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Increasing impacts of wildfires on Western US air quality highlights the need for forecasts of smoke emissions based on dynamic modeled wildfires. This work utilizes knowledge of weather, fuels, topography, and firefighting, combined with machine learning and other statistical methods, to generate 1‐ and 2‐day forecasts of fire radiative energy (FRE). The models are trained on data covering 2019 and 2021 and evaluated on data for 2020. For the 1‐day (2‐day) forecasts, the random forest model shows the most skill, explaining 48% (25%) of the variance in observed daily FRE when trained on all available predictors compared to the 2% (<0%) of variance explained by persistence for the extreme fire year of 2020. The random forest model also shows improved skill in forecasting day‐to‐day increases and decreases in FRE, with 28% (39%) of observed increase (decrease) days predicted, and increase (decrease) days are identified with 62% (60%) accuracy. Error in the random forest increases with FRE, and the random forest tends toward persistence under severe fire weather. Sensitivity analysis shows that near‐surface weather and the latest observed FRE contribute the most to the skill of the model. When the random forest model was trained on subsets of the training data produced by agencies (e.g., the Canadian or US Forest Services), comparable if not better performance was achieved (1‐day R2 = 0.39–0.48, 2‐day R2 = 0.13–0.34). FRE is used to compute emissions, so these results demonstrate potential for improved fire emissions forecasts for air quality models. Plain Language Summary: Increasing wildfire smoke is undoing decades of air quality progress, yet air quality forecasts often miss the most intense smoke events. This is because forecasted smoke is released at constant rates whereas the rate of smoke release from real wildfires varies in time. In this work we teach a machine learning algorithm to predict the daily change in fire heat output, a quantity that is used to calculate wildfire emissions. The machine learning algorithm uses information regarding weather, fuel moisture and amount, and firefighting efforts to make its predictions. We also tried to predict the daily change in fire heat output using only weather information but found the machine learning method to be more successful. Many federal agencies have their own ways of tracking fire weather and fuel moisture, and in this paper, we show that we can apply machine learning to the data from any of several agencies and get the same level of forecasting skill. Key Points: Random forest models trained on weather, fuel, and firefighting data surpass persistence and weather‐based methods to predict fire energyRandom forest models beat persistence across states, for most days in the 2020 fire season, and across levels of fire severityFire weather and latest fire energy predictors add most skill to the random forest; models using agency weather data perform similarly [ABSTRACT FROM AUTHOR]

Abstract. The evolution of organic aerosols (OAs) and their precursors in the boundary layer (BL) of the Colorado Front Range during the Front Range Air Pollution and Photochemistry Éxperiment (FRAPPÉ, July–August 2014) was analyzed by in situ measurements and chemical transport modeling. Measurements indicated significant production of secondary OA (SOA), with enhancement ratio of OA with respect to carbon monoxide (CO) reaching 0.085±0.003 µg m−3 ppbv−1. At background mixing ratios of CO, up to  ∼  1.8 µg m−3 background OA was observed, suggesting significant non-combustion contribution to OA in the Front Range. The mean concentration of OA in plumes with a high influence of oil and natural gas (O&G) emissions was  ∼  40 % higher than in urban-influenced plumes. Positive matrix factorization (PMF) confirmed a dominant contribution of secondary, oxygenated OA (OOA) in the boundary layer instead of fresh, hydrocarbon-like OA (HOA). Combinations of primary OA (POA) volatility assumptions, aging of semi-volatile species, and different emission estimates from the O&G sector were used in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) simulation scenarios. The assumption of semi-volatile POA resulted in greater than a factor of 10 lower POA concentrations compared to PMF-resolved HOA. Including top-down modified O&G emissions resulted in substantially better agreements in modeled ethane, toluene, hydroxyl radical, and ozone compared to measurements in the high-O&G-influenced plumes. By including emissions from the O&G sector using the top-down approach, it was estimated that the O&G sector contributed to

The declining trend in vehicle emissions has underscored the growing significance of volatile organic compound (VOC) emissions from volatile chemical products (VCPs). However, accurately representing VOC chemistry in simplified chemical mechanisms remains challenging due to its chemical complexity including speciation and reactivity. Previous studies have predominantly focused on VOCs from fossil fuel sources, leading to an underrepresentation of VOC chemistry from VCP sources. We developed an integrated chemical mechanism, RACM2B-VCP, that is compatible with WRF-Chem and is aimed at enhancing the representation of VOC chemistry, particularly from VCP sources, within the present urban environment. Evaluation against the Air Quality System (AQS) network data demonstrates that our model configured with RACM2B-VCP reproduces both the magnitude and spatial variability of O3 and PM 2.5 in Los Angeles. Furthermore, evaluation against comprehensive measurements of O3 and PM 2.5 precursors from the Reevaluating the Chemistry of Air Pollutants in California (RECAP-CA) airborne campaign and the Southwest Urban NO x and VOC Experiment (SUNVEx) ground site and mobile laboratory campaign confirm the model's accuracy in representing NO x and many VOCs and highlight remaining biases. Although there exists an underprediction in the total VOC reactivity of observed VOC species, our model with RACM2B-VCP exhibits good agreement for VOC markers emitted from different sectors, including biogenic, fossil fuel, and VCP sources. Through sensitivity analyses, we probe the contributions of VCP and fossil fuel emissions to total VOC reactivity and O3. Our results reveal that 52 % of the VOC reactivity and 35 % of the local enhancement of MDA8 O3 arise from anthropogenic VOC emissions in Los Angeles. Significantly, over 50 % of this anthropogenic fraction of either VOC reactivity or O3 is attributed to VCP emissions. The RACM2B-VCP mechanism created, described, and evaluated in this work is ideally suited for accurately representing ozone for the right reasons in the present urban environment where mobile, biogenic, and VCP VOCs are all important contributors to ozone formation. [ABSTRACT FROM AUTHOR]

Megacities are major sources of anthropogenic fossil fuel CO2 (FFCO2) emissions. The spatial extents of these large urban systems cover areas of 10 000 km2 or more with complex topography and changing landscapes. We present a high-resolution land-atmosphere modelling system for urban CO2 emissions over the Los Angeles (LA) megacity area. The Weather Research and Forecasting (WRF)-Chem model was coupled to a very high-resolution FFCO2 emission product, Hestia-LA, to simulate atmospheric CO2 concentrations across the LA megacity at spatial resolutions as fine as ∼ 1 km. We evaluated multiple WRF configurations, selecting one that minimized errors in wind speed, wind direction, and boundary layer height as evaluated by its performance against meteorological data collected during the CalNex-LA campaign (May-June 2010). Our results show no significant difference between moderate-resolution (4 km) and high-resolution (1.3 km) simulations when evaluated against surface meteorological data, but the high-resolution configurations better resolved planetary boundary layer heights and vertical gradients in the horizontal mean winds. We coupled our WRF configuration with the Vulcan 2.2 (10 km resolution) and Hestia-LA (1.3 km resolution) fossil fuel CO2 emission products to evaluate the impact of the spatial resolution of the CO2 emission products and the meteorological transport model on the representation of spatiotemporal variability in simulated atmospheric CO2 concentrations. We find that high spatial resolution in the fossil fuel CO2 emissions is more important than in the atmospheric model to capture CO2 concentration variability across the LA megacity. Finally, we present a novel approach that employs simultaneous correlations of the simulated atmospheric CO2 fields to qualitatively evaluate the greenhouse gas measurement network over the LA megacity. Spatial correlations in the atmospheric CO2 fields reflect the coverage of individual measurement sites when a statistically significant number of sites observe emissions from a specific source or location. We conclude that elevated atmospheric CO2 concentrations over the LA megacity are composed of multiple fine-scale plumes rather than a single homogenous urban dome. Furthermore, we conclude that FFCO2 emissions monitoring in the LA megacity requires FFCO2 emissions modelling with ∼ 1 km resolution because coarser-resolution emissions modelling tends to overestimate the observational constraints on the emissions estimates.

