FINN - Fire INventory from NCAR
Fire INventory from NCAR (FINN): A daily, high resolution fire emissions product for atmospheric chemistry models
EMAIL LIST: If you are a user of FINN (archived or near-real-time), please complete this form to be added to our email list to be notified of updates.
Open biomass burning makes up an important part of the total global emissions of greenhouse gases, reactive trace gases, and particulate matter. Although episodic in nature and highly variable, open biomass burning emissions can contribute to local, regional, and global air quality problems and climate forcings. The Fire INventory from NCAR (FINN) model provides high resolution, global emission estimates from open burning; these emissions have been developed specifically to provide input needed for modeling atmospheric chemistry and air quality in a consistent framework at scales from local to global. FINN differs from other inventories because it provides a unique combination of high temporal and spatial resolution, global coverage, and estimates for a large number of chemical species.
FINN emission estimates are based on the framework described by Wiedinmyer et al. [2006; 2011; 2023]. FINN uses satellite observations of active fires and land cover, together with emission factors and estimated fuel loadings to provide daily, highly-resolved (1 km) open burning emissions estimates for use in regional and global chemical transport models.
The FINN2 description paper is available at Wiedinmyer et al. (2023) in Geoscientific Model Development.
FINNv2.5 uses an updated algorithm for determining fire size by aggregating adjacent fire detections. For emissions from 2012 onward, fire detections from both MODIS and VIIRS satellite instruments are used. Emissions are also provided for the full period since 2002 based on only MODIS fire detections for consistency for use in long time series analyses or model simulations. Fire data were downloaded from the NASA Fire Information for Resource Management System (FIRMS, https://firms.modaps.eosdis.nasa.gov/download/). See Wiedinmyer et al. (2023) for more information.
The emissions of non-methane organic compounds (NMOC) have been speciated either to the MOZART- T1 chemical mechanism (Emmons et al., JAMES, https://doi.org/10.1029/2019MS001882), the SAPRC99 chemical mechanism (Carter et al., 2000), and the GEOS-CHEM mechanism (Bey et al., 2001; http://www.geos-chem.org/). The mapping of the NMOCs to the SAPRC99 and GEOS-CHEM mechanisms has not changed from FINNv1 and is described by Wiedinmyer et al. (2011; See factors in Tables 4 and 5). The MOZART speciation is described in Wiedinmyer et al. (2023).
Data Access
The FINNv2.5 emissions files for 2002-2021 are available from the NCAR Research Data Archive: https://rda.ucar.edu/datasets/ds312.9/. Go to the 'Data Access' tab.
The Products labeled 'modis' and 'modisviirs' are gridded files at 0.1 degree resolution. The latitude and longitude of these files indicate the centers of the 0.1x0.1 degree grid boxes.
The Products labeled 'eachfire modis' and 'eachfire modisviirs' are text files with emissions for each fire.
NOTE on units: In the text files with emissions for each fire, the aerosols (BC, OC, PM2.5, PM10), as well as the gas-phase lumped species 'NOXasNO' and NMOC, are in units of kg/day, and the gas-phase species are in moles/day. In these files 'AREA' is the area burned in m^2; 'BMASS' is the biomass burned per area burned in kg/m^2. 'GENVEG' specifies the vegetation type assumed for the fire: 1 = grasslands and savanna; 2 = woody savanna/shrublands; 3 = tropical forest; 4 = temperate forest; 5 = boreal forest; 6 = temperate evergreen forest; 9 = croplands; 0 = no vegetation.
In the 0.1x0.1 degree gridded files, all compounds are converted to molecules/cm^2/sec, where aerosols have been converted from kg to molecules using molecular weight of 12 g/mole.
The text files also contain POLYID and FIREID. Each fire point identified by satellites is assigned a POLYID. Each POLYID is assigned to a FIREID. If multiple fire points are located together as part of a big fire, they are assigned to the same FIREID.
FRP (in MW, megawatts) is also included in the text (each_fire) files. FRP is not used in the calculation of FINN emissions, but has been averaged to each 1-km fire in FINN emissions for potential use by users.
