Urban flooding events caused by heavy and short-duration rainfall are challenging to predict and can cause significant socioeconomic impacts. Forecast systems able to predict floods in real-time at a spatial scale suitable for human action (<10 m) and enough lead time (hours to days) can enable timely preparedness and response actions. These forecast systems often depend on precipitation predictions from numerical weather forecast models to produce flood maps. This study evaluates a novel framework for generating inundation maps up to 48-hour lead time in real-time using precipitation forecasts from the High-Resolution Ensemble Forecast version 3 (HREFv3) coupled to a watershed-scale 2D hydrodynamic model (HEC-RAS 2D). We assessed precipitation, streamflow, and flood forecast performance for multiple events between June and September 2021 in Northern Virginia, United States. The results showed that flood forecast performance decreased with increasing lead time when compared to reanalysis simulations forced with precipitation estimates. The system also produced flood forecasts with less than 17% error in mapping the inundated areas up to 29 h before rainfall events begin in real-time. The proposed approach uses open data and software that provides a new pathway for future studies and allows the expansion of the real-time flood forecast system to other areas in the United States.

Environmental justice research historically focuses on assessing and understanding direct impacts of environmental risk on socio-economically varied areas. Limited work has addressed indirect impacts such as how flooding of transportation networks may disproportionately influence different socio-economic groups. Our objective addresses this by investigating how flood-related road closures influence Virginians differently across the state. Firstly, we retrieve flood-related road closure information from the Virginia Department of Transportation along with race and poverty data from the US Census Bureau and aggregate socio-economic data from the Center for Disease Control and Prevention. Analytically, we then explore best-fit linear models between these variables, including spatial comparisons of coastal vs. inland and urban vs. rural areas, to assess how flooding may disproportionately be impacting people of different socio-economic status. Overall, we find more flood-related road closures in areas with less poverty and more white people. Inland areas experience more closures in whiter, less wealthy areas, while coastal areas see more racial diversity subjected to flood-related road closures. We further find that rural areas experience more closures than urban areas, with a bias in coastal rural areas towards census tracts with fewer white people. Our findings show noticeable differences when considering the relationship between flood-related road closures and various socio-economic factors and geographic categorizations in Virginia, though note that our findings are contingent on reported road closure data and therefore are susceptible to biases in reporting. These findings have implications for transportation infrastructure maintenance and prioritization of transportation network improvements from social and environmental justice standpoints.

Increasingly frequent and extreme precipitation events are heightening flood hazard exposure, intensifying the need for efficient and reliable flood risk modeling. Such modeling relies critically on accurate Digital Elevation Models (DEMs), yet raw DEMs often contain artificial obstructions—primarily bridges and culverts—that incorrectly disrupt modeled water flow. While high-resolution elevation data from Light Detection and Ranging (LiDAR) helps address some of these challenges, LiDAR cannot resolve elevations beneath bridges and culverts, necessitating further corrections. Traditional manual methods of DEM correction are labor-intensive and impractical at large scales. To address this issue, we introduce DEMend (Digital Elevation Model mender), a streamlined, automated tool designed to efficiently detect and correct hydrological obstructions in DEMs. DEMend leverages widely available stream and road network datasets and employs locally weighted regression techniques to statistically adjust and smooth terrain elevations. Applied to Northern Virginia’s Accotink watershed, DEMend rapidly identified and corrected 119 artificial obstructions, markedly enhancing stream alignment and facilitating efficient flood modeling workflows. Packaged as an ArcGIS toolbox, DEMend significantly reduces manual preprocessing efforts, offering flood modelers a practical and scalable solution to rapidly improve DEM suitability for accurate flood risk assessments.

