Weather Service Research Articles

Landscape changes and watershed erosion in Prince George's County, Maryland

AbstractThis study evaluated erosion rates and sediment production in streams, and factors potentially influencing them throughout the Anacostia, Patuxent, and Potomac (non‐Anacostia) River watersheds within Prince George's County, Maryland, US. As part of the County's watershed‐scale biological monitoring program, from approx. 1999 to 2008, permanent monuments were established to allow measurement of stream channel cross‐sectional (XS) area. The intent of this study was to characterize the intensity and spatial distribution of fluvial geomorphic instability across the county and use the results to target and plan stormwater management and stream restoration actions. For this study, 78 stream locations were re‐surveyed in 2020, representing a time lapse of from 12 to 21 years. Data collected included XS dimensions, modified Wolman 100‐particle pebble counts, and reach‐specific soil bulk density. Land use/land cover data were compiled from the National Land Cover Dataset (NLCD), precipitation from the National Weather Service Center for Environmental Information (NCEI), and soils from the Natural Resources Conservation Service Web Soil Survey (NRCS/WSS). We calculated percent change in XS area, rates of erosion, sediment yield, and assigned geomorphic classifications, and interpreted them in the context of spatial positions relative to changes in land cover characteristics. Sediment yields among the 78 reaches exhibited a combination of those undergoing enlargement/erosion (67.9%), reduction/deposition (25.6%), and the remaining 6.4% with essentially no change over the period of record. Of the top 20 most geomorphically active reaches surveyed in the County, 12 are in the Anacostia River basin, with the other scattered among the Patuxent River and Potomac River basins.

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.

Analysis and Prediction of Summer Rainfall over Southwestern Utah

Abstract While many studies have examined intense rainfall and flash flooding during the North American Monsoon (NAM) in Arizona, Nevada, and New Mexico, less attention has focused on the NAMS’s extension into southwestern Utah. This study relates flash flood reports and Multi-Radar Multi-Sensor (MRMS) precipitation across southwestern Utah to atmospheric moisture content and instability analyses and forecasts from the High-Resolution Rapid Refresh (HRRR) model during the 2021–23 monsoon seasons. MRMS quantitative precipitation estimates over southwestern Utah during summer depend largely on the areal coverage from the KICX WSR-88D radar near Cedar City, UT. Those estimates are generally consistent with the limited number of precipitation gauge reports in the region except at extended distances from the radar. A strong relationship is evident between days with widespread precipitation and afternoons with above average precipitable water (PWAT) and convective available potential energy (CAPE) estimated from HRRR analyses across the region. Time-lagged ensembles of HRRR forecasts (initialization times from 03–06 UTC) that are 13–18 h prior to the afternoon period when convection is initiating (18–21 UTC) are useful for situational awareness of widespread precipitation events after adjusting for underprediction of afternoon CAPE. Improved skill is possible using random forest classification relying only on PWAT and CAPE to predict days experiencing excessive (upper quartile) precipitation. Such HRRR predictions may be useful for forecasters at the Salt Lake City National Weather Service Forecast Office to assist issuing flash flood potential statements for visitors to national parks and other recreational areas in the region.

SWOL2023: Report of Space Weather Observations Throughout Latinoamerica: Filling the Southern Gaps

AbstractThe “Space Weather Observations throughout Latinoamerica: Filling the Southern gaps” Workshop took place in Ushuaia, Argentina, from 2 to 4 October 2023. This event was exclusively dedicated to Space Weather (SWx), gathering leading international academic and governmental agencies focused on the subject. The meeting addressed the field of SWx from three perspectives: driving phenomena occurring in the solar atmosphere, evolution in the interplanetary environment, and interaction with the Earth. In addition, emphasis was placed on identifying key research directions in solar and space physics that warrant development in Argentina and across the region in the forthcoming years. The workshop also focused on the current observational infrastructure that supports SWx studies, and how to improve and develop it through strategic international partnerships, stressing on the need for real‐time data. The conference fostered discussion among different partners involved in SWx studies, that is, researchers and potential space weather service providers, and the need to include in this debate potential users of space weather services.

