Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire

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,&nbsp;August&nbsp;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&thinsp;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 PM2.5, plume injection, and surface PM2.5 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&nbsp;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&nbsp;August&nbsp;2019, mainly over the transported smoke plumes, owing to the underestimated emissions on 7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM2.5. Overpredictions of sPM2.5 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&ndash;8&nbsp;August, the plume height became deeper, with a day-to-day range of about 2&ndash;9&thinsp;km&thinsp;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&thinsp;d old. Discrepancies in model performance for surface PM2.5 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. &nbsp; Full list of Authors: &nbsp; Xinxin Ye1, Pargoal Arab1, Ravan Ahmadov2,3, Eric James2,3, Georg A. Grell3, Bradley Pierce4, Aditya Kumar4, Paul Makar5, Jack Chen5, Didier Davignon6, Greg R. Carmichael7, Gonzalo Ferrada7, Jeff McQueen8, Jianping Huang8, Rajesh Kumar9, Louisa Emmons10, Farren L. Herron-Thorpe11, Mark Parrington12, Richard Engelen12, Vincent-Henri Peuch12, Arlindo da Silva13, Amber Soja14,15, Emily Gargulinski15, Elizabeth Wiggins15, Johnathan W. Hair15, Marta Fenn15,18, Taylor Shingler15, Shobha Kondragunta16, Alexei Lyapustin13, Yujie Wang13, Brent Holben13, David M. Giles13, and Pablo E. Saide1,17 1Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, USA 2Cooperative Institute for Research in Environmental Sciences, CU Boulder, Boulder, CO, USA 3NOAA Global Systems Laboratory, Boulder, CO, USA 4University of Wisconsin &ndash; Madison Space Science and Engineering Center, Madison, WI, USA 5Air Quality Research Division, Environment and Climate Change Canada, Downsview, Ontario, Canada 6Canadian Meteorological Centre Operations, Environment and Climate Change Canada, Dorval, Quebec, Canada 7College of Engineering, University of Iowa, Iowa City, IA, USA 8NOAA/NWS National Centers for Environment Prediction, Boulder, CO, USA 9Research Application Laboratory (RAL), National Center for Atmospheric Research (NCAR), Boulder, CO, USA 10Atmospheric Chemistry Observations and Modeling (ACOM) Laboratory, NCAR, Boulder, CO, USA 11Washington State Department of Ecology, Lacey, Washington, USA 12European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK 13NASA Goddard Space Flight Center, Greenbelt, MD, USA 14National Institute of Aerospace, Hampton, VA, USA 15NASA Langley Research Center, Hampton, VA, USA 16Center for Satellite Applications and Research, NOAA, Boulder, CO, USA 17Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA, USA 18Science Systems and Applications, Inc., Hampton, VA, USA &nbsp; &nbsp;</p

Improving dust simulations in WRF-Chem v4.1.3 coupled with the GOCART aerosol module

In this paper, we rectify inconsistencies that emerge in the Weather Research and Forecasting model with chemistry (WRF-Chem)&nbsp;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&nbsp;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 PM2.5 diagnostic output by 7&thinsp;% and overestimates PM10 by 5&thinsp;% 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&thinsp;%&ndash;30&thinsp;% 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&thinsp;%&ndash;6&thinsp;%, PM10 surface concentrations by 2&thinsp;%&ndash;4&thinsp;%, and mass of the gravitationally settled dust by 5&thinsp;%&ndash;10&thinsp;%. 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 PM10 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. &nbsp;</p

A fast visible-wavelength 3D radiative transfer model for numerical weather prediction visualization and forward modeling

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. </div

Sources and characteristics of summertime organic aerosol in the Colorado Front Range: perspective from measurements and WRF-Chem modeling

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&amp;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&amp;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&amp;G emissions resulted in substantially better agreements in modeled ethane, toluene, hydroxyl radical, and ozone compared to measurements in the high-O&amp;G-influenced plumes. By including emissions from the O&amp;G sector using the top-down approach, it was estimated that the O&amp;G sector contributed to  &lt;  5 % of total OA, but up to 38 % of anthropogenic SOA (aSOA) in the region. The best agreement between the measured and simulated median OA was achieved by limiting the extent of biogenic hydrocarbon aging and consequently biogenic SOA (bSOA) production. Despite a lower production of bSOA in this scenario, contribution of bSOA to total SOA remained high at 40–54 %. Future studies aiming at a better emissions characterization of POA and intermediate-volatility organic compounds (IVOCs) from the O&amp;G sector are valuable

Los Angeles megacity: a high-resolution land-atmosphere modelling system for urban CO2 emissions

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

Los Angeles megacity: a high-resolution land–atmosphere modelling system for urban CO_2 emissions

