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.
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Estimating Fire Radiative Power Using Weather Radar Products for Wildfires
Abstract Satellite‐based Fire radiative power (FRP) retrievals are used to track wildfire activity but are sometimes not possible or have large uncertainties. Here, we show that weather radar products including composite and base reflectivity and equivalent rainfall integrated in the vicinity of the fires show strong correlation with hourly FRP for multiple fires during 2019–2020. Correlation decreases when radar beams are blocked by topography and when there is significant ground clutter (GC) and anomalous propagation (AP). GC/AP can be effectively removed using a machine learning classifier trained with radar retrieved correlation coefficient, velocity, and spectrum width. We find a power‐law best describes the relationship between radar products and FRP for multiple fires combined (0.67–0.76 R2). Radar‐based FRP estimates can be used to fill gaps in satellite FRP created by cloud cover and show great potential to overcome satellite FRP biases occurring during extreme fire events.
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- PAR ID:
- 10495847
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 50
- Issue:
- 21
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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