Background. The record number of wildfires in the United States in recent years has led to an increased focus on developing tools to accurately forecast their impacts at high spatial and temporal resolutions. Aims. The Warn-on-Forecast System for Smoke (WoFS-Smoke) was developed to improve these forecasts using wildfire properties retrieved from satellites to generate smoke plumes in the system. Methods. The WoFS is a regional domain ensemble data assimilation and forecasting system built around the concept of creating short-term (0-6 h) forecasts of high impact weather. This work extends WoFS-Smoke by ingesting data from the GOES-16 satellite at 15-min intervals to sample the rapidly changing conditions associated with wildfires. Key results. Comparison of experiments with and without GOES-16 data show that ingesting high temporal frequency data allows for wildfires to be initiated in the model earlier, leading to improved smoke forecasts during their early phases. Decreasing smoke plume intensity associated with weakening fires was also better forecast. Conclusions. The results were consistent for a large fire near Boulder, Colorado and a multi-fire event in Texas, Oklahoma, and Arkansas, indicating a broad applicability of this system. Implications. The development of WoFS-Smoke using geostationary satellite data allows for a significant advancement in smoke forecasting and its downstream impacts such as reductions in air quality, visibility, and potentially properties of severe convection. [ABSTRACT FROM AUTHOR]

A physics suite under development at NOAA's Global Systems Laboratory (GSL) includes the aerosol-aware double-moment Thompson–Eidhammer microphysics (TH-E MP) scheme. This microphysics scheme uses two aerosol variables (concentrations of water-friendly aerosol (WFA) and ice-friendly aerosol (IFA) numbers) to include interactions with some of the physical processes. In the original implementation, WFA and IFA depended on emissions derived from climatologies. In our approach, using the Common Community Physics Package (CCPP), we embedded modules of sea-salt emissions, dust emissions, and biomass-burning emissions, as well as of anthropogenic aerosol emissions, into the Unified Forecast System (UFS) to provide realistic aerosol emissions for these two variables. This represents a very simple approach with no additional tracer variables and therefore very limited additional computing cost. We then evaluated a comparison of simulations using the original TH-E MP approach, which derives the two aerosol variables using empirical emission formulas from climatologies (CTL) and simulations that use the online emissions (EXP). Aerosol optical depth (AOD) was derived from the two variables and appears quite realistic in the runs with online emissions when compared to analyzed fields. We found less resolved precipitation over Europe and North America from the EXP run, which represents an improvement compared to observations. Also interesting are moderately increased aerosol concentrations over the Southern Ocean from the EXP run, which invigorate the development of cloud water and enhance the resolved precipitation in those areas. This study shows that a more realistic representation of aerosol emissions may be useful when using double-moment microphysics schemes. [ABSTRACT FROM AUTHOR]

Wildfires have become larger and more frequent because of climate change, increasing their impact on air pollution. Air quality forecasts and climate models do not currently account for changes in the composition of wildfire emissions during the commonly observed progression from more flaming to smoldering combustion. Laboratory measurements have consistently shown decreased nitrogen dioxide (NO2) relative to carbon monoxide (CO) over time, as they transitioned from more flaming to smoldering combustion, while formaldehyde (HCHO) relative to CO remained constant. Here, we show how daily ratios between column densities of NO2 versus those of CO and HCHO versus CO from the Tropospheric Monitoring Instrument (TROPOMI) changed for large wildfires in the Western United States. TROPOMI‐derived emission ratios were lower than those from the laboratory. We discuss reasons for the discrepancies, including how representative laboratory burns are of wildfires, the effect of aerosols on trace gas retrievals, and atmospheric chemistry in smoke plumes. Plain Language Summary: Climate change has led to an increase in the frequency and size of wildfires in the Western United States. The gases and particles released from wildfires impact air quality and climate, so it is important to understand the chemical composition of these emissions. In current air quality forecasts and climate models, the composition of wildfire emissions is based on the dominant vegetation burned and is assumed to be constant over time. In contrast, measurements from laboratory burns indicate that the composition of emissions from fires changes over time, as fires progress from more flaming combustion to flameless burning dominated by smoke (smoldering). It is challenging to have daily field measurements of the emissions from long‐lived wildfires, but there are instruments in space that can make daily observations of wildfires globally. In this study, we show how the composition of emissions from wildfires in California, Oregon, and Washington changed over time, as they progressed from more flaming to more smoldering combustion, using observations from a satellite instrument called TROPOMI. The analysis of the composition of wildfire emissions and their evolution over time using TROPOMI could improve air quality forecasting and climate modeling globally. Key Points: Space‐based remote sensing instruments can be used to observe changes in the composition of wildfire emissions over timeChanges in wildfire emissions composition observed with TROPOMI were caused by evolving combustion conditions rather than aerosol shieldingTROPOMI observations can be used to help parametrize how modeled wildfire emissions should change with evolving combustion conditions [ABSTRACT FROM AUTHOR]

The declining trend in vehicle emissions has underscored the growing significance of Volatile Organic Compound (VOC) emissions from Volatile Chemical Products (VCP). However, accurately representing VOC chemistry in simplified chemical mechanisms remains challenging due to its chemical complexity including speciation and reactivity. Previous studies have predominantly focused on VOCs from fossil fuel sources, leading to an underrepresentation of VOC chemistry from VCP sources. We developed an integrated chemical mechanism, RACM2B-VCP, that is compatible with WRF-Chem and is aimed to enhance the representation of VOC chemistry, particularly from VCP sources, within the present urban environment. Evaluation against the Air Quality System (AQS) network data demonstrates that our model configured with RACM2B-VCP reproduces both the magnitude and spatial variability of O3 as well as PM2.5 in Los Angeles. Furthermore, evaluation against comprehensive measurements of O3 and PM2.5 precursors from the Reevaluating the Chemistry of Air Pollutants in California (RECAP-CA) airborne campaign and the Southwest Urban NOx and VOC Experiment (SUNVEx) ground site and mobile laboratory campaign, confirm the model's accuracy in representing NOx and many VOCs and highlight remaining biases. Although there exists an underprediction in the total VOC reactivity of observed VOC species, our model with RACM2B-VCP exhibits good agreement for VOC markers emitted from different sectors, including biogenic, fossil fuel, and VCP sources. Through sensitivity analyses, we probe the contributions of VCP and fossil fuel emissions to total VOC reactivity and O3. Our results reveal that 52 % of the VOC reactivity and 35 % of the local enhancement of MDA8 O3 arise from anthropogenic VOC emissions in Los Angeles. Significantly, over 50 % of this anthropogenic fraction of either VOC reactivity or O3 is attributed to VCP emissions. The RACM2B-VCP mechanism created, described, and evaluated in this work is ideally suited for accurately representing ozone for the right reasons in the present urban environment where mobile, biogenic, and VCP VOCs are all important contributors to ozone formation. [ABSTRACT FROM AUTHOR]

The declining trend in vehicle emissions has underscored the growing significance of Volatile Organic Compound (VOC) emissions from Volatile Chemical Products (VCP). However, accurately representing VOC chemistry in simplified chemical mechanisms remains challenging due to its chemical complexity including speciation and reactivity. Previous studies have predominantly focused on VOCs from fossil fuel sources, leading to an underrepresentation of VOC chemistry from VCP sources. We developed an integrated chemical mechanism, RACM2B-VCP, that is compatible with WRF-Chem and is aimed to enhance the representation of VOC chemistry, particularly from VCP sources, within the present urban environment. Evaluation against the Air Quality System (AQS) network data demonstrates that our model configured with RACM2B-VCP reproduces both the magnitude and spatial variability of O3 as well as PM2.5 in Los Angeles. Furthermore, evaluation against comprehensive measurements of O3 and PM2.5 precursors from the Reevaluating the Chemistry of Air Pollutants in California (RECAP-CA) airborne campaign and the Southwest Urban NOx and VOC Experiment (SUNVEx) ground site and mobile laboratory campaign, confirm the model's accuracy in representing NOx and many VOCs and highlight remaining biases. Although there exists an underprediction in the total VOC reactivity of observed VOC species, our model with RACM2B-VCP exhibits good agreement for VOC markers emitted from different sectors, including biogenic, fossil fuel, and VCP sources. Through sensitivity analyses, we probe the contributions of VCP and fossil fuel emissions to total VOC reactivity and O3. Our results reveal that 52% of the VOC reactivity and 35% of the local enhancement of MDA8 O3 arise from anthropogenic VOC emissions in Los Angeles. Significantly, over 50% of this anthropogenic fraction of either VOC reactivity or O3 is attributed to VCP emissions. The RACM2B-VCP mechanism created, described, and evaluated in this work is ideally suited for accurately representing ozone for the right reasons in the present urban environment where mobile, biogenic, and VCP VOCs are all important contributors to ozone formation. [ABSTRACT FROM AUTHOR]

A physics suite under development at NOAA's Global System Laboratory (GSL) includes the aerosol-aware double moment Thompson-Eidhammer microphysics scheme (TH-E MP). This microphysics scheme uses two aerosol variables (water friendly (WFA) and ice friendly (IFA) aerosol number concentrations) to include interaction with some of the physical processes. In the original implementation, WFA and IFA depend on emissions derived from climatologies. In our approach, using the Common Community Physics Package (CCPP), we embedded sea-salt, dust, and biomass burning emission modules as well as anthropogenic aerosol emissions into the Unified Forecast System (UFS) to provide realistic aerosol emissions for these two variables. This represents a very simple approach with no additional tracer variables and therefore very limited additional computing cost. We then evaluate a comparison of simulations using the original TH-E MP approach, which derives the two aerosol variables using empirical emission formulas from climatologies (CTL) and simulations that use the online emissions (EXP). Aerosol Optical Depth (AOD) is derived from the 2 variables and appears quite realistic in the runs with online emissions when compared to analyzed fields. We find less resolved precipitation over Europe and North America from the EXP run, which represents an improvement compared to observations. Also interesting are moderately increased aerosol concentrations over Southern Ocean from the EXP run invigorating the development of cloud water and enhances the resolved precipitation in those areas. This study shows that a more realistic representation of aerosol emission may be useful when using double moment microphysics schemes. [ABSTRACT FROM AUTHOR]

