National Weather Service Research Articles

An Iterative Approach toward Development of Ensemble Visualization Techniques for High-Impact Winter Weather Hazards: Part II: Product Evaluation

Abstract We developed five prototype convection-allowing model ensemble visualization products with the goal of improving depictions of the timing of winter weather hazards. These products are interactive, web-based plots visualizing probabilistic onset times and durations of intense snowfall rates, probabilities of heavy snow at rush hour, periods of heightened impacts, and mesoscale snowband probabilities. Prototypes were evaluated in three experimental groups coordinated by the Weather Prediction Center (WPC) Hydrometeorological Testbed (HMT), with a total of 53 National Weather Service (NWS) forecasters. Forecasters were asked to complete a simple forecast exercise for a snowfall event, with a control group using the Storm Prediction Center’s (SPC) High-Resolution Ensemble Forecast (HREF) system viewer, and an experimental group using both the HREF viewer and the five experimental graphics. Forecast accuracy was similar between the groups, but the experimental group exhibited smaller mean absolute error for snowfall duration forecasts. A series of Likert-scale questions saw participants respond favorably to all of the products and indicated that they would use them in operational forecasts and in communicating information to core partners. Forecasters also felt that the new products improved their comprehension of ensemble spread and reduced the time required to complete the forecasting exercise. Follow-up plenary discussions reiterated that there is a high demand for ensemble products of this type, though a number of potential improvements, such as greater customizability, were suggested. Ultimately, we demonstrated that social science methods can be effectively employed in the atmospheric sciences to yield improved visualization products.

On the Detection of Icing Conditions at Altitude in Conjunction with Mesoscale Convective Complexes Using Balloon Sondes

Abstract Balloon-borne radiosondes are launched twice daily at coordinated times worldwide to assist with weather forecasting. Data collection from each flight is usually terminated when the balloon bursts at an altitude above 20 km. This paper highlights cases where the balloon’s turnaround occurs at lower altitudes and is associated with ice formation on the balloon, a weather condition of interest to aviation safety. Four examples of such cases are shown, where the balloon oscillates between 3- and 6-km altitude before rising to high altitudes and bursting. This oscillation is due to the accumulation and melting of ice on the balloon, causing the pattern to repeat multiple times. An analysis of National Weather Service radiosonde data over a 5-yr period and a global dataset from the National Centers for Environmental Information from 1980 to 2020 identified that 0.18% of soundings worldwide satisfied these criteria. This indicates that weather conditions important to aviation safety are not rare in the worldwide database. We recommend that soundings that show descent at altitudes lower than typically expected continue to be tracked, particularly given that these up–down-oscillating soundings can provide valuable information for weather forecasting on days with significant precipitation and icing conditions that might lead to aviation safety concerns.

Struggle to survive: case of flood risk on US community banks

PurposeThe survivorship of firms under extreme weather poses an essential question about the local economy's health. Over 90% of agricultural banks are categorized as community banks, which are important financial institutions promoting local growth. While previous studies suggest that climate change and weather shocks adversely impact community banks' resiliency, studies on whether these institutions engage in risk-reducing management strategies have been limited. In this study, the authors examine strategic choices of local community banks when facing flood events which include (1) safety net increase, (2) portfolio diversification, and (3) branch opening. These strategic choices are the coping mechanisms banks can take to survive while affecting the local competitive lending market.Design/methodology/approachThe authors use panel-fixed effect regressions based on the storm data from National Oceanic and Atmospheric Administration (NOAA)'s National Weather Service (NWS) and the call reports from the Federal Deposit Insurance Corporation (FDIC). The authors focus on community banks' account variable characteristics and the number of offices to examine whether community banks take an active role in managing flood risk.FindingsResults suggest that community banks do employ the selected strategic choices to a certain degree, as it is found that there is an increase in the core capital that absorbs shocks and portfolio diversification. However, the magnitudes of these activities are rather small and not large enough to fully mitigate the climate risk. Also, the authors do not find any evidence of branch expansion associated with local floods.Originality/valueThis study contributes to the literature by examining different strategic choices of community banks in the face of natural uncertainty. Even though concerns of climate risk have been raised in the regulatory setting, a lack of guidance or assessment tools could contribute to the passive action of these community banks, even though climate risks can have a significant economic impact. Thus, the evidence documented from this study calls for further guidelines and the importance of highlighting climate risks on community banks so that they can actively engage in risk-reducing strategies.

