Threat Score Research Articles

MalwareHunter: A Unified Antivirus Scanning Platform

This research paper aims to develop a website that allows users to upload files and scan them for malware. The website will use a variety of malware scanning technologies, including signature-based detection to identify any malicious code in the uploaded files. Upload a file for scanning, analysis your file with few antivirus products, dynamic analysis sandboxes and a myriad of other security tools to produce a threat score and relevant context to understand it. Get a file report by hash, given a {md5, sha1, sha256} hash, retrieves the pertinent analysis report including threat reputation and context produced by antivirus products, dynamic analysis sandboxes and a myriad of other security tools, and datasets. In the same manner the URL can also be scanned, analysis of the scanned URL with few antivirus products/blocklists and a myriad of other security tools occurs to produce a threat score and relevant context to understand it. Get an URL analysis report, given an URL, retrieves the pertinent analysis report including threat reputation and context produced by antivirus products/blocklists and a myriad of other security tools and datasets. For domain reports, get a domain report, given a domain, retrieves the pertinent analysis report including threat reputation and context produced by antivirus products/blocklists and a myriad of other security tools and datasets. Get an IP address report, given an IP address, retrieves the pertinent analysis report including threat reputation and context produced by antivirus products/ blocklists and a myriad of other security tools and datasets. Keywords: Upload, Malware, Sandbox, Hash, URLs, Signature-based detection.

How frequent and which variables of automatic weather station data should be assimilated into WRF-3DVar model? A case study of a squall line event in Beijing

Accurate precipitation forecasting is one of the most challenging problems in mesoscale Numerical Weather Prediction (NWP) models. The utilization of 3-Dimensional Variational (3DVar) Data Assimilation technique based on automatic weather station (AWS) data can significantly enhance the accuracy of precipitation simulations and forecasts using the Weather Research and Forecasting (WRF) model. However, the impacts of different assimilation frequencies and meteorological variables on the accuracy of precipitation forecasts remain unclear. This study comprehensively evaluated the impact of different assimilation frequencies and meteorological variables on precipitation forecasts, as illustrated by a case study of a squall line event on August 2, 2017, in Beijing. Eight experiments were conducted by assimilating different combinations of meteorological variables (air pressure, temperature, relative humidity, wind speed, and wind direction) at various time intervals (1 h, 3 h, and 6 h). The results indicated that the WRF model roughly simulated the evolution of this event but overestimated the precipitation amount, accompanied by a large deviation in precipitation areas. Assimilating detailed AWS data significantly improves the model performance in precipitation forecasts. The experiment assimilating all variables at a 3 h interval yielded the most accurate forecasts, with the maximum threat score (TS) increasing from 0.02 to 0.56. Higher assimilation frequencies do not guarantee a better performance. In practice, a 3 h assimilation frequency emerges as an optimal choice for AWS data assimilation. The assimilation of various variables enhanced precipitation forecasts to varying degrees, with the optimum results achieved when all variables were assimilated. Wind speed and direction were the most significant factors in dynamically enhancing the simulation of precipitation areas. Relative humidity and temperature influenced the precipitation intensity by affecting the evolution of convective precipitation thermodynamically. The findings of this study can contribute to the development of AWS data assimilation strategies for the WRF-3DVar model, thereby enhancing the precipitation forecast accuracy.

Improvement of disastrous extreme precipitation forecasting in North China by Pangu-weather AI-driven regional WRF model