This work aims to identify a mechanism of interaction between soil moisture (SM) state and the incidence of weakly forced synoptic scale MCS events during boreal summer by performing a sensitivity study using the Weather Research and Forecasting (WRF) model over the US Great Plains. A uniformly dry SM patch at a 5 degrees x 5 degrees scale is centered at the point of a documented MCS initiation to observe spatiotemporal changes of the simulated MCS events, totaling 97 cases between 2004 and 2017. A storm-centered composite analysis of SM at the location of simulated MCS events depicted SM heterogeneity [O(100) km] structured as significantly drier soils to the southwest (SW) transitioning to wetter soils northeast (NE) of the mean simulated initiation. Further analyses showed that this SM configuration influenced near-surface fluxes, which created a gradient of 2m-temperature and 2m-humidity, also aligned SW-to-NE, which affected the growth of the planetary boundary layer to trigger MCS initiations earlier in time (similar to 1-2 hr on average) compared to Control simulations. The implementation of the dry SM perturbation introduced drier-to-wetter SM gradients along the edges of the perturbed area, and MCS initiations were subsequently preferred on the drier side of those transition zones, with the most common orientation of simulated MCS events embedded within southwesterly flow. These results emphasize the importance of the low-level wind field alignment to organized SM gradients, which suggests that SM heterogeneity can drive MCS initiation related to near-surface atmospheric variable fluctuations as the main mechanism of interaction in weakly forced synoptic environments.

Curbing the worst impacts of global climate change will require rapidly transitioning away from fossil fuel across all sectors of the economy. This transition will also yield substantial co-benefits, as fossil fuel combustion releases harmful pollutants into the air. In this article, we present an analysis of the co-benefits to health and health-care costs related from decarbonization of the power sector, using the Virginia Clean Economy Act (VCEA) as a case study. Using a model that combines a source-response matrix approach to pollutant concentration modelling tied to health impact functions, our analysis shows that, by 2045, the VCEA will save up to 32 lives per year across the state, and avoid up to $355 million per year in health-related costs. Fossil-fuel free generation will also help the most disadvantaged communities, as counties in the highest poverty rate quintile also avoid the most pollutant-related deaths.

The intensification of extreme precipitation in a warming climate is expected to increase flood risk. In order to support flood resilience efforts, it is important to anticipate and quantify potential changes in design standards under future climate conditions. This study assessed how extreme precipitation is expected to change over the 21st century in relation to current National Oceanic and Atmospheric Administration (NOAA) Atlas 14 design standards over the contiguous United States (CONUS). We used the Community Earth System Model Version 2 large ensemble (CESM2‐LE) simulations from the Coupled Model Intercomparison Project Phase 6 and incorporated future changes into flood engineering design standard with a spatially distributed quantile delta mapping method. Relative changes in extreme daily precipitation were computed for multiple average recurrence intervals (ARIs) up to 100‐year and different planning horizons (2020, 2040, 2060, 2080, and 2100). The results indicated an intensification of extreme precipitation by approximately 10%–40% in northern regions and 20%–80% in southern regions by 2100. The current 100‐year ARI with 24‐hr duration from NOAA Atlas 14 is projected to become the 50‐year ARI in the Northern Great Plains, less than the 25‐year ARI in Southwest areas, and approximately the 25‐year ARI in the other regions by 2100. While a nationwide consensus is still needed, this work presents a possible methodology for incorporating climate uncertainty in engineering design. A comparison across major metropolitan areas also illustrates regional variability in projected changes relative to NOAA Atlas 14, suggesting a need for varied local‐scale responses.

Funded jointly by NOAA’s National Weather Service (NWS) Office of Science and Technology Integration (OSTI) and the Oceanic and Atmospheric Research (OAR) Weather Program Office (WPO), the UFS-R2O Project has made significant progress coordinating a large community of researchers, both inside and outside NOAA for integrating new research into the operational UFS applications. The project began in July 2020 as a collaboration between the National Centers forEnvironmental Prediction (NCEP) EnvironmentalModelling Center, 8 NOAA research labs, the National Center for Atmospheric Research (NCAR), the Naval Research Lab (NRL) and 6 universities and cooperative institutes. The project was conceived with a focus on leveraging the nascent UFS community to build new UFS applications that will replace several existing operational modeling systems and simplify the NCEP Production Suite (NPS). The project consists of three integrated teams covering the global Medium Range Weather/Subseasonal to Seasonal (MRW/S2S); the regional Short Range Weather/Convection Allowing Modeling (SRW/CAM); and the Hurricane applications, and are supported by seven cross-cutting development teams shown in Figure 1. The MRW/S2S team is leading the development of a six-component global coupled (atmosphere/ocean/land/sea-ice/wave/aerosol) ensemble system targeted for combining the Global Forecast System (GFS) and the Global Ensemble Forecast system (GEFS) as a single application, the SRW/CAM team is leading the development of a regional hourly-updating high-resolution and convection-allowing Rapid Refresh Forecast System (RRFS) for prediction of severe weather, and the Hurricane team developing the Hurricane Analysis and Forecast System (HAFS) for high resolution global tropical cyclone predictions. Some of the highlights of the progress accomplished thus far include: (1) testing and evaluation of various prototype versions of the global coupled prediction system with incremental improvements to the component models and the coupling infrastructure; (2) development of a prototype coupled data assimilation system that can update the ocean, sea-ice, atmospheric and land states; (3) development of a limited-area convective-scale short-range ensemble prediction system that formed the basis for the RRFS; and (4) development of moving nest capability within the global or regional domains for the HAFS. This presentation highlights the outcomes of the UFS R2O Project thus far, with emphasis on results from the UFS based coupled model deterministic and ensemble prototypes targeted for medium range and sub-seasonal weather forecasts. We will also discuss on the reanalysis and reforecast strategies for sub-seasonal to seasonal prediction capabilities, and eventual development of the Seasonal Forecast System (SFS) that will replace the existing Climate Forecast System (CFSv2) in operations. Figure 1: Structure and composition of the UFS-R2O Project

