Reports from western U.S. firefighters that nighttime fire activity has been increasing during the spans of many of their careers have recently been confirmed by satellite measurements over the 2003-20 period. The hypothesis that increasing nighttime fire activity has been caused by increased nighttime vapor pressure deficit (VPD) is consistent with recent documentation of positive, 40-yr trends in nighttime VPD over the western United States. However, other meteorological conditions such as near-surface wind speed and planetary boundary layer depth also impact fire behavior and exhibit strong diurnal changes that should be expected to help quell nighttime fire activity. This study investigates the extent to which each of these factors has been changing over recent decades and, thereby, may have contributed to the perceived changes in nighttime fire activity. Results quantify the extent to which the summer nighttime distributions of equilibrium dead woody fuel moisture content, planetary boundary layer height, and near-surface wind speed have changed over the western United States based on hourly ERA5 data, considering changes between the most recent decade and the 1980s and 1990s, when many present firefighters began their careers. Changes in the likelihood of experiencing nighttime meteorological conditions in the recent period that would have registered as unusually conducive to fire previously are evaluated considering each variable on its own and in conjunction (simultaneously) with one another. The main objective of this work is to inform further study of the reasons for the observed increases in nighttime fire activity.

Smoke plume dynamics involve various smoke processes and mechanics in the atmosphere and provide the scientific foundation for the development of tools to simulate and predict smoke and its environmental and human impacts. The increasing occurrence of wildfires and the demands for more extensive application of prescribed fires in the U.S. have posed great challenges and immediate actions for advancing smoke plume dynamics and improving smoke predictions and impact assessments to mitigate smoke impacts. Numerous efforts have been made recently to address these needs and challenges. This paper synthesizes advances in smoke plume dynamics research mainly conducted in the U.S. in the recent decade, identifies gaps, and suggests future research needs. The main advances include smoke data collections from comprehensive field campaigns, new satellite products, improved understanding of smoke plume properties and chemistry, structure and evolution, evaluation and improvement of smoke modeling and prediction systems, the development of coupled smoke models, and applications of machine-learning techniques. The major remaining gaps are the lack of comprehensive simultaneous measurements of smoke, fuels, fire, and atmospheric interactions during wildfires, high-resolution coupled modeling systems of these components, and real-time smoke prediction capacity. The findings from this synthesis study are expected to support smoke research and management to meet various challenges under increasing wildland fires and impacts.