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.

Towards a South American High Impact Weather Reports Database

Abstract Despite Southern South America being recognized as a hotspot for deep convective storms, little is known about the socio-environmental impacts of high impact weather (HIW) events. Although there have been past efforts to collect severe weather reports in the region, they have been highly fragmented among and within countries, sharing no common protocol, and limited to a particular phenomenon, a very specific region or a short period of time. There is a pressing need for a more comprehensive understanding of the present risks linked to HIW events, specifically deep convective storms, on a global scale as well as their variability and potential future evolution in the context of climate change. A database of high-quality and systematic HIW reports and associated socio-environmental impacts is essential to understand the regional atmospheric conditions leading to hazardous weather, to quantify its predictability and to build robust early warning systems. To tackle this problem and following successful initiatives in other regions of the world, researchers, national weather service members, and weather enthusiasts from Argentina, Brazil, Chile, Paraguay and Uruguay have embarked on a multi-national collaboration to generate a standardized database of reports of HIW events principally associated with convective storms and their socio-environmental impacts in South America. The goal of this paper is to describe this unprecedented initiative over the region, to summarize first results and to discuss the potential applications of this collaboration.

Leveraging Collaborative Partnerships to Enhance NWS and Emergency Management Communications Through Exercising

Abstract Tabletop exercises examining weather-related hazards are not uncommon but are often built around somewhat generic scenarios that only touch on the meteorological communication environment at a very shallow level. A recent exercise in central Oklahoma sought to change that. A local emergency manager, personnel from a National Weather Service (NWS) forecast office, and a severe weather researcher with a background in exercise design and facilitation worked together to create and deliver a realistic severe weather simulation. Exercise participants were exposed to detailed forecast information via NWSChat - a dedicated communication tool used to connect NWS forecasters, emergency managers, and media members for real-time information sharing. NWS forecasters were able to both actively play in the exercise due to the use of NWSChat, as well as observe how local decision makers interpreted and utilized the IDSS graphics and short-term forecast updates. The collaborative approach of developing a detailed scenario with numerous real-world Impact-Based Decision Support Services (IDSS) graphics, along with the use of NWSChat for real-time delivery, resulted in overwhelmingly positive feedback from the participants. The local emergency management office identified numerous areas for improvement in communicating real-time forecast information across their jurisdiction, along with gaps in current plans and resources. Meanwhile, the NWS forecast office had the opportunity to experiment with using the new NWSChat platform in a high-impact severe weather environment before a real-world event took place. Forecasters also gained insight into current IDSS graphic interpretation, noting areas for improved messaging to end users, such as adding storm motion to existing severe weather graphics.

Making Social Science Actionable for the NWS: The Brief Vulnerability Overview Tool (BVOT)

Abstract This paper provides an introduction to a new tool that is designed to provide operationally useful vulnerability information to National Weather Service (NWS) Weather Forecasting Offices (WFOs). The Brief Vulnerability Overview Tool (BVOT) is a shapefile containing local known, spatially specific, and weather hazard-related vulnerabilities in a format that is easily integrated into the existing forecasting, warning, and decision support responsibilities and tasks of NWS WFO meteorologists. The methods for gathering vulnerability data and then building a BVOT for a WFO leverages and strengthens the relationships that NWS WFOs already have with their local emergency managers (EMs) and core partners to work together to identify operationally useful, local vulnerability knowledge. The BVOT is populated with discrete, known vulnerabilities to provide NWS meteorologists spatial-situational awareness of those people, places, and things of greatest concern to their core partners. Crucially, the BVOT is a sub-sample of all potential vulnerabilities; its primary purpose is to make meteorologists aware of those weather hazard-specific vulnerabilities that, as we posed to them, “keep them awake at night.” Here we describe the development of the BVOT as a social science-informed operational tool; how the BVOT methods have evolved and how it can be integrated into the culture of the NWS as a tool for building and maintaining relationships with partners; and how the BVOT is designed to be used and its impact on operational decision making as observed in NOAA’s Hazardous Weather Testbed.

