Ingesting GOES-16 fire radiative power retrievals into Warn-on-Forecast System for Smoke (WoFS-Smoke)

Thomas Jones; Ravan Ahmadov; Eric James; Gabriel Pereira; Saulo Freitas; Georg Grell

doi:10.1071/WF23133

RESEARCH ARTICLE (Open Access)

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Ingesting GOES-16 fire radiative power retrievals into Warn-on-Forecast System for Smoke (WoFS-Smoke)

Thomas Jones

^A ^B ^C ^* , Ravan Ahmadov ^D , Eric James ^D , Gabriel Pereira ^E , Saulo Freitas ^F and Georg Grell ^G

+ Author Affiliations

- Author Affiliations

^A Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, 120 David L. Boren Boulevard, Norman, OK 73072, USA.

^B NOAA/National Severe Storms Laboratory, 120 David L. Boren Boulevard, Norman, OK 73072, USA.

^C School of Meteorology, University of Oklahoma, Norman, OK, USA.

^D NOAA/OAR/Global Systems Laboratory, Boulder, CO, USA.

^E Federal University of Sao Joao del-Rei, Sao Joao del-Rei Minas Gerais, Brazil.

^F USRA/GESTAR & NASA Goddard Space Flight Center, Greenbelt, MD, USA.

^G Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA.

^* Correspondence to: Thomas.Jones@noaa.gov

International Journal of Wildland Fire 33, WF23133 https://doi.org/10.1071/WF23133

Submitted: 19 August 2023 Accepted: 8 January 2024 Published: 25 January 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of the Australasian Society for the Study of Brain Impairment. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Background

The record number of wildfires in the United States in recent years has led to an increased focus on developing tools to accurately forecast their impacts at high spatial and temporal resolutions.

Aims

The Warn-on-Forecast System for Smoke (WoFS-Smoke) was developed to improve these forecasts using wildfire properties retrieved from satellites to generate smoke plumes in the system.

Methods

The WoFS is a regional domain ensemble data assimilation and forecasting system built around the concept of creating short-term (0–6 h) forecasts of high impact weather. This work extends WoFS-Smoke by ingesting data from the GOES-16 satellite at 15-min intervals to sample the rapidly changing conditions associated with wildfires.

Key results

Comparison of experiments with and without GOES-16 data show that ingesting high temporal frequency data allows for wildfires to be initiated in the model earlier, leading to improved smoke forecasts during their early phases. Decreasing smoke plume intensity associated with weakening fires was also better forecast.

Conclusions

The results were consistent for a large fire near Boulder, Colorado and a multi-fire event in Texas, Oklahoma, and Arkansas, indicating a broad applicability of this system.

Implications

The development of WoFS-Smoke using geostationary satellite data allows for a significant advancement in smoke forecasting and its downstream impacts such as reductions in air quality, visibility, and potentially properties of severe convection.

Keywords: ensemble data assimilation, fire weather, GOES-R, NWP, probabilistic forecasting, smoke forecasting, weather radar, wildfire.

References

Ahmadov R, Grell G, James E, 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. In ‘IEEE International Symposium on Geoscience and Remote Sensing IGARSS’. pp. 2806–2808. (IEEE: New York, NY, USA)

Benjamin SG, Weygandt SS, Brown JM, Hu M, Alexander CR, Smirnova TG, Olson JB, James EP, Dowell DC, Grell GA, Lin H, Peckham SE, Smith TL, Moninger WR, Kenyon JS, Manikin GS (2016) A North American hourly assimilation and model forecast cycle: the rapid refresh. Monthly Weather Review 144, 1669-1694.
| Crossref | Google Scholar |

Chow FK, Yu KA, Young A, James E, Grell GA, Csiszar I, Tsidulko M, Freitas S, Pereira G, Giglio L, Friberg MD, Ahmadov R (2022) High-resolution smoke forecasting for the 2018 Camp Fire in California. Bulletin of the American Meteorological Society 103(6), E1531-E1552.
| Crossref | Google Scholar |

Csiszar I, Schroeder W, Giglio L, Ellicott E, Vadrevu KP, Justice CO, et al. (2014) Active fires from the Suomi NPP Visible Infrared Imaging Radiometer Suite: product status and first evaluation results. Journal of Geophysical Research: Atmospheres 119(2), 803-816.
| Crossref | Google Scholar |

Dowell DC, Alexander CR, James EP, Weygandt SS, Benjamin SG, Manikin GS, Blake BT, Brown JM, Olson JB, Hu M, Smirnova TG, Ladwig T, Kenyon JS, Ahmadov R, Turner DD, Duda JD, Alcott TI (2022) The High-Resolution Rapid Refresh (HRRR): an hourly updating convection-allowing forecast model. Part I: Motivation and system description. Weather and Forecasting 37(8), 1371-1395.
| Crossref | Google Scholar |