A physics suite under development at NOAA's Global System Laboratory (GSL) includes the aerosol-aware double moment Thompson-Eidhammer microphysics scheme (TH-E MP). This microphysics scheme uses two aerosol variables (water friendly (WFA) and ice friendly (IFA) aerosol number concentrations) to include interaction with some of the physical processes. In the original implementation, WFA and IFA depend on emissions derived from climatologies. In our approach, using the Common Community Physics Package (CCPP), we embedded sea-salt, dust, and biomass burning emission modules as well as anthropogenic aerosol emissions into the Unified Forecast System (UFS) to provide realistic aerosol emissions for these two variables. This represents a very simple approach with no additional tracer variables and therefore very limited additional computing cost. We then evaluate a comparison of simulations using the original TH-E MP approach, which derives the two aerosol variables using empirical emission formulas from climatologies (CTL) and simulations that use the online emissions (EXP). Aerosol Optical Depth (AOD) is derived from the 2 variables and appears quite realistic in the runs with online emissions when compared to analyzed fields. We find less resolved precipitation over Europe and North America from the EXP run, which represents an improvement compared to observations. Also interesting are moderately increased aerosol concentrations over Southern Ocean from the EXP run invigorating the development of cloud water and enhances the resolved precipitation in those areas. This study shows that a more realistic representation of aerosol emission may be useful when using double moment microphysics schemes. [ABSTRACT FROM AUTHOR]

Ground‐level ozone (O3) was unusually high in northern Colorado in the summer of 2021 with maximum daily 8‐hr average (MDA8) concentrations 6 to 8 parts‐per‐billion by volume (ppbv) higher than in 2019, 2020, or 2022. One or more of the monitors on the Colorado Front Range exceeded the 2015 U.S. National Ambient Air Quality Standard (NAAQS) of 70 ppbv on 66 of the 122 days from 1 June to 30 September, and this record number of exceedances coincided with the near daily presence of dispersed smoke haze from wildfires in Arizona, California, and the Pacific Northwest. In this paper, we use regulatory and non‐regulatory surface O3 and PM2.5 measurements in conjunction with ground‐based lidar observations to estimate how much O3 was associated with the wildfire smoke. Analyses of the surface measurements suggest that pyrogenic O3 transported to northern Colorado with the smoke increased the surface concentrations in northern Colorado by an average of 8 ppbv in July, 3 ppbv in August, and 2 ppbv in September. Analysis of the lidar measurements showed these contributions to be as large as 12 ppbv on some days. Production of O3 from reactions of pyrogenic VOCs and locally emitted NOx appears to have been minimal (<3 ppbv) in the Boulder area, but may have been much larger in the suburbs southwest of downtown Denver. Plain Language Summary: Northern Colorado experienced unusually poor air quality in the summer of 2021 with frequent high ozone (O3) episodes and hazy skies caused by smoke from wildfires in Arizona, California, and the Pacific Northwest. In this study, we use surface and lidar measurements to explore the connection between the two. Our analysis suggests that the unusually high O3 was caused primarily by a combination of O3 transported to Colorado with the wildfire smoke and enhancement of local photochemical production by unusually clear skies and warm temperatures coupled with weak winds that led to localized O3 accumulations and fewer than normal thunderstorms that might otherwise have dispersed the O3. Production of O3 by reactions of locally emitted NOx with VOCs in the wildfire smoke may also have been significant in Southwest Denver. Key Points: The impacts of the 2021 western wildfires on ozone in the Denver metropolitan area and rural northern Colorado in 2021 are examinedOzone transported in the smoke from distant wildfires increased the 8‐hr concentrations in northern Colorado by an average of 8 ppbv in JulyUnusual meteorology, including fewer thunderstorms, allowed ozone produced locally to accumulate along the foothills west of Denver [ABSTRACT FROM AUTHOR]

Plume height plays a vital role in wildfire smoke dispersion and the subsequent effects on air quality and human health. In this study, we assess the impact of different plume rise schemes on predicting the dispersion of wildfire air pollution and the exceedances of the National Ambient Air Quality Standards (NAAQS) for fine particulate matter (PM 2.5) during the 2020 western United States wildfire season. Three widely used plume rise schemes (Briggs, 1969; Freitas et al., 2007; Sofiev et al., 2012) are compared within the Community Multiscale Air Quality (CMAQ) modeling framework. The plume heights simulated by these schemes are comparable to the aerosol height observed by the Multi-angle Imaging SpectroRadiometer (MISR) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The performance of the simulations with these schemes varies by fire case and weather conditions. On average, simulations with higher plume injection heights predict lower aerosol optical depth (AOD) and surface PM 2.5 concentrations near the source region but higher AOD and PM 2.5 in downwind regions due to the faster spread of the smoke plume once ejected. The 2-month mean AOD difference caused by different plume rise schemes is approximately 20 %–30 % near the source regions and 5 %–10 % in the downwind regions. Thick smoke blocks sunlight and suppresses photochemical reactions in areas with high AOD. The surface PM 2.5 difference reaches 70 % on the West Coast of the USA, and the difference is lower than 15 % in the downwind regions. Moreover, the plume injection height affects pollution exceedance (>35 µgm-3) predictions. Higher plume heights generally produce larger downwind PM 2.5 exceedance areas. The PM 2.5 exceedance areas predicted by the three schemes largely overlap, suggesting that all schemes perform similarly during large wildfire events when the predicted concentrations are well above the exceedance threshold. At the edges of the smoke plumes, however, there are noticeable differences in the PM 2.5 concentration and predicted PM 2.5 exceedance region. For the whole period of study, the difference in the total number of exceedance days could be as large as 20 d in northern California and 4 d in the downwind regions. This disagreement among the PM 2.5 exceedance forecasts may affect key decision-making regarding early warning of extreme air pollution episodes at local levels during large wildfire events. [ABSTRACT FROM AUTHOR]

This research extends the Warn‐on‐Forecast System for Smoke (WoFS‐Smoke) by adding the capability to assimilate aerosol optical depth (AOD) retrievals from the Geostationary Operational Environmental Satellite Series‐R (GOES‐R) satellites. The WoFS is a rapidly cycling, ensemble‐based analysis and forecasting system designed to generate short term (0–3 hr) forecasts of high impact weather. WoFS‐Smoke provides short‐term forecasts of smoke aerosols injected into the atmosphere from ongoing wildfires. The vertically integrated concentration of smoke aerosols in the atmosphere can be estimated from satellite based AOD retrievals. To assimilate AOD into WoFS‐Smoke, a smoke control variable is added to the prognostic state that is updated during each assimilation cycle. Then, a forward operator is created to relate modeled smoke aerosols to AOD retrievals using a mathematical function developed for the smoke‐AOD conversion used by the High‐Resolution Rapid Refresh for Smoke. Finally, GOES‐R AOD retrievals are quality controlled and are assimilated at 15 min intervals. Comparing analyzed AOD with smoke and other atmospheric variables indicates that assimilating AOD not only directly impacts smoke aerosol concentration in the system, but also has indirect impacts on variables such as temperature, humidity, and wind. Results from two wildfire cases in Oklahoma and Arizona show that assimilating AOD substantially impacts the concentration and distribution of smoke aerosols in the system. Forecast verification against satellite and surface observations indicates the overall impact of assimilating AOD in WoFS‐Smoke can improve forecast skill of smoke and the surrounding environment. Plain Language Summary: Short‐term forecasts of smoke generated from the Warn‐on‐Forecast System for Smoke (WoFS‐Smoke) data assimilation and forecasting system are improved through the assimilation of aerosol characteristics derived from satellites. Satellite measurements of aerosols provide information on the amount and distribution of aerosols in the atmosphere. Assimilating these data into a system designed to track smoke aerosols produces a more accurate analysis of smoke concentration. This work uses aerosol observations from a geostationary orbiting satellite to take advantage of the high temporal frequency (<15 min) needed to continuously update smoke in WoFS‐Smoke. Testing of two wildfire events showed that assimilating aerosol characteristics improved both smoke forecasts and forecasts of the surrounding atmospheric conditions. Key Points: Ensemble data assimilation of Geostationary Operational Environmental Satellite Series‐R aerosol optical depth (AOD) retrievals for short‐term smoke forecastsAssimilation of AOD improves the forecast of smoke aerosols in two wildfire casesImproved smoke forecasts impact forecasts of the surrounding atmospheric conditions [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration (NOAA)'s National Weather Service (NWS) is on its way to deploying various operational prediction applications using the Unified Forecast System (https://ufscommunity.org/ , last access: 18 June 2022), a community-based coupled, comprehensive Earth modeling system. An aerosol model component developed in collaboration between the Global Systems Laboratory, Chemical Science Laboratory, Air Resources Laboratory, and Environmental Modeling Center (GSL, CSL, ARL, EMC) was coupled online with the FV3 Global Forecast System (FV3GFS) using the National Unified Operational Prediction Capability (NUOPC)-based NOAA Environmental Modeling System (NEMS) software framework. This aerosol prediction system replaced the NEMS GFS Aerosol Component version 2 (NGACv2) system in the National Center for Environment Prediction (NCEP) production suite in September 2020 as one of the ensemble members of the Global Ensemble Forecast System (GEFS), dubbed GEFS-Aerosols v1. The aerosol component of atmospheric composition in the GEFS is based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). GEFS-Aerosols includes bulk modules from the Goddard Chemistry Aerosol Radiation and Transport model (GOCART). Additionally, the biomass burning plume rise module from High-Resolution Rapid Refresh (HRRR)-Smoke based on WRF-Chem was implemented. The GOCART dust scheme was replaced by the FENGSHA dust scheme (developed by ARL). The Blended Global Biomass Burning Emissions Product (GBBEPx version 3) provides biomass burning emission and fire radiative power (FRP) data. The global anthropogenic emission inventories are derived from the Community Emissions Data System (CEDS). All sub-grid-scale transport and deposition are handled inside the atmospheric physics routines, which required consistent implementation of positive definite tracer transport and wet scavenging in the physics parameterizations used by the NCEP's operational FV3GFS. This paper describes the details of GEFS-Aerosols model development and evaluation of real-time and retrospective runs using different observations from in situ measurement and satellite and aircraft data. GEFS-Aerosols predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational NGACv2 system with the fundamental updates (e.g., dust and fire emission) in the atmospheric and chemical transport model. [ABSTRACT FROM AUTHOR]

