National Weather Service Research Articles

Improving the atmospheric dispersion forecasts over Washington, D.C. using UrbanNet observations: A study with HYSPLIT model

UrbanNet data have been used to enhance the predictions of the Weather Research and Forecasting (WRF) model through observational nudging to improve temperature and wind predictions. HYSPLIT was utilized to understand the impact of using the observationally nudged WRF fields in dispersion modeling. The meteorological observations collected from the National Weather Service monitoring stations located at two major airports in Washington metropolitan area were also assimilated into WRF modeling. The results showed that observational nudging successfully adjusted WRF wind fields towards the observations and significantly reduced the forecast temperature bias at nighttime. The comparison of HYSPLIT simulations with and without the enhancement of the WRF model using UrbanNet and airport data showed significant differences in the pattern and direction of the dispersion plume especially during the early morning hours. Furthermore, ingesting the data from the closer airport to the downtown area in WRF provided HYSPLIT simulations very similar to the ones using UrbanNet data. This provides strong evidence that local data are essential to adjust weather prediction models routinely used to drive dispersion models. There was also evidence of increased mixing height when using local data collected in the downtown, mainly resulting from the increased surface heating in the city.

The First Operational Version of Taiwan Central Weather Bureau’s One-Tier Global Atmosphere–Ocean Coupled Forecast System for Seasonal Prediction

Abstract The first version of the Taiwan Central Weather Bureau one-tier (TCWB1T) fully coupled global atmospheric and oceanic modeling forecast system had been developed and implemented as a routine operation for seasonal prediction at Central Weather Bureau (CWB) in 2017, with a minor revision in 2020. Based on NCEP CFSv1, the global atmospheric model in NCEP CFSv1 was replaced by CWB’s atmospheric global spectral model (GSM) and coupled with the GFDL MOM3. Several parameters have been tested and tuned in the CWB atmospheric GSM, achieving an optimal configuration with better sea surface temperature (SST) predictions for integration more than one year. Using NCEP CFSR as the initial condition, TCWB1T conducted hindcasts from 1982 to 2011 and forecasts from 2012 to 2019 to analyze its performance. The results of these hindcasts and forecasts show that the TCWB1T can make useful predictions as verified against the observations of OISST, ERSST, CFSR, and GPCP based on the methods of EOF, RMSE, anomaly correlation, ranked probability skill score (RPSS), reliability diagram (RD), and relative operating characteristics (ROCs). TCWB1T also has the same level of skill scores as NCEP CFSv2 and/or the ECMWF fifth-generation seasonal forecast system (SEAS5), based on EOF, anomaly pattern correlation, climatological bias, RMSE, temporal correlation, and anomaly correlation percentage of forecast skill. TCWB1T shows forecast skill that is better in winter than in summer. Overall, it indicates that TCWB1T can be used for seasonal ENSO predictions.

Homogenized gridded dataset for drought and hydrometeorological modeling for the continental United States

We present a novel data set for drought in the continental US (CONUS) built to enable computationally efficient spatio-temporal statistical and probabilistic models of drought. We converted drought data obtained from the widely-used US Drought Monitor (USDM) from its native geo-referenced polygon format to a 0.5 degree regular grid. We merged known environmental drivers of drought, including those obtained from the North American Land Data Assimilation System (NLDAS-2), US Geological Survey (USGS) streamflow data, and National Oceanic and Atmospheric Administration (NOAA) teleconnections data. The resulting data set permits statistical and probabilistic modeling of drought with explicit spatial and/or temporal dependence. Such models could be used to forecast drought at short-range, seasonal to sub-seasonal, and inter-annual timescales with uncertainty, extending the reach and value of the current US Drought Outlook from the National Weather Service Climate Prediction Center. This novel data product provides the first common gridded dataset that includes critical variables used to inform hydrological and meteorological drought.

