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2. Forecasting Daily Fire Radiative Energy Using Data Driven Methods and Machine Learning Techniques.

Author

Thapa, Laura H., Saide, Pablo E., Bortnik, Jacob, Berman, Melinda T., da Silva, Arlindo, Peterson, David A., Li, Fangjun, Kondragunta, Shobha, Ahmadov, Ravan, James, Eric, Romero‐Alvarez, Johana, Ye, Xinxin, Soja, Amber, Wiggins, Elizabeth, and Gargulinski, Emily

Abstract

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]

Published
2024
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3. Impact of Biomass Burning Organic Aerosol Volatility on Smoke Concentrations Downwind of Fires

Author

Pagonis, Demetrios, primary, Selimovic, Vanessa, additional, Campuzano-Jost, Pedro, additional, Guo, Hongyu, additional, Day, Douglas A., additional, Schueneman, Melinda K., additional, Nault, Benjamin A., additional, Coggon, Matthew M., additional, DiGangi, Joshua P., additional, Diskin, Glenn S., additional, Fortner, Edward C., additional, Gargulinski, Emily M., additional, Gkatzelis, Georgios I., additional, Hair, Johnathan W., additional, Herndon, Scott C., additional, Holmes, Christopher D., additional, Katich, Joseph M., additional, Nowak, John B., additional, Perring, Anne E., additional, Saide, Pablo, additional, Shingler, Taylor J., additional, Soja, Amber J., additional, Thapa, Laura H., additional, Warneke, Carsten, additional, Wiggins, Elizabeth B., additional, Wisthaler, Armin, additional, Yacovitch, Tara I., additional, Yokelson, Robert J., additional, and Jimenez, Jose L., additional

Published
2023
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4. Sensitivity of Burned Area and Fire Radiative Power Predictions to Containment Efforts, Fuel Density, and Fuel Moisture Using WRF‐Fire

Author

Turney, Francis A., primary, Saide, Pablo E., additional, Jimenez Munoz, Pedro A., additional, Muñoz‐Esparza, Domingo, additional, Hyer, Edward J., additional, Peterson, David A., additional, Frediani, Maria E., additional, Juliano, Timothy W., additional, DeCastro, Amy L., additional, Kosović, Branko, additional, Ye, Xinxin, additional, and Thapa, Laura H., additional

Published
2023
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6. Fire Influence on Regional to Global Environments and Air Quality (FIREX‐AQ)

Author

Warneke, Carsten, Schwarz, Joshua P., Washenfelder, Rebecca A., Wiggins, Elizabeth B., Moore, Richard H., Anderson, Bruce E., Jordan, Carolyn, Yacovitch, Tara I., Herndon, Scott C., Liu, Shang, Kuwayama, Toshihiro, Jaffe, Daniel, Dibb, Jack, Johnston, Nancy, Selimovic, Vanessa, Yokelson, Robert, Giles, David M., Holben, Brent N., Goloub, Philippe, Popovici, Ioana, Trainer, Michael, Kumar, Aditya, Pierce, R. Bradley, Kalashnikova, Olga, Fahey, David, Roberts, James, Gargulinski, Emily M., Peterson, David A., Ye, Xinxin, Thapa, Laura H., Saide, Pablo E., Fite, Charles H., Holmes, Christopher D., Wang, Siyuan, Frost, Gregory, Coggon, Matthew M., Decker, Zachary C. J., Stockwell, Chelsea E., Xu, Lu, Gkatzelis, Georgios, Aikin, Kenneth, Lefer, Barry, Kaspari, Jackson, Griffin, Debora, Zeng, Linghan, Al-Saad, Jassim, Weber, Rodney, Hastings, Meredith, Chai, Jiajue, Wolfe, Glenn M., Hanisco, Thomas F., Liao, Jin, Campuzano Jost, Pedro, Guo, Hongyu, Jimenez, Jose L., Crawford, James, Brown, Steven S., Brewer, Wm. Alan, Soja, Amber, and Seidel, Felix C.

Subjects
Atmospheric Science, Geophysics, Space and Planetary Science, Earth and Planetary Sciences (miscellaneous), ddc:550
Abstract

The NOAA/NASA Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) experiment was a multi-agency, inter-disciplinary research effort to: (a) obtain detailed measurements of trace gas and aerosol emissions from wildfires and prescribed fires using aircraft, satellites and ground-based instruments, (b) make extensive suborbital remote sensing measurements of fire dynamics, (c) assess local, regional, and global modeling of fires, and (d) strengthen connections to observables on the ground such as fuels and fuel consumption and satellite products such as burned area and fire radiative power. From Boise, ID western wildfires were studied with the NASA DC-8 and two NOAA Twin Otter aircraft. The high-altitude NASA ER-2 was deployed from Palmdale, CA to observe some of these fires in conjunction with satellite overpasses and the other aircraft. Further research was conducted on three mobile laboratories and ground sites, and 17 different modeling forecast and analyses products for fire, fuels and air quality and climate implications. From Salina, KS the DC-8 investigated 87 smaller fires in the Southeast with remote and in-situ data collection. Sampling by all platforms was designed to measure emissions of trace gases and aerosols with multiple transects to capture the chemical transformation of these emissions and perform remote sensing observations of fire and smoke plumes under day and night conditions. The emissions were linked to fuels consumed and fire radiative power using orbital and suborbital remote sensing observations collected during overflights of the fires and smoke plumes and ground sampling of fuels.

Published
2023

7. Understanding the Evolution of Smoke Mass Extinction Efficiency Using Field Campaign Measurements

Author

Saide, Pablo E., primary, Thapa, Laura H., additional, Ye, Xinxin, additional, Pagonis, Demetrios, additional, Campuzano‐Jost, Pedro, additional, Guo, Hongyu, additional, Schuneman, Melinda L., additional, Jimenez, Jose‐Luis, additional, Moore, Richard, additional, Wiggins, Elizabeth, additional, Winstead, Edward, additional, Robinson, Claire, additional, Thornhill, Lee, additional, Sanchez, Kevin, additional, Wagner, Nicholas L., additional, Ahern, Adam, additional, Katich, Joseph M., additional, Perring, Anne E., additional, Schwarz, Joshua P., additional, Lyu, Ming, additional, Holmes, Christopher D., additional, Hair, Johnathan W., additional, Fenn, Marta A., additional, and Shingler, Taylor J., additional

Published
2022
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8. Measurements from inside a Thunderstorm Driven by Wildfire: The 2019 FIREX-AQ Field Experiment

Author

Peterson, David A., primary, Thapa, Laura H., additional, Saide, Pablo E., additional, Soja, Amber J., additional, Gargulinski, Emily M., additional, Hyer, Edward J., additional, Weinzierl, Bernadett, additional, Dollner, Maximilian, additional, Schöberl, Manuel, additional, Papin, Philippe P., additional, Kondragunta, Shobha, additional, Camacho, Christopher P., additional, Ichoku, Charles, additional, Moore, Richard H., additional, Hair, Johnathan W., additional, Crawford, James H., additional, Dennison, Philip E., additional, Kalashnikova, Olga V., additional, Bennese, Christel E., additional, Bui, Thaopaul P., additional, DiGangi, Joshua P., additional, Diskin, Glenn S., additional, Fenn, Marta A., additional, Halliday, Hannah S., additional, Jimenez, Jose, additional, Nowak, John B., additional, Robinson, Claire, additional, Sanchez, Kevin, additional, Shingler, Taylor J., additional, Thornhill, Lee, additional, Wiggins, Elizabeth B., additional, Winstead, Edward, additional, and Xu, Chuanyu, additional

Published
2022
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