Plume height plays a vital role in wildfire smoke dispersion and the subsequent effects on air quality and human health. In this study, we assess the impact of different plume rise schemes on predicting the dispersion of wildfire air pollution, and the exceedances of the National Ambient Air Quality Standards (NAAQS) for fine particulate matter (PM2.5) during the 2020 Western United States Wildfire season. Three widely used plume rise schemes (Briggs 1969, Freitas 2007, Sofiev 2012) are compared within the Community Multiscale Air Quality (CMAQ) modelling framework. The plume heights simulated by these schemes are comparable to the aerosol height observed by the Multi-angle Imaging SpectroRadiometer (MISR). The performance of the simulations with these schemes varies by fire case and weather conditions. On average, simulations with higher plume injection heights predict lower AOD and surface PM2.5 concentrations near the source region but higher AOD and PM2.5 in downwind regions due to the faster spread of the smoke plume once ejected. The two-month mean AOD difference caused by different plume rise schemes is approximately 20–30 % near the source regions and 5–10 % in the downwind regions. Thick smoke blocks sunlight and suppresses photochemical reactions in areas with high AOD. The surface PM2.5 difference reaches 70 % on the west coast and the difference is lower than 15 % in the downwind regions. Moreover, the plume injection height affects pollution exceedance (>35 μg/m3) forecasts. Higher plume heights generally produce larger downwind PM2.5 exceedance areas. The PM2.5 exceedance areas predicted by the three schemes largely overlap, suggesting that all schemes perform similarly during large wildfire events when the predicted concentrations are well above the exceedance threshold. At the edges of the smoke plumes, however, there are noticeable differences in the PM2.5 concentration and predicted PM2.5 exceedance region. This disagreement among the PM2.5 exceedance forecasts may affect key decision-making regarding early warning of extreme air pollution episodes at local levels during large wildfire events. [ABSTRACT FROM AUTHOR]

Plume height plays a vital role in wildfire smoke dispersion and the subsequent effects on air quality and human health. In this study, we assess the impact of different plume rise schemes on predicting the dispersion of wildfire air pollution, and the exceedances of the National Ambient Air Quality Standards (NAAQS) for fine particulate matter (PM2.5) during the 2020 Western United States Wildfire season. Three widely used plume rise schemes (Briggs 1969, Freitas 2007, Sofiev 2012) are compared within the Community Multiscale Air Quality (CMAQ) modelling framework. The plume heights simulated by these schemes are comparable to the aerosol height observed by the Multi-angle Imaging SpectroRadiometer (MISR). The performance of the simulations with these schemes varies by fire case and weather conditions. On average, simulations with higher plume injection heights predict lower AOD and surface PM2.5 concentrations near the source region but higher AOD and PM2.5 in downwind regions due to the faster spread of the smoke plume once ejected. The two-month mean AOD difference caused by different plume rise schemes is approximately 20-30% near the source regions and 5-10% in the downwind regions. Thick smoke blocks sunlight and suppresses photochemical reactions in areas with high AOD. The surface PM2.5 difference reaches 70% on the west coast and the difference is lower than 15% in the downwind regions. Moreover, the plume injection height affects pollution exceedance (>35 µg/m3) forecasts. Higher plume heights generally produce larger downwind PM2.5 exceedance areas. The PM2.5 exceedance areas predicted by the three schemes largely overlap, suggesting that all schemes perform similarly during large wildfire events when the predicted concentrations are well above the exceedance threshold. At the edges of the smoke plumes, however, there are noticeable differences in the PM2.5 concentration and predicted PM2.5 exceedance region. This disagreement among the PM2.5 exceedance forecasts may affect key decision-making regarding early warning of extreme air pollution episodes at local levels during large wildfire events. [ABSTRACT FROM AUTHOR]

The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model with hourly data assimilation that covers the conterminous United States and Alaska and runs in real time at the NOAA/National Centers for Environmental Prediction (NCEP). Implemented operationally at NOAA/NCEP in 2014, the HRRR features 3-km horizontal grid spacing and frequent forecasts (hourly for CONUS and 3-hourly for Alaska). HRRR initialization is designed for optimal short-range forecast skill with a particular focus on the evolution of precipitating systems. Key components of the initialization are radar-reflectivity data assimilation, hybrid ensemble-variational assimilation of conventional weather observations, and a cloud analysis to initialize stratiform cloud layers. From this initial state, HRRR forecasts are produced out to 18 h every hour, and out to 48 h every 6 h, with boundary conditions provided by the Rapid Refresh system. Between 2014 and 2020, HRRR development was focused on reducing model bias errors and improving forecast realism and accuracy. Improved representation of the planetary boundary layer, subgrid-scale clouds, and land surface contributed extensively to overall HRRR improvements. The final version of the HRRR (HRRRv4), implemented in late 2020, also features hybrid data assimilation using flow-dependent covariances from a 3-km, 36-member ensemble ("HRRRDAS") with explicit convective storms. HRRRv4 also includes prediction of wildfire smoke plumes. The HRRR provides a baseline capability for evaluating NOAA's next-generation Rapid Refresh Forecast System, now under development. SIGNIFICANCE STATEMENT: NOAA's operational hourly updating, convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, have led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes. [ABSTRACT FROM AUTHOR]

We use the Weather Research and Forecasting with Chemistry (WRF-Chem) model with new implementations of GOES-16 wildfire emissions and plume rise based on fire radiative power (FRP) to interpret aerosol observations during the 2019 NASA-NOAA FIREX-AQ field campaign and perform model evaluations. We compare simulated aerosol concentrations and optical properties against observations of black carbon aerosol from the NOAA Single Particle Soot Photometer (NOAA-SP2), organic aerosol from the CU High-Resolution Aerosol Mass Spectrometer (HR-AMS), and aerosol backscatter coefficients from the high-spectral-resolution lidar (HSRL) system. This study focuses on the Williams Flats fire in Washington, which was repeatedly sampled during four science flights by the NASA DC-8 (3–8 August 2019). The emissions and plume-rise methodologies are implemented following NOAA's operational High-Resolution Rapid Refresh coupled with Smoke (HRRR-Smoke) forecasting model. In addition, new GOES-16 FRP-based diurnal cycle functions are developed and incorporated into WRF-Chem. The FIREX-AQ observations represented a diverse set of sampled environments ranging from fresh/aged smoke from the Williams Flats fire to remnants of plumes transported over long distances. The Williams Flats fire resulted in significant aerosol enhancements during 3–8 August 2019, which were substantially underestimated by the standard version of WRF-Chem. The simulated black carbon (BC) and organic carbon (OC) concentrations increased between a factor of 92–125 (BC) and a factor of 28–78 (OC) with the new implementation compared to the standard WRF-Chem version. These increases resulted in better agreement with the FIREX-AQ airborne observations for BC and OC concentrations (particularly for fresh smoke sampling phases) and aerosol backscatter coefficients. The model still showed a low bias in simulating the aerosol loadings observed in aged plumes from Williams Flats. WRF-Chem with the FRP-based plume rise simulated similar plume heights to the standard plume-rise model in WRF-Chem. The simulated plume heights (for both versions) compared well with estimated plume heights using the HSRL measurements. Therefore, the better agreement with observations was mainly driven by the higher emissions in the FRP-based version. The model evaluations also highlighted the importance of accurately accounting for the wildfire diurnal cycle and including adequate representation of the underlying chemical mechanisms, both of which could significantly impact model forecasting performance. [ABSTRACT FROM AUTHOR]