In the realm of weather forecasting, the implementation of Artificial Intelligence (AI) represents a transformative approach. However, AI weather forecasting method still faces challenges in accurately predicting meso- and smaller-scale processes and failing to directly capture extreme precipitation due to regression algorithm’s nature, coarse resolution, and limitations in key variables like precipitation. Therefore, we propose a state-of-the-art technology which integrates the strengths of the Pangu-weather AI weather forecasting with the traditional regional weather model, focusing specifically on enhancing the prediction of extreme precipitation events, as mainly exemplified by an unprecedented precipitation in North China from 29 July to 1 August 2023, and an additional extraordinary precipitation event as a supplementary validation to further ensure the accuracy of this technology. The results show that the AI-driven approach exhibits superior performance in capturing the spatial and temporal dynamics of extreme precipitation events. Remarkably, with a threshold of 400 mm, the AI-driven model secures a Threat Score (TS) of 0.1 for forecast lead time reaching up to 8.5 d. This performance notably surpasses the performance of traditional GFS-Driven models, which achieve a similar TS only within a limited 3-day forecast lead time. This considerable enhancement in forecast accuracy, especially over extended lead times illustrates the AI-driven model’s potential to advance in long-term forecasts of extreme precipitation, previously considered challenging, emphasizing the potential of AI in augmenting and refining traditional weather prediction.

Application of Ensemble Algorithm Based on the Feature-Oriented Mean in Tropical Cyclone-Related Precipitation Forecasting

Tropical cyclones (TCs) are characterized by robust vortical motion and intense thermodynamic processes, often causing damage in coastal cities as they result in landfall. Accurately estimating the ensemble mean of TC precipitation is critical for forecasting and remains a foremost global challenge. In this study, we develop an ensemble algorithm based on the feature-oriented mean (FM) suitable for spatially discrete variables in precipitation ensembles. This method can adjust the locations of ensemble precipitation fields to reduce the location-related deviations among ensemble members, ultimately enhancing the ensemble mean forecast skill for TC precipitation. To evaluate the feasibility of the FM in TC precipitation ensemble forecasting, 18 landing TC cases in China from 2019 to 2021 were selected for validation. For precipitation forecasts of the landing TCs with a varying leading time, we conducted a comprehensive quantitative evaluation and comparison of the precipitation forecast skills of the FM and arithmetic mean (AM) algorithms. The results indicate that the field adjustment algorithm in the FM can effectively align with the TC precipitation structure and the location of the ensemble mean, reducing the spatial divergence among precipitation fields. The FM method demonstrates superior performance in the equitable threat score, probability of detection, and false alarm ratio compared with the AM, exhibiting an overall improvement of around 10%. Furthermore, the FM ensemble mean shows a higher pattern of the correlation coefficient with observations and has a smaller root mean square error than the AM ensemble mean, signifying that the FM method can better preserve the characteristics of the precipitation structure. Additionally, an object-based diagnostic evaluation method was used to verify forecast results, and the results suggest that the attribute distribution of FM forecast objects more closely resembles that of observed precipitation objects (including the area, longitudinal and latitudinal centroid locations, axis angle, and aspect ratio).

Insights into key threats and conservation status of the river lapwing, Vanellus duvaucelii (Lesson, 1826) in Northern India

Abstract The river lapwings are inhabitant of river banks with sand or gravel bars and river islands. In this study, we investigated vital threats (natural and anthropogenic) and the conservation status of river lapwing in the riverine ecosystem of Northern India. In this regard, we frequently visited selected study sites along the banks of the river Ganges in the district of Raebareli (Uttar Pradesh), India, from January 2016 to December 2019. To estimate perceived threats for river lapwing, we developed a questionnaire and collected threat scores. The line transect method was used to estimate the density of river lapwing. Predation and farming activities were the most potent threats influencing the survival and abundance of river lapwing. River lapwings were primarily observed at open unvegetated river banks and open unvegetated islands. They were seen in significant density near the water in the breeding season. We concluded that the population of river lapwing is relatively stable in the Gangetic plains of Northern India. However, it is declining in other parts of the world, for example, in Southern Laos. Though it is a relatively common species, robust scientific information about its population and habitat relation is mainly absent. Therefore, accurate counts from other parts of the world are needed to place the estimate for river lapwing into a comprehensible, inclusive perspective. Furthermore, detailed information on habitat relationships is also necessary to develop conservation strategies.

Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest

Precipitation is the basic component of the Earth’s water cycle. Obtaining high-resolution and high-precision precipitation data is of great significance. This paper establishes a precipitation retrieval model based on a random forest classification and regression model during the day and at night with FY-4B/AGRI Level1 data on China from July to August 2022. To evaluate the retrieval effect of the model, the GPM IMERG product is used as a reference, and the retrieval results are compared against those of the FY-4B/AGRI operational precipitation product. In addition, the retrieval results are analyzed according to different underlying surfaces. The results showed that compared with the FY-4B/AGRI operational precipitation product, the retrieval model can better identify precipitation and capture precipitation areas of light rain, moderate rain, heavy rain and torrential rain. Among them, the probability of detection (POD) of the day model increased from 0.328 to 0.680, and the equitable threat score (ETS) increased from 0.252 to 0.432. The POD of the night model increased from 0.337 to 0.639, and the ETS score increased from 0.239 to 0.369. Meanwhile, the precipitation estimation accuracy of the day model increased by 38.98% and that of the night model increased by 40.85%. Our results also showed that due to the surface uniformity of the ocean, the model can identify precipitation better on the ocean than on the land. Our findings also indicated that for the different underlying surfaces of the land, there is no significant difference in each evaluation index of the model. This is a strong argument for the universal applicability of the model. Notably, the results showed that, especially for more vegetated areas and areas covered by water, the model is capable of estimating precipitation. In conclusion, the precipitation retrieval model that is proposed herein can better determine precipitation regions and estimate precipitation intensities compared with the FY-4B/AGRI operational precipitation product. It can provide some reference value for future precipitation retrieval research on FY-4B/AGRI.

Assimilation of new rocket dropsonde data using WRFDA and its impact on numerical simulations of typhoon NORU

On September 26 at 2100 UTC and September 27 at 0900 and 2300 UTC, three rockets platform carrying dropsondes (TFTC-400) devices were launched off the east coast of Hainan Island to conduct a launch experiment aimed at detecting Typhoon NORU (2216). The experiment yielded valuable data that were subsequently analyzed to ascertain temperatures, wind speeds, and relative humidity in the atmosphere. Of the four experiments conducted, employing three distinct control variable configurations (CVs), we utilized the 3DVAR of WRF Data Assimilation (WRFDA) to assimilate rocket sounding data and the NCEP ADP Global Upper Air Observational Weather Data from the research data archive dataset that was jointly produced by the Center for Weather and Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). In one experiment, no data assimilation was performed (CTL). These experiments were designed to assess the impact of these observational datasets on typhoon predictions of the Weather Research & Forecasting Model (WRF) numerical simulations. Utilizing the assimilated background field, a 24-hour forecast was conducted, and the assimilation simulation was analyzed with regard to typhoon path, intensity, precipitation, and improvements in the background field. The results reveal that, on average, the three-assimilation experiment led to a 30 % reduction in track error compared to the CTL. Additionally, the assimilation experiment for CV7 of control variable configurations brought the maximum wind speed closer to observed data than the CTL between 6 and 12 h. The TS (threat score) evaluation of simulated 24-hour precipitation in the model domain indicates that the three assimilation schemes exhibit a degree of improvement in the forecast scores for 24-hour cumulative typhoon precipitation. Nevertheless, the simulation results for minimum sea-level pressure are unsatisfactory. To establish statistical significance, additional cases within the relevant region are necessary for result validation.

Diversity and conservation status of herbal formulations used to treat different diseases in a semi-arid to sub-humid subtropical region