Chapter 9 Profiles of Operational and Research Forecasting of Smoke and Air Quality Around the World Susan M. O'Neill, Susan M. O'Neill Pacific Northwest Research Station, United States Forest Service, Seattle, Washington, USASearch for more papers by this authorPeng Xian, Peng Xian United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorJohannes Flemming, Johannes Flemming European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorMartin Cope, Martin Cope CSIRO Climate Science Centre, Aspendale, Victoria, AustraliaSearch for more papers by this authorAlexander Baklanov, Alexander Baklanov World Meteorological Organization, Geneva, SwitzerlandSearch for more papers by this authorNarasimhan K. Larkin, Narasimhan K. Larkin Pacific Northwest Research Station, United States Forest Service, Seattle, Washington, USASearch for more papers by this authorJoseph K. Vaughan, Joseph K. Vaughan Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington, USASearch for more papers by this authorDaniel Tong, Daniel Tong Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia, USASearch for more papers by this authorRosie Howard, Rosie Howard Earth, Ocean and Atmospheric Sciences Department, The University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorRoland Stull, Roland Stull Earth, Ocean and Atmospheric Sciences Department, The University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorDidier Davignon, Didier Davignon Environment and Climate Change Canada, Dorval, Quebec City, CanadaSearch for more papers by this authorRavan Ahmadov, Ravan Ahmadov CIRES, University of Colorado Boulder, Boulder, Colorado, USA NOAA Global Systems Laboratory, Boulder, Colorado, USASearch for more papers by this authorM. Talat Odman, M. Talat Odman School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USASearch for more papers by this authorJohn Innis, John Innis EPA Tasmania, Hobart, Tasmania, AustraliaSearch for more papers by this authorMerched Azzi, Merched Azzi Department of Planning and Environment, Government of New South Wales, Sydney, AustraliaSearch for more papers by this authorChristopher Gan, Christopher Gan Centre for Climate Research Singapore, Meteorological Service Singapore, SingaporeSearch for more papers by this authorRadenko Pavlovic, Radenko Pavlovic Environment and Climate Change Canada, Dorval, Quebec City, CanadaSearch for more papers by this authorBoon Ning Chew, Boon Ning Chew Centre for Climate Research Singapore, Meteorological Service Singapore, SingaporeSearch for more papers by this authorJeffrey S. Reid, Jeffrey S. Reid United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorEdward J. Hyer, Edward J. Hyer United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorZak Kipling, Zak Kipling European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorAngela Benedetti, Angela Benedetti European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorPeter R. Colarco, Peter R. Colarco NASA Goddard Space Flight Center, Greenbelt, Maryland, USASearch for more papers by this authorArlindo Da Silva, Arlindo Da Silva NASA Goddard Space Flight Center, Greenbelt, Maryland, USASearch for more papers by this authorTaichu Tanaka, Taichu Tanaka Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, JapanSearch for more papers by this authorJeffrey McQueen, Jeffrey McQueen NOAA National Centers for Environmental Prediction, College Park, Maryland, USASearch for more papers by this authorPartha Bhattacharjee, Partha Bhattacharjee I. M. Systems Group, NWS/NCEP/EMC, College Park, Maryland, USASearch for more papers by this authorJonathan Guth, Jonathan Guth Météo-France, Toulouse, FranceSearch for more papers by this authorNicole Asencio, Nicole Asencio Météo-France, Toulouse, FranceSearch for more papers by this authorOriol Jorba, Oriol Jorba Barcelona Supercomputing Center, Barcelona, SpainSearch for more papers by this authorCarlos Pérez García-Pando, Carlos Pérez García-Pando Barcelona Supercomputing Center, Barcelona, Spain Catalan Institution for Research and Advanced Studies, Barcelona, SpainSearch for more papers by this authorRostislav Kouznetsov, Rostislav Kouznetsov Atmospheric Composition Unit, Finnish Meteorological Institute, Helsinki, FinlandSearch for more papers by this authorMikhail Sofiev, Mikhail Sofiev Atmospheric Composition Unit, Finnish Meteorological Institute, Helsinki, FinlandSearch for more papers by this authorMelissa E. Brooks, Melissa E. Brooks Met Office, Exeter, United KingdomSearch for more papers by this authorJack Chen, Jack Chen Environment and Climate Change Canada, Ottawa, Ontario, CanadaSearch for more papers by this authorEric James, Eric James CIRES, University of Colorado Boulder, Boulder, Colorado, USA NOAA Global Systems Laboratory, Boulder, Colorado, USASearch for more papers by this authorFabienne Reisen, Fabienne Reisen CSIRO Climate Science Centre, Aspendale, Victoria, AustraliaSearch for more papers by this authorAlan Wain, Alan Wain Australian Bureau of Meteorology, Melbourne, Victoria, AustraliaSearch for more papers by this authorKerryn McTaggart, Kerryn McTaggart Department of Environment, Land, Water and Planning, Government of Victoria, Melbourne, Victoria, AustraliaSearch for more papers by this authorAngus MacNeil, Angus MacNeil Forest Practices Authority, Hobart, Tasmania, AustraliaSearch for more papers by this author Susan M. O'Neill, Susan M. O'Neill Pacific Northwest Research Station, United States Forest Service, Seattle, Washington, USASearch for more papers by this authorPeng Xian, Peng Xian United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorJohannes Flemming, Johannes Flemming European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorMartin Cope, Martin Cope CSIRO Climate Science Centre, Aspendale, Victoria, AustraliaSearch for more papers by this authorAlexander Baklanov, Alexander Baklanov World Meteorological Organization, Geneva, SwitzerlandSearch for more papers by this authorNarasimhan K. Larkin, Narasimhan K. Larkin Pacific Northwest Research Station, United States Forest Service, Seattle, Washington, USASearch for more papers by this authorJoseph K. Vaughan, Joseph