Abstract Biomass‐burning smoke drives large uncertainty in climate projections of the Earth's radiative balance. This is due to the chemical and physical evolution of smoke and its impact on clouds and radiation. Here we focus on the southeastern Atlantic region and its inflow of African biomass‐burning smoke during August 2017. We evaluate smoke properties and processes in two coupled earth‐system models, the Energy Exascale Earth System Model (E3SM) and Community Earth System Model (CESM). These are compared against in situ aircraft observations from two field campaigns, ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) and CLoud–Aerosol–Radiation Interaction and Forcing: Year 2017 (CLARIFY‐2017). Observations reveal an increase and subsequent decrease in smoke mean diameter, and a steady decrease in the mass ratio of organic aerosol (OA) to black carbon aerosol (BC) (OA:BC) over 4–12 days of aging, neither captured by the base models. Implementation of a photolytic loss scheme for secondary organic aerosol (SOA)—as a proxy for other heterogeneous volatilization chemistry—and a ∼1‐day conversion for primary OA to SOA significantly improves the representation of this loss. In the boundary layer, both models show dimethyl sulfide driving a large increase in the sulfate aerosol mass fraction from the free troposphere, which is consistent with observations. Finally, models tend to underpredict cloud droplet number concentration partially due to weak modeled turbulent updraft strength, and model performance improves when the parameterized turbulent updraft strength is increased substantially. These results are expected to provide insights into future model development to reduce climate model uncertainties.

We use measurements of near-surface aerosol backscatter, extinction, and depolarization acquired by four NASA Langley Research Center airborne High Spectral Resolution Lidars (HSRLs) in machine learning (ML) regression algorithms to derive concentrations of particulate matter (PM) with aerodynamic diameters less than 2.5 µm (PM2.5), 10 µm (PM10), and the PM2.5 / PM10 ratio. The ML regression models are trained using airborne HSRL measurements acquired over major metropolitan regions in the United States and Asia that are coincident with hourly surface PM2.5 and PM10 measurements from the EPA air quality system and similar networks in other countries. We examine several regression methods and find that exponential Gaussian Process regression (GPR) algorithms consistently give the best performance in terms of the lowest root-mean-square (RMS) errors and the highest correlations. When evaluated using surface measurements withheld from the training sets, ML models that use the HSRL near-surface measurements of aerosol backscatter and aerosol intensive properties such as depolarization, backscatter color ratio, and lidar ratio typically give the best performance with RMS differences in PM2.5 retrievals around 5 µg m−3 and correlation coefficients above 0.8, respectively. Corresponding RMS differences and correlation coefficients for PM10 retrievals are 11 µg m−3 and 0.7 and corresponding RMS differences and correlation coefficients for PM2.5 / PM10 are 0.17 and 0.75. This retrieval performance is achieved using airborne HSRL measurements alone and so does not depend on external knowledge of or assumptions regarding aerosol type, aerosol mass extinction efficiency, aerosol hygroscopic growth, the ratio of PM2.5 to PM10, particle density, or relative humidity. PM2.5 values in the training set range from about 5 to 80 µg m−3; PM10 values range from about 10 to 100 µg m−3. Accurate retrievals of PM outside these ranges would require commensurate training data. We present examples of PM retrievals in the United States as well as Asia when HSRL measurements were acquired when the aircraft flew systematic “raster-scan” patterns for several hours over major urban areas. We show that these PM2.5 retrievals are in good agreement with PM2.5 derived from coincident airborne in situ measurements near the surface as well as aloft. We describe also how the distribution of PM2.5 varies with aerosol type and altitude over these regions. We use the HSRL measurements of aerosol extinction and retrievals of surface PM2.5 along with HSRL retrievals of aerosol type to derive estimates of the fine mode aerosol mass extinction efficiency (MEEf) for major aerosol types identified by an updated HSRL aerosol classification method. MEEf ranges from about 2.6 ± 0.5 m2 g−1 for maritime aerosol to 5.0 ± 0.7 m2 g−1 for smoke. These estimates of MEEf are also in good agreement with values derived from airborne in situ measurements. We also discuss how this methodology may be applied to measurements from the Atmospheric Lidar (ATLID) on the EarthCARE satellite.