IntroductionThe current research aimed to discover the diversity and conservation status of medicinal plants used in herbal formulations to treat different diseases in Shakardara and adjacent areas, of Khyber Pakhtunkhwa, Pakistan. MethodsData were collected using semi-structured questionnaires about the preparation of monoherbal and polyherbal recipes that were used locally. Fidelity level, use value index, preference ranking method, and rapid vulnerability assessment were used for data interpretation. Relevant statistical tools (ANOVA and SPSS software) were used for data analysis. ResultsA total of 74 medicinal plants from 39 families were recorded that were used in the preparation of monoherbal and polyherbal formulation. In the study area, 146 recipes were used, of which 101 were monoherbal and 45 were polyherbal. Seeds were the most usable part of plants (28%), followed by fruits (23%) and leaves (21%). The highest informant consensus factor (1.00) was recorded for nail protection and growth, while the lowest (0.83) for wound healing. Withania coagulans Dunal was reported with the highest fidelity level and Peganum harmala L. was the most vulnerable species in the study area, with the highest threat score. ConclusionsConserving the knowledge and resources associated with the herbal remedies is essential for both human health and ecological balance. Monoherbal and polyherbal formulations of medicinal plants highlight the potential of traditional herbal remedies as valuable sources of alternative medicine. Further research in this field is crucial to explore the therapeutic potential and ensure the safe utilization of these natural remedies in modern healthcare.

Spatiotemporal Convolutional Approach for the Short-Term Forecast of Hourly Heavy Rainfall Probability Integrating Numerical Weather Predictions and Surface Observations

Abstract The accurate prediction of short-term rainfall, and in particular the forecast of hourly heavy rainfall (HHR) probability, remains challenging for numerical weather prediction (NWP) models. Here, we introduce a deep learning (DL) model, PredRNNv2-AWS, a convolutional recurrent neural network designed for deterministic short-term rainfall forecasting. This model integrates surface rainfall observations and atmospheric variables simulated by the Precision Weather Analysis and Forecasting System (PWAFS). Our DL model produces realistic hourly rainfall forecasts for the next 13 h. Quantitative evaluations show that the use of surface rainfall observations as one of the predictors achieves higher performance (threat score) with 263% and 186% relative improvements over NWP simulations for the first 3 h and the entire forecast hours, respectively, at a threshold of 5 mm h−1. Noting that the optical-flow method also performs well in the initial hours, its predictions quickly worsen in the final hours compared to other experiments. The machine learning model, LightGBM, is then integrated to classify HHR from the predicted hourly rainfall of PredRNNv2-AWS. The results show that PredRNNv2-AWS can better reflect actual HHR conditions compared with PredRNNv2 and PWAFS. A representative case demonstrates the superiority of PredRNNv2-AWS in predicting the evolution of the rainy system, which substantially improves the accuracy of the HHR prediction. A test case involving the extreme flood event in Zhengzhou exemplifies the generalizability of our proposed model. Our model offers a reliable framework to predict target variables that can be obtained from numerical simulations and observations, e.g., visibility, wind power, solar energy, and air pollution.

Improvement of Albedo and Snow-Cover Simulation during Snow Events over the Tibetan Plateau

Abstract The snow albedo is a vital component of land–atmosphere coupling models. It plays a critical role in regulating land surface energy exchange by controlling incoming solar radiation absorbed by the land surface and influencing the timing and rate of snowmelt. Accurate snow albedo simulation is essential to obtain surface energy balance and snow-cover estimates. Here, the simulation of albedo and snow cover using the Weather Research and Forecasting Model and an improved snow albedo scheme is verified against satellite-retrieved products during and immediately following eight snowfall events over the Tibetan Plateau. The improved model successfully characterizes the spatial pattern and inverted U-shaped temporal pattern of albedo over the entire Tibetan Plateau. This is attributed to the local optimization of snow-age parameters and explicit consideration of snow depth in the improved scheme. Compared with the previous model, the model proposed herein greatly decreases the overestimated albedo (by 0.13–0.27), yielding a bias range of ±0.08, mean relative bias decrease of 70%, and significant increase in the spatial correlation coefficient of 0.03–0.39 (mean: 0.13). The significant improvements of albedo estimates appear in deep snow-covered regions, largely attributed to parameter optimization related to snow albedo decay, while less improvements appear over the shallow snow-covered regions. Accurate reproduction of the spatiotemporal variation in albedo alleviated snow-cover overestimation by small amounts. For snow-cover estimates, the improved model consistently decreases the false-alarm rate by 0.03, and increases the overall accuracy and equitable threat score by 0.04 and 0.03, respectively. Moreover, the improved scheme shows an equivalent improvement of albedo estimates at both 1- and 5-km grid spacing over the eastern Tibetan Plateau; this is also true for snow-cover estimates. Significance Statement Snow albedo schemes in widely used numerical weather prediction models show notable shortcomings in complex mountainous regions, hindering accurate surface energy balance and snow-cover prediction. The purpose of this study is to better understand the role of snow albedo on snow-cover estimates and reveal the application potential of an improved snow albedo scheme across the Tibetan Plateau. This is important because snow albedo influences the timing and rate of snowmelt, and in turn snow-cover estimates, through regulating the surface energy budget. Our results highlight the strong application potential of our improved scheme in reducing snow simulation errors, confirm the importance of snow depth on snow albedo, and provide a new perspective for improving the accuracy of snow forecast over the topographically high Tibetan Plateau.

