Recent surges in wildfire emissions have exacerbated surface ozone pollution in the United States. Using deep learning, we developed a gapless daily surface ozone dataset at 1-kilometer resolution for 2003-2024. This dataset revealed a reversal in national policy-relevant ozone trends that had gone undetected by the sparse monitoring network: from -0.65 parts per billion (ppb) per year (2003-2015) to +0.13 ppb per year (2015-2024). The reversal was primarily driven by increasing wildfire emissions, offsetting 3.9 years of mitigation progress. Premature deaths from fire-sourced ozone have increased by 318 deaths per year since 2013, with post-2013 mortality 46% higher than pre-2013 mortality. During 2022-2024, wildfire emissions exposed 43 million people to nonattainment conditions, effectively preventing a 4-ppb tightening of the ozone standard. These results underscore the growing challenges of sustaining air quality progress as wildfires intensify under climate change.

Sub-pixel fire characterization is essential for quantifying wildfire energetics, combustion phase dynamics, and their atmospheric impacts from satellite observations. This study presents a series of Observing System Simulation Experiments (OSSEs) to systematically evaluate the influence of temperature phase complexity, channel selection, and intra-phase temperature heterogeneity on the performance of a Bayesian retrieval framework. Using synthetic observations derived from a multi-phase radiance model, we assess retrieval skill across key fire properties—including Fire Radiative Power (FRP), visible energy fraction (VEF), and flaming heat flux—under realistic sensor constraints. Results show that a bi-phasic fire model provides a strong balance between accuracy and observational feasibility, outperforming conventional approaches such as Wooster’s FRP regression, particularly in low-intensity fire scenarios. FRP is found to be the most resilient parameter to model and characterize observational uncertainties, while VEF and heat flux estimates are more sensitive to both fire phase partitioning and spectral coverage. Shortwave and mid-infrared bands, especially the day-night band (DNB) and 2.25 \mum m channels, are shown to offer the greatest constraint on retrieval accuracy. We further analyze the impact of intra-phase temperature variability and propose practical filtering strategies for identifying low-confidence cases, such as smoldering-dominated fires. This paper serves as the first part of a companion study on multi-channel fire retrieval. In the subsequent paper, we apply the bi-phasic Bayesian framework to real VIIRS nighttime observations and assess algorithm performance in an operational setting.

Spaceborne lidar offers unique advantages for improving global estimates of fine particulate matter (), traditionally limited by critical data gaps in the vertical dimension. Here, we present a new method to retrieve relying on ensembles on aerosol extinction available within the GEOS Aerosol Data Assimilation. This study uses 1064‐nm backscatter lidar data from the NASA Cloud‐Aerosol Transport System (CATS) and model priors from the GEOS model. First, we developed a 1‐D ensemble‐based variational technique (1‐D EnsVar) to perform vertically resolved retrievals of speciated aerosol extinction and surface . Next, we evaluated the performance of 1‐D EnsVar retrievals of and extinction through an independent validation using measurements from spaceborne, airborne, and ground‐based platforms. This approach overcomes traditional limitations by leveraging the strengths of complementary vertical aerosol information from CATS and GEOS to better resolve speciated aerosol optical properties and mass. Assimilating CATS lidar data with the GEOS model reduced bias in surface prediction by 1.1 over the CONUS in 2016, potentially reducing model errors by up to 20%. Given the unique capability of CATS to process vertical profile data in near real‐time, this work demonstrates the powerful utility of spaceborne lidar for improving air quality forecasting. While this pilot study is not yet performed within a cycling data assimilation system, the developed algorithm can easily be integrated in such systems. These results have broader implications for validating aerosol transport models, refining passive satellite retrievals of , and developing data assimilation techniques for future lidar platforms.

Satellite infrared (IR) cloud imagery has proven valuable in the identification of Pyrocumulonimbus (pyroCb) clouds. The substantial brightness temperature difference observed between warm shortwave IR wavelengths (~4 μm) and window IR wavelengths (~11 μm) has served as a reliable marker for detecting daytime pyroCb. However, this indicator becomes ineffective during nocturnal hours when the enhanced brightness temperature at 4 μm is solely a daytime phenomenon, arising from PyroCb microphysics that increase solar reflectivity of clouds. We have developed a machine learning model designed to detect pyroCb events during nighttime using IR channels from the Advanced Baseline Imager (ABI) aboard GOES-16. The model leverages the distinctive characteristics of daytime IR channels as its training data. We applied the trained model to five intense pyroCb events in western North America during August 2017. Furthermore, we have employed an established cloud-tracking tool known as Tracking and Object-Based Analysis of Clouds (tobac) to analyze the evolution of the clouds plumes and infer their lifetimes. Our research aims to extend this case study on a global scale, with the objective of creating a comprehensive database for the lifetimes of pyroCb events. Such a database will enhance our understanding of pyroCb dynamics, which is helpful for investigating the radiative implications and the potential impact on stratospheric chemistry.