Megacities are major sources of anthropogenic fossil fuel CO_2 (FFCO_2) emissions. The spatial extents of these large urban systems cover areas of 10000 km^2 or more with complex topography and changing landscapes. We present a high-resolution land–atmosphere modelling system for urban CO_2 emissions over the Los Angeles (LA) megacity area. The Weather Research and Forecasting (WRF)-Chem model was coupled to a very high-resolution FFCO_2 emission product, Hestia-LA, to simulate atmospheric CO_2 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 CO_2 emission products to evaluate the impact of the spatial resolution of the CO_2 emission products and the meteorological transport model on the representation of spatiotemporal variability in simulated atmospheric CO_2 concentrations. We find that high spatial resolution in the fossil fuel CO_2 emissions is more important than in the atmospheric model to capture CO_2 concentration variability across the LA megacity. Finally, we present a novel approach that employs simultaneous correlations of the simulated atmospheric CO_2 fields to qualitatively evaluate the greenhouse gas measurement network over the LA megacity. Spatial correlations in the atmospheric CO_2 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 CO_2 concentrations over the LA megacity are composed of multiple fine-scale plumes rather than a single homogenous urban dome. Furthermore, we conclude that FFCO_2 emissions monitoring in the LA megacity requires FFCO_2 emissions modelling with  ∼ 1 km resolution because coarser-resolution emissions modelling tends to overestimate the observational constraints on the emissions estimates

Instrumentation and measurement strategy for the NOAA SENEX aircraft campaign as part of the Southeast Atmosphere Study 2013

Natural emissions of ozone-and-aerosol-precursor gases such as isoprene and monoterpenes are high in the southeastern US. In addition, anthropogenic emissions are significant in the southeastern US and summertime photochemistry is rapid. The NOAA-led SENEX (Southeast Nexus) aircraft campaign was one of the major components of the Southeast Atmosphere Study (SAS) and was focused on studying the interactions between biogenic and anthropogenic emissions to form secondary pollutants. During SENEX, the NOAA WP-3D aircraft conducted 20 research flights between 27 May and 10 July 2013 based out of Smyrna, TN. Here we describe the experimental approach, the science goals and early results of the NOAA SENEX campaign. The aircraft, its capabilities and standard measurements are described. The instrument payload is summarized including detection limits, accuracy, precision and time resolutions for all gas-and-aerosol phase instruments. The inter-comparisons of compounds measured with multiple instruments on the NOAA WP-3D are presented and were all within the stated uncertainties, except two of the three NO2 measurements. The SENEX flights included day- and nighttime flights in the southeastern US as well as flights over areas with intense shale gas extraction (Marcellus, Fayetteville and Haynesville shale). We present one example flight on 16 June 2013, which was a daytime flight over the Atlanta region, where several crosswind transects of plumes from the city and nearby point sources, such as power plants, paper mills and landfills, were flown. The area around Atlanta has large biogenic isoprene emissions, which provided an excellent case for studying the interactions between biogenic and anthropogenic emissions. In this example flight, chemistry in and outside the Atlanta plumes was observed for several hours after emission. The analysis of this flight showcases the strategies implemented to answer some of the main SENEX science questions.</p

Exposure of agricultural workers in California to wildfire smoke under past and future climate conditions

Wildfire activity in the western U.S. has increased in frequency and severity in recent decades. Wildfire smoke emissions contribute to elevated fine particulate matter (PM _2.5 ) concentrations that are dangerous to public health. Due to the outdoor and physically demanding nature of their work, agricultural workers are particularly vulnerable to wildfire smoke pollution. In this study, we quantify the potential exposure of agricultural workers in California to past (2004–2009) and future (2046–2051) smoke PM _2.5 . We find that while absolute increases in smoke PM _2.5 exposure are largest in northern California, agricultural regions in the Central Valley and Central Coast may be highly vulnerable to future increases in smoke PM _2.5 concentrations. We find an increase from 6 to 8 million worker smoke exposure days (+35%) of ‘smokewave’ exposure for agricultural workers across the state under future climate conditions, with the largest increases in Tulare, Monterey, and Fresno counties. Under future climate conditions, we find 1.9 million worker smoke exposure days of agricultural worker exposure to levels of total PM _2.5 pollution deemed ‘Unhealthy for Sensitive Groups.’ This is a 190% increase over past climate conditions. Wildfire smoke PM _2.5 contributes, on average, to more than 90% of these daily PM _2.5 exceedances compared with non-fire sources of air pollution. Using the recent extreme wildfire season of 2020 as a case study, we show that existing monitoring networks do not provide adequate sampling of PM _2.5 in many future at-risk wildfire regions with large numbers of agricultural workers. Policies will need to consider the changing patterns of smoke PM _2.5 exposure under future climate conditions to better protect outdoor agricultural workers