Regional Cloud Forecast Verification Using Standard, Spatial, and Object-Oriented Methods

Abstract This study assesses the accuracy of the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) forecasts for clouds within stable and unstable environments (thereafter refers as “stable” and “unstable” clouds). This evaluation is conducted by comparing these forecasts against satellite retrievals through a combination of traditional, spatial, and object-based methods. To facilitate this assessment, the Model Evaluation Tools (MET) community tool is employed. The findings underscore the significance of fine-tuning the MET parameters to achieve a more accurate representation of the features under scrutiny. The study’s results reveal that when employing traditional pointwise statistics (e.g., frequency bias and equitable threat score), there is consistency in the results whether calculated from Method for Object-Based Diagnostic Evaluation (MODE)-based objects or derived from the complete fields. Furthermore, the object-based statistics offer valuable insights, indicating that COAMPS generally predicts cloud object locations accurately, though the spread of these predicted locations tends to increase with time. It tends to overpredict the object area for unstable clouds while underpredicting it for stable clouds over time. These results are in alignment with the traditional pointwise bias scores for the entire grid. Overall, the spatial metrics provided by the object-based verification methods emerge as crucial and practical tools for the validation of cloud forecasts. Significance Statement As the general Navy meteorological and oceanographic (METOC) community engages in collaboration with the broader scientific community, our goal is to harness community tools like MET for the systematic evaluation of weather forecasts, with a specific focus on variables crucial to the Navy. Clouds, given their significant impact on visibility, hold particular importance in our investigations. Cloud forecasts pose unique challenges, primarily attributable to the intricate physics governing cloud development and the complexity of representing these processes within numerical models. Cloud observations are also constrained by limitations, arising from both top-down satellite measurements and bottom-up ground-based measurements. This study illustrates that, with a comprehensive understanding of community tools, cloud forecasts can be consistently verified. This verification encompasses traditional evaluation methods, measuring general qualities such as bias and root-mean-squared error, as well as newer techniques like spatial and object-based methods designed to account for displacement errors.

Assessing the relative threats from Canadian volcanoes

We assessed 28 Canadian volcanoes in terms of their relative threats to people, aviation, and infrastructure. The methodology we used was developed by the United States Geological Survey for the 2005 National Volcano Early Warning System. Each volcano is scored on multiple hazard and exposure factors, producing an overall threat score. The scored volcanoes are assigned to five threat categories, ranging from Very Low to Very High. We developed a knowledge uncertainty score to provide additional information about assessed threat levels; this does not affect the threat scoring. Two Canadian volcanoes are in the Very High threat category (Mt. Garibaldi and Mt. Meager). Three Canadian volcanoes are in the High threat category (Mt. Cayley, Mt. Price, and Mt. Edziza) and one volcano is in the Moderate threat category (Mt. Silverthrone). We compare the ranked Canadian volcanoes to volcanoes in the USA and assess current levels of monitoring against internationally recognized monitoring strategies. We find that even one of the best-studied volcanoes in Canada (Mt. Meager) falls significantly short of the recommended monitoring level and is currently monitored at a level commensurate with a Very Low threat edifice. All other Canadian volcanoes are unmonitored (apart from falling within a regional seismic network). This threat ranking has been used to prioritize hazard and risk assessment targets and to help select monitoring activities that will most effectively address the undermonitoring of Canadian volcanoes.