Assessment of Natural Moisture Availability of Turkestan Region of the Republic of Kazakhstan

To increase the level of management efficiency in the agricultural sector of the economy, it is necessary to ensure the implementation of the sustainable environmental management principles, taking into account the spatial patterns of climate change and bioclimatic potential of the territory. The assessment of natural moisture availability of the natural areas of the Turkestan region of the Republic of Kazakhstan for 1941-2020 (by providing a comparative analysis of indicators for 1941-1960 and 2001-2020) was conducted based on the use of the natural moisture coefficient and hydrothermal index or "dryness index" predicated on energy resources (total of biologically active air temperatures above 10°С, photosynthetically active radiation, evaporating capacity and water consumption of agricultural land). The conducted survey has used the proven domestic, international and proprietary methods according to estimates of natural water availability in the natural areas. The results of a comparative analysis of climatic indices changes in the natural areas of the Turkestan region (by sixteen weather bureau stations) for 1941 to 2020 have shown that there is an increase in average annual air temperatures in all natural areas, and the annual precipitation tends downward which affects the formation of energy resources and natural water supply. The identified features of changes in the natural moisture coefficient and hydrothermal index or "dryness index" in natural area of Turkestan region, make it possible to adjust the spread of its boundaries and consider these changes in the territorial organization of agricultural nature management.

Evaluation of Probabilistic Snow Forecasts for Winter Weather Operations at Intermountain West Airports

Abstract This work set out to assess the performance of four forecast systems [the Short-Range Ensemble Forecast (SREF), High-Resolution Rapid Refresh Ensemble (HRRRE), the National Blend of Models (NBM), and the Probabilistic Snow Accumulation product (PSA) from the National Weather Service (NWS) Boulder, Colorado, Weather Forecast Office] when predicting snowfall events around the Intermountain West to advise winter weather decision-making processes at Denver International Airport. The goal was to provide airport personnel and the Boulder NWS Forecast Office with operationally relevant verification results on the timing and severity of these events so they are able to make better-informed decisions to minimize negative impacts of storms. Forecasts of snow events using various probability thresholds and a climatological snow-to-liquid ratio of 15:1 were evaluated against Meteorological Aerodrome Reports (METARs) for 24-h periods following four decision-making times spaced equally throughout the day. For the ensembles, a frequentist approach was used: the forecast probability equaled the percentage of ensemble members that predicted a snow event. The results show that the NBM had the best timing of snow events out of the products, while all the products tended to overforecast snow amount. Additionally, NBM had fewer snow events and rarely had high probabilities of snow, unlike the other forecast products.

Evaluating Data Product Exposure Metrics for Use in Epidemiologic Studies of Dust Storms.

Dust storms are increasing in frequency and correlate with adverse health outcomes but remain understudied in the United States (U.S.), partially due to the limited spatio-temporal coverage, resolution, and accuracy of current data sets. In this work, dust-related metrics from four public areal data products were compared to a monitor-based "gold standard" dust data set. The data products included the National Weather Service (NWS) storm event database, the Modern-Era Retrospective analysis for Research and Applications-Version 2, the EPA's Air QUAlity TimE Series (EQUATES) Project using the Community Multiscale Air Quality Modeling System (CMAQ), and the Copernicus Atmosphere Monitoring Service global reanalysis product. California, Nevada, Utah, and Arizona, which account for most dust storms reported in the U.S., were examined. Dichotomous and continuous metrics based on reported dust storms, particulate matter concentrations (PM10 and PM2.5), and aerosol-type variables were extracted or derived from the data products. Associations between these metrics and a validated dust storm detection method utilizing Interagency Monitoring of Protected Visual Environments monitors were estimated via quasi-binomial regression. In general, metrics from CAMS yielded the strongest associations with the "gold standard," followed by the NWS storm database metric. Dust aerosol (0.9-20μm) mixing ratio, vertically integrated mass of dust aerosol (9-20μm), and dust aerosol optical depth at 550nm from CAMS generated the highest standardized odds ratios among all metrics. Future work will apply machine-learning methods to the best-performing metrics to create a public dust storm database suitable for long-term epidemiologic studies.

