Operational Products Research Articles

A comprehensive global mapping of offshore lighting

Abstract. We present the first comprehensive multiyear global mapping of offshore lighting structures derived from low-light imaging satellite observations collected at night. The sensor is the day–night band (DNB) flown as part of the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS). The product merges two operational nighttime light products: VIIRS boat detection (VBD) data and VIIRS cloud-free nighttime light (VNL) data. The two products are spatially complementary, making it possible to fill gaps through a merger. Both product sets have an average DNB radiance layer, and the merger involves preserving the higher of the two average radiances. A wide range of lighting structures is present, i.e., fishing grounds, platforms, anchorages, gas flares, transit routes, and the glow surrounding bright lighting onshore. The richness of the numbers and types of offshore lighting structures can be traced back to the DNB spike detector at the core of the VBD algorithm. The VNL algorithm uses outlier removal to filter out biomass burning, an essential process for mapping electric lighting onshore. The outlier removal drops about 80 % of the offshore lighting detections. We expect that the new product will lead to an improved understanding of fishing grounds, offshore light pollution, and supply chain disruptions at anchorages, thereby aiding in the development of more sustainable and efficient practices. The global datasets are available at https://doi.org/10.25676/11124/179157 (Elvidge et al., 2024).

Optimization of Rubber Sheet Rolling Machine Parameters using a Taguchi-based TOPSIS Linear Programming Model

The Multi-Response Optimization (MRO) problem is a critical aspect of the engineering design, particularly in improving process efficiency and product quality. This study focuses on optimizing the parameters for a rubber sheet rolling machine, a vital component of Thailand's natural rubber industry. The objective is to enhance its operational efficiency and product consistency by addressing key criteria, such as production time and rubber sheet thickness. A novel approach integrating the Taguchi method and the Technique for Order Preference by Similarity to Ideal Solution Linear Programming (TOPSIS-LP) model is proposed. The Taguchi method systematically designs experiments, while the Preference by Similarity to Ideal Solution (TOPSIS) model consolidates multiple performance indicators into a single optimal solution. Optimal roller gaps of 4.5 mm, 3.0 mm, 2.0 mm, and 0.1 mm for the first, second, third, and fourth roller pairs, were, respectively, identified. The results demonstrated a reduction in rubber sheet thickness to 2.06 mm (5.94% improvement) and production time to 9.71 seconds per sheet (1.33% improvement) compared to the original settings. The qualitative analysis confirmed the robustness and reliability of the optimized parameters, achieving consistent results across various evaluation methods. This study presents a significant advancement in the MRO problem, offering a robust framework applicable to similar challenges in industrial settings. The findings provide a foundation for future automation and optimization efforts, driving sustainable improvements in the manufacturing efficiency and product quality.

Analisis Levelized Cost of Electricity (LCoE) pada PLTS Terapung dengan PLTS di Daratan: Study Kasus Pulau Kodingareng, Makassar Sulawesi Selatan

Indonesia, as an archipelagic country with over 17,000 islands, faces significant challenges in providing electricity access, particularly to small and remote islands. One promising solution is Solar Power Plants (PLTS), both land-based and floating solar plants that utilize water surfaces, suitable for areas with limited land availability. This study evaluates the economic feasibility of floating solar plants and land-based solar plants using the Levelized Cost of Electricity (LCoE) approach. LCoE encompasses all costs of construction, operation, maintenance, and energy production over the plant's lifespan, serving as a key benchmark in selecting power generation technologies. The findings show that the LCoE for floating solar plants is Rp11,197.00/kWh, lower than land-based solar plants at Rp11,769.00/kWh, although both exceed the electricity purchase price in Kodingareng, South Sulawesi, at Rp2,460.00/kWh. This difference is influenced by higher construction costs for floating solar plants but offset by greater energy output and lower operation and maintenance costs. This research provides a basis for developing solar power systems in archipelagic regions, emphasizing efficiency and sustainable energy solutions.