The Multi-Angle Imager for Aerosols (MAIA) satellite mission, to be jointly implemented by NASA and the Italian Space Agency, aims to study how different types of particulate matter (PM) pollution affect human health. The investigation will primarily focus on a discrete set of globally distributed Primary Target Areas (PTAs) containing major metropolitan cities, and will integrate satellite observations, ground observations, and chemical transport model (CTM) outputs (meteorology variables and PM concentrations) to generate maps of near-surface total and speciated PM within the PTAs. In addition, the MAIA investigation will provide satellite measurements of aerosols over a set of Secondary Target Areas (STAs), which are useful for studying air quality more broadly. For the CTM, we have developed a Unified Inputs (of initial and boundary conditions) for WRF-Chem (UI-WRF-Chem) modeling framework to support the MAIA satellite mission, building upon the standard WRF-Chem model. The framework includes newly developed modules and major enhancements that aim to improve model simulated meteorology variables, total and speciated PM concentrations as well as AOD. These developments include: (1) application of NASA GEOS FP and MERRA-2 data to provide both meteorological and chemical initial and boundary conditions for performing WRF-Chem simulations at a fine spatial resolution for both forecast and reanalysis modes; (2) application of GLDAS and NLDAS data to constrain surface soil properties such as soil moisture; (3) application of recent available MODIS land data to improve land surface properties such as land cover type; (4) development of a new soil NOx emission scheme – the Berkeley Dalhousie Iowa Soil NO Parameterization (BDISNP); (5) development of a stand-alone emission preprocessor that ingests both global and regional anthropogenic emission inventories as well as fire emissions. Here, we illustrate the model improvements enabled by these developments over four target areas: Beijing in China, CHN-Beijing (STA); Rome in Italy, ITA-Rome (PTA); Los Angeles in the U.S., USA-LosAngeles (PTA), and Atlanta in the U.S., USA-Atlanta (PTA). UI-WRF-Chem is configured as 2 nested domains using an outer domain (D1) and inner domain (D2) with 12 and 4 km spatial resolution, respectively. For each target area, we first run a suite of simulations to test the model sensitivity to different physics schemes and then select the optimal combination based on evaluation of model simulated meteorology with ground observations. For the inner domain (D2), we have chosen to turn off the traditional Grell 3D ensemble (G3D) cumulus scheme. We conducted a case study over USA-Atlanta for June 2022 to demonstrate the impacts of the cumulus scheme on precipitation and subsequent total and speciated PM2.5 concentrations. Our results show that keeping the G3D cumulus scheme turned on results in higher precipitation and lower total and speciated PM2.5 than the simulation with the G3D cumulus scheme turned off. Compared with surface observations of precipitation and PM2.5 concentration, the simulation with the G3D scheme off shows better performance. We focus on two dust intrusion events over CHN-Beijing and ITA-Rome, which occurred in March 2018 and June 2023, respectively. We carried out a suite of sensitivity simulations using UI-WRF-Chem by excluding chemical boundary conditions or including MERRA-2 chemical boundary conditions. Our results show that using MERRA-2 data to provide chemical boundary conditions can help improve model simulation of surface PM concentrations and AOD. Some of the target areas have also experienced significant changes in land cover and land use over the past decade. Our case study over CHN-Beijing in July 2018 investigates the impacts of improved land surface properties with recent available MODIS land data on capturing the urban heat island phenomenon. Model-simulated surface skin temperature shows better agreement with MODIS observed land surface temperature. The updated soil NOx emission scheme in July 2018 also leads to higher NO2 vertical column density (VCD) in rural areas within the CHN-Beijing target area, which matches better with TROPOMI observed NO2 VCD. This in turn affects the simulation of surface nitrate concentration. Lastly, we conducted a case study over USA-LosAngeles to tune dust emissions. These examples illustrate the fine-tuning work conducted over each target area for the purpose of evaluating and improving model performance.