Downslope Windstorm Forecasting: Easier with a Critical Level, but Still Challenging for High-Resolution Ensembles

Abstract Strong downslope windstorms can cause extensive property damage and extreme wildfire spread, so their accurate prediction is important. Although some early studies suggested high predictability for downslope windstorms, more recent analyses have found limited predictability for such winds. Nevertheless, there is a theoretical basis for expecting higher downslope wind predictability in cases with a mean-state critical level, and this is supported by one previous effort to forecast actual events. To more thoroughly investigate downslope windstorm predictability, we compare archived simulations from the NCAR ensemble, a 10-member mesoscale ensemble run at 3-km horizontal grid spacing over the entire contiguous United States, to observed events at 15 stations in the western United States susceptible to strong downslope winds. We assess predictability in three contexts: the average ensemble spread, which provides an estimate of potential predictability; a forecast evaluation based upon binary-decision criteria, which is representative of operational hazard warnings; and a probabilistic forecast evaluation using the continuous ranked probability score (CRPS), which is a measure of an ensemble’s ability to generate the proper probability distribution for the events under consideration. We do find better predictive skill for the mean-state critical-level regime in comparison to other downslope windstorm–generating mechanisms. Our downslope windstorm warning performance, calculated using binary-decision criteria from the bias-corrected ensemble forecasts, performed slightly worse for no-critical-level events, and slightly better for critical-level events, than National Weather Service high-wind warnings aggregated over all types of high-wind events throughout the United States and annually averaged for each year between 2008 and 2019.

A Nowcasting Approach for Low-Earth-Orbiting Hyperspectral Infrared Soundings within the Convective Environment

Abstract Low-Earth-orbiting (LEO) hyperspectral infrared (IR) sounders have significant yet untapped potential for characterizing thermodynamic environments of convective initiation and ongoing convection. While LEO soundings are of value to weather forecasters, the temporal resolution needed to resolve the rapidly evolving thermodynamics of the convective environment is limited. We have developed a novel nowcasting methodology to extend snapshots of LEO soundings forward in time up to 6 h to create a product available within National Weather Service systems for user assessment. Our methodology is based on parcel forward-trajectory calculations from the satellite-observing time to generate future soundings of temperature (T) and specific humidity (q) at regularly gridded intervals in space and time. The soundings are based on NOAA-Unique Combined Atmospheric Processing System (NUCAPS) retrievals from the Suomi National Polar-Orbiting Partnership (Suomi NPP) and NOAA-20 satellite platforms. The tendencies of derived convective available potential energy (CAPE) and convective inhibition (CIN) are evaluated against gridded, hourly accumulated rainfall obtained from the Multi-Radar Multi-Sensor (MRMS) observations for 24 hand-selected cases over the contiguous United States. Areas with forecast increases in CAPE (reduced CIN) are shown to be associated with areas of precipitation. The increases in CAPE and decreases in CIN are largest for areas that have the heaviest precipitation and are statistically significant compared to areas without precipitation. These results imply that adiabatic parcel advection of LEO satellite sounding snapshots forward in time are capable of identifying convective initiation over an expanded temporal scale compared to soundings used only during the LEO satellite overpass time. Significance Statement Advection of low-Earth-orbiting (LEO) satellite observations of temperature and specific humidity forward in time exhibits skill in determining where and when convection eventually initiates. This approach provides a foundation for a new nowcasting methodology leveraging thermodynamic soundings derived from hyperspectral infrared (IR) sounders on LEO satellite platforms. This method may be useful for creating time-resolved soundings with the constellation of LEO satellites until hyperspectral infrared soundings are widely available from geostationary platforms.

