NOAA Air Resources Laboratory

Abstract. Ozone (O3) over South Korea has risen in recent years, underscoring the need to accurately quantify emissions of nitrogen oxides (NOx) and volatile organic compounds (VOC). We develop a hybrid inverse modeling framework that couples the Finite Difference Mass Balance (FDMB) method with four-dimensional variational (4D-Var) assimilation using the Community Multiscale Air Quality (CMAQ) model to jointly constrain spatiotemporal NOx and VOC emissions. The inversion is constrained by Tropospheric Monitoring Instrument (TROPOMI) NO2 and HCHO columns and by surface NO2 and O3 from the air quality monitoring station network. The analysis covers 1–14 May 2022, a period of climatologically high O3. Optimized NOx emissions exhibit strong diurnal adjustments relative to the prior (nighttime reductions up to 51 % and daytime increases up to 14 %). The joint inversion of NOx and VOC delivers the largest improvement in O3 simulations, achieving the best agreement with observations (IOA > 0.8). Constrained emissions shift O3 sensitivity from VOC-sensitive to NOx-sensitive across much of the domain, improving spatial consistency with TROPOMI-derived formaldehyde-to-NO2 ratio (FNR) diagnostics. Adjoint-based hourly ΔO3 responses reveal regime- and hour-dependent behavior: VOC controls are most effective under VOC-sensitive conditions, whereas NOx controls are more direct under NOx-sensitive conditions. Importantly, because O3 titration is immediate while photochemical production requires finite reaction time, emissions released approximately 1–2 hours earlier have the greatest influence on current O3, motivating hour-specific, regime-specific controls. Overall, the hybrid framework improves O3 simulations and sensitivity-regime diagnosis, enabling spatiotemporally resolved precursor emission reduction guidance for effective O3 mitigation.

MRI plays a central role in the diagnosis, treatment planning, and post-treatment follow-up of musculoskeletal tumors. Key morphological features on MRI help differentiate benign from malignant lesions, and functional imaging further enhances diagnostic confidence. Emerging tools such as artificial intelligence and structured reporting systems show promise in improving patient care and outcomes; however, challenges remain in achieving widespread integration.

Abstract. Biogenic isoprene emissions play an important role in air quality so it is important to quantify their response to extreme events such as drought. This study aims at constraining drought stress on biogenic isoprene emissions in South Korea using satellite formaldehyde (HCHO) column, the key product of isoprene oxidation, and a chemistry transport model (GEOS-Chem) with Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1). It was found that the HCHO column from the Ozone Monitoring Instrument (OMI) increased by 5.4 % under the drought condition compared to the normal condition, but GEOS-Chem simulated a 20.23 % increase indicating an overestimation of isoprene emissions under drought. We implemented two existing drought stress algorithms in MEGAN2.1 and found they were not effective to reduce HCHO column biases in South Korea because those algorithms were proposed and developed for the Southeast United States (SE US). To improve this, we applied an iterative finite difference mass balance (IFDMB) method to estimate isoprene emissions using the OMI HCHO column. With this method, isoprene emissions were reduced by 60 % under the drought condition compared to those simulated by the standard MEGAN2.1 implemented in GEOS-Chem. The increase of HCHO column under the drought condition compared to the normal condition was also reduced to 10.71 %, which is comparable to that in the satellite retrievals. Based on isoprene emission difference between MEGAN2.1 and IFDMB, we developed the empirical equations to adjust isoprene emissions in South Korea that also improved model prediction of the secondary pollutant such as ozone.

Abstract The rise of digital platforms has significantly transformed the dissemination and consumption of weather and climate information, leading to the emergence of digital meteorology as a distinct field. In this article, we explore the current digital landscape, highlighting how weather and climate information are increasingly consumed on digital platforms such as social media and mobile applications. We review evolving industry standards, specifically highlighting the American Meteorological Society’s (AMS) Certified Digital Meteorologist (CDM) seal. This certification recognizes English- and Spanish-speaking meteorologists who demonstrate both scientific competence and effective communication in their digital weather presentations. Additionally, we present a recent example of integrating digital meteorology practices into a university broadcasting operation, in partnership with the growing roster of CDM seal holders. Through these discussions, we lay the groundwork for applications, curriculum development, and applied research in digital meteorology to enhance public understanding and engagement with weather and climate information.