There is a need to quickly convert aerosol microphysical properties into optical properties for global modeling, data assimilation, and remote sensing applications. This is generally accomplished through look-up tables (LUTs) of aerosol mass extinction coefficients (MEC), mass absorption coefficients (MAC), asymmetry parameters, normalized phase functions, etc. Unfortunately, many scientists are using outdated LUTs that are based upon measurements and computational techniques first published by Shettle and Fenn (1979) and later updated by Hess et al. (1998). Thus, the computations in common use are still largely based upon Mie theory and in situ information that has not been updated during this century.The Table of Aerosol Optics (TAO) is an open relational database (under construction) that expands upon existing LUTs by including recent measurements and new computational techniques for non-spherical particles (https://science.larc.nasa.gov/mira-wg/topics/tao/). The ‘open’ aspect of TAO is important, since the measurements and techniques of today will undoubtedly yield to different values in the future. This open architecture allows specialists to add new tables and gain exposure for their work and benefits modelers and remote sensing scientists by giving them easy access to computations that utilize the latest techniques. Quality is controlled by requiring methods to be peer-reviewed in the scientific literature.Thus far, we have computed mass extinction coefficients, mass absorption coefficients, lidar ratios, etc., at 73 wavelengths ranging from 0.25-40 µm for black carbon (BC), brown carbon (BrC), non-absorbing organic carbon, and mineral dust. For mineral dust, we use hexahedra shapes and mineral mixtures of montmorillonite, illite, hematite, and goethite. The illite volume fraction varies from 0 to 59% to capture the range of real refractive indices found in AERONET climatologies; the sum of the hematite and goethite mass fractions are ~2%. Additional mixtures will be added as appropriate.We have also computed optical properties for 22 size distributions of bare aggregated BC using the Multi-Sphere T-Matrix (MSTM) code (https://github.com/dmckwski/MSTM) at several remote sensing wavelengths. Our MSTM computations use aggregates of 20-nm spherules with particle-cluster growth. We obtained mass absorption coefficients (MACs) of 7.2-7.5 m2/g at a mid-visible wavelength (532 nm) when the BC fractal dimension was fixed at Df = 1.8 (i.e., fresh BC), consistent with values commonly recommended in literature reviews.We will present the TAO vision and example results for several aerosol types. TAO is part of the Models, In situ, and Remote sensing of Aerosols (MIRA) working group. MIRA seeks to build collaboration, consistency, and openness amongst the aerosol disciplines. We seek community feedback from aerosol scientists regarding the construction and content of TAO, especially in this early phase. Check out the MIRA webpage at https://science.larc.nasa.gov/mira-wg/ and subscribe to our mailing list at https://espo.nasa.gov/lists/listinfo/mira.Hess et al. (1998): Optical properties of aerosols and clouds: The software package OPAC, BAMS, 79, 831–844.Shettle and Fenn (1979): Tech. Rep. AFGL-TR-790214, Air Force Geophysics Laboratory, 1979.

Atmospheric aerosols are an important component of the Earth’s climate system and contribute substantial uncertainties to predictions of future climate change. In the southeast Atlantic, where expansive light-absorbing smoke aerosol plumes overlie semi-permanent stratocumulus clouds, the direct aerosol radiative effect (DARE) induces warming, but the magnitude of this effect varies widely among climate models. Thus, it is essential to improve estimates based on observations to help constrain model uncertainties. However, the impact of the observed cloud diurnal cycle on DARE remains unclear. Here we quantify DARE using radiative transfer modelling based on hourly satellite observations of clouds focusing on the region 20° S–0° and 10° W–15° E. We find that accounting for the observed cloud diurnal cycle over the southeast Atlantic, rather than assuming a constant early-afternoon cloud field throughout the entire day, results in a more than twofold increase (+1.7 ± 0.4 W m−2) in the regional mean aerosol radiative warming. The increase in DARE results from morning hours when cloud fractions and optical depths are higher. Neglect of the cloud diurnal cycle adds to the underestimated radiative warming in the southeast Atlantic associated with underestimated aerosol absorption among climate models. Future observations-based estimates of aerosol climatic effects need to account for the cloud diurnal cycle. Accounting for the diurnal cycle in low clouds more than doubles estimated aerosol radiative warming in the southeast Atlantic, according to radiative transfer modelling and satellite observations.