Study on the Application of Typhoon Experience Parameter Analysis in Taiwan’s Offshore Wind Farms

Due to the rapid development of computers, researchers have made efforts since the 1990s to develop typhoon forecasting models and stochastic typhoon simulation models to assess typhoon disasters and risks. Typhoon forecasting models are primarily used to predict and track the movement of typhoons and provide warning information to the general public before landfall. Stochastic typhoon simulation models can assess extreme wind speeds and compensate for the limitations of current observations and simulation data length. Taiwan experiences approximately three to four typhoons yearly, of varying intensities and paths. Whether the marine meteorological data includes events of strong typhoon centers passing through will affect the results of frequency analysis. The development of offshore wind power in Taiwan is closely related to the unique marine meteorological conditions throughout the lifecycle stages, including wind farm site selection, feasibility studies, planning and design, construction and installation, operation and maintenance, and decommissioning. This study references relevant research and analyzes sixty-three scenarios using nine types of maximum storm wind speed radii and seven Holland-B parameters. The data from Japan Meteorological Agency Best Track Data (JMA BTD) is utilized, explicitly selecting 20 typhoon events after 2000 for wind speed simulation using a typhoon wind speed model. After validating the typhoon wind speeds with observation data from the Central Weather Bureau (CWB) in Hsinchu and the Longdong buoy, the technique of Monte Carlo simulation is utilized to generate synthetic typhoons randomly. The average of the relative absolute errors for the simulated maximum wind speeds is calculated, and through comprehensive evaluation, optimal parameter combinations (Rm, B) are obtained.

Library of rough hailstone backscattering coefficients at 2.8 GHz

The rough hailstone scattering library is created to fill the need for results of non-spheroidal hailstone backscattering characteristics. The listed backscattering coefficients at the S-band (2.8 GHZ) are specifically tailored for computing the polarimetric variables. The National Weather Service’s radar operates in this band; hence the library is pertinent to those studying hail in the USA. The library contains several axis ratios and a range of hail sizes from 5 mm to 100 mm. Moreover, hailstone surface roughness is expressed as a percentage of the equatorial diameter. Our computational electromagnetic approach to modeling hailstones is compared with typically employed simpler methods. The polarimetric variables calculated using our models can be compared to observations. Furthermore, we illustrate the potency of the CEM approach to hydrometeors with extreme axis ratios for which simpler methods (i.e., the T-matrix) fail.

Data aggregation, ML ready datasets, and an API: leveraging diverse data to create enhanced characterizations of monsoon flood risk

Monsoon precipitation and severe flooding is highly variable and often unpredictable, with a range of flood conditions and impacts across metropolitan regions or a county. County and storm specific watches or warnings issued by the National Weather Service (NWS) alert the public to current flood conditions and risks, but floods are not limited to the area that is under alert and these zones can be relatively coarse depending on the data these warnings are based on. Research done by the Arizona Institute for Resilient Environments and Societies (AIRES) has produced an Application Programming Interface (API) accessible data warehouse of time series precipitation totals across the state of Arizona which consists of higher resolution geographically disperse data that helped create improved characterizations of monsoon precipitation variability. There is an opportunity to leverage these data to address flood risk particularly where advanced Computer Science methodologies and Machine Learning techniques may offer additional spatial and temporal insight into flood events. This can be especially useful during rainfall events where precipitation station reporting frequencies are increased and near real-time totals are accessible via the AIRES API. A Machine-Learning-ready dataset structured to train ML models facilitates an anticipatory approach to predicting/characterizing flood risk. This presents an opportunity for new inputs into management and decision making opportunities, in addition to describing precipitation and flood patterns after an event. In this paper we will be the first to make use of the AIRES API by taking the initial step of the Machine Learning process and assembling the precipitation data into a ML-ready dataset. We then look closer at the dataset assembled and call attention to characteristics of the dataset that can be further explored through machine learning processes. Finally, we will summarize future directions for research and climate services using this dataset and API.