K. Vaughan Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington, USASearch for more papers by this authorDaniel Tong, Daniel Tong Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia, USASearch for more papers by this authorRosie Howard, Rosie Howard Earth, Ocean and Atmospheric Sciences Department, The University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorRoland Stull, Roland Stull Earth, Ocean and Atmospheric Sciences Department, The University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorDidier Davignon, Didier Davignon Environment and Climate Change Canada, Dorval, Quebec City, CanadaSearch for more papers by this authorRavan Ahmadov, Ravan Ahmadov CIRES, University of Colorado Boulder, Boulder, Colorado, USA NOAA Global Systems Laboratory, Boulder, Colorado, USASearch for more papers by this authorM. Talat Odman, M. Talat Odman School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USASearch for more papers by this authorJohn Innis, John Innis EPA Tasmania, Hobart, Tasmania, AustraliaSearch for more papers by this authorMerched Azzi, Merched Azzi Department of Planning and Environment, Government of New South Wales, Sydney, AustraliaSearch for more papers by this authorChristopher Gan, Christopher Gan Centre for Climate Research Singapore, Meteorological Service Singapore, SingaporeSearch for more papers by this authorRadenko Pavlovic, Radenko Pavlovic Environment and Climate Change Canada, Dorval, Quebec City, CanadaSearch for more papers by this authorBoon Ning Chew, Boon Ning Chew Centre for Climate Research Singapore, Meteorological Service Singapore, SingaporeSearch for more papers by this authorJeffrey S. Reid, Jeffrey S. Reid United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorEdward J. Hyer, Edward J. Hyer United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorZak Kipling, Zak Kipling European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorAngela Benedetti, Angela Benedetti European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorPeter R. Colarco, Peter R. Colarco NASA Goddard Space Flight Center, Greenbelt, Maryland, USASearch for more papers by this authorArlindo Da Silva, Arlindo Da Silva NASA Goddard Space Flight Center, Greenbelt, Maryland, USASearch for more papers by this authorTaichu Tanaka, Taichu Tanaka Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, JapanSearch for more papers by this authorJeffrey McQueen, Jeffrey McQueen NOAA National Centers for Environmental Prediction, College Park, Maryland, USASearch for more papers by this authorPartha Bhattacharjee, Partha Bhattacharjee I. M. Systems Group, NWS/NCEP/EMC, College Park, Maryland, USASearch for more papers by this authorJonathan Guth, Jonathan Guth Météo-France, Toulouse, FranceSearch for more papers by this authorNicole Asencio, Nicole Asencio Météo-France, Toulouse, FranceSearch for more papers by this authorOriol Jorba, Oriol Jorba Barcelona Supercomputing Center, Barcelona, SpainSearch for more papers by this authorCarlos Pérez García-Pando, Carlos Pérez García-Pando Barcelona Supercomputing Center, Barcelona, Spain Catalan Institution for Research and Advanced Studies, Barcelona, SpainSearch for more papers by this authorRostislav Kouznetsov, Rostislav Kouznetsov Atmospheric Composition Unit, Finnish Meteorological Institute, Helsinki, FinlandSearch for more papers by this authorMikhail Sofiev, Mikhail Sofiev Atmospheric Composition Unit, Finnish Meteorological Institute, Helsinki, FinlandSearch for more papers by this authorMelissa E. Brooks, Melissa E. Brooks Met Office, Exeter, United KingdomSearch for more papers by this authorJack Chen, Jack Chen Environment and Climate Change Canada, Ottawa, Ontario, CanadaSearch for more papers by this authorEric James, Eric James CIRES, University of Colorado Boulder, Boulder, Colorado, USA NOAA Global Systems Laboratory, Boulder, Colorado, USASearch for more papers by this authorFabienne Reisen, Fabienne Reisen CSIRO Climate Science Centre, Aspendale, Victoria, AustraliaSearch for more papers by this authorAlan Wain, Alan Wain Australian Bureau of Meteorology, Melbourne, Victoria, AustraliaSearch for more papers by this authorKerryn McTaggart, Kerryn McTaggart Department of Environment, Land, Water and Planning, Government of Victoria, Melbourne, Victoria, AustraliaSearch for more papers by this authorAngus MacNeil, Angus MacNeil Forest Practices Authority, Hobart, Tasmania, AustraliaSearch 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.ch9Book Series:Geophysical Monograph Series AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary Biomass burning has shaped many of the ecosystems of the planet and for millennia humans have used it as a tool to manage the environment. When widespread fires occur, the health and daily lives of millions of people can be affected by the smoke leading to a range of health consequences such as respiratory issues, cardiovascular issues, and mortality. It is critical to include smoke and its consequences in atmospheric modeling systems to meet needs such as informing and protecting the public during smoke episodes. This chapter profiles many of the global and regional smoke prediction systems available. It is not an exhaustive list, but rather a profile of many of the systems to give examples of the creativity and complexity needed to simulate the phenomenon of smoke. The global smoke prediction systems are advanced, and many are self-organizing into a powerful ensemble. Regional and national systems are being developed independently for example in Europe (11 systems), North America (7 systems), and Australia (3 systems). Finally, the World Meteorological Organization is bringing together global and regional systems to form an ensemble to support countries with smoke issues and who lack resources. For each system we discuss how fire activity information is obtained, how fire emissions are calculated, and how atmospheric transport and chemical transformation of the smoke plume is treated. REFERENCES Adams , C. , McLinden , C. A. , Shephard , M. W. , Dickson , N. , Dammers , E. , Chen , J. , et al. ( 2019 ). Satellite-derived emissions of carbon