The Goddard Chemistry Aerosol Radiation and Transport (GOCART) model, which controls the sources, sinks, and chemistry of aerosols within the Goddard Earth Observing System (GEOS), recently underwent a major refactoring and update, including a revision of the emissions datasets and the addition of brown carbon. A 4-year benchmark simulation utilizing the new version of the model code, termed GOCART Second Generation (GOCART-2G) and coupled to the Goddard Earth Observing System (GEOS) model, was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development. A comparison of simulated aerosol optical depth between GOCART-2G and MODIS retrievals indicates the model captures the overall spatial pattern and seasonal cycle of aerosol optical depth but overestimates aerosol extinction over dusty regions and underestimates aerosol extinction over Northern Hemisphere boreal forests, requiring further investigation and tuning of emissions. This MODIS-based analysis is corroborated by comparisons to MISR and selected AERONET stations; however, discrepancies between the Aqua and Terra satellites indicate there is a diurnal component to biases in aerosol optical depth over southern Asia and northern Africa. Despite the underestimate of aerosol optical depth in biomass burning regions in GEOS, there is an overestimate in the surface mass of organic carbon in the United States, especially during the summer months. Over Europe, GOCART-2G is unable to match the summertime peak in aerosol optical depth, opposing the observed late fall and early spring peaks in surface mass concentration. A comparison of the vertical profile of attenuated backscatter to observations from CALIPSO indicates the GEOS model is capable of capturing the vertical profile of aerosol; however, the mid-troposphere plumes of dust in the North Atlantic and smoke in the southeastern Atlantic are perhaps too low in altitude. The results presented highlight priorities for future development with GOCART-2G, including improvements for dust, biomass burning aerosols, and anthropogenic aerosols.

The presence of an aerosol layer in the upper troposphere/lower stratosphere (UT/ LS) in South America was identified with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2). This layer, which we shall refer to as the South American tropopause aerosol layer (SATAL), was identified over the Amazon basin at altitudes between 11 and 14 km. It exhibits a seasonal behavior similar to the Asian tropopause aerosol layer (ATAL) and the North American tropopause aerosol layer (NATAL). The SATAL is observed from October to March, coinciding with the presence of the South American monsoon. It forms first in the eastern Amazon basin in October, then moves to the southern Amazon, where it weakens in December-Januar y and finally dissipates in February-March. We hypothesize that two main factors influence the SATAL formation in the UT/LS: 1) the source of aerosols from Africa and 2) the updraft mass flux from deep convective systems during the active phase of the South American monsoon system that transports aerosols to the UT/LS. Further satellite observations of aerosols and field campaigns are needed to provide useful information to find the origin and composition of the aerosols in the UT/LS during the South American monsoon.

The regional climate variability in peninsular Southeast Asia (PSEA) can influence springtime biomass burning (BB) aerosol emissions and associated transport patterns. To comprehend the interannual variation of regional climate and its impact on PSEA BB, a diagnostic analysis based on the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) dataset and Moderate Resolution Imaging Spectroradiometer (MODIS) observations from 2000 to 2019 has presented. Employing principal component, composite, and correlation analyses, this study identified four climatic factors governing the emission and transport of PSEA BB aerosols: (i) a low-level anticyclone (suppressed monsoon trough) in the Bay of Bengal, (ii) the relative strength of the anticyclone over the South China Sea, (iii) the Pacific subtropical high, and (iv) low-level westerlies from PSEA to Taiwan. Additionally, BB emissions and transport significantly correlate with the El Niño-Southern Oscillation (ENSO). In the El Niño year, increased anticyclones in the Bay of Bengal and South China Sea accompanied the stronger westerlies, which enhanced BB aerosol emission and transport. The diagnostic results of this study can contribute to a better understanding and improved model simulations of aerosol-climate interactions in South and Southeast Asian monsoon regions.

Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting. While the parallel developments in the AI literature focus on foundation models – models that can be effectively tuned to address multiple, different use cases – the developments on the weather and climate side largely focus on single-use cases with particular emphasis on mid-range forecasting. We close this gap by introducing Prithvi WxC, a 2.3 billion parameter foundation model developed using 160 variables from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Prithvi WxC employs an encoder-decoder-based architecture, incorporating concepts from various recent transformer models to effectively capture both regional and global dependencies in the input data. The model has been designed to accommodate large token counts to model weather phenomena in different topologies at fine resolutions. Furthermore, it is trained with a mixed objective that combines the paradigms of masked reconstruction with forecasting. We test the model on a set of challenging downstream tasks namely: Autoregressive rollout forecasting, Downscaling, Gravity wave flux parameterization, and Extreme events estimation. The pretrained model with 2.3 billion parameters, along with the associated fine-tuning workflows, has been publicly released as an open-source contribution via Hugging Face.