This research begins the process of creating an ensemble-based forecast system for smoke aerosols generated from wildfires using a modified version of the National Severe Storms Laboratory (NSSL) Warn-on-Forecast System (WoFS). The existing WoFS has proven effective in generating short-term (0–3 h) probabilistic forecasts of high-impact weather events such as storm rotation, hail, severe winds, and heavy rainfall. However, it does not include any information on large smoke plumes generated from wildfires that impact air quality and the surrounding environment. The prototype WoFS-Smoke system is based on the deterministic High-Resolution Rapid Refresh-Smoke (HRRR-Smoke) model. HRRR-Smoke runs over a continental United States (CONUS) domain with a 3-km horizontal grid spacing, with hourly forecasts out to 48 h. The smoke plume injection algorithm in HRRR-Smoke is integrated into the WoFS forming WOFS-Smoke so that the advantages of the rapidly cycling, ensemble-based WoFS can be used to generate short-term (0–3 h) probabilistic forecasts of smoke. WoFS-Smoke forecasts from three wildfire cases during 2020 show that the system generates a realistic representation of wildfire smoke when compared against satellite observations. Comparison of smoke forecasts with radar data show that forecast smoke reaches higher levels than radar-detected debris, but exceptions to this are noted. The radiative effect of smoke on surface temperature forecasts is evident, which reduces forecast errors compared to experiments that do not include smoke. [ABSTRACT FROM AUTHOR]

Smoke from the 2018 Camp Fire in Northern California blanketed a large part of the region for 2 weeks, creating poor air quality in the "unhealthy" range for millions of people. The NOAA Global System Laboratory's HRRR-Smoke model was operating experimentally in real time during the Camp Fire. Here, output from the HRRR-Smoke model is compared to surface observations of PM2.5 from AQS and PurpleAir sensors as well as satellite observation data. The HRRR-Smoke model at 3-km resolution successfully simulated the evolution of the plume during the initial phase of the fire (8-10 November 2018). Stereoscopic satellite plume height retrievals were used to compare with model output (for the first time, to the authors' knowledge), showing that HRRR-Smoke is able to represent the complex 3D distribution of the smoke plume over complex terrain. On 15-16 November, HRRR-Smoke was able to capture the intensification of PM2.5 pollution due to a high pressure system and subsidence that trapped smoke close to the surface; however, HRRR-Smoke later underpredicted PM2.5 levels due to likely underestimates of the fire radiative power (FRP) derived from satellite observations. The intensity of the Camp Fire smoke event and the resulting pollution during the stagnation episodes make it an excellent test case for HRRR-Smoke in predicting PM2.5 levels, which were so high from this single fire event that the usual anthropogenic pollution sources became insignificant. The HRRR-Smoke model was implemented operationally at NOAA/NCEP in December 2020, now providing essential support for smoke forecasting as the impact of U.S. wildfires continues to increase in scope and magnitude. [ABSTRACT FROM AUTHOR]

We use the Weather Research and Forecasting with Chemistry (WRF-Chem) model with new implementations of GOES-16 fire radiative power (FRP) based wildfire emissions and plume-rise to interpret aerosol observations during the 2019 NASA-NOAA FIREX-AQ field campaign and perform model evaluations. We compare simulated aerosol concentrations and optical properties against observations of black carbon aerosol from the NOAA Single Particle Soot Photometer (NOAA-SP2), organic aerosol from the CU High Resolution Aerosol Mass Spectrometer (HR36 AMS) and aerosol backscatter coefficients from the High Spectral Resolution Lidar (HSRL) system. This study focuses on the Williams Flats fire in Washington, which was repeatedly sampled during four science flights by the NASA DC-8 (August 3 – August 8, 2019). The emissions and plume-rise methodologies are implemented following NOAA’s operational High Resolution Rapid Refresh coupled with Smoke (HRRR-Smoke) forecasting model. In addition, new GOES-16 FRP based diurnal cycle functions are developed and incorporated in WRF-Chem. The FIREX-AQ observations represented a diverse set of sampled environments ranging from fresh/aged smoke from the Williams Flats fire to remnants of plumes transported over long distances. The Williams Flats fire resulted in significant aerosol enhancements during August 3- 8, 2019, which were substantially underestimated by the standard version of WRF-Chem. The simulated BC and OC concentrations increased between 92 – 125 times (BC) and 28-78 times (OC) with the new implementation compared to the standard WRF-Chem version. These increases resulted in better agreement with the FIREX-AQ airborne observations for BC and OC concentrations (particularly for fresh smoke sampling phases) and aerosol backscatter coefficients. The model still showed a low bias in simulating the aerosol loadings observed in aged plumes from Williams Flats. WRF-Chem with the FRP-based plumerise simulated similar plume heights to the standard plumerise model in WRF-52 Chem. The simulated plume heights (for both versions) compared well with estimated plume heights using the HSRL measurements. Therefore, the improvements in the model simulation were mainly driven by the higher emissions in the FRP55 based version. The model evaluations also highlighted the importance of accurately accounting for the wildfire diurnal cycle and including adequate representation of the underlying chemical mechanisms, both of which could significantly impact model forecasting performance. [ABSTRACT FROM AUTHOR]

Birds, energetics, migration, movement, population connectivity, smoke, telemetry, wildfire Average smoke concentrations within migration flight elevations (<4,000 m) greater than 160 µg m-3 (red) resulted in disruption of typical migratory behavior; including extended at-sea rafting, novel stopover site use, recursive migration paths ("impact points"; yellow circles). Keywords: birds; energetics; migration; movement; population connectivity; smoke; telemetry; wildfire EN birds energetics migration movement population connectivity smoke telemetry wildfire 1 5 5 01/05/22 20220101 NES 220101 In 2020, the fire season affecting the western United States reached unprecedented levels. [Extracted from the article]

NOAA's National Weather Service (NWS) is on its way to deploy various operational prediction applications using the Unified Forecast System (https://ufscommunity.org/), a community-based coupled, comprehensive Earth modeling system. An aerosol model component developed in a collaboration between the Global Systems Laboratory, Chemical Science Laboratory, the Air Resources Laboratory, and Environmental Modeling Center (GSL, CSL, ARL, EMC) was coupled online with the FV3 Global Forecast System (FV3GFS) using the National Unified Operational Prediction Capability (NUOPC)-based NOAA Environmental Modeling System (NEMS) software framework. This aerosol prediction system replaced the NEMS GFS Aerosol Component (NGAC) system in the National Center for Environment Prediction (NCEP) production suite in September 2020 as one of the ensemble members of the Global Ensemble Forecast System (GEFS), dubbed GEFS-Aerosols v1. The aerosol component of atmospheric composition in GEFS is based on the Weather Research and Forecasting model (WRF-Chem) that was previously included into FIM-Chem (Zhang et al, 2021). GEFS- Aerosols includes bulk modules from the Goddard Chemistry Aerosol Radiation and Transport model (GOCART). Additionally, the biomass burning plume rise module from High-Resolution Rapid Refresh (HRRR)-Smoke was implemented; the GOCART dust scheme was replaced by the FENGSHA dust scheme (developed by ARL); the Blended Global Biomass Burning Emissions Product (GBBEPx V3) provides biomass burning emission and Fire Radiative Power (FRP) data; and the global anthropogenic emission inventories are derived from the Community Emissions Data System (CEDS). All sub-grid scale transport and deposition is handled inside the atmospheric physics routines, which required consistent implementation of positive definite tracer transport and wet scavenging in the physics parameterizations used by NCEP's operational Global Forecast System based on FV3 (FV3GFS). This paper describes the details of GEFS-Aerosols model development and evaluation of real -time and retrospective runs using different observations from in situ measurement, satellite and aircraft data. GEFS-Aerosols predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational NGAC system. [ABSTRACT FROM AUTHOR]

Wildfire smoke is one of the most significant concerns of human and environmental health, associated with its substantial impacts on air quality, weather, and climate. However, biomass burning emissions and smoke remain among the largest sources of uncertainties in air quality forecasts. In this study, we evaluate the smoke emissions and plume forecasts from 12 state-of-the-art air quality forecasting systems during the Williams Flats fire in Washington State, US, August 2019, which was intensively observed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Model forecasts with lead times within 1 d are intercompared under the same framework based on observations from multiple platforms to reveal their performance regarding fire emissions, aerosol optical depth (AOD), surface PM 2.5 , plume injection, and surface PM 2.5 to AOD ratio. The comparison of smoke organic carbon (OC) emissions suggests a large range of daily totals among the models, with a factor of 20 to 50. Limited representations of the diurnal patterns and day-to-day variations of emissions highlight the need to incorporate new methodologies to predict the temporal evolution and reduce uncertainty of smoke emission estimates. The evaluation of smoke AOD (sAOD) forecasts suggests overall underpredictions in both the magnitude and smoke plume area for nearly all models, although the high-resolution models have a better representation of the fine-scale structures of smoke plumes. The models driven by fire radiative power (FRP)-based fire emissions or assimilating satellite AOD data generally outperform the others. Additionally, limitations of the persistence assumption used when predicting smoke emissions are revealed by substantial underpredictions of sAOD on 8 August 2019, mainly over the transported smoke plumes, owing to the underestimated emissions on 7 August. In contrast, the surface smoke PM 2.5 (sPM 2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM 2.5. Overpredictions of sPM 2.5 are found for the models driven by FRP-based emissions during nighttime, suggesting the necessity to improve vertical emission allocation within and above the planetary boundary layer (PBL). Smoke injection heights are further evaluated using the NASA Langley Research Center's Differential Absorption High Spectral Resolution Lidar (DIAL-HSRL) data collected during the flight observations. As the fire became stronger over 3–8 August, the plume height became deeper, with a day-to-day range of about 2–9 km a.g.l. However, narrower ranges are found for all models, with a tendency of overpredicting the plume heights for the shallower injection transects and underpredicting for the days showing deeper injections. The misrepresented plume injection heights lead to inaccurate vertical plume allocations along the transects corresponding to transported smoke that is 1 d old. Discrepancies in model performance for surface PM 2.5 and AOD are further suggested by the evaluation of their ratio, which cannot be compensated for by solely adjusting the smoke emissions but are more attributable to model representations of plume injections, besides other possible factors including the evolution of PBL depths and aerosol optical property assumptions. By consolidating multiple forecast systems, these results provide strategic insight on pathways to improve smoke forecasts. [ABSTRACT FROM AUTHOR]