Benchmarking passive-microwave-satellite-derived freeze–thaw datasets

Abstract. Satellite-derived soil surface state has been identified to be of added value for a wide range of applications. Frozen versus unfrozen conditions are operationally mostly derived using passive microwave (PMW) measurements from various sensors and different frequencies. Products differ thematically, as well as in terms of spatial and temporal characteristics. All of them offer only comparably coarse spatial resolutions on the order of several kilometers to tens of kilometers, which limits their applicability. Quality assessment is usually limited to comparisons with in situ point records, but a regional benchmarking dataset is, thus far, missing. Synthetic aperture radar (SAR) offers high spatial detail and, thus, is potentially suitable for assessment of the operational products. Specifically, dual-polarized C-band data acquired by Sentinel-1, operating in interferometric wide (IW) swath mode with a ground resolution of 5 m×20 m in range and azimuth, provide dense time series in some regions and are therefore a suitable basis for benchmarking. We developed a robust freeze–thaw (FT) detection approach that is suitable for tundra regions, applying a constant threshold to the combined C-band VV (vertically sent and received) and VH (vertically sent and horizontally received) polarization ratios. The achieved performance (91.8 %) is similar to previous methods which apply an empirical local threshold to single-polarized VV backscatter data. All global products, tested with the resulting benchmarking dataset, are of value for freeze–thaw retrieval, although differences were found depending on the season, particularly during the spring and autumn transition. Fusion can improve the representation of thaw and freeze-up, but a multi-purpose applicability cannot be obtained since the transition periods are not fully captured by any of the operational coarse-resolution products.

Mitigating Supply Disruption Risks: Impact of Product Upgrading Strategy

Supply disruption is one of the crucial risks faced by supply chains in the changing external environment. To mitigate supply disruption risk and enhance resilience, product upgrading with Research and Development (R&D) investment is widely adopted by firms. In this paper, we establish a game-theoretic model to study the operation decisions and product upgrading strategies for a supply chain. Specifically, Firm 1 faces supply disruption risks when selling a normal product with a key component sourced from an unreliable supplier. To achieve supply resilience, the firm has an incentive to replace the unreliable supplier with product upgrading supported by R&D. It has an option to make high or low investments in R&D projects, indicating a high or low success rate. Our findings reveal that Firm 1 prefers to make investments in R&D for the upgraded product at a high investment level when the additional production cost of the upgraded product is relatively low or both the upgrading cost and supply disruption risk are relatively high. Extending our model to a duopoly scenario, we investigate the impact of competition on the product upgrading strategy selection. It is shown that a firm with a sufficiently large market share can drive its competitor out of the market by making R&D investments. Compared to the monopoly scenario, Firm 1 has a stronger incentive to upgrade the product under the duopoly scenario if its market share is relatively low. Finally, we identify the conditions where consumers get benefits from product upgrading. Our findings shed light on the importance of product upgrading strategy in a volatile business environment and provide insights into how firms can effectively mitigate supply disruption risk through R&D investments.

A Review on Thermal and Electrical Hazards in the Pharmaceutical Industry

Thermal and electrical hazards in the pharmaceutical industry pose a significant risk to operational safety, product integrity, and regulatory compliance. These risks arise from the high-temperature processes of exothermic reactions, electrostatic discharge, and electrical system malfunctions. As advanced technologies in continuous manufacturing and high-speed automation are increasingly integrated, the complexity and magnitude of these hazards are accelerating. According to research findings, thermal and electrical breakdowns represent over 60% of accidents in this sector, an aspect that emphasizes the greater need for advanced safety techniques. This paper compiles all current information that would define the causes and implications of hazards in this review. This focuses on many often neglected critical factors, including high energy content in processes, the volatile nature of solvents, and inadequately developed safety systems. The latest innovations in hazard mitigation, such as predictive maintenance using IoT-enabled sensors, dynamic thermal management systems, and AI-based fault detection algorithms, have demonstrated good potential for reducing incident rates. Additionally, adherence to evolving standards like ISO 45001 and advanced Occupational Safety and Health Administration (OSHA). compliance frameworks is essential for addressing these risks effectively. This review underscores the need to integrate advanced safety technologies and robust risk management frameworks by identifying gaps in current safety practices and proposing evidence-based strategies for ensuring sustainable and compliant pharmaceutical operations. Keywords: Thermal Hazards, Electrical Hazards, Pharmaceutical Industry Safety, Workplace Hazards in Pharma, Occupational Safety in Pharmaceuticals, Industrial Electrical Safety

Performance Measurement of Digitalisation Strategies by 21st Century Businesses: An Empirical Study