monoxide, ammonia, and nitrogen dioxide from the 2016 Horse River wildfire in the Fort McMurray area . Atmospheric Chemistry and Physics , 19 ( 4 ), 2577 – 2599 . Ahmadov , R. , Grell , G. , James , E. , Csiszar , I. , Tsidulko , M. , Pierce , B. , et al. ( 2017 ). Using VIIRS fire radiative power data to simulate biomass burning emissions, plume rise and smoke transport in a real-time air quality modeling system . Paper presented at the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Akagi , S. K. , Yokelson , R. J. , Wiedinmyer , C. , Alvarado , M. J. , Reid , J. S. , Karl , T. , et al. ( 2011 ). Emission factors for open and domestic biomass burning for use in atmospheric models . Atmospheric Chemistry and Physics , 11 ( 9 ), 4039 – 4072 . Al Mahmud , A. A. ( 2005 ). Evaluation of the AIRPACT2 air quality forecast system for the Pacific Northwest . Doctoral dissertation, Washington State University . Alman , B. L. , Pfister , G. , Hao , H. , Stowell , J. , Hu , X. , Liu , Y. , & Strickland , M. J. ( 2016 ). The association of wildfire smoke with respiratory and cardiovascular emergency department visits in Colorado in 2012: A case crossover study . Environmental Health , 15 ( 1 ), 64 . https://www.ncbi.nlm.nih.gov/pubmed/27259511 Anderson , G. K. , Sandberg , D. V. , & Norheim , R. A. ( 2004 ). Fire Emission Production Simulator (FEPS) user's guide . Seattle, WA, USA . Badia , A. , Jorba , O. , Voulgarakis , A. , Dabdub , D. , Pérez García-Pando , C. , et al. ( 2017 ). Description and evaluation of the Multiscale Online Nonhydrostatic AtmospheRe Chemistry model (NMMB-MONARCH) version 1.0: Gas-phase chemistry at global scale . Geoscientific Model Development , 10 ( 2 ), 609 – 638 . Baklanov , A. , Chew , B. N. , Frassoni , A. , Gan , C. , Goldammer , J. , Keywood , M. , et al. ( 2021 ). The WMO Vegetation Fire and Smoke Pollution Warning Advisory and Assessment System (VFSP-WAS): Concept, current capabilities, research and development challenges and the way ahead . Biodiversidade Brasileira: BioBrasil ( 2 ), 179 – 201 . https://doi.org/10.37002/biobrasil.v11i2.1738 Barna , M. , Lamb , B. , O'Neill , S. , Westberg , H. , Figueroa-Kaminsky , C. , Otterson , S. , et al. ( 2000 ). Modeling ozone formation and transport in the Cascadia region of the Pacific northwest . Journal of Applied Meteorology , 39 ( 3 ), 349 – 366 . https://journals.ametsoc.org/view/journals/apme/39/3/1520-0450_2000_039_0349_mofati_2.0.co_2.xml Benedetti , A. , Morcrette , J. J. , Boucher , O. , Dethof , A. , Engelen , R. J. , Fisher , M. , et al. ( 2009 ). Aerosol analysis and forecast in the European centre for Medium-Range Weather Forecasts Integrated Forecast System: 2. Data assimilation . Journal of Geophysical Research , 114 ( D13 ). Benedetti , A. , Reid , J. S. , & Colarco , P. R. ( 2011 ). International cooperative for aerosol prediction workshop on aerosol forecast verification . Bulletin of the American Meteorological Society , 92 ( 11 ), ES48–ES53 . Benjamin , S. G. , Weygandt , S. S. , Brown , J. M. , Hu , M. , Alexander , C. R. , Smirnova , T. G. , et al. ( 2016 ). A North American hourly assimilation and model forecast cycle: The rapid refresh . Monthly Weather Review , 144 ( 4 ), 1669 – 1694 . BOM ( 2020a ). Special climate statement 73: Extreme heat and fire weather in December 2019 and January 2020 . http://www.bom.gov.au/climate/current/statements/scs73.pdf BOM ( 2020b ). State of the climate 2020 . http://www.bom.gov.au/state-of-the-climate/documents/State-of-the-Climate-2020.pdf Bowman , D. M. J. S. , Balch , J. K. , Artaxo , P. , Bond , W. J. , Carlson , J. M. , Cochrane , M. A. , et al. ( 2009 ). Fire in the Earth system . Science , 324 ( 5926 ), 481 – 484 . https://www.ncbi.nlm.nih.gov/pubmed/19390038 Bowman , D. M. J. S. , Kolden , C. A. , Abatzoglou , J. T. , Johnston , F. H. , van der Werf , G. R. , & Flannigan , M. ( 2020 ). Vegetation fires in the Anthropocene . Nature Reviews Earth and Environment , 1 ( 10 ), 500 – 515 . Bowman , D. M. J. S. , Williamson , G. J. , Gibson , R. K. , Bradstock , R. A. , & Keenan , R. J. ( 2021 ). The severity and extent of the Australia 2019–20 eucalyptus forest fires are not the legacy of forest management . Nature Ecology and Evolution. https://www.ncbi.nlm.nih.gov/pubmed/33972737 Briggs , G. A. ( 1969 ). Plume rise: A critical survey (No. TID-25075). Air Resources Atmospheric Turbulence and Diffusion Lab ., Oak Ridge, TN . https://www.osti.gov/servlets/purl/4743102 Briggs , G. A. ( 1972 ). Chimney plumes in neutral and stable surroundings . Atmospheric Environment (1967) , 6 ( 7 ), 507 – 510 . Briggs , G. A. ( 1982 ). Plume rise predictions . In Lectures on air pollution and environmental impact analyses . Boston : American Meteorological Society . Brown , T. , Clements , C. , Larkin , N. K. , Anderson , K. , Butler , B. , Goodrick , S. , et al. ( 2014 ). Validating the next generation of wildland fire and smoke models for operational and research use: A national plan . Final report to the Joint Fire Science Program, Project #13-S-1-1. http://www.firescience.gov Byun , D. , & Schere , K. L. ( 2006 ). Review of the governing equations, computational algorithms, and other components of the Models-3 Community Multiscale Air Quality (CMAQ) modeling system . Applied Mechanics Reviews , 59 ( 2 ). Campbell , S. L. , Jones , P. J. , Williamson , G. J. , Wheeler , A. J. , Lucani , C. , Bowman , D. M. J. S. , et al. ( 2020 ). Using digital technology to protect health in prolonged poor air quality episodes: A case study of the AirRater app during the Australian 2019–20 fires . Fire , 3 ( 3 ). Chang , L. T.-C. , Barthelemy , X. , Watt , S. , Jiang , N. , Riley , M. , & Azzi , M. ( 2021 ). The use of HYSPLIT in NSW in air quality Management and forecasting . Paper presented at the Clean Air Society of Australia and New Zealand (CASANZ) Conference, Online. Chang , L. T.-C. , Duc , H. , Scorgie , Y. , Trieu , T. , Monk , K. , & Jiang , N. ( 2018 ). Performance evaluation of CCAM-CTM regional airshed modelling for the New South Wales Greater Metropolitan Region . Atmosphere , 9 ( 12 ), 486 . http://www.mdpi.com/2073-4433/9/12/486 Chen , H. ,