Complicated by seasonal transport of biomass burning aerosol plumes primarily overlaying the marine stratocumulus-to-cumulus transition, the complexity of the atmosphere over the southeast Atlantic Ocean is difficult to represent in models. Biases with respect to the height of the aerosol plume and excessive subsidence have previously been documented in MERRA-2. This paper further diagnoses these biases, and addresses the unreasonable subsidence over the southeast Atlantic region currently simulated by the Goddard Earth Observing System (GEOS) model during the first deployment of the ORACLES campaign in September 2016. Our study is based on model simulations where the meteorological state is constrained by the GEOS-based MERRA-2 reanalysis, as well as by ERA5, using the so-called replay technique. Free running simulations with the GEOS model show excessive subsidence that begins over land such that aerosol transported by the easterly jet reaches a strip of complex terrain and is forced downward; a similar behavior can be found in the MERRA-2 reanalysis. This excessive subsidence has been somewhat reduced by improved parameterizations found in recent versions of the GEOS model. However, any replay constrained by MERRA-2 meteorology, even those based on recent versions of GEOS, suffer from the same excessive subsidence. An additional set of simulations in which the GEOS model was free-running was used to quantify the role of aerosol itself in adjusting the profile of vertical motion. While self-lofting of biomass burning aerosol reduces the subsidence over the ocean, the magnitude of its impact is only a fraction of the bias in MERRA-2.

Satellite observations and ground-based measurements have indicated a high variability in the aerosol optical depth (AOD) in the Middle East region in recent decades. In the period that extends from 2003 to 2012, observations show a positive AOD trend of 0.01–0.04 per year or a total increase of 0.1–0.4 per decade. This study aimed to investigate if the observed trend was also captured by the NASA Goddard Earth Observing System (GEOS) model. To this end, we examined changes in the simulated dust emissions and dust AOD during this period. To understand the factors driving the increase in AOD in this region we also examined meteorological and surface parameters important for dust emissions, such as wind fields and soil moisture. Two GEOS model simulations were used in this study: the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis (with meteorological and aerosol AOD data assimilated) and MERRA-2 Global Modeling Initiative (GMI) Replay (with meteorology constrained by the MERRA-2 reanalysis but without aerosol assimilation). We did not find notable changes in the modeled 10 m wind speed and soil moisture. However, analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data did show an average decrease of 8 % per year in the region encompassing Syria and Iraq, which prompted us to quantify the effects of vegetation on dust emissions and AOD in the Middle East region. This was done by performing a sensitivity experiment in which we enhanced dust emissions in grid cells where the NDVI decreased. The simulation results supported our hypothesis that the loss of vegetation cover and the associated increase in dust emissions over Syria and Iraq can partially explain the increase in AOD downwind. The model simulations indicated dust emissions need to be 10-fold larger in those grid cells in order to reproduce the observed AOD and trend in the model.

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.

The emergence of aerosol reanalyses in recent years has facilitated a comprehensive and systematic evaluation of aerosol optical depth (AOD) trends and attribution over multi-decadal timescales. Notable multi-year aerosol reanalyses currently available include NAAPS-RA from the US Naval Research Laboratory, the NASA MERRA-2, JRAero from the Japan Meteorological Agency (JMA), and CAMSRA from Copernicus/ECMWF. These aerosol reanalyses are based on differing underlying meteorology models, representations of aerosol processes, as well as data assimilation methods and treatment of AOD observations. This study presents the basic verification characteristics of these four reanalyses versus both AERONET and MODIS retrievals in monthly AOD properties and identifies the strength of each reanalysis and the regions where divergence and challenges are prominent. Regions with high pollution and often mixed fine-mode and coarse-mode aerosol environments, such as South Asia, East Asia, Southeast Asia, and the Maritime Continent, pose significant challenges, as indicated by higher monthly AOD root mean square error. Moreover, regions that are distant from major aerosol source areas, including the polar regions and remote oceans, exhibit large relative differences in speciated AODs and fine-mode versus coarse-mode AODs among the four reanalyses. To ensure consistency across the globe, a multi-reanalysis consensus (MRC, i.e., ensemble mean) approach was developed similarly to the International Cooperative for Aerosol Prediction Multi-Model Ensemble (ICAP-MME). Like the ICAP-MME, while the MRC does not consistently rank first among the reanalyses for individual regions, it performs well by ranking first or second globally in AOD correlation and RMSE, making it a suitable candidate for climate studies that require robust and consistent assessments.

