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The yellow stemborer, Scirpophaga incertulas , is a monophagous pest of rice, attacking the crop from its vegetative to reproductive stages. Microorganisms are crucial in influencing the insect’s life cycle, evolution, and ecology, presenting an avenue for understanding and improving management strategies. Present research employed advanced next-generation sequencing technology to investigate the microbiota of S. incertulas , a previously unexplored area for developmental stage associated microbial diversity. The study used 16 S rRNA V3–V4 region amplicon sequencing to determine the diversity of bacteria associated with different developmental stages of S. incertulas . Taxonomically, bacterial communities were classified into 25 phyla, encompassing 46 classes, 101 orders, 197 families, and 364 genera. The major phyla identified were Proteobacteria (39%), Firmicutes (39%), Actinobacteria (11%), and Bacteroidetes (7%), with Proteobacteria being the most predominant across all developmental stages except the larval stage, where Firmicutes took precedence. Moraxellaceae, Bacillaceae, Xanthomonadaceae, Sphingobacteriaceae, and Flavobacteriaceae were predominant families across all the developmental stages. However, in the egg and adult stages, the abundance of Bacillaceae was notably lower, whereas Prevotellaceae found significantly higher in adult stages. Dominant genera across all stages included Acinetobacter , Bacillus , Lactobacillus , Enterococcus , and Pseudomonas . The result showed that the highest number of Operational Taxonomic Units (OTUs) were in the larval stage (426 OTUs), the lowest in adults (251 OTUs), and the egg stage (254 OTUs). This suggests that the microbiota may play a role in the growth and development of S. incertulas . The predicted functional assessment of the associated S. incertulas microbiota revealed that the microbiota primarily participated in metabolic pathways, secondary metabolite biosynthesis, energy metabolism, signaling, and cellular processes. Our findings shed light on the significant variations in the microbial community and their predicted functions present in S. incertulas across developmental stages. The present study findings will help in developing novel microbiota-based management strategies.

We investigated the predatory potential and food preference of different life stages of Coccinella septempunctata L. for a nutritious aphid (mustard aphid, Lipaphis erysimi) and toxic aphid (cabbage aphid, Brevicoryne brassicae). We provided all the life stages of C. septempunctata with either L. erysimi or B. brassicae and found that the second, third and fourth instar larvae and adult females of this predator consumed daily greater numbers of L. erysimi. However, the first instar larvae and adult males consumed similar numbers of both of these aphids. In choice condition, each larva, adult males and females were each provided separately with a mixed aphid diet in three proportions (i.e. low: high, equal: equal and high: low densities of L. erysimi: B. brassicae). We hypothesized that life stages of C. septempunctata will prefer L. erysimi regardless of its proportions. Laboratory experiments supported this hypothesis only at the adult level in terms of high values of β and C preference indices. However, it rejects this hypothesis at the larval level, as larvae preferred B. brassicae when provided with certain combinations and showed no preference in a few combinations. We infer that mixtures of nutritious and toxic aphids may enable this ladybird to overcome any probable nutritional deficiency and/or reduce the toxicity of a toxic diet, especially for the larvae. Results of the treatment in which a high proportion of B. brassicae were consumed along with fewer L. erysimi indicates that a mixed diet could be better for the development of immature stages of C. septempunctata.

Background and Aims: Post-operative pain is a major concern for day care surgeries like laparoscopic cholecystectomy. This study aimed to compare the efficacy of ultrasound guided abdominal field blocks (USAFB) with port site infiltrations for post-operative analgesia in terms of quality of pain relief, opioid consumption and patient satisfaction for day care surgeries Methods : Eighty patients presenting for laparoscopic cholecystectomy were randomly allocated to two groups either to receive port-site infiltration of local anaesthetic (n = 40, Group A) or USAFB (n = 40, Group B group). Numeric rating scores (NRS) were measured postoperatively to primarily assess the pain severity and opioid requirements. Data were analysed using Chi-Square test/Fisher′s exact test for categorical data and Mann-Whitney test/unpaired t-test for quantitative data. Results: The study group (Group B) had significantly reduced NRS and opioid consumption over 24 h. The overall fentanyl consumption in patients receiving port infiltrations was approximately twice (200 ΁ 100 μg) as compared to patients in USAFB group (120 ΁ 74 μg) (P < 0.0001). Maximum fentanyl consumption was 400 μg (Group A) and 262 μg (Group B) over 24 h and the minimum requirement was 50 μg and zero, respectively. Conclusion : Superior post-operative analgesia was observed with USAFB which may help in minimising opioid-related adverse effects and facilitating faster recovery.