The use of weather, water, ice, and climate (WWIC) information in the Polar Regions: What is known after the decade-long Polar Prediction Project?

Abstract The Polar Regions are facing a wide range of compounding challenges, from climate change to increased human activity. Infrastructure, rescue services and disaster-response capabilities are limited in these remote environments. Relevant and usable weather, water, ice and climate (WWIC) information is vital for safety, activity success, adaptation and environmental protection. This has been a key focus for the World Meteorological Organization’s (WMO) Polar Prediction Project (PPP), and in particular its ‘Societal and Economic Research and Applications’ (PPP-SERA) Task Team, which together over a decade have sought to understand polar WWIC information use in relation to operational needs, constraints and decision contexts to inform the development of relevant services. To understand research progress and gaps on WWIC information use during the PPP (2013–2023), we undertook a systematic bibliometric review of aligned scholarly peer-reviewed journal articles (n=43), examining collaborations, topics, methods and regional differences. Themes to emerge included activity and context, human factors, information needs, situational awareness, experience, local and Indigenous knowledge, and sharing of information. We observed an uneven representation of disciplinary backgrounds, geographic locations, research topics and sectoral foci. Our review signifies an overall lack of Antarctic WWIC services research and a dominant focus on Arctic sea-ice operations and risks. We noted with concern a mismatch between user needs and services provided. Our findings can help to improve WWIC services’ dissemination, communication effectiveness and actionable knowledge provision for users, and guide future research as the critical need for salient weather services across the Polar Regions remains beyond the PPP.

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.

An Assessment of the GLE Alert++ Warning System

Over the last years the Athens Cosmic Ray Group of the National & Kapodistrian University of Athens has implemented a warning tool called GLE Alert, which is a highly credible application that issues alerts when a ground level enhancement (GLE) starts due to very high energy solar energetic particles reaching the Earth. This application warns of a high intensity solar energetic particle event up to several minutes before it reaches near the near-Earth space environment. In this work, an assessment of the latest updated version of GLE Alert, GLE Alert++, is presented. GLE Alert++ is a federated product of the ESA S2P SWE Space Radiation Expert Service Centre, which is part of the ESA Space WEather Service NETwork (SWESNET) project. The assessment of the GLE Alert++, which was finalized in October 2022, focused on: (a) the availability of the real-time data provided by the neutron monitor stations that contribute to the GLE Alert++, (b) the behaviour of each station regarding the different Alert levels status (Watch, Warning and Alert), and (c) the definition of the real-time assessment index. The results of this work are of essential importance since they ensure a reliable and trustworthy warning tool, and can be highly useful in protecting humans during extreme solar energetic events.

Forecasting High Wind Events in the HRRR Model over Wyoming and Colorado. Part II: Sensitivity of Surface Wind Speeds to Model Resolution and Physics

Abstract Strong wind events can cause severe economic loss and societal impacts. Peak winds over Wyoming and Colorado occur during the wintertime months, and in Part I of this two-part series, it was shown that the High Resolution Rapid Refresh (HRRR) model displays large negative biases with respect to strong wind events. In Part II of this study, we address two questions: 1) does increasing the horizontal resolution improve the representation of strong wind events over this region, and 2) are the biases in HRRR-forecasted winds related to the selected planetary boundary layer (PBL), surface layer (SL), and/or land surface model (LSM) parameterizations? We conduct Weather Research and Forecasting (WRF) simulations to address these two main questions. Increasing the horizontal resolution leads to a small improvement in the simulation of strong wind speeds over the complex terrain of Wyoming and Colorado. In general, changes in the PBL, SL, and LSM parameterizations show much larger changes in simulated wind speeds compared to increasing the model resolution alone. Specifically, changing from the Mellor-Yamada-Nakanishi-Niino scheme to the Mellor-Yamada-Janjić PBL and SL schemes results in nearly no change in the r2 values, but there is a decrease in the magnitude of the strong wind speed bias (−12.52 m s−1 to −10.16 m s−1). We attribute these differences to differences in the diagnosis of surface wind speeds and mixing in the boundary layer. Further analysis is conducted to determine the value of 1-km forecasts of strong winds compared with wind speed diagnostics commonly used by the National Weather Service.