Dozier J (1981) A method for satellite identification of surface temperature fields of subpixel resolution. Remote Sensing of Environment 11, 221-229.
| Crossref | Google Scholar |

Freitas SR, Longo KM, Andreae MO (2006) Impact of including the plume rise of vegetation fires in numerical simulations of associated atmospheric pollutants. Geophysical Research Letters 33, L17808.
| Crossref | Google Scholar |

Freitas SR, Longo KM, Chatfield R, Latham D, Silva Dias MAF, Andreae MO, Prins E, Santos JC, Gielow R, Carvalho Jr JA (2007) Including the sub-grid scale plume rise of vegetation fires in low resolution atmospheric transport models. Atmospheric Chemistry and Physics 7(13), 3385-3398.
| Crossref | Google Scholar |

Freitas SR, Longo KM, Trentmann J, Latham D (2010) Technical note: Sensitivity of 1‐D smoke plume rise models to the inclusion of environmental wind drag. Atmospheric Chemistry and Physics 10(2), 585-594.
| Crossref | Google Scholar |

Fromm M, Bevilacqua R, Servranckx R, Rosen J, Thayer JP, Herman J, Larko D (2005) Pyro-cumulonimbus injection of smoke to the stratosphere: observations and impact of a super blowup in northwestern Canada on 3–4 August 1998. Journal of Geophysical Research 110, D08205.
| Crossref | Google Scholar |

Fromm M, Tupper A, Rosenfeld D, Servranckx R, McRae R (2006) Violent pyro-convective storm devastates Australia’s capital and pollutes the stratosphere. Geophysical Research Letters 33, L05815.
| Crossref | Google Scholar |

Fromm M, Lindsey DT, Servranckx R, Yue G, Trickl T, Sica R, Doucet P, Godin-Beekmann S (2010) The untold story of pyrocumulonimbus. Bulletin of the American Meteorological Society 91, 1193-1210.
| Crossref | Google Scholar |

Fromm M, Kablick III G, Caffrey P (2016) Dust-infused baroclinic cyclone storm clouds: the evidence, meteorology, and some implications. Geophysical Research Letters 43, 12643-12650.
| Crossref | Google Scholar |

Fovell RG, Brewer MJ, Garmong RJ (2022) The December 2021 Marshall Fire: predictability and gust forecasts from operational models. Atmosphere 13(5), 765.
| Crossref | Google Scholar |

Giglio L, Kendall JD (2001) Application of the dozier retrieval to wildfire characterization: a sensitivity analysis. Remote Sensing of Environment 77, 34-49.
| Crossref | Google Scholar |

Giglio L, Boschetti L, Roy DP, Humber ML, Justice CO (2018) The Collection 6 MODIS burned area mapping algorithm and product. Remote Sensing of the Environment 217, 72-85.
| Crossref | Google Scholar | PubMed |

Halofsky JE, Peterson DL, Harvey BJ (2020) Changing wildfire, changing forests: the effects of climate change on fire regimes and vegetation in the Pacific Northwest, USA. Fire Ecology 16, 4.
| Crossref | Google Scholar |

Holden ZA, Swanson A, Luce CH, Jolly WM, Maneta M, Oyler JW, Warren DA, Parsons R, Affleck D (2018) Decreasing fire season precipitation increased recent western US forest wildfire activity. Proceedings of the National Academy of Sciences of the United States of America 115(36), E8349-E8357.
| Crossref | Google Scholar | PubMed |

Hu M, Ge G, Zhou C, Stark D, Shao H, Newman K, Beck J, Zhang X (2018) ‘Grid-point Statistical Interpolation (GSI) User’s Guide Version 3.7.’ 149 pp. (Developmental Testbed Center) Available at http://www.dtcenter.org/com-GSI/users/docs/index.php

Iacono MJ, Delamere JS, Mlawer EJ, Clough SA, Morcrette J-J, Hou Y-T (2004) Development and evaluation of RRTMG_SW, a shortwave radiative transfer model for general circulation model applications. In ‘Proceedings 14th Atmospheric Radiation Measurement (ARM) Science Team Meeting’, Albuquerque, NM, DOE. 10 pp.