Understanding the spatial and temporal characteristics of both long- and short-term exposure to ground-level ozone is crucial for refining environmental management and improving health studies. However, such studies have been constrained by the availability of high-resolution spatiotemporal data. To address this gap, we characterized ground-level ozone variations and exposure risks across multiple spatial (pixel, county, region, and national) and temporal (daily, monthly, seasonal, and annual) scales using daily 1 km ozone data from 2000 to 2020, derived from satellite-sourced land surface temperature data via a machine-learning hindcast method. The model provided reliable estimates, validated through rigorous cross-validation and direct comparison with external ground-level ozone measurements. Our long-term estimates revealed seasonal shifts in high-exposure ozone centers: spring in eastern China, summer in the North China Plain (NCP), and autumn in the Pearl River Delta (PRD). A non-monotonic trend was observed, with ozone levels rising from 2001-2007 at a rate of 0.47 mu gm-3yr-1, declining after 2008 (-0.58 mu gm-3yr-1), and increasing significantly from 2016-2020 (1.16 mu gm-3yr-1), accompanied by regional and seasonal fluctuations. Notably, ozone levels increased by 0.63 mu gm-3yr-1 in summer in the NCP during the second phase and by 6.38 mu gm-3yr-1 in autumn in the PRD during the third phase. Exposure levels over 100 mu gm-3 have shifted from June to May, and levels exceeding 160 mu gm-3 were primarily seen in the NCP, showing an expanding trend. Our day-to-day analysis highlights the influence of meteorological factors on extreme events. These findings emphasize the need for increased public health awareness and stronger mitigation efforts.

The 2020 wildfire season in the Western U.S. was historic in its intensity and impact on the land and atmosphere. This study aims to characterize satellite retrievals of carbon monoxide (CO), a tracer of combustion and signature of those fires, from two key satellite instruments: the Cross-track Infrared Sounder (CrIS) and the Tropospheric Monitoring Instrument (TROPOMI). We evaluate them during this event and assess their synergies. These two retrievals are matched temporally, as the host satellites are in tandem orbit and spatially by aggregating TROPOMI to the CrIS resolution. Both instruments show that the Western U.S. displayed significantly higher daily average CO columns compared to the Central and Eastern U.S. during the wildfires. TROPOMI showed up to a factor of two larger daily averages than CrIS during the most intense fire period, likely due to differences in the vertical sensitivity of the two instruments and representative of near-surface CO abundance near the fires. On the other hand, there was excellent agreement between the instruments in downwind free tropospheric plumes (scatter plot slopes of 0.96–0.99), consistent with their vertical sensitivities and indicative of mostly lofted smoke. Temporally, TROPOMI CO column peaks were delayed relative to the Fire Radiative Power (FRP), and CrIS peaks were delayed with respect to TROPOMI, particularly during the intense initial weeks of September, suggesting boundary layer buildup and ventilation. Satellite retrievals were evaluated using ground-based CO column estimates from the Network for the Detection of Atmospheric Composition Change (NDACC) and the Total Carbon Column Observing Network (TCCON), showing Normalized Mean Errors (NMEs) for CrIS and TROPOMI below 32% and 24%, respectively, when compared to all stations studied. While Normalized Mean Bias (NMB) was typically low (absolute value below 15%), there were larger negative biases at Pasadena, likely associated with sharp spatial gradients due to topography and proximity to a large city, which is consistent with previous research. In situ CO profiles from AirCore showed an elevated smoke plume for 15 September 2020, highlighted consistency between TROPOMI and CrIS CO columns for lofted plumes. This study demonstrates that both CrIS and TROPOMI provide complementary information on CO distribution. CrIS’s sensitivity in the middle and lower free troposphere, coupled with TROPOMI’s effectiveness at capturing total columns, offers a more comprehensive view of CO distribution during the wildfires than either retrieval alone. By combining data from both satellites as a ratio, more detailed information about the vertical location of the plumes can potentially be extracted. This approach can enhance air quality models, improve vertical estimation accuracy, and establish a new method for assessing lower tropospheric CO concentrations during significant wildfire events.