Enhanced Tropical Cyclone Precipitation Prediction in the Northwest Pacific Using Deep Learning Models and Ensemble Techniques

This study focuses on optimizing precipitation forecast induced by tropical cyclones (TCs) in the Northwest Pacific region, with lead times ranging from 6 to 72 h. The research employs deep learning models, such as U-Net, UNet3+, SE-Net, and SE-UNet3+, which utilize precipitation forecast data from the Global Forecast System (GFS) and real-time GFS environmental background data using a U-Net structure. To comprehensively make use of the precipitation forecasts from these models, we additionally use probabilistic matching (PM) and simple averaging (AVR) in rainfall prediction. The precipitation data from the Global Precipitation Measurement (GPM) Mission serves as the rainfall observation. The results demonstrate that the root mean squared errors (RMSEs) of U-Net, UNet3+, SE-UNet, SE-UNet3+, AVR, and PM are lowered by 8.7%, 10.1%, 9.7%, 10.0%, 11.4%, and 11.5%, respectively, when compared with the RMSE of the GFS TC precipitation forecasts, while the mean absolute errors are reduced by 9.6%, 11.3%, 9.0%, 12.0%, 12.8%, and 13.0%, respectively. Furthermore, the neural network model improves the precipitation threat scores (TSs). On average, the TSs of U-Net, UNet3+, SE-UNet, SE-UNet3+, AVR, and PM are raised by 12.8%, 21.3%, 19.3%, 20.7%, 22.5%, and 22.9%, respectively, compared with the GFS model. Notably, AVR and PM outperform all other individual models, with PM’s performance slightly better than AVR’s. The most important feature variables in optimizing TC precipitation forecast in the Northwest Pacific region based on the UNet-based neural network include GFS precipitation forecast data, land and sea masks, latitudinal winds at 500 hPa, and vertical winds at 500 hPa.

The Retrieval Relationship between Lightning and Maximum Proxy Reflectivity Based on Random Forest

Using the SWAN (Severe Weather Automatic Nowcasting) maximum reflectivity mosaic product and the lightning positioning observations (LPOs) from the ADTD (Advanced Direction and Time of Arrival Detection) system obtained during the 2018–2020 warm season (May to September), adding multi-characteristic LPO parameters in addition to lightning density, the retrieval relationship between lightning and maximum proxy reflectivity, deemed FRST, is constructed by using random forest. The FRST is compared with two empirical relationships from the GSI (Gridpoint Statistical Interpolation) assimilation system, and the results show that the FRST retrieved result better reflects the frequency distribution structure and peak interval of maximum reflectivity. The correlation coefficient between the FRST retrieved result and the observed maximum reflectivity is 0.7037, which is 3.38 (3.12) times greater than that of empirical GSI relationships. The root mean square error and the mean absolute error are 50.85% (28.05%) and 57.15% (35.19%) lower than those for the empirical GSI relationships, respectively. The equitable threat score (ETS) and bias score (BIAS) for FRST are better than those of the empirical GSI relationships in all three maximum reflectivity intervals.

Analisis Strategi Pemasaran dalam Meningkatkan Omzet Penjualan pada Usaha Apotek Aditara 2 Surabaya

The purpose of this research is to analyze and understand the implementation of the best marketing strategies at Apotek Aditara 2 in Surabaya. This research employs a descriptive method with a qualitative approach implemented through SWOT analysis and data collection techniques involving in-depth interviews, observations, and documentation related to the 7P marketing strategy, namely product, place, price, promotion, people, process, and physical evidence. The IFAS and EFAS analyses indicate a strength score of 2.23 and a weakness score of 0.77. Meanwhile, the EFAS results show an opportunity score of 2.56 and a threat score of 0.72. Based on the SWOT Analysis diagram, Apotek Aditara 2 is positioned in quadrant 1, applying an aggressive strategy by leveraging its strengths and opportunities. The applicable SO strategy for Apotek Aditara 2 in Surabaya includes maintaining a complete and varied product supply, preserving the cleanliness and tidiness of the pharmacy, sustaining preventive healthcare services, upholding good service provision, and establishing collaborations with health centers and doctor's practices.