Extreme weather events such as cloudburst and thunderstorms are great threat to life and property. It is a great challenge for the forecasters to nowcast such hazardous extreme weather events. Mesoscale model (ARPS) with real-time assimilation of DWR data has been operationally implemented in India Meteorological Department (IMD) for real-time nowcast of weather over Indian region. Three-dimensional variational (ARPS3DVAR) technique and cloud analysis procedure are utilized for real-time data assimilation in the model. The assimilation is performed as a sequence of intermittent cycles and complete process (starting from reception, processing and assimilation of DWR data, running of ARPS model and Web site updation) takes less than 20 minutes. Thus, real-time nowcast for next 3 h from ARPS model is available within 20 minutes of corresponding hour. Cloudburst event of September 15, 2011, and thunderstorm event of October 22, 2010, are considered to demonstrate the capability of ARPS model to nowcast the extreme weather events in real time over Indian region. Results show that in both the cases, ARPS3DVAR and cloud analysis technique are able to extract hydrometeors from radar data which are transported to upper levels by the strong upward motion resulting in the distribution of hydrometeors at various isobaric levels. Dynamic and thermodynamic structures of cloudburst and thunderstorm are also well simulated. Thus, significant improvement in the initial condition is noticed. In the case of cloudburst event, the model is able to capture the sudden collisions of two or more clouds during 09–10 UTC. Rainfall predicted by the model during cloudburst event is over 100 mm which is very close to the observed rainfall (117 mm). The model is able to predict the cloudburst with slight errors in time and space. Real-time nowcast of thunderstorm shows that movement, horizontal extension, and north–south orientation of thunderstorm are well captured during first hour and deteriorate thereafter. The amount of rainfall predicted by the model during thunderstorm closely matches with observation with slight errors in the location of rainfall area. The temporal and spatial information predicted by ARPS model about the sudden collision/merger and broken up of convective cells, intensification, weakening, and maintaining intensity of convective cells has added value to a human forecast.

For the accurate and effective forecasting of a cyclone, it is critical to have accurate initial structure of the cyclone in numerical models. In this study, Kolkata Doppler weather radar (DWR) data were assimilated for the numerical simulation of a land-falling Tropical Cyclone Aila (2009) in the Bay of Bengal. To study the impact of radar data on very short-range forecasting of a cyclone's path, intensity and precipitation, both reflectivity and radial velocity were assimilated into the weather research and forecasting (WRF) model through the ARPS data assimilation system (ADAS) and cloud analysis procedure. Numerical experiment results indicated that radar data assimilation significantly improved the simulated structure of Cyclone Aila. Strong influences on hydrometeor structures of the initial vortex and precipitation pattern were observed when radar reflectivity data was assimilated, but a relatively small impact was observed on the wind fields at all height levels. The assimilation of radar wind data significantly improved the prediction of divergence/convergence conditions over the cyclone's inner-core area, as well as its wind field in the low-to-middle troposphere (600–900 hPa), but relatively less impact was observed on analyzed moisture field. Maximum surface wind speed produced from DWR–Vr and DWR–ZVr data assimilation experiments were very close to real-time values. The impact of radar data, after final analysis, on minimum sea level pressure was relatively less because the ADAS system does not adjust for pressure due to the lack of pressure observations, and from not using a 3DVAR balance condition that includes pressure. The greatest impact of radar data on forecasting was realized when both reflectivity and wind data (DWR–ZVr and DWR–ZVr00 experiment) were assimilated. It is concluded that after final analysis, the center of the cyclone was relocated very close to the observed position, and simulated cyclone maintained its intensity for a longer duration. Using this analysis, different stages of the cyclone are better captured, and cyclone structure, intensification, direction of movement, speed and location are significantly improved when both radar reflectivity and wind data are assimilated. As compared to other experiments, the maximum reduction in track error was noticed in the DWR–ZVr and DWR–ZVr00 experiments, and the predicted track in these experiments was very close to the observed track. In the DWR–ZVr and DWR–ZVr00 experiments, rainfall pattern and amount of rainfall forecasts were remarkably improved and were similar to the observation over West Bengal, Orissa and Jharkhand; however, the rainfall over Meghalaya and Bangladesh was missed in all the experiments. The influence of radar data reduces beyond a 12-h forecast, due to the dominance of the flow from large-scale, global forecast system models. This study also demonstrates successful coupling of the data assimilation package ADAS with the WRF model for Indian DWR data.