Fire location and burning area are essential parameters for estimating fire emissions. However, ground-based fire data (such as fire perimeters from incident reports) are often not available with the timeliness required for real-time forecasting. Fire detection products derived from satellite instruments such as the GOES-16 Advanced Baseline Imager or MODIS, on the other hand, are available in near real-time. Using a ground fire dataset of 2699 fires during 2017–2019, we fit a series of linear models that use multiple satellite fire detection products (HMS aggregate fire product, GOES-16, MODIS, and VIIRS) to assess the ability of satellite data to detect and estimate total burned area. It was found that on average models fit with fire detections from GOES-16 products performed better than those developed from other satellites in the study (modelled R2 = 0.84 and predictive R2 = 0.88). However, no single satellite product was found to best estimate incident burned area, highlighting the need for an ensemble approach. With our proposed modelling ensemble, we demonstrate its ability to estimate burned area and suggest its further use in daily fire tracking and emissions-modeling frameworks.

AbstractSmoke plume dynamic science focuses on understanding the various smoke processes that control the movement and mixing of smoke. A current challenge facing this research is providing timely and accurate smoke information for the increasing area burned by wildfires in the western USA. This chapter synthesizes smoke plume research from the past decade to evaluate the current state of science and identify future research needs. Major advances have been achieved in measurements and modeling of smoke plume rise, dispersion, transport, and superfog; interactions with fire, atmosphere, and canopy; and applications to smoke management. The biggest remaining gaps are the lack of high-resolution coupled fire, smoke, and atmospheric modeling systems, and simultaneous measurements of these components. The science of smoke plume dynamics is likely to improve through development and implementation of: improved observational capabilities and computational power; new approaches and tools for data integration; varied levels of observations, partnerships, and projects focused on field campaigns and operational management; and new efforts to implement fire and stewardship strategies and transition research on smoke dynamics into operational tools. Recent research on a number of key smoke plume dynamics has improved our understanding of coupled smoke modeling systems, modeling tools that use field campaign data, real-time smoke modeling and prediction, and smoke from duff burning. This new research will lead to better predictions of smoke production and transport, including the influence of a warmer climate on smoke.