<strong class="journal-contentHeaderColor">Abstract.</strong> This paper describes efforts to establish aerosol data assimilation capabilities for NOAA&rsquo;s National Air Quality Forecasting Capability (NAQFC), a regional online air quality modeling (AQM) system under NOAA&rsquo;s Unified Forecast System (UFS), by assimilating measurements of fine particulate matter (PM_2.5, particles with diameters less than 2.5 &micro;m). PM_2.5assimilation is developed within the Joint Effort for Data assimilation Integration (JEDI) framework and tested using its 3D-Var data assimilation (DA) component. The PM_2.5 observation operator is constructed by combining newly developed PM_2.5 transformation recipes in the JEDI Variable Derivation Repository (VADER) with a general spatial interpolation operator in the Unified Forward Operator (UFO). Cycled DA and forecast experiments were conducted from 1 to 21 September 2020, during a period of Western U.S. wildfires, to assess the impact of assimilating PM_2.5 observations from the AirNow and PurpleAir networks. The control and analysis variables include individual aerosol species, with background error standard deviations generated by scaling their respective background values. Prognostic variables such as aerosol particle number and total particulate surface area are updated accordingly following each analysis update. All DA experiments use a 3-hourly cycling interval, with PM_2.5 observations assimilated every 3 hours. The control experiment uses the same configuration but without any data assimilation. Results show that assimilating either AirNow or PurpleAir PM_2.5 data reduces 1&ndash;24 h forecast errors in terms of mean absolute error (MAE) and root mean square error (RMSE) compared to the control run over CONUS. Forecast skill, measured using the Critical Success Index (CSI) for PM_2.5 thresholds of 5, 12, and 35 &micro;g/m&sup3;, also improves. AirNow observations have a greater impact overall, while PurpleAir shows its strongest impact over Nevada, northern Utah, Colorado, and southwestern New Mexico &ndash; regions with persistent underpredictions in the control run at forecast hour 1. Overall, the assimilation of PurpleAir observations in addition to AirNow data leads to a slight reduction in 3&ndash;24 h MAE.

Between June 6 and 8, 2023, wildfires in Quebec, Canada generated massive smoke plumes that traveled long distances and deteriorated air quality across the Northeastern United States (US). Surface daily PM2.5 observations exceeded 100 µg m-3, affecting major cities such as New York City and Philadelphia, while many areas lacked PM2.5 monitors, making it difficult to assess local air quality conditions. To address this gap, we developed a WRF-CMAQ-BenMAP modeling system to provide rapid, spatially continuous estimates of wildfire-attributable PM2.5 concentrations and associated health impacts, particularly benefiting regions lacking air quality monitoring. CMAQ simulations driven by two wildfire emissions datasets and two meteorological drivers showed good agreement with PM2.5 observations, with linear regression results of R2 ∼0.6 and slope ∼0.9. We further quantified uncertainties introduced by varying emissions and meteorological drivers and found the choice of wildfire emissions dataset alone can alter PM2.5 simulations by up to 40 µg m-3 (∼40%). Short-term health impacts were evaluated using the BenMAP model. Validation against asthma-associated emergency department (ED) visits in New York State confirmed the framework's ability to replicate real-world outcomes, with ED visits increased up to ∼40%. The modeling results identified counties most severely affected by wildfire plumes, the majority of which lack regulatory air quality monitors. Our approach highlights the value of integrated modeling for identifying vulnerable populations and delivering timely health burden estimates, regardless of local monitoring availability.