Binge drinking associated with mean temperature: a cross-sectional study among Mexican adults living in cities

BackgroundThe association between environmental temperature and alcohol consumption has not been widely explored despite the potential that increasing temperatures could promote the consumption of alcoholic beverages and the alcohol-related burden of disease. We aimed to explore the association between temperature and binge drinking in Mexican adults from urban cities, overall, and by alcoholic beverage type.MethodsData on 10,552 adults ≥ 18 years was obtained from the 2016 National Survey on Drug, Alcohol, and Tobacco Consumption. The mean annual temperature at the municipality was obtained from the Mexican National Weather Service using monthly temperatures from 2015 to 2016. We analyzed binge drinking for all alcoholic beverages in the last year and by type of alcohol as beer, liquor, wine, and coolers. Associations between mean temperature over the past year and binge drinking over the past year among current drinkers were estimated using multilevel Poisson models with robust standard errors adjusted for age, sex, education level, marital status, and household socioeconomic status, with a fixed effect by region.ResultsWe observed a non-significant increase in the prevalence of binge drinking for every difference of 1 °C between municipalities of the same region. By alcohol type, a 1 °C increase in mean annual temperature across municipalities of the same region increased the prevalence of beer binge drinking in the past year by 0.9% (PR = 1.009, 95%CI 1.005, 1.013) among beer consumers and the prevalence of coolers’ binge drinking by 3.0% (PR = 1.030, 95%CI 1.003, 1.057) in coolers consumers. We observed non-significant results for liquor binge drinking (PR = 1.047, 95%CI 0.994, 1.102) and wine binge drinking (PR = 1.047, 95% 0.944, 1.161).ConclusionPeople living in municipalities with higher temperatures reported a higher beer binge drinking in Mexican cities. This could account for 196,000 cases of beer binge drinking in 2016. The context of each country needs to be considered when generalizing these findings, and they need to be further explored with longitudinal data as there might be implications for climate change. If our findings are confirmed given the forecasted rising temperatures, we could expect an increase in binge drinking and therefore, in the alcohol burden of disease.

SNN-PDE: Learning Dynamic PDEs from Data with Simplicial Neural Networks

Dynamics of many complex systems, from weather and climate to spread of infectious diseases, can be described by partial differential equations (PDEs). Such PDEs involve unknown function(s), partial derivatives, and typically multiple independent variables. The traditional numerical methods for solving PDEs assume that the data are observed on a regular grid. However, in many applications, for example, weather and air pollution monitoring delivered by the arbitrary located weather stations of the National Weather Services, data records are irregularly spaced. Furthermore, in problems involving prediction analytics such as forecasting wildfire smoke plumes, the primary focus may be on a set of irregular locations associated with urban development. In recent years, deep learning (DL) methods and, in particular, graph neural networks (GNNs) have emerged as a new promising tool that can complement traditional PDE solvers in scenarios of the irregular spaced data, contributing to the newest research trend of physics informed machine learning (PIML). However, most existing PIML methods tend to be limited in their ability to describe higher dimensional structural properties exhibited by real world phenomena, especially, ones that live on manifolds. To address this fundamental challenge, we bring the elements of the Hodge theory and, in particular, simplicial convolution defined on the Hodge Laplacian to the emerging nexus of DL and PDEs. In contrast to conventional Laplacian and the associated convolution operation, the simplicial convolution allows us to rigorously describe diffusion across higher order structures and to better approximate the complex underlying topology and geometry of the data. The new approach, Simplicial Neural Networks for Partial Differential Equations (SNN PDE) offers a computationally efficient yet effective solution for time dependent PDEs. Our studies of a broad range of synthetic data and wildfire processes demonstrate that SNN PDE improves upon state of the art baselines in handling unstructured grids and irregular time intervals of complex physical systems and offers competitive forecasting capabilities for weather and air quality forecasting.