See project description for general summary of data structure. The attached file is table reporting the estimates of the posterior daily wildfire size time series for the South Fork Complex in Oregon 2014.

Data on wildfire growth are useful for multiple research purposes but are frequently unavailable and often have data quality problems. For these reasons, we developed a protocol for collecting daily burned area time series from the InciWeb website, Incident Management Situation Reports (IMSRs), and other sources. We apply this protocol to create the Warehouse of Multiple Burned Area Time Series (WoMBATS) data, which are a collection of burned area time series with cross-check data for 514 wildfires in the United States for the years 2018–2020. We compare WoMBATS-derived distributions of wildfire occurrence and size to those derived from MTBS data to identify potential biases. We also use WoMBATS data to cross tabulate the frequency of missing data in InciWeb and IMSRs and calculate differences in size estimates. We identify multiple instances where WoMBATS data fails to reproduce wildfire occurrence and size statistics derived from MTBS data. We show that WoMBATS data are typically much more complete than either of the two constituent data sources, and that the data collection protocol allows for the identification of otherwise undetectable errors. We find that although disagreements between InciWeb and IMSRs are common, the magnitude of these differences are usually small. We illustrate how WoMBATS data can be used in practice by validating two simple wildfire growth forecasting models.

Amid reports from western US wildland fire managers that, compared to when many started their careers, fires are burning longer throughout the day before reducing in intensity overnight, we examined decadal changes in nighttime vapor pressure deficits over the western United States with a focus on the summer fire season. We calculated changes using a recently updated observation‐assimilating reanalysis (ERA5) available at hourly resolution over the 1980–2019 period. Analysis identifies the proximate cause (atmospheric temperature vs. moisture content) of the observed changes and the extent to which they have been captured in climate model simulations. Increases in nighttime vapor pressure deficits of >50% in 40 years are evident over foothills of mountains ranges adjacent to arid plateaus. The largest observed increases greatly exceed the forced climate‐model response. Correlation analysis reveals a broad link between the variability of western US summer‐nighttime vapor pressure deficit and the Pacific Decadal Oscillation.

Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8-20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke.Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health.

See project description for general summary of data structure. The attached file is table reporting the estimates of the posterior daily wildfire size time series for the Beaver Fire in California 2014.