Global climate models and long-term observational records point to the intensification of extreme precipitation due to global warming. Such intensification has direct implications for worsening floods and damage to life and property. This study investigates the projected trends (2015–2100) in precipitation climatology and daily extremes using Community Earth System Model Version 2 large ensemble (CESM2-LE) simulations at regional and seasonal scales. Specifically, future extreme precipitation is examined in National Climate Assessment (NCA) regions over the Contiguous United States using SSP3-7.0 (Shared Socioeconomic Pathway). Extreme precipitation is analyzed in terms of daily maximum precipitation and simple daily intensity index (SDII) using Mann-Kendall (5% significance level) and Theil-Sen (TS) regression. The most substantial increases occur in the highest precipitation values (95th) during summer and winter clustered in the Midwest and Northeast, respectively, according to long-term extreme trends evaluated in quantiles (i.e., 25, 50, 75, and 95th). Seasonal climatology projections suggest wetting and drying patterns, with wetting in spring and winter in the eastern areas and drying during summer in the Midwest. Lower quantiles in the central U.S. are expected to remain unchanged, transitioning to wetting patterns in the fall due to heavier precipitation. Winter positive trends (at a 5% significance level) are most prevalent in the Northeast and Southeast, with an overall ensemble agreement on such trends. In spring, these trends are predominantly found in the Midwest. In the Northeast and Northern Great Plains, the intensity index shows a consistent wetting pattern in spring, winter, and summer, whereas a drying pattern is projected in the Midwest during summer. Normalized regional changes are a function of indices, quantiles, and seasons. Specifically, seasonal accumulations present larger changes (~30% and above) in summer and lower changes (< ~20%) in winter in the Southern Great Plains and the Southwestern U.S. Examining projections of extreme precipitation change across distinct quantiles provides insights into the projected variability of regional precipitation regimes over the coming decades.

Many large metropolitan areas are especially susceptible to floods generated by heavy, short-duration rainfall. The high density of people, buildings and infrastructure in these areas underline the importance of developing flood resilient cities and communities. An accurate real-time flood forecasting system can support decision making for launching preparedness and response actions in short-range (hours to days), and assist mitigating the disturbances caused by floods. This study explores the short-range predictive capability of a real-time flood forecast system by coupling the High-Resolution Rapid Refresh (HRRR) meteorological forecasted variables with a fully distributed hydrological model (WRF-Hydro). We provide a comprehensive analysis of short-range (36 h) forecasts for 19 flood events generated by heavy rainfall in small urban and suburban watersheds (<200 km2) in the National Capital Region of the United States. Flood forecast performance are then assessed using different metrics based on observed data from U.S. Geological Survey stream gauges and reanalysis simulations using the Stage IV quantitative precipitation estimates. Results show that despite the high variability of the HRRR cycle-to-cycle precipitation forecasts, the real-time flood forecast system can produce skillful forecasts with similar overall performance as the reanalysis simulations, achieving 65 % of flood detection rate. This variability had more impact on forecast skill in smaller subbasins, and flood forecasts were more consistent for longer duration and larger spatial extent rainfall. Hourly flood forecasts performed well even in smaller watersheds, correctly detecting flood occurrence with up to 34 h lead time and resulting in low peak flow magnitude and timing errors.