Jaffe DA, O’Neill SM, Larkin NK, et al. (2020) Wildfire and prescribed burning impacts on air quality in the United States. Journal of the Air & Waste Management Association 70, 583-615.
| Crossref | Google Scholar |

James EP, Alexander CR, Dowell DC, Weygandt SS, Benjamin SG, Manikin GS, Brown JM, Olson JB, Hu M, Smirnova TG, Ladwig T, Kenyon JS, Turner DD (2022) The High-Resolution Rapid Refresh (HRRR): an hourly updating convection-allowing forecast model. Part II: Forecast performance. Weather and Forecasting 37(8), 1397-1417.
| Crossref | Google Scholar |

Jones TA, Christopher SA (2009) Injection heights of biomass burning debris estimated from WSR-88D radar observations. IEEE Trans Geoscience Remote Sensing 47(8), 2599-2605.
| Crossref | Google Scholar |

Jones TA, Christopher SA, Petersen W (2009) Dual-polarization radar characteristics of an apartment fire. Journal of Atmospheric and Oceanic Technology 26, 2257-2269.
| Crossref | Google Scholar |

Jones TA, Christopher SA (2010a) Satellite and radar observations of the 9 April 2009 Texas and Oklahoma grassfires. Bulletin of the American Meteorological Society 91(4), 455-460.
| Crossref | Google Scholar |

Jones TA, Christopher SA (2010b) Satellite and radar remote sensing of Southern Plains grassfires: a case study. Journal of Applied Meteorology and Climatology 49, 2133-2146.
| Crossref | Google Scholar |

Jones TA, Knopfmeier K, Wheatley D, Creager G, Minnis P, Palikonda R (2016) Storm-scale data assimilation and ensemble forecasting with the NSSL experimental Warn-on-Forecast system. Part II: Combined radar and satellite data experiments. Weather and Forecasting 31, 297-327.
| Crossref | Google Scholar |

Jones TA, Skinner P, Yussouf N, Knopfmeier K, Reinhart A, Wang X, Bedka K, Smith Jr W, Palikonda R (2020) Assimilation of GOES-16 radiances and retrievals into the Warn-on-Forecast System. Monthly Weather Review 148, 1829-1859.
| Crossref | Google Scholar |

Jones TA, Ahmadov R, James E, Periria G, Freitas S, Grell G (2022a) Prototype of a Warn-on-Forecast System for Smoke (WoFS-Smoke). Weather and Forecasting 37, 1191-1209.
| Crossref | Google Scholar |

Jones TA, Ahmadov R, James E (2022b) Assimilation of aerosol optical depth into the Warn-on-Forecast System for Smoke (WoFS-Smoke). Journal of Geophysical Research: Atmospheres 127(24), e2022JD037454.
| Crossref | Google Scholar |

Kaufman YJ, Hobbs PV, Kirchhoff VWJH, et al. (1998a) Smoke, Clouds, and Radiation‐Brazil (SCAR‐B) experiment. Journal of Geophysical Research 103, 31,783-31,805.
| Crossref | Google Scholar |

Kaufman YJ, Kleidman R, King MD (1998b) SCAR-B fires in the tropics: Properties and remote sensing from EOS-MODIS. Journal of Geophysical Research 103, 31,955-31,968.
| Crossref | Google Scholar |

Kleist DT, Parrish DF, Derber JC, Treadon R, Wu W-S, Lord S (2009) Introduction of the GSI into the NCEP Global Data Assimilation System. Weather and Forecasting 24, 1691-1705.
| Crossref | Google Scholar |

Lareau NP, Clements CB (2015) Cold Smoke: smoke-induced density currents cause unexpected smoke transport near large wildfires. Atmospheric Chemistry and Physics 15, 11513-11520.
| Crossref | Google Scholar |

Lareau NP, Clements CB (2016) Environmental controls on pyrocumulus and pyrocumulonimbus initiation and development. Atmospheric Chemistry and Physics 16, 4005-4022.
| Crossref | Google Scholar |

Lareau NP, Nauslar NJ, Abatzoglou JT (2018) The Carr Fire vortex: a case of pyrotornadogenesis? Geophysical Research Letters 45, 13107-13115.
| Crossref | Google Scholar |

Li F, Zhang X, Roy DP, Kondragunta S (2019) Estimation of biomass-burning emissions by fusing the fire radiative power retrievals from polar-orbiting and geostationary satellites across the conterminous United States. Atmospheric Environment 211, 274-287.
| Crossref | Google Scholar |

Li F, Zhang X, Kondragunta S, Lu X, Csiszar I, Schmidt CC (2022) Hourly biomass burning emissions product from blended geostationary and polar-orbiting satellites for air quality forecasting applications. Remote Sensing of Environment 281, 113237.
| Crossref | Google Scholar |

Mansell ER, Ziegler CL, Bruning EC (2010) Simulated electrification of a small thunderstorm with two-moment bulk microphysics. Journal of the Atmospheric Sciences 67, 171-194.
| Crossref | Google Scholar |