Pyrocumulonimbus (pyroCb) is a form of deep convection that is generated by the heating from large wildfires and specific meteorology known for producing lightning. We study the lightning characteristics of five pyroCb events in British Columbia, Canada, from June 29 to July 1 of 2021, and compare them to other clean and smoke-filled high-based thunderstorms in the same region and season using ground-based lightning detection data, satellite retrievals, meteorological and atmospheric composition reanalysis, and observed thermodynamic profiles. One large pyroCb event over the Sparks Lake fire that generated persistent overshooting tops had a remarkable amount of lightning activity, with 5,600 total lightning strikes, while the rest of the pyroCb events corresponded with lower injection altitudes and minimal to no observed lightning activity. The cloud-to-cloud (CC) to cloud-to-ground (CG) lightning ratio (CC:CG) in this Sparks Lake pyroCb was significantly higher than in other high-based storms but displayed similar lightning density and slightly lower peak current distributions. All clean and smoke-filled thunderstorms produced significant levels of lightning activity, regardless of their cloud-top altitudes. However, ingestion of smoke significantly reduced the percentage of positive polarity CG strikes when compared to clean cases. These results set a reference for identifying the characteristics of pyrogenic lightning and improved predictions of lightning-caused fire ignitions, which will aid in understanding pyroCb activity and related impacts.

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 R-2 = 0.39-0.48, 2-day R-2 = 0.13-0.34). FRE is used to compute emissions, so these results demonstrate potential for improved fire emissions forecasts for air quality models.

Accurately mapping ground-level ozone concentrations at high spatiotemporal resolution (daily, 1 km) is essential for evaluating human exposure and conducting public health assessments. This requires identifying and understanding a proxy that is well-correlated with ground-level ozone variation and available with spatiotemporal high-resolution data. This study introduces a high-resolution ozone modeling method utilizing the XGBoost algorithm with satellite-derived land surface temperature (LST) as the primary predictor. Focusing on China in 2019, our model achieved a cross-validation R2 of 0.91 and a root-mean-square error (RMSE) of 13.51 μg/m3. We provide detailed maps highlighting ground-level ozone concentrations in urban areas, uncovering spatial variations previously unresolved, along with time series aligning with established understandings of ozone dynamics. Our local interpretation of the machine learning model underscores the significant contribution of LST to spatiotemporal ozone variations, surpassing other meteorological, pollutant, and geographical predictors in its influence. Validation results indicate that model performance decreases as spatial resolution becomes coarser, with R2 decreasing from 0.91 for the 1 km model to 0.85 for the 25 km model. The methodology and data sets generated by this study offer new insights into ground-level ozone variability and mapping and can significantly aid in exposure assessment and epidemiological research related to this critical environmental challenge.

Predicting the evolution of burned area, smoke emissions, and energy release from wildfires is crucial to air quality forecasting and emergency response planning yet has long posed a significant scientific challenge. Here we compare predictions of burned area and fire radiative power from the coupled weather/fire‐spread model WRF‐Fire (Weather and Research Forecasting Tool with fire code), against simpler methods typically used in air quality forecasts. We choose the 2019 Williams Flats Fire as our test case due to a wealth of observations and ignite the fire on different days and under different configurations. Using a novel re‐gridding scheme, we compare WRF‐Fire's heat output to geostationary satellite data at 1‐hr temporal resolution. We also evaluate WRF‐Fire's time‐resolved burned area against high‐resolution imaging from the National Infrared Operations aircraft data. Results indicate that for this study, accounting for containment efforts in WRF‐Fire simulations makes the biggest difference in achieving accurate results for daily burned area predictions. When incorporating novel containment line inputs, fuel density increases, and fuel moisture observations into the model, the error in average daily burned area is 30% lower than persistence forecasting over a 5‐day forecast. Prescribed diurnal cycles and those resolved by WRF‐Fire simulations show a phase offset of at least an hour ahead of observations, likely indicating the need for dynamic fuel moisture schemes. This work shows that with proper configuration and input data, coupled weather/fire‐spread modeling has the potential to improve smoke emission forecasts.