Impact of Severe Winter Weather on Operations of a Radiation Oncology Department

IntroductionDuring winter of 2022, Western New York faced two major storms with blizzard conditions and record-breaking snowfall. The severe weather resulted in power outages, and travel bans. This study investigates the impact of these conditions on patient adherence to radiotherapy. Combining data from a large academic center and its satellite clinic, this single-center study is shedding light on the challenges faced by cancer care facilities during severe weather and proposing suggestions to prevent and mitigate harm done by severe weather. MethodsIn this study, data was collected using the MOSAIQ Record and Verify system (v. 2.81) to generate deidentified reports of scheduled and treated patients. The treatment adherence rate was calculated by dividing the number of patients treated by the total number of patients scheduled. Data was specifically collected for patients undergoing treatment on linear accelerators at a primary academic center and a satellite facility. The study focused on working days from November 1, 2022, to March 31, 2023, excluding weekends and holidays (as treatments are not routinely scheduled). Severe weather days were identified using advisories from the National Weather Service and local institution, including specific periods in November, December, and January. ResultsIn the study, 15,010 scheduled treatment visits were recorded across the academic center and the satellite clinic. The mean daily treatment adherence rate was 91.7%. Severe weather conditions led to a significant reduction in adherence, with rates dropping to 77.8%. Adherence rates during non-severe weather days were notably higher at 93.9%. Statistical analysis confirmed the substantial impact of severe weather on adherence (p<0.001). Severe weather had a more pronounced impact on the satellite clinic during periods of severe weather with absolute reduction in adherence rates of 21.9% vs. 15% in primary hospital). Moreover, adherence rates at the satellite clinic vs. primary hospital site was lower even under standard operating conditions (92.2% vs 94.0%, p<0.001). ConclusionAs a part of operational planning, it is important to be aware how severe weather can impact treatment adherence. The study findings underscore the importance of proactive measures to ensure patient access to healthcare services during adverse weather events and highlight the broader significance of incorporating Social Determinants of Health (SDOH) considerations into contingency planning for maintaining treatment continuity.

The impact of probabilistic tornado warnings on risk perceptions and responses.

Many warnings issued to members of the public are deterministic in that they do not include event likelihood information. This is true of the current polygon-based tornado warning used by the American National Weather Service, although the likelihood of a tornado varies within the boundaries of the polygon. To test whether adding likelihood information benefits end users, two experimental studies and one in-person interview study were conducted. The experimental studies compared five probabilistic formats, two with color and three with numeric probabilities alone, to the deterministic polygon. In both experiments, probabilistic formats led to better understanding of tornado likelihood and higher trust than the polygon alone, although color-coding led to several misunderstandings. When the polygon boundary was drawn at 10% chance, those using probabilistic formats made fewer correct shelter decisions at low probabilities and more correct shelter decisions at high probabilities compared to those using the deterministic warning, although overall decision quality, operationalized as expected value, did not differ. However, when the polygon boundary was drawn around 30%, participants with probabilistic forecasts had higher expected value. The interview study revealed that, although tornado-experienced individuals would not shelter at 10% chance, they would take intermediate actions, such as information-seeking and sharing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Hazard or Non-Hazard Flood: Post Analysis for Paddy Rice, Wetland, and Other Potential Non-Hazard Flood Extraction from the VIIRS Flood Products

VIIRS flood products have been widely used by the National Weather Service (NWS) for river flood monitoring, and by the Federal Emergency Management Agency (FEMA) and the International Charter Program for rescue and relief efforts. However, some water bodies, like irrigated or flooded paddy rice fields, and water in seasonal wetlands, are detected as floodwater instead of permanent, seasonal, controlled, or normal water in the VIIRS flood products. Because these kinds of floodwater don’t cause any severe disasters, they should be delineated as non-hazard floods to differentiate from real hazardous floods, the latter of which generally pose a significant risk and damage to human life, property, and the environment. Hazardous floods are usually caused by intensive rainfall, rapid snowmelt, or ice jams, they appear accidentally and tend to be short-term events. Non-hazard floodwaters generally result from tides, wetlands, agricultural irrigation, and other human activities, and thus they appear regularly and often stand for a longer time. Therefore, non-hazard floodwaters are some kind of standing water and usually have much larger long-time flood frequency or probability than hazardous floods. Based on this feature, it’s possible to combine long-time flood frequency calculated as the ratio of total flooding days over total clear days, including both flooding and no-flooding days, from VIIRS flood products with some additional information, such as agriculture statistics and land cover and land use map, to identify non-hazard floodwater in paddy rice fields and wetland. In this study, non-hazard floodwater bodies are classified into four types: paddy rice fields, wetlands, riparian and coastal tidal flooding, and other non-hazard floods, including possible fishing ponds. In this study, we selected several examples, such as the 2023 California flood event and some large paddy rice fields in China, to demonstrate how to identify water in paddy rice fields. The Florida flood event due to Hurricane Ian in 2022 is used to demonstrate how to separate wetlands from real hazardous floods. In addition to non-hazard floodwaters in wetlands, some coastal tidal flooding in Florida is also detected. The non-hazard floodwaters extracted from the proposed methods are compared with change detection analysis and good agreement is found. The paddy rice fields obtained from this study are compared and evaluated with the rice distribution data from the USDA. The quantitative evaluation results from the confusion matrix calculation demonstrate that the accuracy is 99.96%, recall is 83.69%, precision is 61.57%, and the F1-score is 70.95%. The high accuracy indicates that the method developed in this study for paddy rice field extraction from the VIIRS flood products is feasible.

