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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]

Aerosol optical depth (AOD) is a vital parameter in atmospheric research. Using observations of the Visible Infrared Imaging Radiometer Suite (VIIRS), onboard Suomi National Polar‐orbiting Partnership (Suomi‐NPP) and NOAA‐20 satellites, National Oceanic and Atmospheric Administration (NOAA) produces near‐real time AOD product with high pixel resolution (750 m), wide swath width (3,040 km), and a 16‐day repeat cycle. Here we report the evaluation of the NOAA/VIIRS AOD using a comprehensive aerosol data set, derived from a global‐scale, multi‐seasonal airborne mission, the NASA Atmospheric Tomography Mission (ATom). This data set includes rich physical and chemical information, such as size distributions, chemical compositions, optical properties, and hygroscopicities of major aerosol types, including dust, sea salt, smoke, internally mixed sulfate/nitrate/organics particles (non‐smoke), black carbon, etc. Globally, VIIRS AOD (Suomi‐NPP and NOAA‐20) shows good agreement with the ATom AOD in the moderate to high AOD range (>0.3), with respect to measurement uncertainties (orthogonal distance regression fitting slope: 1.5 ± 0.2 for Suomi‐NPP and 1.6 ± 0.5 for NOAA‐20; correlation coefficient: 0.85 for Suomi‐NPP and 0.73 for NOAA‐20). There is a persistent bias in the low AOD range (<0.3) on the order of 0.03, likely reflecting systematic errors on VIIRS and/or the ATom AOD product. Ångström exponent reported by VIIRS shows excellent agreement with ATom results within expected uncertainties. Given the unique insights revealed by the ATom AOD and aerosol property data set, it is desirable to have ATom‐like comprehensive payloads in future airborne satellite validation programs. Plain Language Summary: Aerosol optical depth (AOD) plays a crucial role in air quality and climate. Satellites have been used to retrieve AOD and other aerosol properties for decades; but satellite retrievals are indirect measurements and need validation using direct measurements. Visible Infrared Imaging Radiometer Suite (VIIRS) is the state‐of‐the‐art instrument that retrieves AOD along with other aerosol properties. In this work, we use the comprehensive aerosol data set from the recent global‐scale, multi‐seasonal ATom field campaign, to evaluate the operational VIIRS AOD product on two satellites (NOAA‐20 and SNPP). This data set includes rich physical and chemical information, such as size distributions, chemical compositions, optical properties, and hygroscopicities of major aerosol types, including dust, sea salt, smoke, internally mixed sulfate/nitrate/organics particles, black carbon, etc. Our analysis shows that the operational VIIRS AOD product is generally accurate and robust. A small bias in the low AOD range has been identified, which could be partially explained by the atmospheric variability, but likely reflects systematic errors on VIIRS and/or the ATom AOD product. Our effort further justifies the global scale suborbital airborne missions (such as HIPPO and ATom) and its unique role in atmospheric/climate research as well as operations. Key Points: National Oceanic and Atmospheric Administration (NOAA)/Visible Infrared Imaging Radiometer Suite (VIIRS) Aerosol optical depth (AOD) (Suomi National Polar‐orbiting Partnership [Suomi‐NPP] and NOAA‐20) generally shows good agreement with the AOD product derived from global‐scale airborne observationsThere is a persistent bias in the low AOD range (<0.3), likely reflecting systematic errors on VIIRS and/or the ATom AOD productComprehensive aerosol observations offer invaluable information for satellite validation and model evaluation [ABSTRACT FROM AUTHOR]

In September 2020, a global aerosol forecasting model was implemented as an ensemble member of the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Global Ensemble Forecasting System (GEFS) v12.0.1 (hereafter referred to as "GEFS-Aerosols"). In this study, GEFS-Aerosols simulation results from September 1, 2019 to September 30, 2020 were evaluated using an aerosol budget analysis. These results were compared with results from other global models as well as reanalysis data. From this analysis, the global average lifetimes of black carbon (BC), organic carbon (OC), dust, sea salt, and sulfate are 4.06, 4.29, 4.59, 0.34 and 3.3 days, respectively, with the annual average loads of 0.135, 1.29, 4.52, 6.80 and 0.50 TG. Compared to National Aeronautics and Space Administration (NASA)'s Goddard Earth Observing System-Goddard Chemistry Aerosol and Radiation Transport Model (GEOS4-GOCART), the aerosols in GEFS-Aerosols have a relatively short lifetime because of the faster removal processes in GEFS-Aerosols. Meanwhile, in GEFS-Aerosols, aerosol emissions are the determining factor for the mass and composition of aerosols in the atmosphere. The size (bin) distribution of aerosol emissions is as important as its total emissions, especially in simulations of dust and sea salt. Also most importantly, the strong monthly and interannual variations in natural sources of aerosols in GEFS-Aerosols suggests that improving the accuracy of prognostic concentrations of aerosols is important for applying aerosol feedback to weather and climate predictions. [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]

The mass concentration of fine particulate matter (PM2.5; diameters less than 2.5 μm) estimated from geostationary satellite aerosol optical depth (AOD) data can supplement the network of ground monitors with high temporal (hourly) resolution. Estimates of PM2.5 over the United States were derived from NOAA's operational geostationary satellites' Advanced Baseline Imager (ABI) AOD data using a geographically weighted regression with hourly and daily temporal resolution. Validation versus ground observations shows a mean bias of −21.4% and −15.3% for hourly and daily PM2.5 estimates, respectively, for concentrations ranging from 0 to 1000 μg m−3. Because satellites only observe AOD in the daytime, the relation between observed daytime PM2.5 and daily mean PM2.5 was evaluated using ground measurements; PM2.5 estimated from ABI AODs were also examined to study this relationship. The ground measurements show that daytime mean PM2.5 has good correlation (r > 0.8) with daily mean PM2.5 in most areas of the United States, but with pronounced differences in the western United States due to temporal variations caused by wildfire smoke; the relation between the daytime and daily PM2.5 estimated from the ABI AODs has a similar pattern. While daily or daytime estimated PM2.5 provides exposure information in the context of the PM2.5 standard (>35 μg m−3), the hourly estimates of PM2.5 used in nowcasting show promise for alerts and warnings of harmful air quality. The geostationary satellite based PM2.5 estimates inform the public of harmful air quality 10 times more than standard ground observations (1.8 versus 0.17 million people per hour). Significance Statement: Fine particulate matter (PM2.5; diameters less than 2.5 μm) are generated from smoke, dust, and emissions from industrial, transportation, and other sectors. They are harmful to human health and even lead to premature mortality. Data from geostationary satellites can help estimate surface PM2.5 exposure by filling in gaps that are not covered by ground monitors. With this information, people can plan their outdoor activities accordingly. This study shows that availability of hourly PM2.5 observations covering the entire continental United States is more informative to the public about harmful exposure to pollution. On average, 1.8 million people per hour can be informed using satellite data compared to 0.17 million people per hour based on ground observations alone. [ABSTRACT FROM AUTHOR]