The Office of Water Prediction's Analysis of Record for Calibration, version 1.1: Dataset description and precipitation evaluation

AbstractHydrologic models operated by the National Weather Service call for an accurate, consistent, high‐resolution, multi‐decade, continental‐scale record of hydrometeorological fields to serve as forcing data for model calibration. To serve this purpose, the Analysis of Record for Calibration was developed, and version 1.1 of the dataset is described in this study. Geospatial and scientific requirements, methods used in dataset generation, and input data sources are described. Given the prominent role of precipitation in model calibration, accurate and consistent precipitation is a particularly high priority for the analysis. To evaluate the analysis from this perspective, its daily precipitation is compared with surface observing stations over 43 years. The analysis exhibits low bias compared with other similar products. It also displays nonstationary bias behavior after 2015 due to the lack of a climatological constraint, as well as frequent occurrences of heavy‐to‐extreme precipitation that are often difficult to verify. These findings should be taken into account when the product is used for model calibration.

Prospects for Machine Learning Activity within the United States National Weather Service

Abstract The National Weather Service (NWS) Office of Science and Technology Integration commissioned a report to assess the status of artificial intelligence (AI) and machine learning (ML) activity within the agency with a view toward identifying existing obstacles and recommending future directions. The purpose of this essay is to communicate the steps that the NWS plans to take to realize the potential benefits of AI in operations. AI activities are growing rapidly within atmospheric sciences, and the NWS is part of this growth. However, the activity is fragmented and lacks the needed infrastructure for improved coordination of effort. Current obstacles to progress include insufficient workforce training in AI/ML, a lack of curated datasets and software that can be used for development and evaluation of these approaches, the absence of a centralized clearing house available to NWS personnel for technical expertise and consultation, limited operational compute resources, and a lack of a clear end-to-end project pathway that encompasses exploration, development, testbed/proving ground, and operational implementation. These limitations are addressable. Training materials specific to NWS interests can be developed through collaboration with existing NOAA centers. Establishing a reference library staffed with AI/ML consultants tasked with collaborating with operational units would reduce siloed efforts and enhance productivity. Establishing funding vehicles for theme-based projects with a sustainable pathway through operational implementation would help bridge the research-to-operations “valley of death.” Given the growth of AI/ML across the U.S. Weather Enterprise and the already substantial involvement of academic and private sector entities, these developments within the NWS will be of interest to the atmospheric science field.

The 2021 Hazardous Weather Testbed Experimental Warning Program Radar Convective Applications Experiment: A Forecaster Evaluation of the Tornado Probability Algorithm and the New Mesocyclone Detection Algorithm

Abstract Developed as part of a larger effort by the National Weather Service (NWS) Radar Operations Center to modernize their suite of single-radar severe weather algorithms for the WSR-88D network, the Tornado Probability Algorithm (TORP) and the New Mesocyclone Detection Algorithm (NMDA) were evaluated by operational forecasters during the 2021 National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) Experimental Warning Program Radar Convective Applications experiment. Both TORP and NMDA leverage new products and advances in radar technology to create rotation-based objects that interrogate single-radar data, providing important summary and trend information that aids forecasters in issuing time-critical and potentially life-saving weather products. Utilizing virtual resources like Google Workspace and cloud instances on Amazon Web Services, 18 forecasters from the NOAA/NWS and the U.S. Air Force participated remotely over three weeks during the spring of 2021, providing valuable feedback on the efficacy of the algorithms and their display in an operational warning environment, serving as a critical step in the research-to-operations process for the development of TORP and NMDA. This article will discuss the details of the virtual HWT experiment and the results of each algorithm’s evaluation during the testbed. Significance Statement Before transitioning newly developed radar-based severe weather applications to forecasting operations, an experiment simulating the use of these tools by end users issuing severe weather warnings is helpful to identify both how they are best utilized and address any needed improvements to increase their operational readiness. Conducted in 2021, this study describes the forecaster evaluation of the single-radar Tornado Probability Algorithm (TORP) and the New Mesocyclone Detection Algorithm (NMDA) in one of the first completely virtual Hazardous Weather Testbed (HWT) experiments. Participants stated both TORP and NMDA offered marked improvement over the currently available algorithms by helping the operational forecaster build their confidence when issuing severe weather warnings and increasing their overall situational awareness of storms within their domain.