The NOAA/NASA Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) experiment was a multi-agency, inter-disciplinary research effort to: (a) obtain detailed measurements of trace gas and aerosol emissions from wildfires and prescribed fires using aircraft, satellites and ground-based instruments, (b) make extensive suborbital remote sensing measurements of fire dynamics, (c) assess local, regional, and global modeling of fires, and (d) strengthen connections to observables on the ground such as fuels and fuel consumption and satellite products such as burned area and fire radiative power. From Boise, ID western wildfires were studied with the NASA DC-8 and two NOAA Twin Otter aircraft. The high-altitude NASA ER-2 was deployed from Palmdale, CA to observe some of these fires in conjunction with satellite overpasses and the other aircraft. Further research was conducted on three mobile laboratories and ground sites, and 17 different modeling forecast and analyses products for fire, fuels and air quality and climate implications. From Salina, KS the DC-8 investigated 87 smaller fires in the Southeast with remote and in-situ data collection. Sampling by all platforms was designed to measure emissions of trace gases and aerosols with multiple transects to capture the chemical transformation of these emissions and perform remote sensing observations of fire and smoke plumes under day and night conditions. The emissions were linked to fuels consumed and fire radiative power using orbital and suborbital remote sensing observations collected during overflights of the fires and smoke plumes and ground sampling of fuels. Plain Language Summary The NOAA/NASA Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) experiment was aimed at understanding how fuel and fire conditions at the point of emission influence the chemistry of smoke, what conditions and processes control the rise of smoke plumes, what happens to smoke as it is distributed in the atmosphere, and how chemical transformation of smoke impacts air quality, weather, and climate downwind. Lessons learned from FIREX-AQ will also be used to assess and improve the effectiveness of satellites for estimating the emissions from wildfires and prescribed burns and to reduce uncertainties associated with modeling and forecasting of smoke. Here we present an overview of the FIREX-AQ effort, its motivation and design, with detailed descriptions of the measurements and analyses carried out, their connections to FIREX-AQ science goals, and the early findings of this exceptionally broad effort to understand fire and its many impacts on the atmosphere.

Biomass burning (BB) is one of the largest sources of absorbing aerosols globally and accounts for about 40% of black carbon in the atmosphere. The Southern African region contributes approximately 35% of Earth’s BB aerosol emissions. During the Southern Hemisphere winter, smoke is transported over the southeast Atlantic Ocean, overlying and mixing with a semi-permanent stratocumulus cloud deck. Aerosol-cloud interactions contribute the largest uncertainty to anthropogenic forcing, and the southeast Atlantic region exhibits a large model-to-model divergence of climate forcing. This makes the region particularly valuable for understanding these interactions and was one of the factors motivating the three-year NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) mission. Previous studies using ORACLES datasets have explored the distribution of aerosol and cloud particles, however, changes in some aerosol properties during transport are not well documented. This study investigates the evolution of biomass burning aerosol properties from emission within Southern Africa, transport over land, and then over the Atlantic. Measurements from a collection of airborne in situ and remote-sensing instruments including 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) along with ground-based AERONET (Aerosol Robotic Network) are combined with results from two regional models, the WRF-AAM and WRF-CAM5 to explore the changes in the optical properties of these smoke plumes as they age. The aerosol age is determined using tracers from the WRF-AAM configured with 12 km resolution over the region’s spatial domain (41 ºS – 14 ºN, 34 ºW – 51 ºE). Changes in extinction, single scattering Albedo (SSA) and angstrom exponent (AE) with age as well as a comparative analysis between observations and model results were carried out using datasets from airborne PSAP (Particle Soot Absorption Photometer) and nephelometers, 4STAR, AERONET, and WRF-CAM5.