Breathing on the job: investigating predictors of air quality protective actions and information seeking among outdoor workers

ABSTRACT Background Air quality issues, exacerbated by wildfire smoke and excessive ozone that is worsened by climate change, pose significant health risks to outdoor workers, who are often overlooked in regulatory protection and communication efforts. This study examined how outdoor worker demographics, risk perceptions, and efficacy beliefs predict air quality protective actions and information seeking. Additionally, it investigates the sources of information that this population relies on for understanding air quality. Method A survey was conducted with 256 outdoor workers in Colorado, a state regularly affected by wildfire smoke and ozone. Measures included demographics, perceived risk, efficacy beliefs, air quality actions, and information seeking behavior. Results Both perceived risk and efficacy beliefs influenced health-protective actions during poor air quality events. Interestingly, efficacy beliefs were found to be a more reliable predictor of air quality information seeking than perceived risk. The top sources of air quality information among outdoor workers were local news media, The Weather Channel, mobile apps, state public health authorities, and the National Weather Service. Conclusions These findings enhance our understanding of how perceived risk and efficacy beliefs promote health-protective behaviors among outdoor workers. They lay the groundwork for future research and initiatives to improve air quality communication and promote health-protective actions for this population group. Promoting the efficacy of health-protective actions and seeking information are important components of air quality communication.

Annual Daily Irradiance Analysis of Clusters in Mexico by Machine Learning Algorithms

The assessment of solar resources involves the utilization of physical or satellite models for the determination of solar radiation on the Earth’s surface. However, a critical aspect of model validation necessitates comparisons against ground-truth measurements obtained from surface radiometers. Given the inherent challenges associated with establishing and maintaining solar radiation measurement networks—characterized by their expense, logistical complexities, limited station availability and the imperative consideration of climatic criteria for siting—countries endowed with substantial climatic diversity face difficulties in station placement. In this investigation, the measurements of annual solar irradiation, from meteorological stations of the National Weather Service in Mexico, were compared in different regions clustered by similarities in altitude, TL Linke, albedo and cloudiness index derived from satellite images; the main objective is to find the best ratio of annual solar irradiation in a set of clusters. Employing machine learning algorithms, this research endeavors to identify the most suitable model for predicting the ratio of annual solar irradiation and to determine the optimal number of clusters. The findings underscore the efficacy of the L-method as a robust technique for regionalization. Notably, the cloudiness index emerges as a pivotal feature, with the Random Forest algorithm yielding superior performance with a R2 score of 0.94, clustering Mexico into 17 regions.