The current business scenario is a buzz with the concept of digitalization, influence companies to undergo quick transformations. However, it is essential to understand that every business progresses at its own pace and yield varied outcomes. The focus of technological transformation must revolve around optimization of operational processes, enrichment of customer experiences, and reshaping business models. Effective business transformation requires strong leadership from management. Digitalization plays an essential role in enhancement of performance of Indian businesses and overcoming financial difficulties as it offers alternate options of financing. The increased convenience to alternate finance has significantly elevated the operational performance, profitability, and productivity of SMEs in India. It's vital to note that digitalizing a business doesn't mean creating an entirely new entity. Rather, it involves remodelling present business to leverage present assets in novel ways. Companies worldwide aspire to invest in Digital India, recognizing it as a growth opportunity in the 21st century. A sample of 219 respondents was collected from different business sectors.

Improved GODAS reanalysis with MOM5 and impact of altimeter assimilation

The National Centers for Environmental Prediction (NCEP) produces operational reanalysis products based on the Global Ocean Data Assimilation System (GODAS) with the Modular Ocean Model (MOM3/MOM4p0d) as a physical model and 3D-Var as the data assimilation method. The Indian National Center for Ocean Information Services (INCOIS) also produces operational ocean analysis with the same GODAS with MOM4p0d. In this study, GODAS is upgraded with MOM5. The improved reanalysis is compared with respect to the EN4 and Ocean ReAnalysis System 5 (ORAS5) for the subsurface temperature and salinity. The microwave-based satellite-derived sea surface temperature (SST) is employed for the independent evaluation of the reanalysis SST products. We have assimilated observed temperature and salinity profiles from all in situ platforms over the global ocean and also assimilated along-track sea level anomaly from altimeter (Jason1 and Jason2) to produce the improved reanalysis. There is a significant improvement in the SST with biases of temperatures reduced from 1.5 to ~ 0.2 °C as compared to without assimilation. Moreover, a significant improvement is found in the subsurface temperature and salinity fields over the northwest Atlantic and Pacific Ocean, Nino 3.4, and Indian Ocean thermocline ridge regions. The biases in this new, improved reanalysis are even lower than ORAS5 when compared with EN4 analysis. Thermocline depth also shows improvement in terms of better representation and capturing seasonal variability. Altimeter assimilation further reduces Root Mean Square Deviation (RMSD) in SST over the global ocean. We also show the improvement in the new reanalysis with respect to the reanalysis based on MOM4p0d.

Evaluation and Adjustment of Precipitable Water Vapor Products from FY-4A Using Radiosonde and GNSS Data from China

The geostationary meteorological satellite Fengyun-4A (FY-4A) has rapidly advanced, generating abundant high spatiotemporal resolution atmospheric precipitable water vapor (PWV) products. However, remote sensing satellites are vulnerable to weather conditions, and these latest operational PWV products still require systematic validation. This study presents a comprehensive evaluation of FY-4A PWV products by separately using PWV data retrieved from radiosondes (RS) and the Global Navigation Satellite System (GNSS) from 2019 to 2022 in China and the surrounding regions. The overall results indicate a significant consistency between FY-4A PWV and RS PWV as well as GNSS PWV, with mean biases of 7.21 mm and −8.85 mm, and root mean square errors (RMSEs) of 7.03 mm and 3.76 mm, respectively. In terms of spatial variability, the significant differences in mean bias and RMSE were 6.50 mm and 2.60 mm between FY-4A PWV and RS PWV in the northern and southern subregions, respectively, and 5.36 mm and 1.73 mm between FY-4A PWV and GNSS PWV in the northwestern and southern subregions, respectively. The RMSE of FY-4A PWV generally increases with decreasing latitude, and the bias is predominantly negative, indicating an underestimation of water vapor. Regarding temporal differences, both the monthly and daily biases and RMSEs of FY-4A PWV are significantly higher in summer than in winter, with daily precision metrics in summer displaying pronounced peaks and irregular fluctuations. The classic seasonal, regional adjustment model effectively reduced FY-4A PWV deviations across all regions, especially in the NWC subregion with low water vapor distribution. In summary, the accuracy metrics of FY-4A PWV show distinct spatiotemporal variations compared to RS PWV and GNSS PWV, and these variations should be considered to fully realize the potential of multi-source water vapor applications.