In this study, we describe the first prototype version of global aerosol reanalysis at the National Oceanic and Atmospheric Administration (NOAA), the prototype NOAA Aerosol Reanalysis version 1.0 (pNARA v1.0) that was produced for the year 2016. In pNARA v1.0, the forecast model is an early version of the operational Global Ensemble Forecast System-Aerosols (GEFS-Aerosols) model. The three-dimensional ensemble-variational (3D-EnVar) data assimilation (DA) system configuration is built using elements of the Joint Effort for Data Assimilation Integration (JEDI) framework being developed at the Joint Center for Satellite Data Assimilation (JCSDA). The Neural Network Retrievals (NNR) of aerosol optical depth (AOD) at 550 nm from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments are assimilated to provide reanalysis of aerosol mass mixing ratios. We evaluate pNARA v1.0 against a wide variety of Aerosol Robotic Network (AERONET) observations, against the National Aeronautics and Space Administration's Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2; Gelaro et al., 2017; Randles et al., 2017; Buchard et al., 2017) and the European Centre for Medium-Range Weather Forecasts' Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA; Inness et al., 2019), and against measurements of surface concentrations of particulate matter 2.5 (PM2.5) and aerosol species. Overall, the 3D-EnVar DA system significantly improves AOD simulations compared with observations, but the assimilation has limited impact on chemical composition and size distributions of aerosols. We also identify deficiencies in the model's representations of aerosol chemistry and their optical properties elucidated from evaluation of pNARA v1.0 against AERONET observations. A comparison of seasonal profiles of aerosol species from pNARA v1.0 with the other two reanalyses exposes significant differences among datasets. These differences reflect uncertainties in simulating aerosols in general.

This study addresses two key uncertainties in the fire radiative power (FRP) retrieval, which is essential for improving global top-down fire emission inventories. First, it proposes a novel FRP retrieval method by combining the similar to 4 and similar to 8.6 mu m channels based on Monte Carlo simulation, which is verified using the Visible Infrared Imaging Radiometer Suite (VIIRS). The inclusion of the similar to 8.6 mu m channel significantly improves the accuracy of FRP retrieval, especially for highly smoldering fires. Second, atmospheric correction is conducted using outputs from the state-of-the-art unified linearized vector radiative transfer model (UNL-VRTM). The importance of atmospheric correction is demonstrated through the single-channel (similar to 4 mu m) FRP retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire (AF), VIIRS AF, and VIIRS second-generation fire light detection algorithm (FILDA-2) products. Post-correction results show effective mitigation of nighttime FRP angular dependency, achieved by considering the enhanced atmospheric attenuation due to longer path length off-nadir. However, a residual daytime FRP angular dependency remains, likely due to the angular dependency of the thresholds used for daytime fire detection. Additionally, an enhanced agreement is observed between the VIIRS FILDA-2 FRP retrievals from the Suomi National Polar-orbiting Partnership (NPP) and National Oceanic and Atmospheric Administration (NOAA)-20 satellites after correction. Lastly, a global FRP increase is noted across all three products, with VIIRS AF and VIIRS FILDA-2 showing more significant increases (65.8% and 62.5%, respectively) than MODIS AF (20.8%). These advancements in FRP retrievals may enhance the downstream fire emission products, which will benefit the air pollution modeling community.