The southeastern Atlantic is home to an expansive smoke aerosol plume overlying a large cloud deck for approximately a third of the year. The aerosol plume is mainly attributed to the extensive biomass burning activities that occur in southern Africa. Current Earth system models (ESMs) reveal significant differences in their estimates of regional aerosol radiative effects over this region. Such large differences partially stem from uncertainties in the vertical distribution of aerosols in the troposphere. These uncertainties translate into different aerosol optical depths (AODs) in the planetary boundary layer (PBL) and the free troposphere (FT). This study examines differences of AOD fraction in the FT and AOD differences among ESMs (WRF-CAM5, WRF-FINN, GEOS-Chem, EAM-E3SM, ALADIN, GEOS-FP, and MERRA-2) and aircraft-based measurements from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign. Models frequently define the PBL as the well-mixed surface-based layer, but this definition misses the upper parts of decoupled PBLs, in which most low-level clouds occur. To account for the presence of decoupled boundary layers in the models, the height of maximum vertical gradient of specific humidity profiles from each model is used to define PBL heights. Results indicate that the monthly mean contribution of AOD in the FT to the total-column AOD ranges from 44 % to 74 % in September 2016 and from 54 % to 71 % in August 2017 within the region bounded by 25∘ S–0∘ N–S and 15∘ W–15∘ E (excluding land) among the ESMs. ALADIN and GEOS-Chem show similar aerosol plume patterns to a derived above-cloud aerosol product from the Moderate Resolution Imaging Spectroradiometer (MODIS) during September 2016, but none of the models show a similar above-cloud plume pattern to MODIS in August 2017. Using the second-generation High Spectral Resolution Lidar (HSRL-2) to derive an aircraft-based constraint on the AOD and the fractional AOD, we found that WRF-CAM5 produces 40 % less AOD than those from the HSRL-2 measurements, but it performs well at separating AOD fraction between the FT and the PBL. AOD fractions in the FT for GEOS-Chem and EAM-E3SM are, respectively, 10 % and 15 % lower than the AOD fractions from the HSRL-2. Their similar mean AODs reflect a cancellation of high and low AOD biases. Compared with aircraft-based observations, GEOS-FP, MERRA-2, and ALADIN produce 24 %–36 % less AOD and tend to misplace more aerosols in the PBL. The models generally underestimate AODs for measured AODs that are above 0.8, indicating their limitations at reproducing high AODs. The differences in the absolute AOD, FT AOD, and the vertical apportioning of AOD in different models highlight the need to continue improving the accuracy of modeled AOD distributions. These differences affect the sign and magnitude of the net aerosol radiative forcing, especially when aerosols are in contact with clouds.

To quantify the relative roles of long-range transport (LRT) versus locally emitted aerosol and ozone precursors during polluted periods in Korea, high-resolution (4 km) Weather Research and Forecasting with Chemistry model simulations were performed. The model was evaluated using surface and airborne observations collected during the KORea and United States Air Quality campaign. Ozone above 40 ppb had mean bias of −5.9 ppb. PM2.5 was biased high (8.2 µg/m3), with a relative bias of 30% given the mean observed value of 26.8 µg/m3. The absolute amounts and shifts between phases for all PM2.5 species except nitrate reasonably match observations across all 4 phases. Notable limitations include an underestimation of nighttime planetary boundary layer height. Transport versus domestic emissions influence was studied by model runs with perturbed emissions and by comparing east-west fluxes over the Yellow Sea to Korean emissions and other normalization metrics. Domestic anthropogenic emission contributions to surface air quality were quantified by location across Korea, segregated by synoptic meteorological phase. The largest contributions from Korean emissions were found under high-pressure stagnant conditions and the smallest for conditions with strong westerly winds. For example, at Seoul, domestic contributions of PM2.5 averaged 49% and 29% in the aforementioned meteorological phases, respectively. Surface concentrations of NOx and toluene in Seoul were over 85% due to domestic emissions. CO and black carbon had both local and remote contributions. Nitrate and ammonium contributions varied greatly by phases in Seoul, with 7%–51% nitrate and 42%–70% of ammonium from remote sources. Variation in direction (west-to-east vs. east-to-west) and magnitude of fluxes support the model sensitivity results. Analysis using fluxes facilitates the quantification of source contributions for secondary species and, in many cases, can be done using a single model run or reanalysis result. The analysis presented shows the importance of using models with high spatial resolution to capture pollutant transport and mixing around Korea. However, there remain uncertainties in secondary aerosol production mechanisms and indications that local production at times could be higher than those modeled in this analysis. Therefore, the results presented here should be viewed as an upper limit on the importance of LRT.