Assessment of the relationship between body weight and body measurements among Morkaraman, Tushin and Awassi sheeps with different ages

This study aimed to identify morphological predictors of body weight in Morkaraman, Tushin, and Awassi sheep across different age groups, providing valuable insights for effective livestock management and breeding programs. Data were collected from 260 Morkaraman, 109 Tushin, and 96 Awassi sheep, with body measurements including body length (BL), wither height (WH), chest girth (CG), chest width (CW), chest depth (CD), rump width (RW), rump height (RH), ear length (EL), head length (HL), forehead width (FW), cannon forelimb circumference (CFC), and cannon hindlimb circumference (CHC). Pearson correlation analysis was conducted to assess relationships between body weight and these morphological traits. A multiple linear regression model was developed using the backward stepwise elimination method to estimate body weight based on the collected measurements. The results revealed that the Morkaraman breed and the 3-year-old age group had the highest average body weights. Among the morphological traits, CG (r = 0.702), RH (r = 0.694), BL (r = 0.678), and WH (r = 0.677) exhibited the strongest correlations with body weight. The final regression model identified CG, RW, RH, and HL as the most significant predictors, collectively explaining approximately 78.3% of the variation in body weight. These findings underscore the importance of selecting specific morphological traits for accurate body weight estimation in sheep. The results of this study have significant implications for sheep farming, particularly in enhancing breeding, management, and selection strategies. By focusing on key morphological predictors, livestock producers can improve decision-making processes, optimize resource allocation, and promote the overall productivity and sustainability of sheep farming operations.

An Analytical Study on Opportunities and Challenges towards Implementation of Industry 4.0 Tools for Manufacturing SMEs in Karnataka

In the era of Industry 4.0, there is a significant shift in manufacturing paradigms, offering substantial opportunities for operational efficiency, product quality, and supply chain integration. However, small and medium-sized enterprises (SMEs) in Karnataka face numerous challenges in adopting these advanced technologies. This study investigates the readiness and capacity of manufacturing SMEs in Karnataka to implement Industry 4.0 tools, focusing on financial capacity, workforce skills, infrastructure support, and comparative regional analysis. Data were collected from ten SMEs through structured questionnaires and discussions with officials, supplemented by secondary data from books, research papers, journals, annual reports, and websites. The data were analyzed using SPSS software, employing descriptive statistics, correlation analysis, and ANOVA. The findings reveal moderate to strong correlations between financial capacity, workforce skills, and infrastructure support, highlighting the interconnected nature of these factors. Despite uniform challenges across industries, Karnataka's SMEs demonstrate a high level of readiness and capacity for digital transformation, suggesting that broad-based initiatives could effectively support their adoption of Industry 4.0 technologies. The study provides actionable insights for policymakers and business leaders to facilitate the successful implementation of these advanced manufacturing tools.

Novel and cost-effective CNC tool condition monitoring through image processing techniques

CNC machining is an important part of the manufacturing industry. This paper introduces a novel and efficient approach for tool condition monitoring in CNC machine operations through the application of image processing techniques. By utilizing a consumer-grade camera capable of recording videos at 60 frames per second, the study demonstrates a cost-effective method to detect tool breakage and identify edge fractures. Basic image processing techniques, including frame extraction, background subtraction, thresholding, and morphological operations, are applied to analyze captured images and videos. This research not only offers a practical solution to enhance the efficiency and accuracy of CNC machine operations but also aligns with advancements in smart manufacturing and Industry 4.0. The findings will showcase the proposed system’s effectiveness in actual CNC machine environments, underscoring its potential to enhance maintenance strategies and operational productivity for small and medium-sized manufacturers without incurring high costs. Furthermore, it sets the stage for future investigations in this domain, indicating the possibility for enhancements through machine learning and an expanded application of these monitoring techniques.

Literature Review : The Role of Digital Technology in Enhancing Entrepreneurship in the Era of Industry 4.0

This study examines the role of digital technology in enhancing entrepreneurship in the Era of industry 4.0 through a systematic literature analysis of various sources from 2013 to 2023. By adopting this methodological approach, the research elucidates the benefits, challenges, and solutions associated with the adoption of digital technology for business practitioners, particularly in the micro, small and medium enterprises (MSMEs). The analysis demonstrates that digital technology significantly enhances operational efficiency, productivity, and market reach, while also fostering innovation in business models. However, challenges such as resource limitations, a lack of digital literacy, and the digital divide persist as significant obstacles. Furthermore, the study identifies strategic solutions to mitigate these challenges, including digital literacy training, technology subsidies, and improvements to digital infrastructure. The limitations of this research include a constrained data range from 2013 to 2023 and insufficient exploration of practical dimensions and ethical issues pertaining to the adoption of digital technology. Future research is encouraged to investigate these topics in greater depth, particularly within localized contexts and their implications for business sustainability. These findings underscore the critical role of digital technology as a fundamental enabler for developing competitive and sustainable entrepreneurship in Era 4.0.