Statistical post-processing of multiple meteorological elements using the multimodel integration embedded method

Statistical post-processing of systematic errors is required for numerical weather predictions to obtain accurate and credible forecasts. Traditionally, this is accomplished separately with different individual models and for one specific element of focus. Here, a promising new method is proposed for the post-processing of meteorological elements output by the European Centre for Medium-Range Weather Forecasts (ECMWF), based on the integration of several different models. For 24-h precipitation, 2-m temperature, and 10-m wind speed, our new method, called the Multimodel Integration Embedded Method (MMIEM), outperformed the single models in terms of several skill scores, while being computationally more convenient. The mean average error of the MMIEM post-processed daily maximum 2-m temperature, minimum 2-m temperature, and maximum 10-m wind speed, was 18%, 26% and 29% lower than that of the raw ECMWF forecast, respectively. Also, compared with ECMWF, the threat score of the rainstorm forecast was improved by 9%. Key attributions to this improvement were the use of multiple models containing different advantages, with help from the embedding process and the interaction of multiple features in the model training. Furthermore, MMIEM can be extended to other statistical and forecast problems. It is anticipated that, with ever-increasing amounts of data, machine learning methods can transform the post-processing of numerical weather forecasts by gaining insight into the importance of different meteorological elements.

Sensitivity analysis of different parameterization schemes of the Weather Research and Forecasting (WRF) model to simulate heavy rainfall events over the Mahi River Basin, India

A reliability-based assessment of heavy rainfall events can provide insights for developing appropriate measures and predictions to control flood risk. One of the contemporary challenges is to model heavy rainfall events, which depend on different synoptic conditions and model configurations. In this study, the regional mesoscale Weather Research and Forecasting (WRF) model was used to evaluate the applicability of different parameterization schemes for simulating rainfall events over the Mahi River basin between 22 and 26 August 2020 with the maximum rainfall occurring on 23 August 2020. A high-resolution triple-nested (27, 9, and 3 km) WRF model was configured, and data for initial boundary conditions were obtained from NCEP/NCAR FNL with a resolution of 1° × 1° (six-hourly). The model results were evaluated using gridded rainfall of ECMWF Reanalysis v5 (ERA5) and India Meteorological Department (IMD) at a spatial resolution of 0.25° × 0.25°. Further to verify the model output, GPM-IMERG was used to compute the skill score (bias, threat score, probability of detection, and accuracy) metrics at 24 hourly and 3 hourly scales. Eight sensitivity tests were conducted to identify the optimal sets of microphysics (WSM6 and Goddard), cumulus (Kain and BMJ), and planetary boundary layer (YSU and ACM2) parameterization schemes for rainfall simulation of the selected events. The Kain-Goddard-YSU and Kain-Goddard-ACM2 parameterization schemes demonstrated the optimal performance for simulating the rainfall event. Relative humidity and reflectivity were used for analyzing moisture and cloud brightness temperature, respectively. The model score revealed that the Kain-Goddard scheme outperformed other schemes in case of heavy precipitation, with a threshold of 60 mm/day over the Mahi basin. The findings of this study showed that the simulation of heavy rainfall events with a set of selected combinations of parameterization schemes can reproduce the structure of convective organization as well as prominent synoptic features associated with heavy rainfall events over the Mahi basin. Moreover, this information may help to better understand the future forecast trend of precipitation and explore solutions for sustainable water management to support decision-makers and operational water managers.