A large part of the uncertainty in climate projections comes from uncertain aerosol properties and aerosol–cloud interactions as well as the difficulty in remotely sensing them. The southeastern Atlantic functions as a natural laboratory to study biomass-burning smoke and to constrain this uncertainty. We address these gaps by comparing the Weather Research and Forecasting with Chemistry Community Atmosphere Model (WRF-CAM5) to the multi-campaign observations ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS), CLARIFY (CLoud–Aerosol–Radiation Interaction and Forcing), and LASIC (Layered Atlantic Smoke Interactions with Clouds) in the southeastern Atlantic in August 2017 to evaluate a large range of the model's aerosol chemical properties, size distributions, processes, and transport, as well as aerosol–cloud interactions. Overall, while WRF-CAM5 is able to represent smoke properties and transport, some key discrepancies highlight the need for further analysis. Observations of smoke composition show an overall decrease in aerosol mean diameter as smoke ages over 4–12 d, while the model lacks this trend. A decrease in the mass ratio of organic aerosol (OA) to black carbon (BC), OA:BC, and the OA mass to carbon monoxide (CO) mixing ratio, OA:CO, suggests that the model is missing processes that selectively remove OA from the particle phase, such as photolysis and heterogeneous aerosol chemistry. A large (factor of ∼2.5) enhancement in sulfate from the free troposphere (FT) to the boundary layer (BL) in observations is not present in the model, pointing to the importance of properly representing secondary sulfate aerosol formation from marine dimethyl sulfide and gaseous SO2 smoke emissions. The model shows a persistent overprediction of aerosols in the marine boundary layer (MBL), especially for clean conditions, which multiple pieces of evidence link to weaker aerosol removal in the modeled MBL than reality. This evidence includes several model features, such as not representing observed shifts towards smaller aerosol diameters, inaccurate concentration ratios of carbon monoxide and black carbon, underprediction of heavy rain events, and little evidence of persistent biases in modeled entrainment. The average below-cloud aerosol activation fraction (NCLD/NAER) remains relatively constant in WRF-CAM5 between field campaigns (∼0.65), while it decreases substantially in observations from ORACLES (∼0.78) to CLARIFY (∼0.5), which could be due to the model misrepresentation of clean aerosol conditions. WRF-CAM5 also overshoots an observed upper limit on liquid cloud droplet concentration around NCLD= 400–500 cm−3 and overpredicts the spread in NCLD. This could be related to the model often drastically overestimating the strength of boundary layer vertical turbulence by up to a factor of 10. We expect these results to motivate similar evaluations of other modeling systems and promote model development to reduce critical uncertainties in climate simulations.

Background. Accurately estimating burned area from satellites is key to improving biomass burning emission models, studying fire evolution and assessing environmental impacts. Previous studies have found that current methods for estimating burned area of fires from satellite active-fire data do not always provide an accurate estimate.Aims and methods. In this work, we develop a novel algorithm to estimate hourly accumulated burned area based on the area from boundaries of non-convex polygons containing the accumulated Visible Infrared Imaging Radiometer Suite (VIIRS) active-fire detections. Hourly time series are created by combining VIIRS estimates with Fire Radiative Power (FRP) estimates from GOES-17 (Geostationary Operational Environmental Satellite) data.Conclusions, key results and implication. We evaluate the performance of the algorithm for both accumulated and change in burned area between airborne observations, and specifically examine sensitivity to the choice of the parameter controlling how much the boundary can shrink towards the interior of the area polygon. Results of the hourly accumulation of burned area for multiple fires from 2019 to 2020 generally correlate strongly with airborne infrared (IR) observations collected by the United States Forest Service National Infrared Operations (NIROPS), exhibiting correlation coefficient values usually greater than 0.95 and errors <20%.