Mesoscale convective systems (MCS) are known to develop under ideal conditions of temperature and humidity profiles and large-scale dynamic forcing. Recent work, however, has shown that summer MCS events can occur under weak synoptic forcing or even unfavorable large-scale environments. When baroclinic forcing is weak, convection may be triggered by anomalous conditions at the land surface. This work evaluates land surface conditions for summer MCS events forming in the U.S. Great Plains using an MCS database covering the contiguous United States east of the Rocky Mountains, in boreal summers 2004-2016. After isolating MCS cases where synoptic-scale influences are not the main driver of development (i.e. only non-squall line storms), antecedent soil moisture conditions are evaluated over two domain sizes (1.25° and 5° squares) centered on the mean position of the storm initiation. A negative correlation between soil moisture and MCS initiation is identified for the smaller domain, indicating that MCS events tend to be initiated over patches of anomalously dry soils of ~100-km scale, but not significantly so. For the larger domain, soil moisture heterogeneity, with anomalously dry soils (anomalously wet soils) located northeast (southwest) of the initiation point, is associated with MCS initiation. This finding is similar to previous results in the Sahel and Europe that suggest that induced meso-β circulations from surface heterogeneity can drive convection initiation.

A series of reforecasts have been generated with prototype versions of the coupled Unified Forecast System (UFS) to evaluate progress in the model development. The forecast skill and biases of the UFS Prototypes 3 and 5 reforecast sets—called Benchmark 3 and Benchmark 5, respectively—are analyzed and compared with the NCEP Climate Forecast System version 2 (CFSv2) reforecasts from the Subseasonal Prediction Experiment (SubX). The evaluation focuses on surface variables typically provided in the subseasonal outlooks at weekly-averaged timescales, namely 2-meter air temperature, precipitation rate, and sea surface temperature. Additional assessment of the structure of the systematic error in total diabatic heating over three broad layers of the atmosphere (850-650 hPa, 650-450 hPa and 450-50 hPa) has been performed as a function of season and forecast lead. In terms of forecast skill, all models still experience a skill drop-off of varying degree by week 3. In general, however, the UFS prototypes considerably reduce the marked diminution of variability with lead time displayed in their predecessor, CFSv2. Moreover, the prototypes have reduced systematic error compared to CFSv2, particularly for 2-meter temperature and precipitation. A systematic overestimate of diabatic cooling is noted in the upper atmosphere (diabatic heating too negative compare to ERA-5 estimates) during boreal winter.

Tropical cyclone (TC) landfalls over the U.S. mid-Atlantic region are very infrequent. However, when they do occur, the resulting human and material losses can be severe, as was the case with Hurricane Sandy in 2012. Therefore, it is important to predict these land-falling events as far in advance as possible. In this study, we investigate the relationship between mid-Atlantic TC landfalls and the Madden-Julian Oscillation (MJO), which is the dominant source of climate variability in the tropics on intraseasonal time scales. This is largely accomplished by using a high-atmospheric-resolution ensemble prediction system based on the European Centre for Medium-Range Weather Forecasts (ECMWF) operational model (Project Minerva) to compile the statistics of these rare events, and the velocity potential MJO (VPM) index to define the phase and amplitude of the MJO. We find that at longer lead times (between 14 and 7 days prior to landfall), statistically significant peak landfall probabilities are present during MJO phases 1 and 7 and, to some extent, phase 8. This result is largely supported by observational data. At shorter lead times (between 6-day lead and landfall), phase 1 is strongly favored with some contribution from phases 2 and 3. These findings suggest a potential for extended-range predictions of the mid-Atlantic TC landfall risk based on the phase of the MJO.

