which are found to depend on the altitude and geographic location of a station. The mean drop in the maximum and ambient air temperatures are found to be $$2.0{^{\circ }}$$ and $$0.7{^{\circ }}\hbox {C}$$ , respectively, while the mean rise in the minimum air temperature was found to be $$0.8{^{\circ }}\hbox {C}$$ . A mean drop in the surface atmospheric pressure and a mean increase in the relative humidity are found to be 0.9 hPa and 19.5%, respectively, in a 24-hr time interval. The mean precipitation amount and mean maximum precipitation amount associated with the active WDs in the 24-hr time interval are found to be 8.9 and 68.8 mm, respectively. The results are briefly discussed in the paper. The findings of this study can be useful for operational weather forecasting and a selection of precursor variables for developing a real-time local scale weather forecast model(s) for remote areas of the NWH for the winter season.

Road traffic safety is emerging as an area of increased attention and concern in many countries including India. Safety evaluation by conflict study using surrogate measures is a proactive method which does not require any accident data. Conflict study may be carried out using post encroachment time (PET) as a surrogate measure, where critical conflicts are determined based on certain threshold value of PET. Identifying critical conflicts using a threshold value of PET is good and correct for highways and major roads where traffic follows posted speed, but on highways with mixed traffic for varied speeds this is not correct. It can be seen that conflicts with PET less than the threshold value may not be critical if the speed of conflicting vehicle is less. Similarly, conflicts with PET more than threshold value may also be critical if the speed of conflicting vehicle is high. The speed of conflicting vehicle is also important in evaluating severity of conflict. Hence, the use of PET alone for conf...

This is the peer reviewed version of the following article: Tiwari, P. R., Kar, S. C., Mohanty, U. C., Dey, S., Sinha, P., Raju, P. V. S. and Shekhar, M. S., ‘On the dynamical downscaling and bias correction of seasonal-scale winter precipitation predictions over North India’, quarterly Journal of the Royal Meteorological Society, Vol. 142 (699):2398-2410, June 2016, which has been published in final form at DOI: https://doi.org/10.1002/qj.2832. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. © 2016 Royal Meteorological Society

This document is the Accepted Manuscript version of the following article: Tiwari, P.R., Kar, S.C., Mohanty, U.C. et al. Pure Appl. Geophys. (2016) 173: 657. https://doi.org/10.1007/s00024-015-1102-1. The final publication is available at Springer via: http://dx.doi.org/10.1007/s00024-015-1102-1. © Springer Basel 2015.

Relativistic Electron Precipitation (REP) in the atmosphere can contribute significantly to electron loss from the outer radiation belts. In order to estimate the contribution to this loss, it is important to estimate the spatial extent of the precipitation region. We observed REP with the zenith pointing (0o) Medium Energy Proton Electron Detector (MEPED) on board Polar Orbiting Environmental Satellites (POES), for 15 years (2000-2014) and used both single and multi satellite measurements to estimate an average extent of the region of precipitation in L shell and Magnetic Local Time (MLT). In the duration of 15 years (2000-2014), 31035 REP events were found in this study. Events were found to split into two classes; one class of events coincided with proton precipitation in the P1 channel (30-80 keV), were located in the dusk and early morning sector, and were more localized in L shell (dL

The impact of climate change over Indian Himalaya has received a great deal of attention worldwide. The present study focuses the recent winter climate, snow cover and albedo variability over north-west Himalaya (NWH). We analyze the trends of climate and snow cover variations in past two decades (1991–2011) and compare with the recent years 2001–2014, in three different snow climatic zones of NWH viz. Lower Himalaya (LH), Great Himalaya (GH) and Karakoram Himalaya (KH). The analysis of past two decades suggests that the maximum temperatures have increased and minimum temperatures have decreased in all the three zones of NWH. The trends of winter mean temperatures have been found increasing in LH and GH, and decreasing in KH. The increasing trend in diurnal temperature range (DTR) was observed over all the three zones and attributed to increasing maximum and decreasing minimum temperatures. Significant increasing trends in total precipitation (solid and liquid) were observed in LH and GH, and insignificant decreasing trend in KH. The field observed winter snow albedo of the same period suggest significant decreasing trend in GH and no trend in LH. The snow cover area (SCA) and albedo was monitored using MODIS sensor for the winter period 2001–14. These recent years (2001 onward) analysis suggests that the trends of SCA and snow albedo are insignificantly increasing over NWH. This has been supported by insignificant decreasing winter mean temperatures in all the three zones or vice-versa. These decreasing trend in temperature and increasing trends in SCA and albedo are attributed to recent ‘Hiatus’ in rising winter mean temperatures and thus global warming. The trends of these climatic variations in different periods also support the climate change impact on Himalayan snow cover and glaciers.

