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.

Evaluation of Wildfire Plume Injection Heights Estimated from Operational Weather Radar Observations Using Airborne Lidar Retrievals

“…Additionally, other WSR-88D returns that happen at low altitude such as ground clutter and anomalous propagation (that don't play a role for the fire analyzed here) would also need to be filtered out for other fires. This could be done using additional radar observables (e.g., spectrum width, radial velocity) and machine learning algorithms (Saide et al, 2023). Lastly, rapid changes in fire characteristics may correspond to rapid changes in buoyant updraft, thus making the Doppler velocity a key consideration for future, more advanced algorithms (Kingsmill et al, 2023;Rodriguez et al, 2020).…”