Examining Extreme Rainfall Forecast and Communication Processes in the South-Central United States

Abstract Extreme rainfall events are hazardous and costly. They have increased in parts of the United States, and climate models project that trend to continue. Effective communication of potential threats and impacts associated with extreme rainfall events is one of the foci of a weather forecaster’s job and aligns with the National Weather Service (NWS)’s mission to protect life and property. This research investigated how NWS forecasters processed and communicated information about extreme rainfall events that occurred in the south-central United States between 2015 and 2019. The study also explored forecasters’ perceptions of the relationship between the events and climate change and whether those perceptions impacted the forecasts, including how forecast information was communicated. Semistructured interviews were conducted with 21 NWS forecasters about how they internally processed and externally communicated model outliers and anomalous rainfall events. Thematic analysis of the interview data identified components of sensemaking and decision-making conceptual frameworks as well as principles of forecasting. These components were then combined to create an extreme event forecast communication process model to illustrate the findings. Although forecast and communication processes are complex and vary between offices and forecasters, the communication process model presents a high-level conceptualization of how forecasters translate highly technical and disparate material into usable information for their audiences within the context of rare meteorological events. Significance Statement This study presents an extreme event forecast communication process model that helps to explain how National Weather Service forecasters process and communicate extreme rainfall events. Forecasters were interviewed about their experience with extreme rainfall events. Effective communication of such events is important because they can lead to significant, and sometimes deadly, impacts. In the future, the extreme event forecast communication process model might provide a framework for best practices and be incorporated into forecaster training materials. Additional research is needed to determine whether the model applies to regions outside the south-central United States.

What Impact? Communicating Severity Forecast Information through the Winter Storm Severity Index

Abstract Communicating the threat of severe winter weather is not simply a matter of the number of inches of snow or degrees of cold; it also considers the potential impacts of the storm. The winter storm severity index (WSSI) is a graphical product from the National Weather Service that presents anticipated impacts from forecast winter weather for a range of winter conditions. To assess the utility of the WSSI and how an impact-based winter weather forecast product is interpreted and used to inform decision-making, a mixed-methods social science study was conducted by the Nurture Nature Center in coordination with the Weather Prediction Center. Through focus groups and surveys, testing in the Hydrometeorological Testbed, and iterations on design and category descriptions, several themes emerged about how professional stakeholders understand, interpret, and use this product for communicating about impending winter weather. There is perceived utility in the WSSI for situational awareness and as part of a package of other information to inform decision-making. However, there is variability in interpretations of impacts, resulting from differences in geography, community readiness, and experience, among other factors, which creates complications in communicating the forecast. Furthermore, many users seek quantities related to winter weather, suggesting that education about what impact-based products include and what data are shown is necessary. Understanding the factors that influence perspectives on impact levels and the variable needs for winter weather information across regions improves forecasters’ abilities to effectively communicate and provide critical information that helps end users prepare for severe winter weather. Significance Statement Effectively communicating severe winter weather is critical to supporting communities in being prepared for and mitigating weather-related losses and damages. The winter storm severity index focuses on impacts to provide awareness of impending winter weather, information that is useful but not always interpreted consistently, requiring an understanding of factors influencing perspectives on impact levels and user education.

Lives Saved versus Time Lost: Direct Societal Benefits of Probabilistic Tornado Warnings

Abstract The National Weather Service is planning to implement the system of probabilistic tornado warnings. In this paper, I estimate and compare the full societal costs of tornadoes with existing deterministic and potential probabilistic warnings. These full costs include the value of statistical lives lost as well as the value of the time spent sheltering. I find that probabilistic tornado warnings would decrease total expected fatalities. The improvement in decision-making would also decrease the total opportunity cost of time spent sheltering, even though the total sheltering time is likely to increase. In total, probabilistic warnings should lower the societal costs of tornadoes relative to deterministic warnings by approximately $76–139 million per year, with a large portion of this improvement coming from fewer casualties. Significance Statement I measure societal benefits of probabilistic and deterministic tornado warnings in the United States by evaluating their effects on expected casualties and sheltering costs. I find that probabilistic warnings deliver almost twice as much net societal benefit as deterministic ones. These gains happen as a result of fewer casualties and making protective behavior more responsive to risks and sheltering costs. This paper provides additional evidence of the need to implement probabilistic extreme weather warnings.