Author(s): Peter M. Edwards [1, 2, 8]; Steven S. Brown (corresponding author) [1]; James M. Roberts [1]; Ravan Ahmadov [1, 2]; Robert M. Banta [1]; Joost A. deGouw [1, 2]; [...]

Volcanic explosions are the most critical replenishing mechanism of the stratospheric aerosol Junge layer. A fresh volcanic cloud comprises mostly sulfur‐bearing gases, volcanic ash, and water vapor. It is commonly assumed that only sulfate aerosols remain in an aged volcanic cloud. Accurate simulation of the initial evolution of multicomponent fresh volcanic clouds is largely missing due to insufficient spatial resolution and a lack of relevant physics in global climate models. However, this initial stage is essential, as the vertical structure, composition, and altitude of a freshly developed volcanic cloud affect its long‐term evolution. To fill this gap, we modified a regional WRF‐Chem model to study the dispersion of a Pinatubo‐size volcanic cloud in the equatorial belt with a 25 km grid spacing explicitly accounting for the SO2, ash, sulfate, water vapor, and hydrometeors radiative effects. The model best reproduces the observed evolution of the Pinatubo optical depth when eruptive products are injected above the cold tropical tropopause at 17 km. During the first week, the volcanic cloud in our simulations rises 1 km/day. Ash is primarily responsible for the heating and lofting of the volcanic products. Radiative heating of SO2 is weaker than that of ash and sulfate but is sufficient to position the core of the SO2 layer 1–2 km above the sulfate layer. Utilizing a more realistic description of the volcanic cloud's initial stage potentially improves overall volcanic cloud predictability. It might also be essential to designing geoengineering technologies based on the injection of aerosol precursors in the lower stratosphere. Key Points: The model better reproduces the observed evolution of the Pinatubo optical depth when eruptive products are injected at 17 kmThe short‐lived ash is primarily responsible for the heating of the volcanic cloudRadiative heating of SO2 is weaker than that of ash and sulfate aerosols but still is significant to position the core of the SO2 cloud 1–2 km above the sulfate layer [ABSTRACT FROM AUTHOR]

The global Flow-following finite-volume Icosahedral Model (FIM), which was developed in the Global Systems Laboratory of NOAA/ESRL, has been coupled inline with aerosol and gas-phase chemistry schemes of different complexity using the chemistry and aerosol packages from WRF-Chem v3.7, named as FIM-Chem v1. The three chemistry schemes include 1) the simple aerosol modules from the Goddard Chemistry Aerosol Radiation and Transport model that includes only simplified sulfur chemistry, bulk aerosols, and sectional dust and sea salt modules (GOCART); 2) the photochemical gas-phase mechanism RACM coupled to GOCART to determine the impact of more realistic gas-phase chemistry on the GOCART aerosols simulations (RACM_GOCART); and 3) a further sophistication within the aerosol modules by replacing GOCART with a modal aerosol scheme that includes secondary organic aerosols (SOA) based on the VBS approach (RACM_SOA_VBS). FIM-Chem is able to simulate aerosol, gas-phase chemical species and SOA at various spatial resolutions with different levels of complexity and quantify the impact of aerosol on numerical weather predictions (NWP). We compare the results of RACM_GOCART and GOCART schemes which uses the default climatological model fields for OH, H2O2, and NO3. We find significant reductions of sulfate that are on the order of 40 % to 80 % over the eastern US and are up to 40 % near the Beijing region over China when using the RACM_GOCART scheme. We also evaluate the model performance by comparing with the Atmospheric Tomography Mission (ATom-1) aircraft measurements in 2016 summer. FIM-Chem shows good performance in capturing the aerosol and gas-phase tracers. The model predicted vertical profiles of biomass burning plumes and dust plumes off the western Africa are also reproduced reasonably well. [ABSTRACT FROM AUTHOR]

In this paper, we rectify inconsistencies that emerge in the Weather Research and Forecasting model with chemistry (WRF-Chem) v3.2 code when using the Goddard Chemistry Aerosol Radiation and Transport (GOCART) aerosol module. These inconsistencies have been reported, and corrections have been implemented in WRF-Chem v4.1.3. Here, we use a WRF-Chem experimental setup configured over the Middle East (ME) to estimate the effects of these inconsistencies. Firstly, we show that the old version underestimates the PM 2.5 diagnostic output by 7 % and overestimates PM 10 by 5 % in comparison with the corrected one. Secondly, we demonstrate that submicron dust particles' contribution was incorrectly accounted for in the calculation of optical properties. Therefore, aerosol optical depth (AOD) in the old version was 25 %–30 % less than in the corrected one. Thirdly, we show that the gravitational settling procedure, in comparison with the corrected version, caused higher dust column loadings by 4 %–6 %, PM 10 surface concentrations by 2 %–4 %, and mass of the gravitationally settled dust by 5 %–10 %. The cumulative effect of the found inconsistencies led to the significantly higher dust content in the atmosphere in comparison with the corrected WRF-Chem version. Our results explain why in many WRF-Chem simulations PM 10 concentrations were exaggerated. We present the methodology for calculating diagnostics we used to estimate the impacts of introduced code modifications. We share the developed Merra2BC interpolator, which allows processing Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) output for constructing initial and boundary conditions for chemical species and aerosols. [ABSTRACT FROM AUTHOR]

Solar radiation is the ultimate source of energy flowing through the atmosphere; it fuels all atmospheric motions. The visible-wavelength range of solar radiation represents a significant contribution to the earth's energy budget, and visible light is a vital indicator for the composition and thermodynamic processes of the atmosphere from the smallest weather scales to the largest climate scales. The accurate and fast description of light propagation in the atmosphere and its lower-boundary environment is therefore of critical importance for the simulation and prediction of weather and climate. Simulated Weather Imagery (SWIm) is a new, fast, and physically based visible-wavelength three-dimensional radiative transfer model. Given the location and intensity of the sources of light (natural or artificial) and the composition (e.g., clear or turbid air with aerosols, liquid or ice clouds, precipitating rain, snow, and ice hydrometeors) of the atmosphere, it describes the propagation of light and produces visually and physically realistic hemispheric or 360 ∘ spherical panoramic color images of the atmosphere and the underlying terrain from any specified vantage point either on or above the earth's surface. Applications of SWIm include the visualization of atmospheric and land surface conditions simulated or forecast by numerical weather or climate analysis and prediction systems for either scientific or lay audiences. Simulated SWIm imagery can also be generated for and compared with observed camera images to (i) assess the fidelity and (ii) improve the performance of numerical atmospheric and land surface models. Through the use of the latter in a data assimilation scheme, it can also (iii) improve the estimate of the state of atmospheric and land surface initial conditions for situational awareness and numerical weather prediction forecast initialization purposes. [ABSTRACT FROM AUTHOR]

In this paper, we rectify inconsistencies that emerge in the WRF-Chem code when using the Goddard Chemistry Aerosol Radiation and Transport (GOCART) aerosol module. These inconsistencies have been reported and corrections have been implemented in WRF-Chem v4.1.3. Here, we use a WRF-Chem experimental setup configured over the Middle East (ME) to estimate the effects of these inconsistencies. Firstly, we show that the diagnostic output of PM2.5 surface concentration was underestimated by 7% and PM10 was overestimated by 5 %. Secondly, we demonstrate that the contribution of sub-micron dust particles was underestimated in the calculation of optical properties and thus, Aerosol Optical Depth (AOD) was consequently underestimated by 25-30 %. Thirdly, we show that an inconsistency in the process of gravitational settling led to the overestimation of the dust column loadings by 4-6 %, PM10 surface concentrations by 2-4 %, and the rate of dust gravitational settling by 5-10 %. We present a methodology to calculate diagnostics that can be used to estimate the effects of these applied changes. Our corrections also help to explain the overestimation of PM10 surface concentrations encountered in many WRF-Chem simulations. We also share the developed Merra2BC interpolator, which allows constructing initial and boundary conditions for chemical species and aerosols based on MERRA-2 reanalysis. The results of this work can be useful for those who simulate the dust cycle using the WRF-Chem model coupled with the GOCART aerosol module. [ABSTRACT FROM AUTHOR]

