Creation of a Wildland Fire Analysis: Products to enable Earth Science

Wildland fire science and related applications have benefitted from a wide range of space-based and airborne fire observations, each with different spatial resolutions and revisit frequencies, as additional sensors and constellations continue to be added by both the public and private sector. Greater use of such observations for analysis and modeling of wildland fire occurrence, behavior, and effects such as emissions is hampered by limitations including: (1) the observations' disparate resolutions and extent, (2) temporal or spatial gaps due to cloud cover, satellite revisit timing, and other issues, and (3) partial fire mapping resulting from (1,2) with crudely mosaicked image segments. Our purpose is to develop the methodology to create, test, and assess the first wildland fire analysis (sometimes called "reanalysis") products - standardized, gridded outputs of desired wildfire products produced at regular intervals. The idea is to develop fire products analogous to atmospheric analyses -- powerful products used across atmospheric science to initialize atmospheric models and support many types of scientific studies. Reanalyses integrate dissimilar, disconnected, asynchronous observations using the physical consistency of a model and data assimilation system to fill gaps in time and space, estimate variables that are not directly observed, and create a physically balanced, more complete image of reality and how it changed over time. Similar needs exist for sectors of wildland fire science and operations but no analogous process or product exists; instead, investigators have attempted to numerically interpolate between widely separated observations or to estimate or retrieve information from spatially and/or temporally coarse data. We aim to develop the fire reanalysis methodology using fire detection products (e.g. Suomi-NPP VIIRS, Landsat OLI, Sentinel-2, Terra/Aqua MODIS, AVHRR, and Sentinel-3 SLSTR), and other products (e.g. ASTER). Subsequently, we will investigate assimilation of supplementary infrared observations from small satellite constellations and private sector products (e.g. DigitalGlobe WorldView-3), airborne observations such as USDA's National Infrared Operations (NIROPs), or experimental platforms (e.g. German Aerospace Center TET-1) that reflect the growing number of remote sensing observations. The CAWFE coupled numerical weather prediction - wildland fire model, which has been successfully used to model many wildfire events, will assimilate this remotely-sensed fire detection data to simulate fires' evolution while minimizing variance from those observations. In this exploratory study, we will produce reanalysis products for a sample of different types of wildfire events. Products would include 2-D gridded variables including cumulative burned area extent, active burning areas, and heat release rate (related to Fire Radiative Power) in common data formats at hourly intervals (or other frequency to be determined by users - team members who develop additional products, e.g. biomass burning emissions inventories, for the research community and decision makers). As part of the project, users will test the products and assess their utility in smoke and emissions modeling, where the infrequency of fire detection observations (e.g., 2 per day for the original VIIRS) currently creates errors in predicted fire emissions due to the diurnal variability in fire activity. Also, 3-D products will be considered, including atmospheric state variables and smoke concentration, which could indicate injection height, and smoke concentration profiles needed to initialize air quality models. Data, software, documentation, products, and metadata will be released in a publicly accessible repository, allowing the community to further develop the methods and product library, and outreach will be made by initiating workshops and sessions at scientific conferences.

Advance Earth system science knowledge through the identification, development, and demonstration of innovative information systems technologies

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