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Abstract This paper is a collaborative effort that originated at the International Space Science Institute Workshop on “Physical links between Weather and Climate in Space and the Lower Atmosphere” held 22–26 January 2024. Many scientists attended that workshop and contributed their expertise related to polar vortex impacts on upper atmosphere variability. This paper summarizes well-known and newly reported signatures of polar vortex weakening on mesosphere–lower-thermosphere (MLT) temperature, winds, composition, planetary waves, gravity waves, tides, and ionospheric foF2. A variety of observational and modeling results are shown and are consistent with previously published variations in the dynamical and chemical state of the MLT and ionosphere during weak vortex events. We present Superposed Epoch Analysis (SEA) of upper atmosphere diagnostics and phenomena where day 0 is the onset of major SSWs. We also present SEAs where day 0 is the onset of stratopause warmings followed by elevated stratopause events. Our goal in performing two SEAs is to test the sensitivity of 10 hPa versus 1 hPa winds to predict upper atmosphere variability. Results suggest that zonal winds and the semidiurnal migrating solar tide (SW2) in the MLT are more sensitive to zonal wind reversals at 1 hPa rather than 10 hPa. Alternatively, the non-migrating DW2 tide in the equatorial upper mesosphere is best predicted by planetary wave-1 amplitudes in the winter high-latitude upper stratosphere rather than zonal wind reversals. A notable aspect of both SEAs is extremely large event-to-event variability in all diagnostics. Thus, conclusions drawn based on any one event are less robust than those based on many events. 

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.