Background Long-term improvements in air quality and public health in the continental USA were disrupted over the past decade by increased fire emissions that potentially offset the decrease in anthropogenic emissions. This study aims to estimate trends in black carbon and PM2 center dot 5 concentrations and their attributable mortality burden across the USA. Methods In this study, we derived daily concentrations of PM2 center dot 5 and its highly toxic black carbon component at a 1-km resolution in the USA from 2000 to 2020 via deep learning that integrated big data from satellites, models, and surface observations. We estimated the annual PM2 center dot 5-attributable and black carbon-attributable mortality burden at each 1-km2 grid using concentration-response functions collected from a national cohort study and a meta-analysis study, respectively. We investigated the spatiotemporal linear-regressed trends in PM2 center dot 5 and black carbon pollution and their associated premature deaths from 2000 to 2020, and the impact of wildfires on air quality and public health. Findings Our results showed that PM2 center dot 5 and black carbon estimates are reliable, with sample-based cross-validated coefficients of determination of 0 center dot 82 and 0 center dot 80, respectively, for daily estimates (0 center dot 97 and 0 center dot 95 for monthly estimates). Both PM2 center dot 5and black carbon in the USA showed significantly decreasing trends overall during 2000 to 2020 (22% decrease for PM2 center dot 5and 11% decrease for black carbon), leading to a reduction of around 4200 premature deaths per year (95% CI 2960-5050). However, since 2010, the decreasing trends of fine particles and premature deaths have reversed to increase in the western USA (55% increase in PM2 center dot 5, 86% increase in black carbon, and increase of 670 premature deaths [460-810]), while remaining mostly unchanged in the eastern USA. The western USA showed large interannual fluctuations that were attributable to the increasing incidence of wildfires. Furthermore, the black carbon-to-PM2 center dot 5 mass ratio increased annually by 2 center dot 4% across the USA, mainly due to increasing wildfire emissions in the western USA and more rapid reductions of other components in the eastern USA, suggesting a potential increase in the relative toxicity of PM2 center dot 5. 100% of populated areas in the USA have experienced at least one day of PM2 center dot 5 pollution exceeding the daily air quality guideline level of 15 jig/m3 during 2000-2020, with 99% experiencing at least 7 days and 85% experiencing at least 30 days. The recent widespread wildfires have greatly increased the daily exposure risks in the western USA, and have also impacted the midwestern USA due to the long-range transport of smoke. Interpretation Wildfires have become increasingly intensive and frequent in the western USA, resulting in a significant increase in smoke-related emissions in populated areas. This increase is likely to have contributed to a decline in air quality and an increase in attributable mortality. Reducing fire risk via effective policies besides mitigation of climate warming, such as wildfire prevention and management, forest restoration, and new revenue generation, could substantially improve air quality and public health in the coming decades. Funding National Aeronautics and Space Administration (NASA) Applied Science programme, NASA MODIS maintenance programme, NASA MAIA satellite mission programme, NASA GMAO core fund, National Oceanic and Atmospheric Administration (NOAA) GEO-XO project, NOAA Atmospheric Chemistry, Carbon Cycle, and Climate (AC4) programme, and NOAA Educational Partnership Program with Minority Serving Institutions.

We present the second-generation FIre Light Detection Algorithm (FILDA-2), which includes advances in fire detection and retrievals of radiative power (FRP), fire visible energy fraction (VEF), and fire modified combustion efficiency (MCE) at nighttime from the holistic use of multiple-spectral radiances measured by the visible infrared imaging radiometer suite (VIIRS) aboard Suomi-NPP (VNP) and National Oceanic and Atmospheric Administration (NOAA)-20/joint polar satellite system (JPSS)-1 (VJ1) satellites. Key enhancements include: 1) a new fast algorithm that maps VIIRS day/night band (DNB) radiances to the pixel footprints of VIIRS moderate (M) and imagery (I) bands; 2) identification of potential fire pixels through the use of the DNB anomalies and I-band thermal anomalies; 3) dynamic thresholds for contextual testing of fire pixels; and 4) pixel-specific estimates of FRP, VEF, and MCE. The global benchmark test demonstrates that FILDA-2 can detect approximately 25%–30% smaller and cooler fires than the operational VIIRS active fire 375-m I-band algorithm with the added benefit of providing daily global pixel-level characterizations of MCE for nighttime surface fires. The MCE derived by FILDA-2 is in good agreement with limited ground-based observations near the fires. Additionally, FILDA-2 reduces angular dependence in FRP estimates and significantly reduces the “bow-tie” (double-counting) effect in fire detection compared with the AF-I product. The cross-validation of FILDA-2 products from VNP and VJ1 retrievals confirms good consistency in FRP and MCE retrievals globally. FILDA-2 is being implemented by the National Aeronautics and Space Administration (NASA) to generate a new VIIRS data product for fire monitoring, chemical-speciated fire emission estimates, and fire line characterization.