Two events of extremely heavy rainfall occurred over Rajasthan during 7–9 August 2016 and 19–21 August 2016. Due to these events, flooding occurred over east Rajasthan and affected the normal life of people. A low-pressure area lying over northwest Madhya Pradesh on 7 August 2016 moved north-westward and lay near east Rajasthan and adjoining northwest Madhya Pradesh on 8 and 9 August 2016. Under the influence of this low-pressure system, Chittorgarh district and adjoining areas of Rajasthan received extremely heavy rainfall of 23 cm till 0300 UTC of 8 August 2016 and 34 cm on 0300 UTC of 9 August 2016. A deep depression lying over extreme south Uttar Pradesh and adjoining northeast Madhya Pradesh on 19 August 2016 moved westward and gradually weakened into a depression on 20 August 2016. It further weakened into a low-pressure area and lay over east Rajasthan on 21 and 22 August 2016. Under the influence of this deep depression, Jhalawar received 31 cm and Baran received 25 cm on 19 August. On 20 August 2016, extremely heavy rainfall (EHR) occurred over Banswara (23.5 cm), Pratapgarh (23.2 cm) and Chittorgarh (22.7 cm) districts. In this paper, the performance of the National Centers for Environmental Prediction (NCEP) global forecast system (GFS) model for real-time forecast and warning of heavy to very heavy/EHR that occurred over Rajasthan during 7–9 August 2016 and 19–21 August 2016 has been examined. The NCEP GFS forecast rainfall (Day 1, Day 2 and Day 3) was compared with the corresponding observed gridded rainfall. Based on the predictions given by the NCEP GFS model for heavy rainfall and with their application in real-time rainfall forecast and warnings issued by the Regional Weather Forecasting Center in New Delhi, it is concluded that the model has predicted the wind pattern and EHR event associated with the low-pressure system very accurately on day 1 and day 2 forecasts and with small errors in intensity and space for day 3.

In this study, both reflectivity and radial velocity are assimilated into the Weather Research and Forecasting (WRF) model using ARPS 3DVAR technique and cloud analysis procedure for analysis and very short range forecast of cyclone ÁILA. Doppler weather radar (DWR) data from Kolkata radar are assimilated for numerical simulation of landfalling tropical cyclone. Results show that the structure of cyclone AILA has significantly improved when radar data is assimilated. Radar reflectivity data assimilation has strong influence on hydrometeor structures of the initial vortex and precipitation pattern and relatively less influence is observed on the wind fields. Divergence/convergence conditions over cyclone inner-core area in the low-to-middle troposphere (600–900 hPa) are significantly improved when wind data are assimilated. However, less impact is observed on the moisture field. Analysed minimum sea level pressure (SLP) is improved significantly when both reflectivity and wind data assimilated simultaneously (RAD-ZVr experiment), using ARPS 3DVAR technique. In this experiment, the centre of cyclone is relocated very close to the observed position and the system maintains its intensity for longer duration. As compared to other experiments track errors are much reduced and predicted track is very much closer to the best track in RAD-ZVr experiment. Rainfall pattern and amount of rainfall are better captured in this experiment. The study also reveals that cyclone structure, intensification, direction of movement, speed and location of cyclone are significantly improved and different stages of system are best captured when both radar reflectivity and wind data are assimilated using ARPS 3DVAR technique and cloud analysis procedure. Thus optimal impact of radar data is realized in RAD-ZVr experiment. The impact of DWR data reduces after 12 h forecast and it is due to the dominance of the flow from large-scale global forecast system model. Successful coupling of data assimilation package ARPS 3DVAR with WRF model for Indian DWR data is also demonstrated.