<strong class="journal-contentHeaderColor">Abstract.</strong> The age of smoke, meaning the time elapsed since it was produced in a fire, is an important parameter for interpreting measurements of evolving smoke composition. This study describes the smoke age estimates developed for large plumes sampled in the 2019 NASA-NOAA FIREX-AQ field experiment. Smoke ages are computed using two methods and applied to observations from two aircraft: the NASA DC-8 and a NOAA Twin Otter. The first method uses measurements of mean horizontal wind speed, as observed by the sampling aircraft, and distance to the fire to provide a single age estimate for each plume-crossing performed by the aircraft. While this "mean-wind method" uses accurate wind measurements, it can be systematically biased by assumptions that plume rise time is negligible and that winds are homogeneous horizontally and in time during the plume transport. Wind inhomogeneities due to terrain effects and day-to-night transition, among other factors, affected some plumes during FIREX-AQ. The mean-wind method therefore performs best for short-range transport over level terrain with steady winds. The second method relies on upwind air parcel trajectories and plume rise computed with multiple high-resolution meteorological datasets. This "trajectory-based method" quantifies age uncertainty from the meteorological ensemble, plume rise speed, wind speed errors, and fire location. The second method also resolves age differences from the center to edge of a transect. Still, it is susceptible to errors in the meteorological model. With careful comparison of the simulated trajectories to smoke transport observed from geostationary satellite imagery described here, we filter out many trajectory errors and improve the smoke age estimates. The two age methods are strongly correlated (<em>R</em> = 0.93) for the periods during FIREX-AQ when both ages are available. The mean-wind age is systematically 14&thinsp;% younger than the trajectory-based age and the median absolute difference between them is 19&thinsp;% (23&thinsp;% for mean). The favorable agreement between the two age methods reflects that the mean-wind method was selectively applied to plumes with little wind variability. Trajectory-based ages are available for more of the FIREX-AQ smoke observations than the mean-wind ages. The median trajectory-based age uncertainty during FIREX-AQ is 24&thinsp;% and the mean uncertainty is 37&thinsp;%, due to a long-tailed distribution. The main source of age uncertainty is spread within the meteorological ensemble, followed by discrepancy between measured and modeled wind speed, then other factors like plume rise. The age uncertainty variable enables the user to identify periods with high or low confidence in the age estimate, thereby informing studies of smoke aging.

<strong class="journal-contentHeaderColor">Abstract.</strong> <span>The presence of dense forest canopies significantly alters the near-field dynamical, physical, and chemical environment, with implications for atmospheric composition and air quality variables such as boundary layer ozone (O₃). Observations show profound vertical gradients in O₃ concentration beneath forest canopies; however, most chemical transport models (CTMs) used in the operational and research community, such as the Community Multiscale Air Quality (CMAQ) model, cannot account for such effects due to inadequate canopy representation and lack of sub-canopy processes. </span><span>To address this knowledge gap, we implemented detailed forest canopy processes&mdash;including in-canopy photolysis attenuation and turbulence&mdash;into the CMAQv5.3.1 model, driven by the Global Forecast System and enhanced with high-resolution vegetation datasets. Simulations were conducted for August 2019 over the contiguous U.S. The canopy-aware model shows substantial improvement, with mean O₃ bias reduced from +0.70 ppb (Base) to &minus;0.10 ppb (Canopy), and fractional bias from +9.71% to +6.37%. Monthly mean O₃ in the lowest model layer (~0&ndash;40 m) decreased by up to 9 ppb in dense forests, especially in the East. Process analysis reveals a 75.2% drop in first-layer O₃, with daily surface production declining from 673 to 167 ppb d⁻&sup1;, driven by suppressed photolysis and vertical mixing. This enhances NOₓ titration and reduces O₃ formation under darker, stable conditions. The results highlight the critical role of canopy processes in atmospheric chemistry and demonstrate the importance of incorporating realistic vegetation-atmosphere interactions in CTMs to improve air quality forecasts and health-relevant exposure assessments.</span><span></span>