Estimating Fire Radiative Power Using Weather Radar Products for Wildfires.

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. Plain Language Summary: The radiant energy emitted by wildfires (FRP) is an important variable that controls many aspects of the smoke plume including the amount of emission released into the atmosphere and how high it travels. Biomass burning debris, which are large particles generated by combustion, can be suspended in the atmosphere along with smoke. Weather radars can detect and retrieve information from these large particles, which can be used for a variety of applications. In this work, we show that retrievals from weather radars can be used to estimate FRP with reasonable results. Echoes associated with ground artifacts affect these results but can be effectively screened using machine learning algorithms trained on manually selected data of representative plume and non‐plume cases. Satellite FRP can often be missing or underpredicted especially for extreme fire events, and thus this technique offers an alternative way to provide a more accurate depiction of fire evolution that can be used to predict smoke impacts. Key Points: Weather radar products integrated in the vicinity of fires can be used to estimate Fire radiative power (FRP) through a power‐law relationshipThe relationships between FRP and radar products are skillful only after removing artifacts using a machine learning classifierRadar‐based FRP has potential to fill gaps and overcome satellite FRP biases occurring during extreme fire events [ABSTRACT FROM AUTHOR]