Chapter 7 Data Assimilation for Numerical Smoke Prediction Edward J. Hyer, Edward J. Hyer Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorChristopher P. Camacho, Christopher P. Camacho Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorDavid A. Peterson, David A. Peterson Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorElizabeth A. Satterfield, Elizabeth A. Satterfield Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorPablo E. Saide, Pablo E. Saide Department of Atmospheric and Oceanic Sciences, and Institute of the Environment and Sustainability, University of California Los Angeles, Los Angeles, California, USASearch for more papers by this author Edward J. Hyer, Edward J. Hyer Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorChristopher P. Camacho, Christopher P. Camacho Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorDavid A. Peterson, David A. Peterson Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorElizabeth A. Satterfield, Elizabeth A. Satterfield Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorPablo E. Saide, Pablo E. Saide Department of Atmospheric and Oceanic Sciences, and Institute of the Environment and Sustainability, University of California Los Angeles, Los Angeles, California, USASearch for more papers by this author Book Editor(s):Tatiana V. Loboda, Tatiana V. LobodaSearch for more papers by this authorNancy H. F. French, Nancy H. F. FrenchSearch for more papers by this authorRobin C. Puett, Robin C. PuettSearch for more papers by this author First published: 20 October 2023 https://doi.org/10.1002/9781119757030.ch7Citations: 1Book Series:Geophysical Monograph Series AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary Skillful forecasts of weather phenomena in numerical models begin with the most accurate set of initial conditions achievable from observational data sets. The process of combining observations with numerical model predictions is called data assimilation . This chapter describes the types of observations available for data assimilation in models that predict the transport, fate, and impacts of smoke pollution. Observation properties needed for effective data assimilation are identified based on experiences with a variety of observation types in data assimilation experiments, compiled from the published literature. The second half of the chapter surveys the data assimilation methodologies that have been applied to smoke aerosols, and describes specific problems associated with the smoke observations that require innovative techniques in data assimilation. The chapter concludes by providing an outlook for future research and development in data assimilation for smoke prediction models. Data assimilation for prediction of smoke is an emerging area of development that promises to greatly improve forecast skill as new data sets and techniques are applied. REFERENCES Alexandrov , M. D. , Geogdzhayev , I. V. , Tsigaridis , K. , Marshak , A. , Levy , R. , & Cairns , B. ( 2016 ). New statistical model for variability of aerosol optical thickness: Theory and application to MODIS data over ocean . 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Biomass burning particulate matter (BBPM) affects regional air quality and global climate, with impacts expected to continue to grow over the coming years. We show that studies of North American fires have a systematic altitude dependence in measured BBPM normalized excess mixing ratio (NEMR; ΔPM/ΔCO), with airborne and high-altitude studies showing a factor of 2 higher NEMR than ground-based measurements. We report direct airborne measurements of BBPM volatility that partially explain the difference in the BBPM NEMR observed across platforms. We find that when heated to 40-45 °C in an airborne thermal denuder, 19% of lofted smoke PM1 evaporates. Thermal denuder measurements are consistent with evaporation observed when a single smoke plume was sampled across a range of temperatures as the plume descended from 4 to 2 km altitude. We also demonstrate that chemical aging of smoke and differences in PM emission factors can not fully explain the platform-dependent differences. When the measured PM volatility is applied to output from the High Resolution Rapid Refresh Smoke regional model, we predict a lower PM NEMR at the surface compared to the lofted smoke measured by aircraft. These results emphasize the significant role that gas-particle partitioning plays in determining the air quality impacts of wildfire smoke.

Aerosol over the remote southeastern Atlantic is some of the most sunlight-absorbing aerosol on the planet: the in situ free-tropospheric single-scattering albedo at the 530 nm wavelength (SSA(530 nm)) ranges from 0.83 to 0.89 within ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) aircraft flights from late August-September. Here we seek to explain the low SSA. The SSA depends strongly on the black carbon (BC) number fraction, which ranges from 0.15 to 0.4. Low organic aerosol (OA)-to-BC mass ratios of 8-14 and modified combustion efficiency values > 0 :975 point indirectly to the dry, flame-efficient combustion of primarily grass fuels, with back trajectories ending in the miombo woodlands of Angola. The youngest aerosol, aged 4-5 d since emission, occupied the top half of a 5 km thick plume sampled directly west of Angola with a vertically consistent BC : Delta CO (carbon monoxide) ratio, indicating a homogenization of the source emissions. The younger aerosol, transported more quickly off of the continent by stronger winds, overlaid older, slowermoving aerosol with a larger mean particle size and fraction of BC-containing particles. This is consistent with ongoing gas condensation and the coagulation of smaller non-BC particles upon the BC-containing particles. The particle volumes and OA : BC mass ratios of the older aerosol were smaller, attributed primarily to evaporation following fragmentation, instead of dilution or thermodynamics. The CLARIFY (CLoud-Aerosol-Radiation Interaction and Forcing: Year 2017) aircraft campaign sampled aerosols that had traveled further to reach the more remote Ascension Island. CLARIFY reported higher BC number fractions, lower OA : BC mass ratios, and lower SSA yet larger mass absorption coefficients compared to this study's. Values from one ORACLES 2017 flight, held midway to Ascension Island, are intermediate, confirming the long-range changes. Overall the data are most consistent with continuing oxidation through fragmentation releasing aerosols that subsequently enter the gas phase, reducing the OA mass, rather than evaporation through dilution or thermodynamics. The data support the following best fit: SSA(530) (nm) = 0 :801 + 0055 (OA : BC) (r = 0 :84). The fires of southern Africa emit approximately one-third of the world's carbon; the emitted aerosols are distinct from other regional smoke emissions, and their composition needs to be represented appropriately to realistically depict regional aerosol radiative effects.