Melnikov VM, Zrnic DS, Rabin RM (2009) Polarimetric radar properties of smoke plumes: a model. Journal of Geophysical Research 114, D21204.
| Crossref | Google Scholar |

O’Neill SM, Diao M, Raffuse S, Al-Hamdan M, Barik M, Jia Y, et al. (2021) A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires. Journal of the Air & Waste Management Association 71(7), 791-814.
| Crossref | Google Scholar | PubMed |

Powers JG, Klemp JB, Skamarock WC, Davis CA, Dudhia J, Gill DO, et al. (2017) The Weather Research and Forecasting (WRF) model: Overview, system efforts, and future directions. Bulletin of the American Meteorological Society 98(8), 1717-1737.
| Crossref | Google Scholar |

Prins EM, Menzel WP (1992) Geostationary satellite detection of biomass burning in South America. International Journal of Remote Sensing 13, 2783-2799.
| Crossref | Google Scholar |

Prins EM, Menzel WP (1994) Trends in South American biomass burning detected with the GOES visible infrared spin scan radiometer atmospheric sounder from 1983 to 1991. Journal of Geophysical Research 99(D8), 16,719-16,735.
| Crossref | Google Scholar |

Prins EM, Feltz JM, Menzel WP, Ward DE (1998) An overview of GOES-8 diurnal fire and smoke results for SCAR-B and 1995 fire season in South America. Journal of Geophysical Research 103(D24), 31821-31835.
| Crossref | Google Scholar |

Roberts G, Wooster MJ, Perry GLW, Drake N, Rebelo L-M, Dipotso F (2005) Retrieval of biomass combustion rates and totals from fire radiative power observations: application to southern Africa using geostationary SEVIRI imagery. Journal of Geophysical Research 110, D21111.
| Crossref | Google Scholar |

Robock A (1988) Enhancement of surface cooling due to forest fire smoke. Science 242(4880), 911-913.
| Crossref | Google Scholar |

Robock A (1991) Surface cooling due to forest fire smoke. Journal of Geophysical Research 96(D11), 20869-20878.
| Crossref | Google Scholar |

Skamarock WC, et al. (2008) A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-4751STR. 113 pp. University Corporation for Atmospheric Research. 10.5065/D68S4MVH

Skinner PS, Wheatley DM, Knopfmeier KH, et al. (2018) Object-based verification of a prototype Warn-on-Forecast system. Weather and Forecasting 33, 1225-1250.
| Crossref | Google Scholar | PubMed |

Troy A, Moghaddas J, Schmidt D, Romsos JS, Sapsis DB, Brewer W, Moody T (2022) An analysis of factors influencing structure loss resulting from the 2018 Camp Fire. International Journal of Wildland Fire 31(6), 586-598.
| Crossref | Google Scholar |

Twomey S (1974) Pollution and the planetary albedo. Atmospheric Environment 8, 1251-1256.
| Crossref | Google Scholar |

Weaver JF, Purdom JFW, Schneider TL (1995) Observing forest fires with the GOES-8, 3.9-μm imaging channel. Weather and Forecasting 10, 803-808.
| Crossref | Google Scholar |

Weaver JF, Lindsey D, Bikos D, Schmidt CC, Prins E (2004) Fire detection using GOES rapid scan imagery. Weather and Forecasting 19, 496-510.
| Crossref | Google Scholar |

Wheatley DM, Knopfmeier KH, Jones TA, Creager GJ (2015) Storm-scale data assimilation and ensemble forecasting with the NSSL Experimental Warn-on-Forecast System. Part I: Radar data experiments. Weather and Forecasting 30, 1795-1817.
| Crossref | Google Scholar |

Whitaker JS, Hamill TM, Wei X, Song Y, Toth Z (2008) Ensemble data assimilation with the NCEP Global Forecast System. Monthly Weather Review 136, 463-482.
| Crossref | Google Scholar |

Wooster MJ, Roberts G, Perry GLW, Kaufman YJ (2005) Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release. Journal of Geophysical Research 110, D24311.
| Crossref | Google Scholar |

Xu X, Jia G, Zhang X, et al. (2020) Climate regime shift and forest loss amplify fire in Amazonian forests. Global Change Biology 26, 5874-5885.
| Crossref | Google Scholar | PubMed |

Yussouf N, Knopfmeier KH (2019) Application of the Warn-on-Forecast System for Flash-Flood-Producing heavy convective rainfall events. Quarterly Journal of the Royal Meteorological Society 145, 2385-2403.
| Crossref | Google Scholar |

Zrnic D, Zhang P, Melnikov V, Mirkovic D (2020) Of fire and smoke plumes, polarimetric radar characteristics. Atmosphere 11, 363.
| Crossref | Google Scholar |