Tailored Investments Needed to Support Weather, Water, Ice, and Climate Services in the Polar Regions

Abstract The Polar Prediction Project (PPP), one of the flagship programs of the World Meteorological Organization’s (WMO) World Weather Research Programme (WWRP), has come to an end after a decade of intensive and coordinated international observing, modeling, verification, user engagement, and education activities. While PPP facilitated many advancements in modeling and forecasting, critical investment is now required to turn prediction science into salient environmental services for the polar regions. In this commentary, the members of the Societal and Economic Research and Applications task team of PPP, a group of social scientists and service delivery specialists, identify a number of insights and lessons that are critical for the implementation of the follow-up program Polar Coupled Analysis and Prediction for Services (PCAPS). We argue that in order to raise the societal value of polar environmental services, we need to better understand the diversity of highly specific user contexts; to tailor the actionability of weather, water, ice, and climate (WWIC) service development in the polar regions through inclusive transdisciplinary approaches to coproduction; to assess the societal impact of improved environmental services in the polar regions; and to invest and provide dedicated funding for involving the social sciences in research and tailoring processes across all the polar regions.

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).

NWS Regional and Local Climate Services: Past 20 years, Present, and Future

Abstract National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) has been providing national, regional, and local climate services for more than 20 years. The NWS climate services building blocks consist of service provision infrastructure, partnership and outreach, discovery of user needs and requirements, and service delivery at national, regional, local, and tribal levels. To improve services, the NWS climate services program accelerated user engagement through customer surveys, workshops, and collaborations. Since 2002, the annual Climate Prediction Applications Science Workshop has developed a community of climate information producers and users through sharing of climate science applications, decision support tools, and effective communication practices. Although NWS had been producing operational climate monitoring and prediction products for several decades, the Weather Research and Forecasting Innovation Act of 2017 (US Public Law 115-25, 2017) specifically mandated that NWS deliver services at subseasonal to seasonal (S2S) time scales, including periods from two weeks to two years. Looking ahead, both the Department of Commerce (DOC) and NOAA have included climate services in their new 2022-2026 strategic plans, including DOC’s goal to address the climate crisis through mitigation, adaptation, and resilience efforts and NOAA’s initiatives to build a Climate Ready Nation (CRN). The NWS Climate Services Program supports these strategic goals and CRN initiatives through integrating climate information into Impact-based Decision Support Services, the most critical element for implementation of the NWS strategy for a Weather-Ready Nation. This includes application of state-of-the-art climate monitoring and prediction products to the most societally relevant impacts while empowering regional and local climate delivery of enhanced services.

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.

Reliable precipitation nowcasting using probabilistic diffusion models

Abstract Precipitation nowcasting is a crucial element in current weather service systems. Data-driven methods have proven highly advantageous, due to their flexibility in utilizing detailed initial hydrometeor observations, and their capability to approximate meteorological dynamics effectively given sufficient training data. However, current data-driven methods often encounter severe approximation/optimization errors, rendering their predictions and associated uncertainty estimates unreliable. Here a probabilistic diffusion model-based precipitation nowcasting methodology is introduced, overcoming the notorious blurriness and mode collapse issues in existing practices. Diffusion models learn a sequential of neural networks to reverse a pre-defined diffusion process that generates the probability distribution of future precipitation fields. The precipitation nowcasting based on diffusion model results in a 3.7% improvement in continuous ranked probability score compared to state-of-the-art generative adversarial model-based method. Critically, diffusion model significantly enhance the reliability of forecast uncertainty estimates, evidenced in a 68% gain of spread-skill ratio skill. As a result, diffusion model provides more reliable probabilistic precipitation nowcasting, showing the potential to better support weather-related decision makings.

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.