Atmospheric Rivers in Southeast Alaska: Meteorological Conditions Associated With Extreme Precipitation

AbstractExtreme precipitation events associated with atmospheric rivers (ARs) trigger floods, landslides, and avalanches that threaten lives and livelihoods in Southeast Alaska. Six rural and indigenous communities (Hoonah, Klukwan, Skagway, Yakutat, Craig, and Kasaan) identified specific needs regarding these hazards and joined the Southeast Alaska Coastlines and People (CoPe) Kutí Hub to address the shared challenge of understanding and predicting these events. This study presents a climatology (1980–2019) of synoptic, mesoscale, and local meteorological characteristics of ARs and heavy precipitation across this region. High‐amplitude upper‐level patterns across the northeastern Pacific Ocean favor ARs reaching Southeast Alaska, where moisture is orographically lifted, resulting in heavy precipitation. In the six communities, ARs occur 8–15 days per month, yet only 6 AR days per year account for up to 68%–91% of precipitation extremes. Furthermore, 80%–96% of days with extreme precipitation have &gt;75th percentile integrated water vapor transport (IVT), demonstrating the strong relationship between IVT and extreme precipitation. This study also highlights the relationship between IVT direction and complex coastal topography in determining precipitation extremes. For example, in Klukwan and Skagway, 80%–90% of extreme AR days have south‐southwesterly or south‐southeasterly IVT. Coastal communities like Yakutat experience higher IVT and precipitation overall, and although southeasterly IVT is more common, extreme precipitation events are most common with southwesterly IVT. Collaboration with the National Weather Service in Juneau, Alaska will lead to improved situational awareness, forecasts, and Impact Decision Support Services to communities, saving lives and property in a region vulnerable to the impacts of climate change.

Application of the Flood Guidance Model (FG) in the Yi River basin in Uruguay

This study focuses on the application/adaptation of the Flash Flood Guidance (FFG) model developed by the National Weather Service (NWS). The FFG is defined as the total amount of rainfall of a given duration sufficient to cause bankfull flow at the outlet of the watershed. Since flash floods do not occur in Uruguay due to the slower hydrologic response of the watershed, an adaptation of the above model is evaluated and renamed Flood Guidance (FG). Nine storm events that occurred between 2014 and 2020 in the Yi River basin in Uruguay were analyzed. The Natural Resources Conservation Service (NRCS) Curve Number model, a simple and widely used model, is used to determine effective precipitation. Two alternatives are considered to determine the antecedent soil moisture: the use of an antecedent precipitation index (API), and the use of SMOS satellite imagery. The FG is then calculated and compared to the precipitation forecast to determine if there is a risk of flooding at the watershed outlet. The results are compared to observations to evaluate the quality of the warning forecast. The application for forecasting flooded rural and agricultural areas using Sentinel-2 satellite imagery and the NDWI index is considered and analyzed.

Seasonal forecasts of the world’s coastal waterline: what to expect from the coming El Niño?

The central-eastern tropical Pacific is currently significantly warmer than normal, and the likelihood of a strong El Niño developing by early 2024 is 75–85%, according to the National Weather Service’s Climate Prediction Center. Disruptions in ecosystem services and increased vulnerability, in particular in the coastal zones, are expected in many parts of the world. In this comment, we review the latest seasonal forecasts and showcase the potential for predicting the world’s coastlines based on data-driven modeling.

Forecasters, emergency managers, and residents: Building better risk communication.

The US National Weather Service (NWS) and emergency managers (EMs) around the country are tasked with communicating severe weather information to the public. Frequent interaction between professionals and residents is essential to building effective partnerships. This paper investigates these interactions and also explores the perspectives of NWS forecasters, EMs, and rural residents related to the efficacy of warning communication, message understanding, preferred platforms, and engagement in protective actions. Data for this study were collected through three original survey instruments that were directed to NWS forecasters and EMs across the country, and residents in four rural communities. Findings reveal that residents generally understand warning messages and generally feel tornado risk communication is effective in their communities. However, residents do not appear to have a plan of action formulated prior to a warning and are, therefore, making, rather than implementing, a plan when warning is issued. This study gives rural residents a voice in the warning communication process and a chance for forecasters and EMs to gain valuable information as they better plan to serve these communities.