TEMPO-Mediated Direct C(sp2)-H Alkoxylation/Aryloxylation of 1,4-Quinones.

A convenient and efficient transition-metal-free method has been developed for the C(sp2)-H alkoxylation/aryloxylation of 1,4-quinones by direct cross-dehydrogenative coupling with readily available alcohols and phenols in the presence of TEMPO under simple and mild conditions. The method allowed the installation of a wide range of alkoxy/aryloxy groups, exhibited high functional group tolerance, showed a broad substrate scope, afforded good to excellent yields of products in a simple one-pot operation, and could be performed on a gram scale. Mechanistic investigation indicated the involvement of the radical pathway.

Classification of palm oil fruit ripeness based on AlexNet deep Convolutional Neural Network

The palm oil industry faces significant challenges in accurately classifying fruit ripeness, which is crucial for optimizing yield, quality, and profitability. Manual methods are slow and prone to errors, leading to inefficiencies and increased costs. Deep Learning, particularly the AlexNet architecture, has succeeded in image classification tasks and offers a promising solution. This study explores the implementation of AlexNet to improve the efficiency and accuracy of palm oil fruit maturity classification, thereby reducing costs and production time. We employed a dataset of 1500 images of palm oil fruits, meticulously categorized into three classes: raw, ripe, and rotten. The experimental setup involved training AlexNet and comparing its performance with a conventional Convolutional Neural Network (CNN). The results demonstrated that AlexNet significantly outperforms the traditional CNN, achieving a validation loss of 0.0261 and an accuracy of 0.9962, compared to the CNN's validation loss of 0.0377 and accuracy of 0.9925. Furthermore, AlexNet achieved superior precision, recall, and F-1 scores, each reaching 0.99, while the CNN scores were 0.98. These findings suggest that adopting AlexNet can enhance the palm oil industry's operational efficiency and product quality. The improved classification accuracy ensures that fruits are harvested at optimal ripeness, leading to better oil yield and quality. Reducing classification errors and manual labor can also lead to substantial cost savings and increased profitability. This study underscores the potential of advanced deep learning models like AlexNet in revolutionizing agricultural practices and improving industrial outcomes.

Alternative approaches for bovine mastitis treatment: A critical review of emerging strategies, their effectiveness and limitations.

This review examined alternative treatments for bovine mastitis, driven by the rising issue of antibiotic resistance that limits the effectiveness of antibiotic therapies. As few new antibiotics are being developed, exploring non-antibiotic options is essential. Data were gathered by searching five databases, including PubMed, Scopus, Google Scholar, NCBI, and CABI, for articles on alternative treatments for bovine mastitis. Full texts of relevant studies published from 2013 onwards, both in vitro and in vivo, were screened and retrieved. A deductive approach was used to identify key themes from the review. Data were presented using tables and graphs created with R software for visualisation. Eligible studies included 69 articles tapered from an initial search of 1696 after removing duplicates and irrelevant records. Phytotherapy was found to be the most extensively researched approach, demonstrating significant effectiveness against mastitis pathogens, including multidrug-resistant Staphylococcus aureus, coagulase-negative S. aureus (CoNS), Escherichia coli, different species of Streptococci, though concerns about the degradation of active compounds in milk and variability in natural product composition exist. Bacteriophage therapy also showed promise, with studies indicating its effectiveness and low risk of inducing bacterial resistance. Treatments such as Trisodium citrate, pheromonicin-NM, and lactoferrin therapy exhibited statistically significant results, particularly against biofilm formation, a major challenge in mastitis management. Many of these treatments lacked extensive in vivo validation. The review highlights the geographic concentration of research, predominantly in countries like China and India, and emphasises the need for more standardised protocols to improve comparison across studies. The review highlighted phytotherapy, followed by bacteriophage therapy, as the next most researched alternative treatment for mastitis, effective against various pathogens despite concerns about compound degradation. Future research should prioritise the long-term effects of these therapies and their real-world effectiveness in enhancing dairy cow welfare and improving productivity in dairy operations.