Accurately capturing cloud condensation nuclei (CCN) concentrations is key to understanding the aerosol–cloud interactions that continue to feature the highest uncertainty amongst numerous climate forcings. In situ CCN observations are sparse, and most non-polarimetric passive remote sensing techniques are limited to providing column-effective CCN proxies such as total aerosol optical depth (AOD). Lidar measurements, on the other hand, resolve profiles of aerosol extinction and/or backscatter coefficients that are better suited for constraining vertically resolved aerosol optical and microphysical properties. Here we present relationships between aerosol backscatter and extinction coefficients measured by the airborne High Spectral Resolution Lidar 2 (HSRL-2) and in situ measurements of CCN concentrations. The data were obtained during three deployments in the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) project, which took place over the southeast Atlantic (SEA) during September 2016, August 2017, and September–October 2018. Our analysis of spatiotemporally collocated in situ CCN concentrations and HSRL-2 measurements indicates strong linear relationships between both data sets. The correlation is strongest for supersaturations (S) greater than 0.25 % and dry ambient conditions above the stratocumulus deck, where relative humidity (RH) is less than 50 %. We find CCN–HSRL-2 Pearson correlation coefficients between 0.95–0.97 for different parts of the seasonal burning cycle that suggest fundamental similarities in biomass burning aerosol (BBA) microphysical properties. We find that ORACLES campaign-average values of in situ CCN and in situ extinction coefficients are qualitatively similar to those from other regions and aerosol types, demonstrating overall representativeness of our data set. We compute CCN–backscatter and CCN–extinction regressions that can be used to resolve vertical CCN concentrations across entire above-cloud lidar curtains. These lidar-derived CCN concentrations can be used to evaluate model performance, which we illustrate using an example CCN concentration curtain from the Weather Research and Forecasting Model coupled with physics packages from the Community Atmosphere Model version 5 (WRF-CAM5). These results demonstrate the utility of deriving vertically resolved CCN concentrations from lidar observations to expand the spatiotemporal coverage of limited or unavailable in situ observations.

Concentrations of ambient particulate matter (such as PM2.5 and PM10) have come to represent a serious environmental problem worldwide, causing many deaths and economic losses. Because of the detrimental effects of PM2.5 on human health, many countries and international organizations have developed and operated regional and global short-term PM2.5 prediction systems. The short-term predictability of PM2.5 (and PM10) is determined by two main factors: the performance of the air quality model and the precision of the initial states. While specifically focusing on the latter factor, this study attempts to demonstrate how information from classical ground observation networks, a state-of-the-art geostationary (GEO) satellite sensor, and an advanced air quality modeling system can be synergistically combined to improve short-term PM2.5 predictability over South Korea. Such a synergistic combination of information can effectively overcome the major obstacle of scarcity of information, which frequently occurs in PM2.5 prediction systems using low Earth orbit (LEO) satellite-borne observations. This study first presents that the scarcity of information is mainly associated with cloud masking, sun-glint effect, and ill-location of satellite-borne data, and it then demonstrates that an advanced air quality modeling system equipped with synergistically-combined information can achieve substantially improved performances, producing enhancements of approximately 10%, 19%, 29%, and 10% in the predictability of PM2.5 over South Korea in terms of index of agreement (IOA), correlation coefficient (R), mean biases (MB), and hit rate (HR), respectively, compared to PM2.5 prediction systems using only LEO satellite-derived observations.