Quantifying Changes in the Florida Synoptic-Scale Sea-Breeze Regime Climatology

Abstract Florida’s summertime precipitation patterns are in part influenced by convergence between the synoptic-scale wind and local sea-breeze fronts that form along the east and west coasts of the peninsula. While the National Weather Service previously defined nine sea-breeze regimes resulting from variations in the synoptic-scale vector wind field near Tampa, Florida, these regimes were developed using a shorter 18-yr period and examined primarily for the purposes of short-term weather prediction. This study employs reanalysis data to develop a full 30-yr climatology of the Florida sea-breeze regime distribution and analyze the composite mean atmospheric conditions associated with each regime. Further, given that 1) the synoptic-scale wind primarily varies as a result of movement in the western ridge of the North Atlantic subtropical high (NASH), and 2) previous studies suggest long-term shifts in the mean position of the NASH western ridge, this study also examines variability and trends in the sea-breeze regime distribution and its relationship to rainy-day frequency over a longer 60-yr period. Results indicate that synoptic-scale flow from the west through southwest, which enhances precipitation probabilities along the eastern half of the peninsula, has increased in frequency, while flow from the east through northeast has decreased in frequency. These changes in the sea-breeze regime distribution may be partially responsible for increases in rainy-day frequency during June–August over northeastern Florida, though results suggest that other factors likely contribute to interannual variability in precipitation across the southern peninsula.

Collaborative Exploration of Storm-Scale Probabilistic Guidance for NWS Forecast Operations

Abstract The operational utility of the NOAA National Severe Storm Laboratory’s storm-scale probabilistic Warn-on-Forecast System (WoFS) was examined across the watch-to-warning time frame in a virtual NOAA Hazardous Weather Testbed (HWT) experiment. Over four weeks, 16 NWS forecasters from local Weather Forecast Offices, the Storm Prediction Center, and the Weather Prediction Center participated in simulated forecasting tasks and focus groups. Bringing together multiple NWS entities to explore new guidance impacts on the broader forecast process is atypical of prior NOAA HWT experiments. This study therefore provides a framework for designing such a testbed experiment, including methodological and logistical considerations necessary to meet the needs of both local office and national center NWS participants. Furthermore, this study investigated two research questions: 1) How do forecasters envision WoFS guidance fitting into their existing forecast process? and 2) How could WoFS guidance be used most effectively across the current watch-to-warning forecast process? Content and thematic analyses were completed on flowcharts of operational workflows, real-time simulation interactions, and focus group activities and discussions. Participants reported numerous potential applications of WoFS, including improved coordination and consistency between local offices and national centers, enhanced hazard messaging, and improved operations planning. Challenges were also reported, including the knowledge and training required to incorporate WoFS guidance effectively and forecasters’ trust in new guidance and openness to change. The solutions identified to these challenges will take WoFS one step closer to transition, and in the meantime, improve the capabilities of WoFS for experimental use within the operational community. Significance Statement A first-of-its-kind experiment brought together forecasters from local weather forecast offices and national centers to examine the experimental Warn-on-Forecast System’s (WoFS’s) potential applications across watch-to-warning scales. This experiment demonstrated that WoFS can provide great benefit to forecasters, though a few challenges remain. Benefits provided by WoFS frequently overlap roles and responsibilities at local and national scales, suggesting the potential for enhanced cross-office collaboration. The challenges anticipated for WoFS operational use are far fewer than the benefits, and some solutions to these challenges are now being implemented. Finally, the mixed-methods experimental framework described herein also provides guidance for future collaborative experiments in testbed research that examine impacts of new technologies across NWS entities.