Face Recognition (FR) has been on the forefront of research efforts for the past two decades. In spite of considerable strides, it still suffers from the curse of false matches in the presence of variations in terms of parameters such as affine, scale, rotation and illumination. Since, real world images inherently consists of such variations, an effective FR system, should handle such variations deftly. Hence, in this paper, we propose a robust, yet simple and cost effective technique for overcoming some of the aforementioned challenges. The first stage of the proposed system deals with illumination variations by performing logarithm transform on the input face images. Further, the Non-subsampled Contourlet Transform (NSCT) is used to decompose the logarithm transformed facial images into low frequency and high frequency components. Subsequently, histogram equalization is carried out on the low frequency components. Finally, we employ Affine Scale Invariant Feature Transform (ASIFT) to find corresponding points that are translation and scale invariant. We will demonstrate by carrying out extensive experimentations on the benchmark datasets: ORL, Grimace, Face95 and Yale, that the proposed technique is more robust and yields comparable efficacy to most of the contemporary approaches.

The high Himalayan mountains in the north of India are important sources for generating and maintaining the climate over the entire northern belt of the Indian subcontinent. They also influence extreme weather events, such as the western disturbances over the region during winter. The work presented here describes some current trends in weather and climate over the western Himalaya and suggests some possible explanations in the context of climate change. The work also shows how the special features of Indian orography in the western Himalaya affect climate change in the long term, changing the pattern of precipitation over the region. Data analysis of different ranges of the western Himalaya shows significant variations in temperature and snowfall trends in the past few decades. Possible explanations for the changing climate over the western Himalaya are proposed, in terms of variations in cloudiness. The possible effects of climate change on the number of snowfall days and the occurrences of western disturbances over the western Himalaya are also analysed.

This paper describes a novel streamline-based rate allocation approach to maximize oil recovery from polymerfloods and its application to an ongoing polymerflood in the Mangala field, India which is one of the largest polymerfloods in the world. With over 130 injectors and producers, the field-scale optimization is challenging because of the long simulation times, operational constraints at the well, group and field level and changing field conditions.The FM-1 unit which is the main producing zone of the Mangala field comprises of multi-layered single storied fluvial channel sands of thickness ranging 3-10m with excellent flow characteristics. The gross thickness of FM-1 is about 90m. The sand is under a five-spot pattern polymerflood with a well spacing of aproximately 200m. A finite difference simulator is used for modeling the polymerflood performance with all relevant physics (mobility control, adsorption, residual resistance factor, polymer rheology/shear thinning, inaccessible pore volume and time-dependent degradation). Streamlines are generated from the flux field extracted from the finite difference simulation and are used to calculate pair-wise efficiency for each injector-producer streamline bundle. The pair-wise efficiency quantifies how much oil is recovered given a barrel of water injection. The streamline-based optimizer iteratively reallocates the fluids to injectors and producers by diverting the injected fluid to high efficiency pairs located in unswept oil regions. After optimization, injection efficiency plots as well as streamline/time of flight plots are used to examine and visualize the flow pattern changes leading to the improved flood performance.The reservoir model is first calibrated with 10 years of waterflood history. The polymer injection concentration and tapering schemes are determined based on extensive laboratory experiments. The optimization focuses on reallocating the well rates while maintaining the well, group and field level constraints. Two optimization scenarios were studied to examine the potential benefits of rate allocation: (1) optimization of rate allocation for both injectors and producers, (2) optimization of rate allocation for injectors only. Both the scenarios yield improved oil recovery (~5 MMSTB), accelerated production and reduced polymer usage (utility factor). The streamline and injection efficiency plots clearly reveal regions of improved sweep efficiencies, providing an intuitive explanation of the enhanced performance.