A novel energy optimization framework to enhance the performance of sensor nodes in Industry 4.0

AbstractIndustry 4.0 is a term used to refer to the fourth industrial revolution, characterized by the introduction of new technologies, such as the Internet of Things, Big Data, and artificial intelligence (AI). As the number of connected devices in industrial settings grows, energy optimization of such sensors becomes increasingly essential. This paper proposes an energy optimization framework for sensor nodes in Industry 4.0. The framework is based on energy efficiency, energy conservation, and energy harvesting principles. It is designed to optimize the energy consumption of sensor nodes while maintaining their performance. The framework includes dynamic power management, scheduling, and harvesting techniques to reduce energy consumption while maintaining performance. In addition, the framework provides a comprehensive approach to energy optimization, including advanced analytics and AI to predict energy consumption and optimize energy use. The proposed model reached 96.93% sensitivity, 91.36% false discovery rate, 11.28% false omission rate, 90.12% prevalence threshold, and 91.24% threat score. The proposed framework is expected to improve the performance of sensor nodes in Industry 4.0, enabling increased efficiency and cost savings.

Study and application on the optimal quantile forecast of precipitation in an ensemble forecast system

AbstractQuantiles of precipitation are widely used in ensemble forecast systems. At present, the common practice is to provide precipitation amounts corresponding to different quantiles to users directly, which will make it difficult for users to extract reliable forecast information. Therefore, this study investigates the statistically optimal (using threat score (TS) as a metric) quantiles of precipitation in an ensemble forecast system constructed using the WRF V4.0 model. The main conclusions are as follows: The threat‐score‐optimal quantiles for light rain, moderate rain, heavy rain, rainstorm, and heavy rainstorm forecasts are 40%–60%, 60%–70%, 60%–80%, 70%–80%, and 80%, respectively. Overall, the optimal quantile increases with the rise in precipitation magnitude or the extension of forecast lead time. All the optimal quantile forecast products have higher TS than the corresponding control forecast, ensemble mean forecast, and probability‐matched ensemble mean forecast products. The merged threat‐score‐optimal quantile forecast product formed by combining the optimal quantile forecasts of different precipitation magnitudes shows obvious advantages over other products in statistical verification and case studies, and it shows good potential to be operationally implemented in the future.

Methods for Validating HRRR Simulated Cloud Properties for Different Weather Phenomena Using Satellite and Radar Observations

Abstract In this study, we evaluate the ability of the High-Resolution Rapid Refresh (HRRR) model to forecast cloud characteristics through comparison of observed and simulated satellite brightness temperatures (BTs) and radar reflectivity during different weather phenomena in December 2021: the Mayfield, Kentucky, tornado on 11 December, a heavy snow event in Minnesota from 10 to 11 December, and the Midwest derecho on 15 December. This is done to illustrate the importance of examining model accuracy across a range of weather phenomena. Observation and forecast objects were created using the Method for Object-Based Diagnostic Evaluation (MODE). HRRR accurately depicted the spatial displacements between observation cloud (defined using BTs) and radar reflectivity objects, namely, the centers of cloud objects are to the east of the radar objects for the tornado and derecho events, and generally west of the radar objects for the snow event. However, HRRR had higher (less intense) simulated BTs and higher (more intense) radar reflectivity than the observations for the tornado event. Simulated radar reflectivity is higher and BTs are lower than the observations during the middle of the snow event. Also, simulated radar reflectivity is higher and BTs are lower than the observations during the derecho event. Of the three weather events, the HRRR forecasts are most accurate for the snow event, based on the object-based threat score, followed by the derecho and tornado events. The tornado event has lower accuracy because matches between paired simulated and observation objects are worse than for the snow event, with less similarity in size forecast objects and greater distance between paired object centers. Significance Statement The purpose of this study is to assess the accuracy of forecast cloud and radar objects, defined using simulated satellite brightness temperatures and radar reflectivity, from the High-Resolution Rapid Refresh (HRRR) model. This assessment was conducted for a tornado, snow, and derecho event from December 2021. Results from these three events indicate that the HRRR model accurately represents the observed displacement between the center of cloud and radar objects for the tornado and derecho events, and is the most accurate overall for the snow event.