A preference group consensus method with three-way decisions and regret theory under multi-scale information systems

The foundation of intelligent computing and expert systems relies on the data processing conducted within the realm of multi-scale information systems (MSISs). Data processing within MSISs caters to diverse analytical needs, where the preference hierarchy among various factors plays a vital role in the consensus reaching process (CRP). Additionally, the three-way decisions (TWD) theory serves as an efficient tool, while regret theory (RT) quantifies decision-makers' risk inclinations associated with various psychological behaviors. Thus, the paper aims to introduce an innovative approach known as the CRP-TWD-RT-MSIS method. The study draws inspiration from spatial geometry, incorporating concepts like points, lines, surfaces, and bodies to create MSISs. This process generates a matrix of fuzzy preference relations (FPRs) among distinct decision-makers through data preprocessing. In addition, the paper explores feedback mechanisms based on identification and directional rules, as well as those rooted in minimal adjustments or cost considerations. Simultaneously, it takes negotiation and discussion time into account, culminating in the development of a personalized adjustment optimization model that focuses on minimizing time costs. In the end, the method's effectiveness and superiority are validated through a case study and a comparative analysis of real data from the Chinese Weather and Meteorological Bureau's website.

NOAA's National Water Model: Advancing operational hydrology through continental‐scale modeling

AbstractThe National Weather Service (NWS) Office of Water Prediction (OWP), in conjunction with the National Center for Atmospheric Research and the NWS National Centers for Environmental Prediction (NCEP) implemented version 2.1 of the National Water Model (NWM) into operations in April of 2021. As with the initial version implemented in 2016, NWM v2.1 is an hourly cycling analysis and forecast system that provides streamflow guidance for millions of river reaches and other hydrologic information on high‐resolution grids. The NWM provides complementary hydrologic guidance at current NWS river forecast locations and significantly expands guidance coverage and water budget information in underserved locations. It produces a full range of hydrologic fields, which can be leveraged by a broad cross section of stakeholders ranging from the emergency responder and water resource communities, to transportation, energy, recreation and agriculture interests, to other water‐oriented applications in the government, academic and private sectors. Version 2.1 of the NWM represents the fifth major version upgrade and more than doubles simulation skill with respect to hourly streamflow correlation, Nash Sutcliffe Efficiency, and bias reduction, over its original inception in 2016. This paper will discuss the driving factors underpinning the creation of the NWM, provide a brief overview of the model configuration and performance, and discuss future efforts to improve NWM components and services.

Smart Installation Weather Warning Decision Support

Army installation commanders need timely weather information to make installation closure decisions before or during adverse weather events (e.g., hail, thunderstorms, snow, and floods). We worked with the military installation in Fort Carson, CO, and used their Weather Warning, Watch, and Advisory (WWA) criteria list to establish the foundation for our algorithm. We divided the Colorado Springs area into 2300 grids (2.5 square kilometers areas) and grouped the grids into ten microclimates, geographically and meteorologically unique regions, per pre-defined microclimate regions provided by the Fort Carson Air Force Staff Weather Officers (SWOs). Our algorithm classifies each weather event in the WWA list using the National Weather Service’s and National Digital Forecast Database’s data. Our algorithm assigns each event a criticality level: none, advisory, watch, or warning. The traffic network data highlight the importance of each road segment for travel to and from Fort Carson. The algorithm also uses traffic network data to assign weight to each grid, which enables the aggregation to the region and installation levels. We developed a weather dashboard in ArcGIS Pro to verify our algorithm and visualize the forecasted warnings for the grids and regions that are or may be affected by weather events.

The first 30 min hidden aftershocks of the 2022 September 17, ML 6.4, Guanshan, Taiwan earthquake and its seismological implications

The first 30 min of the aftershock sequence following the 2022 September 17, ML 6.4, Guanshan, Taiwan earthquake is investigated by the back-projection (BP) method. Based on the Automatic Gain Control (AGC) to process individual earthquake record and sort waveforms by epicentral distance, 80 events are identified near the Guanshan earthquake epicenter to verify its temporal variation. However, most of these events cannot clearly image by the BP method to determine its spatial locations for its small amplitude and short time separation. In total, 29 aftershocks are imaged by this study to identify its spatial locations during this time period. The relocated events are compared with the Central Weather Bureau (CWB) rapid report catalogue. Only 5 events have been reported by the CWB rapid report catalogue. These unreported hidden aftershocks are identified to distribute surrounding the epicenter and extended to the east. This extracted information of hidden aftershocks is crucial for assessing the potential for future large aftershocks and estimating the associated seismic hazards in a given region, and provide extra message to evaluate the source physics in general.