While destruction associated with floods during the monsoon season and cyclones receives wide attention, the extreme weather in the form of hail, lightning and high winds have also caused widespread devastation over India on a small spatial scale in recent years, especially during the period of March to June. India Meteorological Department (IMD) organized a special forecast improvement campaign during the period March to June of 2017–2019 when the weather forecasts at all offices of IMD were targeted towards an accurate forecast of the extreme form of thunderstorms and their associated impact in short range to nowcasting timescale and their dissemination. The purpose of this study is to quantify the improvement in operational thunderstorm forecast accuracy, in short range (24 h Severe Weather Guidance at subdivisional level) and nowcast scale (nowcasts for individual stations valid for 3 h and issued every three hours) during March to June of 2017 to 2019 and compare the same with the accuracy of previous years. As a result of these efforts, there has been a significant jump in forecast accuracy in the 24-h thunderstorm forecast as well as 3-h nowcast guidance for thunderstorms across the country. Probability of Detection (POD) scores for India as a whole for the 24-h thunderstorm forecast has doubled, while the false alarms (FAR) have remained at the same level as before the start of the forecast campaign. The results indicate that since a thunderstorm is a disastrous weather event, the forecasters generally tend towards spatial over-forecasting. However, this is not uniform across the months. There is systematic lower accuracy in the season transition months of March (winter to summer) and June (dry summer to wet summer). While POD decreases in both March and June, FAR decrease throughout the season. The significant evolution of atmospheric parameters (moisture in particular) as the season changes, favours the maturation of thunderstorms to cumulonimbus stage as the season progresses, and the problem of over forecasting in March becomes a problem of under forecasting of thunderstorms in June. Another reason for false alarms is the unconscious linkage of the thunderstorm with the pattern of rainfall occurrence. However, since all rain-giving clouds over India do not necessarily mature to the cumulonimbus stage, and vice versa, the two are not always related. This is particularly true for the more arid regions of the country, especially in March, where false alarms are higher. The poor density of reporting observatories compared to the mesoscale nature of the events may also increase false alarms, especially over the small maritime islands and the arid regions of the mainland. The accuracy of the All India 3 hourly station level nowcast also improved systematically since 2017. Despite these constraints, the improvements at all scales were possible due to (a) augmentation of observation network by the rapid expansion of Doppler radars network throughout the Indian mainland as well as the installation of a ground-based lightning detection network, (b) numerical modeling products introduced in 2019 to provide short-range forecasts for all aspects of convection; both of which are incorporated into the forecast framework through Standard Operating Procedures (SOP) to standardize the forecast procedure throughout the Indian region. A more objective forecast strategy, using data generated from a denser network of DWRs and crowdsourcing methods as well as more accurate mesoscale models will go a long way to further improve the thunderstorm forecasts.

This paper demonstrates the usefulness of Indian Doppler Weather Radar (DWR) data for nowcasting applications, and assimilation into a mesoscale Numerical Weather Prediction (NWP) model. Warning Decision Support System Integrated Information (WDSS-II) developed by National Severe Storm Laboratory (NSSL) and Advanced Regional Prediction System (ARPS) developed at the Centre for Analysis and Prediction, University of Oklahoma are used for this purpose. The study reveals that the WDSS-II software is capable of detecting and removing anomalous propagation echoes from the Indian DWR data. The software can be used to track storm cells and mesocyclones through successive scans. Radar reflectivity mosaics are created for a land-falling tropical cyclone--Khaimuk of 14 November 2008 over the Bay of Bengal using observations from three DWR stations, namely, Visakhapatnam, Machilipatnam and Chennai. Assimilation of the quality-controlled radar data (DWR, Chennai) of the WDSS-II software in a very high-resolution NWP model (ARPS) has a positive impact for improving mesoscale prediction. This has been demonstrated for a land-falling tropical cyclone Nisha of 27 November 2008 of Tamil Nadu coast. This paper also discusses the optimum scan strategy and networking considerations. This work illustrates an important step of transforming research to operation.