Earlier versions of FINN (v1.5), as well as a trash burning inventory, emission factor tables and a gridding program are available at: https://www.acom.ucar.edu/Data/fire/.
FINNv1 fire emissions are calculated in real time and are processed as inputs to the WACCM real time forecasts and are available from the ACOM forecast website.
Near-Real-Time Emissions
FINNv2.5 is also run each day using Rapid Response fire detections from MODIS (Terra+Aqua) and VIIRS (Suomi-NPP), which are available from NASA FIRMS. The emissions for each fire (1km resolution) for each day are provided in text files at https://www.acom.ucar.edu/acresp/MODELING/finn_emis_txt/ with filenames starting with 'FINNv2.5.1_modvrs_nrt_'. VOC speciation is provided for MOZART-4, SAPRC99 and GEOS-Chem, and a separate file has only NOX, CO, aerosols, total NMOC, etc.
Emissions from FINNv1 are also still available in this directory (GLOB_*), but production of these may be discontinued soon.
2024 gridded MOZART species emissions for Jan 1 - June 30 are available at: https://www.acom.ucar.edu/acresp/MODELING/finn_emis_txt/2024_gridded_0.1deg/
Code Access
The FINN model code is available on github: https://github.com/NCAR/finn
The fortran 'fire_emis' gridding program (for WRF-Chem or global models) is available at: https://www.acom.ucar.edu/Data/fire/
Science Applications
In recent years, FINN emissions have been used in many various modeling studies that simulate the chemical and climate impacts from fires. By using FINN emissions within the WRF-Chem model, Jiang et al. (2012) explored the impacts of fire plumes on ozone chemistry during a wildfire event in Idaho and Montana during August 2007. WRF-chem simulated the immediate addition fire emissions combined with the changes in photolysis rates, boundary layer height, and biogenic emissions. The results highlighted the importance of including the radiative impacts of fire plumes. Val Martin et al. (2013) used FINN emissions in conjunction with satellite observations to explore the importance of fire smoke or air quality and regional climate in Colorado. Tang et al. (2022) used FINNv2 to explore the impact of applying diurnal cycles and vertical distribution to fire emissions, and evaluated the results with FIREX-AQ observations.
References
Jiang, X., C. Wiedinmyer, A.G. Carlton. Aerosols from fires: an examination of the effects on ozone photochemistry in the Western United States. Environmental Science & Technology, 46(21), 442-460, doi:10.1021/es301541k, 2012.
Tang, Wenfu, et al., Effects of fire diurnal variation and plume rise on U.S. air quality during FIREX-AQ and WE-CAN based on the Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICAv0), Journal of Geophysical Research: Atmospheres, 127, e2022JD036650, doi:10.1029/2022JD036650, 2022.
Val Martin, M., Heald, C. L., Ford, B., Prenni, A. J., and Wiedinmyer, C.: A decadal satellite analysis of the origins and impacts of smoke in Colorado, Atmos. Chem. Phys., 13, 7429-7439, doi:10.5194/acp-13-7429-2013, 2013.
Wiedinmyer, Christine, Brad Quayle, Chris Geron, Angle Belote, Don McKenzie, Xiaoyang Zhang, Susan O'Neill, and Kristina Klos Wynne, Estimating Emissions from Fires in North America for Air Quality Modeling, Atmospheric Environment, 40, 3419-32, 2006.
Wiedinmyer, C., S. K. Akagi, R. J. Yokelson, L. K. Emmons, J. A. Al-Saadi, J. J. Orlando, and A. J. Soja, The Fire Inventory from NCAR (FINN): A High Resolution Global Model to Estimate the Emissions from Open Burning, Geoscientific Model Development, 4, doi:10.5194/gmd-4-625-2011, 2011.
Wiedinmyer, C., Kimura, Y., McDonald-Buller, E. C., Emmons, L. K., Buchholz, R. R., Tang, W., Seto, K., Joseph, M. B., Barsanti, K. C., Carlton, A. G., and Yokelson, R.: The Fire Inventory from NCAR version 2.5: an updated global fire emissions model for climate and chemistry applications, Geosci. Model Dev., 16, 3873–3891, https://doi.org/10.5194/gmd-16-3873-2023, 2023.