Wildland fire emissions from both wildfires and prescribed fires represent a major component of overall U.S. emissions. Obtaining an accurate, time-resolved inventory of these emissions is important for many purposes, including to account for emissions of greenhouse gases and short-lived climate forcers, as well as to model air quality for health, regulatory, and planning purposes. For the U.S. Environmental Protection Agency's 2011 and 2014 National Emissions Inventories, a new methodology was developed to reconcile the wide range of available fire information sources into a single coherent inventory. The Comprehensive Fire Information Reconciled Emissions (CFIRE) inventory effort utilized satellite fire detections as well as a large number of national, state, tribal, and local databases. The methodology and results for CONUS and Alaska were documented and compared against other fire emissions databases, and the efficacy of the overall effort was evaluated. Results show the overall spatial pattern differences and relative seasonality of wildfires and prescribed fires across the country. Prescribed burn emissions occurred primarily in non-summer months were concentrated in the Southeast, Northwest, and lower Midwest, and were relatively consistent year to year. Wildfire emissions were much more variable but occurred primarily in the summer and fall. Overall, CFIRE represents a third of total emitted PM2.5 across all sources in the National Emissions Inventory, with prescribed fires accounting for nearly half of all CFIRE emissions. Compared with other wildland fire emissions inventories derived solely from satellite detections, the CFIRE inventory shows markedly increased emissions, reflecting the importance of the multiple national and regional databases included in CFIRE in capturing small fires and prescribed fires in particular. Implications: Wildland fire emissions inventories need to incorporate multiple sources of fire information in order to better represent the full range of fire activity, including prescribed burns and smaller fires. For the 2011 and 2014 U.S. National Emissions Inventory, a methodology was developed to collect, associate, and reconcile fire information from satellite data as well as a large number of national, regional, state, local, and tribal fire information databases across the country. The resulting emissions inventory shows the importance of this type of integration and reconciliation when compared against other emissions inventories for the same period.

The Fire and Smoke Model Evaluation Experiment (FASMEE) is designed to collect integrated observations from large wildland fires and provide evaluation datasets for new models and operational systems. Wildland fire, smoke dispersion, and atmospheric chemistry models have become more sophisticated, and next-generation operational models will require evaluation datasets that are coordinated and comprehensive for their evaluation and advancement. Integrated measurements are required, including ground-based observations of fuels and fire behavior, estimates of fire-emitted heat and emissions fluxes, and observations of near-source micrometeorology, plume properties, smoke dispersion, and atmospheric chemistry. To address these requirements the FASMEE campaign design includes a study plan to guide the suite of required measurements in forested sites representative of many prescribed burning programs in the southeastern United States and increasingly common high-intensity fires in the western United States. Here we provide an overview of the proposed experiment and recommendations for key measurements. The FASMEE study provides a template for additional large-scale experimental campaigns to advance fire science and operational fire and smoke models.

There is an urgent need for next-generation smoke research and forecasting (SRF) systems to meet the challenges of the growing air quality, health and safety concerns associated with wildland fire emissions. This review paper presents simulations and experiments of hypothetical prescribed burns with a suite of selected fire behaviour and smoke models and identifies major issues for model improvement and the most critical observational needs. The results are used to understand the new and improved capability required for the next-generation SRF systems and to support the design of the Fire and Smoke Model Evaluation Experiment (FASMEE) and other field campaigns. The next-generation SRF systems should have more coupling of fire, smoke and atmospheric processes. The development of the coupling capability requires comprehensive and spatially and temporally integrated measurements across the various disciplines to characterise flame and energy structure (e.g. individual cells, vertical heat profile and the height of well-mixing flaming gases), smoke structure (vertical distributions and multiple subplumes), ambient air processes (smoke eddy, entrainment and radiative effects of smoke aerosols) and fire emissions (for different fuel types and combustion conditions from flaming to residual smouldering), as well as night-time processes (smoke drainage and super-fog formation).

Record-breaking droughts and high temperatures in 2015 across the Pacific Northwest, USA, provide an opportunistic glimpse into potential future thermal regimes of rivers and their implications for freshwater fishes. We applied spatial stream network models to data collected every 30 min for 4 years at 42 sites on the Snoqualmie River (Washington, United States) to compare water temperature patterns, summarized with relevance to particular life stages of native and nonnative fishes, in 2015 with more typical conditions (2012–2014). Although 2015 conditions were drier and warmer than what had been observed since 1960, patterns were neither consistent over the year nor on the network. Some locations showed dramatic increases in air and water temperature, whereas others had temperatures that differed little from typical years; these results contrasted with existing forecasts of future thermal landscapes. If we will observe years like 2015 more frequently in the future, we can expect conditions to be less favorable to native, cool-water fishes such as Chinook salmon (Oncorhynchus tshawytscha) and bull trout (Salvelinus confluentus) but beneficial to warm-water nonnative species such as largemouth bass (Micropterus salmoides).