The Unified Forecast System (UFS) is a community-based coupled Earth modeling system, designed to support the Weather Enterprise and also be the source system for NOAA’s operations. NOAA’s Unified Forecast System Research to Operations Project (UFS-R2O) aims to develop the next generation coupled Global Forecast System (GFS v17)/Global Ensemble Forecast System (GEFS v13) targeting operational implementation in FY24. The Project is part of the larger UFS community and includes scientists from NOAA Labs and Centers, NCAR, UCAR, NRL and several U.S. universities. The UFS is targeted to be a six-way coupled Earth prediction system, consisting of the FV3 dynamical core with the Common Community Physics Package (CCPP) for the atmosphere, MOM6 for the ocean, CICE6 for the sea ice, WW3 for ocean waves, Noah-MP for the land surface and GOCART for aerosols. Currently, four of the six model components have been coupled using the Community Mediator for Earth Prediction Systems (CMEPS). All the components of the coupled system will be initialized with a weakly coupled data assimilation system based on the Joint Effort for Data Assimilation Integration (JEDI) framework. A 30-year coupled reanalysis and reforecast will be conducted for model calibration and post-processing forecast products. The UFS is the basis for the future updates of the deterministic GFS medium-range weather forecast up to 16 days, the ensemble GEFS subseasonal forecast up to 45 days, and the seasonal forecasts up to one year using the new Seasonal Forecast System (SFS) planned to replace the operational Climate Forecast System (CFSv2). Several prototypes of a four-way coupled atmosphere-ocean-ice-wave model have been built and tested with a C384 horizontal grid (~25km) and 64 vertical levels for the atmospheric model, and a ¼ degree tripolar grid for the ocean and ice model components. The presentation will highlight the results of these prototype runs. The UFS-R2O Project has made the latest UFS prototype (S2Sp5) output available on Amazon Web Services (AWS). Researchers interested in the S2S prediction and model development are invited to evaluate the UFS S2Sp5 data. Analysis of the data may include process-based evaluations, diagnostic measures that reveal coupled feedback processes, model biases and S2S forecast skill estimations. To identify and prioritize key metrics in evaluating the UFS applications, the UFS-R2O Project is soliciting community inputs through a online survey and UFS Evaluation Metric Workshop in Feb 2021. The metrics will be incorporated into the METplus verification tools for both research and operation. A few more prototypes are planned beyond S2Sp5 which include increasing the vertical resolution of the atmospheric model to 127 vertical levels, the transition of land model from Noah to Noah-MP, inclusion of aerosol component, advanced physics suites as well as stochastic physics parameterizations to account for uncertainties in each model component. Coarser and higher resolution configurations along with coupled ensemble prototypes are also being built in order to evaluate the resolution-dependence of forecast biases and to assess the benefit vs cost of higher resolution. The development code is available on Github, and the UFS community contributes to the development through a R2O process.

This study conducts a robust assessment of the Coupled Model Intercomparison Project Phase 6 to capture the observed temperature trends and variability at global and regional scales. The warming rate in the second half of the twentieth century (0.19°C/decade) is twice as large as in the full analysis period (1901–2014; 0.10°C/decade). Multidecadal climate variability results in considerable uncertainties in the regional temperature trend, but the multidecadal variability does not represent a statistically significant trend. Globally, the spatial pattern of trends is most similar among ensemble members of the same model, then among climate models, and the least similar between models and observations. The structural uncertainty and internal variability of climate models provide a range of temperature trends that generally encompass the regional scale observations. Some single model large ensembles also have variability comparable to the multimodel large ensemble, encompassing the regional scale observations.

A tracking algorithm based upon a multiple object tracking method is developed to identify, track, and classify Tropical Intraseasonal Oscillations (TISO) on the basis of their direction of propagation. Daily National Oceanic and Atmospheric Administration Outgoing Longwave Radiation anomalies from 1979–2017 are Lanczos band‐pass filtered for the intraseasonal time scale (20–100 days) and spatially averaged with nine neighboring points to get spatially smoothed anomalies over large spatial scales (~105 km2). TISO events are tracked by using a two‐stage Kalman filter predictor‐corrector method. Two dominant components of the TISO (Eastward propagating and Northward propagating) are classified, and it is found that TISO remains active throughout the year. Eastward‐propagating TISO events occur from November to April with a phase speed of ~4 m/s and northward‐propagating TISO events occur from May to October with a phase speed of ~2.5 m/s in both the Indian and Pacific Ocean basins. Composites of the mean background states (wind; sea surface temperature, SST; and moisture) reveal that the co‐occurrence of warm SST and mean westerly zonal wind plays an important role in the direction of propagation and the geographical location of TISO events. In mean state sensitivity experiments with Sp‐CAM4, we have found that the seasonality of TISO in terms of the geographical location of occurrences and direction of propagation is primarily associated with the annual march of the maximum SST and low level zonal wind which tends to follow the SST.