Toward Operational Real-Time Identification of Frontal Boundaries Using Machine Learning

Abstract We present and evaluate a deep learning first-guess front-identification system that identifies cold, warm, stationary, and occluded fronts. Frontal boundaries play a key role in the daily weather around the world. Human-drawn fronts provided by the National Weather Service’s Weather Prediction Center, Ocean Prediction Center, Tropical Analysis and Forecast Branch, and Honolulu Forecast Office are treated as ground-truth labels for training the deep learning models. The models are trained using ERA5 data with variables known to be important for distinguishing frontal boundaries, including temperature, equivalent potential temperature, and wind velocity and direction at multiple heights. Using a 250-km neighborhood over the contiguous U.S. domain, our best models achieve critical success index scores of 0.60 for cold fronts, 0.43 for warm fronts, 0.48 for stationary fronts, 0.45 for occluded fronts, and 0.71 using a binary classification system (front/no front), whereas scores over the full unified surface analysis domain were lower. For cold and warm fronts and binary classification, these scores significantly outperform prior baseline methods that utilize 250-km neighborhoods. These first-guess deep learning algorithms can be used by forecasters to locate frontal boundaries more effectively and expedite the frontal analysis process. Significance Statement Fronts are boundaries that affect the weather that people experience daily. Currently, forecasters must identify these boundaries through manual analysis. We have developed an automated machine learning method for detecting cold, warm, stationary, and occluded fronts. Our automated method provides forecasters with an additional tool to expedite the frontal analysis process.

Hyperlocal Observations Reveal Persistent Extreme Urban Heat in Southeast Florida

Abstract Cities around the world are experiencing the effects of climate change via increasing extreme heat worsened by urbanization. Within cities, there are disparities in extreme heat exposure that are apparent in various surface and remotely sensed observations, as well as in the health impacts. There are, however, large data gaps in our ability to quantify the heat experienced by people in their daily lives across urban areas. In this paper, we use hyperlocal observations to measure heat around Miami–Dade County, Florida. Temperature and humidity measurements were collected at sites throughout the county between 2018 and 2021 with low-cost sensors. By comparing these hyperlocal observations with a National Weather Service (NWS) site at the Miami International Airport (MIA), we show that maximum temperatures are on average 6°F (3.3°C) higher and maximum heat index values are 11°F (6.1°C) higher at sites in the county than at MIA. These measurements show that many sites frequently record a heat index above the local threshold value for heat advisory. This is in contrast with the fact that few forecast advisories are issued, and there are correspondingly few exceedances of the threshold at MIA. We use these results to motivate a discussion about the issues of this particular threshold for Miami–Dade County. We highlight the need for data that are closer to residents’ lived experience to assess the impacts of heat and help inform local and regional decision-making, particularly where heat exposure may be underappreciated as a potential public health hazard.

Generating a reference flow network with improved connectivity to support durable data integration and reproducibility in the coterminous US

This report presents a reference flow network for the conterminous United States that is built from the best available information from the U.S. Geological Survey, the National Oceanic and Atmospheric Administration National Weather Service, and the U.S. Environmental Protection Agency. The work is intended to support durable data integration and reproducibility. Originating from the National Hydrography Dataset Plus (NHDPlus) V2.1, the reference flow network incorporates network connectivity enhancements from federal agency efforts. After incorporating these network improvements, many original NHDPlus attributes were regenerated to enable network navigation and related operations. After introducing the motivation and background for this work, this report describes the attribute generation workflow and data quality checks that were performed in preparation of the dataset. The reference flow network follows the NHDPlus data model and is described using terms defined in the Mainstem and Drainage Basin logical model and WaterML2 Part3: Surface Hydrology Features conceptual model.