Fine particulate matter (PM2.5) is a well-established risk factor for public health. To support both health risk assessment and epidemiological studies, data are needed on spatial and temporal patterns of PM2.5 exposures. This review article surveys publicly available exposure datasets for surface PM2.5 mass concentrations over the contiguous U.S., summarizes their applications and limitations, and provides suggestions on future research needs. The complex landscape of satellite instruments, model capabilities, monitor networks, and data synthesis methods offers opportunities for research development, but would benefit from guidance for new users. Guidance is provided to access publicly available PM2.5 datasets, to explain and compare different approaches for dataset generation, and to identify sources of uncertainties associated with various types of datasets. Three main sources used to create PM2.5 exposure data are ground-based measurements (especially regulatory monitoring), satellite retrievals (especially aerosol optical depth, AOD), and atmospheric chemistry models. We find inconsistencies among several publicly available PM2.5 estimates, highlighting uncertainties in the exposure datasets that are often overlooked in health effects analyses. Major differences among PM2.5 estimates emerge from the choice of data (ground-based, satellite, and/or model), the spatiotemporal resolutions, and the algorithms used to fuse data sources. Implications: Fine particulate matter (PM2.5) has large impacts on human morbidity and mortality. Even though the methods for generating the PM2.5 exposure estimates have been significantly improved in recent years, there is a lack of review articles that document PM2.5 exposure datasets that are publicly available and easily accessible by the health and air quality communities. In this article, we discuss the main methods that generate PM2.5 data, compare several publicly available datasets, and show the applications of various data fusion approaches. Guidance to access and critique these datasets are provided for stakeholders in public health sectors.

A new statistical model for predicting daily ground level fine scale particulate matter (PM2.5) concentrations at monitoring sites in the western United States was developed and tested operationally during the 2016 and 2017 wildfire seasons. The model is site-specific, using a multiple linear regression schema that relies on the previous day's PM2.5 value, along with fire and smoke related variables from satellite observations. Fire variables include fire radiative power (FRP) and the National Fire Danger Rating System Energy Release Component index. Smoke variables, in addition to ground monitored PM2.5, include aerosol optical depth (AOD) and smoke plume perimeters from the National Oceanic and Atmospheric Administration's Hazard Mapping System. The overall statistical model was inspired by a similar system developed for British Columbia (BC) by the BC Center for Disease Control, but it has been heavily modified and adapted to work in the United States. On average, our statistical model was able to explain 78% of the variance in daily ground level PM2.5. A novel method for implementation of this model as an operational forecast system was also developed and was tested and used during the 2016 and 2017 wildfire seasons. This method focused on producing a continuously-updating prediction that incorporated the latest information available throughout the day, including both updated remote sensing data and real-time PM2.5 observations. The diurnal pattern of performance of this model shows that even a few hours of data early in the morning can substantially improve model performance. Implications: Wildfire smoke events produce significant air quality impacts across the western United States each year impacting millions. We present and evaluate a statistical model for making updating predictions of fine particulate (PM2.5) levels during smoke events. These predictions run hourly and are being used by smoke incident specialists assigned to wildfire operations, and may be of interest to public health officials, air quality regulators, and the public. Predictions based on this model will be available on the web for the 2019 western U.S. wildfire season this summer.

Large wildfires are an increasing threat to the western U.S. In the 2017 fire season, extensive wildfires occurred across the Pacific Northwest (PNW). To evaluate public health impacts of wildfire smoke, we integrated numerical simulations and observations for regional fire events during August-September of 2017. A one-way coupled Weather Research and Forecasting and Community Multiscale Air Quality modeling system was used to simulate fire smoke transport and dispersion. To reduce modeling bias in fine particulate matter (PM2.5) and to optimize smoke exposure estimates, we integrated modeling results with the high-resolution Multi-Angle Implementation of Atmospheric Correction satellite aerosol optical depth and the U.S. Environmental Protection Agency AirNow ground-level monitoring PM2.5 concentrations. Three machine learning-based data fusion algorithms were applied: An ordinary multi-linear regression method, a generalized boosting method, and a random forest (RF) method. 10-Fold cross-validation found improved surface PM2.5 estimation after data integration and bias correction, especially with the RF method. Lastly, to assess transient health effects of fire smoke, we applied the optimized high-resolution PM2.5 exposure estimate in a short-term exposure-response function. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 183 (95% confidence interval: 0, 432), with 85% of the PM2.5 pollution and 95% of the consequent multiple-cause mortality contributed by fire emissions. This application demonstrates both the profound health impacts of fire smoke over the PNW and the need for a high-performance fire smoke forecasting and reanalysis system to reduce public health risks of smoke hazards in fire-prone regions.