Solar radiation is the ultimate source of energy for all atmospheric motions. The visible wavelength range of solar radiation represents a significant contribution to the Earth’s energy budget and visible light is a vital indicator for the composition and thermodynamic processes of the atmosphere from the smallest weather to the largest climate scales. The accurate and fast description of light propagation in the atmosphere and its lower boundary environment is therefore of critical importance for the simulation and prediction of weather and climate. Simulated Weather Imagery (SWIm) is a new, fast and physically based visible wavelength 3-dimensional radiative transfer model. Given the location and intensity of the sources of light (natural or artificial) and the composition (e.g., clear or turbid air with aerosols, liquid or ice clouds, and precipitating rain, snow, or ice hydrometeors) of the atmosphere, it describes the propagation of light and produces visually and physically realistic hemispheric or 360° spherical panoramic color images of the atmosphere and the underlying terrain from any specified vantage point either on or above the Earth's surface. Applications of SWIm include the visualization of atmospheric and land surface conditions simulated or forecast by numerical weather or climate analysis and prediction systems for either scientific or lay audiences. Simulated SWim imagery can also be generated for and compared with observed camera images to (i) assess the fidelity, (ii) and improve the performance of numerical atmospheric and land surface models, as well as (iii) through their inclusion into an observational data assimilation scheme, improve the estimate of the state of atmospheric and land surface initial conditions for situational awareness and NWP forecast initialization applications. [ABSTRACT FROM AUTHOR]

To study regional-scale carbon dioxide (CO2) transport, temporal variability, and budget over the Southern California Air Basin (SoCAB) during the California Research at the Nexus of Air Quality and Climate Change (CalNex) 2010 campaign period, a model that couples the Weather Research and Forecasting (WRF) Model with the Vegetation Photosynthesis and Respiration Model (VPRM) has been used. Our numerical simulations use anthropogenic CO2 emissions of the Hestia Project 2010 fossil-fuel CO2 emissions data products along with optimized VPRM parameters at 'FLUXNET' sites, for biospheric CO2 fluxes over SoCAB. The simulated meteorological conditions have been validated with ground and aircraft observations, as well as with background CO2 concentrations from the coastal Palos Verdes site. The model captures the temporal pattern of CO2 concentrations at the ground site at the California Institute of Technology in Pasadena, but it overestimates the magnitude in early daytime. Analysis of CO2 by wind directions reveals the overestimate is due to advection from the south and southwest, where downtown Los Angeles is located. The model also captures the vertical profile of CO2 concentrations along with the flight tracks. The optimized VPRM parameters have significantly improved simulated net ecosystem exchange at each vegetation-class site and thus the regional CO2 budget. The total biospheric contribution ranges approximately from −24% to −20% (daytime) of the total anthropogenic CO2 emissions during the study period. [ABSTRACT FROM AUTHOR]

Nitrous acid (HONO) photolysis is an important source of hydroxyl radicals (OH) in the lower atmosphere, in particular in winter when other OH sources are less efficient. The nighttime formation of HONO and its photolysis in the early morning have long been recognized as an important contributor to the OH budget in polluted environments. Over the past few decades it has become clear that the formation of HONO during the day is an even larger contributor to the OH budget and additionally provides a pathway to recycle NOx. Despite the recognition of this unidentified HONO daytime source, the precise chemical mechanism remains elusive. A number of mechanisms have been proposed, including gas-phase, aerosol, and ground surface processes, to explain the elevated levels of daytime HONO. To identify the likely HONO formation mechanisms in a wintertime polluted rural environment we present LP-DOAS observations of HONO, NO2, and O3 on three absorption paths that cover altitude intervals from 2 to 31, 45, and 68 m above ground level (a.g.l.) during the UBWOS 2012 experiment in the Uintah Basin, Utah, USA. Daytime HONO mixing ratios in the 2–31 m height interval were, on average, 78 ppt, which is lower than HONO levels measured in most polluted urban environments with similar NO2 mixing ratios of 1–2 ppb. HONO surface fluxes at 19 m a.g.l., calculated using the HONO gradients from the LP-DOAS and measured eddy diffusivity coefficient, show clear upward fluxes. The hourly average vertical HONO flux during sunny days followed solar irradiance, with a maximum of (4.9 ± 0.2)  ×  1010 molec. cm−2 s−1 at noontime. A photostationary state analysis of the HONO budget shows that the surface flux closes the HONO budget, accounting for 63 ± 32 % of the unidentified HONO daytime source throughout the day and 90 ± 30 % near noontime. This is also supported by 1-D chemistry and transport model calculations that include the measured surface flux, thus clearly identifying chemistry at the ground as the missing daytime HONO source in this environment. Comparison between HONO surface flux, solar radiation, NO2 and HNO3 mixing ratios, and results from 1-D model runs suggest that, under high NOx conditions, HONO formation mechanisms related to solar radiation and NO2 mixing ratios, such as photo-enhanced conversion of NO2 on the ground, are most likely the source of daytime HONO. Under moderate to low NO2 conditions, photolysis of HNO3 on the ground seems to be the main source of HONO. [ABSTRACT FROM AUTHOR]

Nitrous acid (HONO) and nitryl chloride (ClNO2/ - through their photolysis - can have profound effects on the nitrogen cycle and oxidation capacity of the lower troposphere. Previous numerical studies have separately considered and investigated the sources/processes of these compounds and their roles in the fate of reactive nitrogen and the production of ozone (O3/, but their combined impact on the chemistry of the lower part of the troposphere has not been addressed yet. In this study, we updated the WRFChem model with the currently known sources and chemistry of HONO and chlorine in a new chemical mechanism (CBMZ_ReNOM), and applied it to a study of the combined effects of HONO and ClNO2 on summertime O3 in the boundary layer over China. We simulated the spatial distributions of HONO, ClNO2, and related compounds at the surface and within the lower troposphere. The results showed that the modeled HONO levels reached up to 800-1800 ppt at the surface (0-30 m) over the North China Plain (NCP), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) regions and that HONO was concentrated within a 0-200m layer. In comparison, the simulated surface ClNO2 mixing ratio was around 800-1500 ppt over the NCP, YRD, and central China regions and was predominantly present in a 0-600m layer. HONO enhanced daytime ROx (OHCHO2 CRO2/ and O3 at the surface (0- 30 m) by 2.8-4.6 ppt (28-37 %) and 2.9-6.2 ppb (6-13 %), respectively, over the three most developed regions, whereas ClNO2 increased surface O3 in the NCP and YRD regions by 2.4-3.3 ppb (or 5-6 %) and it also had a significant impact (3-6 %) on above-surface O3 within 200-500 m. The combined effects increased surface O3 by 11.5, 13.5, and 13.3% in the NCP, YRD, and PRD regions, respectively. Over the boundary layer (0-1000 m), the HONO and ClNO2 enhanced O3 by up to 5.1 and 3.2 %, respectively, and their combined effect increased O3 by 7.1-8.9% in the three regions. The new module noticeably improved O3 predictions at ~900 monitoring stations throughout China by reducing the mean bias from -4.3 to 0.1 ppb. Our study suggests the importance of considering these reactive nitrogen species simultaneously into chemical transport models to better simulate the formation of summertime O3 in polluted regions. [ABSTRACT FROM AUTHOR]

Nitrous acid (HONO) photolysis is an important source of hydroxyl radicals (OH) in the lower atmosphere, in particular in winter when other OH sources are less efficient. The nighttime formation of HONO and its photolysis in the early morning have long been recognized as an important contributor to the OH budget in polluted environments. Over the past few decades it has become clear that the formation of HONO during the day is an even larger contributor to the OH budget, and additionally provides a pathway to recycle NOx. Despite the recognition of this unidentified HONO daytime source, the precise chemical mechanism remains elusive. A number of mechanisms have been proposed, including gas-phase, aerosol, and ground surface processes, to explain the elevated levels of daytime HONO. To identify the likely HONO formation mechanisms in a wintertime polluted rural environment we present LP-DOAS observations of HONO, NO2, and O3 on three absorption paths that cover altitude intervals from 2 m to 31 m, 45 m, and 68 m agl during the UBWOS 2012 experiment in the Uintah Basin, Utah, USA. Daytime HONO mixing ratios in the 2-31 m height interval were, on average, 78 ppt, which is lower than HONO levels measured in most polluted urban environments with similar NO2 mixing ratios of 1-2 ppb. HONO surface fluxes at 16 m agl, calculated using the HONO gradients from the LP-DOAS and measured eddy diffusivity coefficient, show clear upward fluxes. The hourly average vertical HONO flux during sunny days followed solar irradiance, with a maximum of (4.9 ± 0.2) × 1010 molec. cm-2 s-1 at noontime. A photo-stationary state analysis of the HONO budget shows that the surface flux closes the HONO budget, accounting for 63 ± 32 % of the unidentified HONO daytime source throughout the day and 90 ± 30 % near noontime. This is also supported by 1D chemistry and transport model calculations that include the measured surface flux, thus clearly identifying chemistry at the ground as the missing daytime HONO source in this environment. Comparison between HONO surface flux, solar radiation, NO2 and HNO3 mixing ratios and results from 1D model runs suggest that, under high NOx conditions, HONO formation mechanisms related to solar radiation and NO2 mixing ratios, such as photo-enhanced conversion of NO2 on the ground, are most likely the source of daytime HONO. Under moderate to low NO2 conditions, photolysis of HNO3 on the ground seems to be the main source of HONO. [ABSTRACT FROM AUTHOR]