Development Strategy of Agribusiness Teaching Factory Pilot at PSDKU Sidoarjo using SWOT Analysis and Lean Canvas Business Model

Abstract The Teaching Factory is a strategic initiative of Politeknik Negeri Jember for the year 2020-2024, implementing the one department, one teaching factory policy. The TEFA most suitable for establishment at Campus 4 is TEFA Agribusiness. This teaching factory adopts the structure of a minimarket, encompassing all aspects of agribusiness operations from processing to product marketing. The objective of this research was to develop a TEFA Agribusiness design strategy using the lean canvas business model and SWOT analysis. The research findings indicated that PSDKU Sidoarjo’s agribusiness teaching factory pilot has significant viability for development. The SWOT analysis classified the teaching factory startup as being in cell I, which represents growth and build. The techniques for implementation include intense operations (market penetration, market development, and product creation) and integrative approaches (backward integration, forward integration, and horizontal integration).

Sensitivity analysis of proppant transportation and settling in a hydraulic fracture

Hydraulic fracturing is the core technology for stimulating unconventional oil-gas reservoirs. The effective placement of proppant is directly linked to the success of the fracturing operation and reservoir productivity. In-depth investigations into the migration and settling behavior of proppant can provide a scientific foundation for optimizing fracturing parameters and fracture conductivity. Numerical simulations were employed to analyze the effects of many factors, such as the fracturing fluid velocity and viscosity, proppant size and density, and fracture surface roughness and injection position, on proppant migration within a planar fracture. Some interesting findings are: ① As the fracturing fluid velocity increases, the maximum height of the sand dune initially increases and then decreases, whereas the horizontal distance between the highest point of the sand dune and the entrance increases. ② Excessively high fracturing fluid viscosity results in a significant portion of the proppant remaining suspended within the fracturing fluid, thereby reducing the settling velocity and causing proppant to travel further. ③ Smaller proppant particles exhibit longer migration distances, making them more likely to travel further before settlement. ④ Proppant with lower densities exhibit superior transportability, with a larger proportion of the proppant remaining suspended in the fluid, resulting in more efficient fracture filling. ⑤ As the roughness of the fracture surface increases, the maximum height of the sand dune also increases, and fractures with rougher surfaces exhibit a greater sand dune area. ⑥ As the injection position decreases, the maximum height of the sand dune increases and then decreases.

Operational satellite cloud products need local adjustment – The Galapagos case of ecoclimatic cloud zonation

Operational satellite cloud products need local adjustment – The Galapagos case of ecoclimatic cloud zonation

Hybrid Numerical Weather Prediction: Downscaling GraphCast AI Forecasts for Downslope Windstorms

Abstract Preemptively managing electrical circuits during periods of elevated wildfire risk, such as downslope windstorms in complex terrain, necessitates accurate, high-resolution forecasts days in advance. Currently available high-resolution operational guidance is limited with respect to the forecasting period and/or spatial detail it can provide. As a consequence, many public utilities support their own NWP systems, primarily WRF-based, with initial conditions and boundary forcings supplied by operational global models such as the GFS. This study demonstrates pairing Google’s artificial intelligence (AI)-based GraphCast (GC) model, which operates on a 0.25° grid and produces global forecasts every 6 h, with a “last mile” WRF (WRF-LM) downscaling framework to refine GC’s spatial and temporal resolution for utility forecasting needs. We illustrate the potential of this system through a case study involving the December 2021 Marshall Fire windstorm in the Boulder, Colorado, area. Our results suggest that GC-initialized WRF forecasts are competitive with those guided by NCEP and ECMWF operational products, offering a promising hybrid approach for utility weather forecasting. Significance Statement Downslope windstorms are difficult to forecast with small errors potentially affecting the timing, magnitude, and location of concerning winds. Due to the elevated wildfire danger caused by these windstorms, accurate forecasts with lead times greater than 48 h are often needed to allow utilities time to prepare for the de-energization of transmission lines to reduce ignition risk. This paper examines methods of downscaling coarse synoptic-scale forecasts using the downslope windstorm associated with the 2021 Marshall Fire as our case study. A novel aspect of this work is the incorporation of machine learning weather prediction models and downscaling their forecasts using a high-resolution numerical model.