The uptake of dinitrogen pentoxide (N2O5/ on aerosol surfaces and the subsequent production of nitryl chloride (ClNO2/ can have a significant impact on the oxidising capability and thus on secondary pollutants such as ozone. The range of such an impact, however, has not been well quantified in different geographical regions. In this study, we applied the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) model to investigate the impact of the N2O5 uptake processes in the Hong Kong-Pearl River Delta (HK-PRD) region, where the highest ever reported N2O5 and ClNO2 concentrations were observed in our recent field study. We first incorporated into the WRF-Chem an aerosol thermodynamics model (ISORROPIA II), recent parameterisations for N2O5 heterogeneous uptake and ClNO2 production and gas-phase chlorine chemistry. The revised model was then used to simulate the spatiotemporal distribution of N2O5 and ClNO2 over the HK-PRD region and the impact of N2O5 uptake and Cl activation on ozone and reactive nitrogen in the planetary boundary layer (PBL). The updated model can generally capture the temporal variation of N2O5 and ClNO2 observed at a mountaintop site in Hong Kong, but it overestimates N2O5 uptake and ClNO2 production. The model results suggest that under average conditions, elevated levels of ClNO2 (> 0.25 ppb within the PBL) are present in the south-western PRD, with the highest values (> 1.00 ppb) predicted near the ground surface (0-200m above ground level; a.g.l.). In contrast, during the night when very high levels of ClNO2 and N2O5 were measured in well-processed plumes from the PRD, ClNO2 is mostly concentrated within the residual layer (~300ma.g.l.). The addition of N2O5 heterogeneous uptake and Cl activation reduces the NO and NO2 levels by as much as 1.93 ppb (~7.4%) and 4.73 ppb (~16.2%), respectively, and it increases the total nitrate and ozone concentrations by up to 13.45 μgm-3 (~57.4%) and 7.23 ppb (~16.3%), respectively, in the PBL. Sensitivity tests show that the simulated chloride and ClNO2 concentrations are highly sensitive to chlorine emission. Our study suggests the need to measure the vertical profiles of N2O5 = ClNO2 under various meteorological conditions, to consider the chemistry of N2O5 = ClNO2 in the chemical transport model and to develop an updated chlorine emission inventory over China. [ABSTRACT FROM AUTHOR]

Megacities are major sources of anthropogenic fossil fuel CO2 (FFCO2) emissions. The spatial extents of these large urban systems cover areas of 10 000 km² or more with complex topography and changing landscapes. We present a high-resolution land-atmosphere modelling system for urban CO2 emissions over the Los Angeles (LA) megacity area. The Weather Research and Forecasting (WRF)-Chem model was coupled to a very high-resolution FFCO2 emission product, Hestia-LA, to simulate atmospheric CO2 concentrations across the LA megacity at spatial resolutions as fine as ~1 km. We evaluated multiple WRF configurations, selecting one that minimized errors in wind speed, wind direction, and boundary layer height as evaluated by its performance against meteorological data collected during the CalNex-LA campaign (May-June 2010). Our results show no significant difference between moderate-resolution (4 km) and high-resolution (1.3 km) simulations when evaluated against surface meteorological data, but the highresolution configurations better resolved planetary boundary layer heights and vertical gradients in the horizontal mean winds. We coupled our WRF configuration with the Vulcan 2.2 (10 km resolution) and Hestia-LA (1.3 km resolution) fossil fuel CO2 emission products to evaluate the impact of the spatial resolution of the CO2 emission products and the meteorological transport model on the representation of spatiotemporal variability in simulated atmospheric CO2 concentrations. We find that high spatial resolution in the fossil fuel CO2 emissions is more important than in the atmospheric model to capture CO2 concentration variability across the LA megacity. Finally, we present a novel approach that employs simultaneous correlations of the simulated atmospheric CO2 fields to qualitatively evaluate the greenhouse gas measurement network over the LA megacity. Spatial correlations in the atmospheric CO2 fields reflect the coverage of individual measurement sites when a statistically significant number of sites observe emissions from a specific source or location. We conclude that elevated atmospheric CO2 concentrations over the LA megacity are composed of multiple fine-scale plumes rather than a single homogenous urban dome. Furthermore, we conclude that FFCO2 emissions monitoring in the LA megacity requires FFCO2 emissions modelling with ~1 km resolution because coarser-resolution emissions modelling tends to overestimate the observational constraints on the emissions estimates. [ABSTRACT FROM AUTHOR]

The uptake of dinitrogen pentoxide (N2O5) on aerosol surfaces and the subsequent production of nitryl chloride (ClNO2) can have significant impact on the oxidising capability and thus on secondary pollutants such as ozone. The range of such impact, however, has not well been quantified in different geographical regions. In this study, we applied Weather Research and Forecasting coupled with Chemistry (WRF-Chem) model to investigate the impact of the N2O5 uptake processes in the Hong Kong-Pearl River Delta (HK-PRD) region, where the highest ever-reported N2O5 and ClNO2 concentrations were observed in our recent field study. We first incorporated into the WRF-Chem an aerosol thermodynamics model (ISORROPIA II), recent parameterisations for N2O5 heterogeneous uptake and ClNO2 production and gas-phase chlorine chemistry. The revised model was then used to simulate the spatiotemporal distribution of N2O5 and ClNO2 over the HK-PRD region and the impact of N2O5 uptake and Cl activation on ozone and reactive nitrogen in the planetary boundary layer (PBL). The updated model is capable of reproducing the temporal patterns of N2O5 and ClNO2 observed at a mountain-top site in Hong Kong, but overestimates N2O5 uptake and ClNO2 production. The model results suggest that under average meteorological conditions, elevated levels of ClNO2 (> 0.25 ppb within the PBL) are present in the south-western PRD, with the highest values (> 1.00 ppb) predicted near the ground surface (0-200 m above ground level (a.g.l.)). In contrast, during the night when very high levels of ClNO2 and N2O5 were measured in well-processed plumes from the PRD, ClNO2 is mostly concentrated within the residual layer (~ 300 m a.g.l.). The addition of N2O5 heterogeneous uptake and Cl activation reduces the NO and NO2 levels by as much as 1.93 ppb (~ 7.4%) and 4.73 ppb (~ 16.2%), respectively, increases the total nitrate and ozone concentrations by up to 13.45 µg m-3 (~ 57.4%) and 7.23 ppb (~ 16.3%), respectively, in the PBL. Sensitivity tests show that the simulated chloride and ClNO2 concentrations are highly sensitive to chlorine emission. Our study suggests the need to measure the vertical profiles of N2O5/ClNO2 under various meteorological conditions, to consider the chemistry of N2O5/ClNO2 in the chemical transport model, and to develop an updated chlorine emission inventory over China. [ABSTRACT FROM AUTHOR]

The western US experienced one of the worst fire seasons in 2018. During summer 2018 air quality in the northwestern US was dramatically affected by the large wildfires in the western US and Canada. It is a huge challenge to accurately forecast biomass burning emissions from rapidly changing wildland fires across the US and surrounding regions, the transport of smoke near the surface and aloft on local and regional scales, and the impact of smoke on visibility and weather.We present an experimental smoke forecasting system, which leverages the existing Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) numerical weather prediction models running operationally at NOAA/NWS. The RAP domain (13.5 km resolution) covers all of North America, and other regions. The HRRR model is nested within RAP and runs on a very high resolution (3km) domain over CONUS. The RAP-Smoke model enables simulation of smoke over Canada, thus providing boundary conditions of smoke to the HRRR domain. The RAP/HRRR-Smoke modeling system is simulated in real time by ingesting the real-time satellite (Suomi-NPP, NOAA-20 and MODIS Aqua/Terra) fire radiative power data. The rapidly updated forecast products of smoke (near surface and aloft), visibility and other related variables are provided to a wide range of operational users and researchers across the US. Here, we discuss RAP/HRRR-Smoke simulations for August, 2018 over the northwestern US. The smoke and visibility forecasts are evaluated using the available ground particulate matter and satellite based (e.g. VIIRS AOD) measurements. Additionally, detailed verification of the meteorological forecasts for the case study is presented. We demonstrate the improvements in weather forecasting, when smoke feedback on meteorological processes is enabled in the coupled HRRR-Smoke model. [ABSTRACT FROM AUTHOR]