26Cloud phase and microphysical properties control the radiative effects of clouds in the climate 27 system and are therefore crucial to characterize in a variety of conditions and locations. An 28 Arctic-specific, ground-based, multi-sensor cloud retrieval system is described here and applied 29 to two years of observations from Barrow, Alaska. Over these two years, clouds occurred 75% 30 of the time, with cloud ice and liquid each occurring nearly 60% of the time. Liquid water 31 occurred at least 25% of the time even in winter, and existed up to heights of 8 km. The 32 vertically integrated mass of liquid was typically larger than that of ice. While it is generally 33 difficult to evaluate the overall uncertainty of a comprehensive cloud retrieval system of this 34 type, radiative flux closure analyses were performed where flux calculations using the derived 35 microphysical properties were compared to measurements at the surface and top-of-atmosphere. 36 Radiative closure biases were generally smaller for cloudy scenes relative to clear skies, while 37 the variability of flux closure results was only moderately larger than under clear skies. The best 38 closure at the surface was obtained for liquid-containing clouds. Radiative closure results were 39 compared to those based on a similar, yet simpler, cloud retrieval system. These comparisons 40 demonstrated the importance of accurate cloud phase and type classification, and specifically the 41 identification of liquid water, for determining radiative fluxes. Enhanced retrievals of liquid 42 water path for thin clouds were also shown to improve radiative flux calculations.43 44 45 3

A 2-yr cloud microphysical property dataset derived from ground-based remote sensors at the Atmospheric Radiation Measurement site near Barrow, Alaska, was used as input into a radiative transfer model to compute radiative heating rate (RHR) profiles in the atmosphere. Both the longwave (LW; 5–100 μm) and shortwave (SW; 0.2–5 μm) RHR profiles show significant month-to-month variability because of seasonal dependence in the vertical profiles of cloud liquid and ice water contents, with additional contributions from the seasonal dependencies of solar zenith angle, water vapor amount, and temperature. The LW and SW RHR profiles were binned to provide characteristic profiles as a function of cloud type and liquid water path (LWP). Single-layer liquid-only clouds are shown to have larger (10–30 K day−1) LW radiative cooling rates at the top of the cloud layer than single-layer mixed-phase clouds; this is due primarily to differences in the vertical distribution of liquid water between the two classes. However, differences in SW RHR profiles at the top of these two classes of clouds are less than 3 K day−1. The absolute value of the RHR in single-layer ice-only clouds is an order of magnitude smaller than in liquid-bearing clouds. Furthermore, for double-layer cloud systems, the phase and condensed water path of the upper cloud strongly modulate the radiative cooling both at the top and within the lower-level cloud. While sensitivity to cloud overlap and phase has been shown previously, the characteristic RHR profiles are markedly different between the different cloud classifications.

Corroboration of Geostationary Operational Environmental Satellite-17 (GOES-17) wildland fire detection capabilities occurred during the 24 October 2019 (evening of 23 October LST) ignition of the Kincade Fire in northern California. Post-analysis of remote sensing data compared to observations by the ALERTWildfire fire surveillance video system suggests that the emerging Kincade Fire hotspot was visually evident in GOES17 shortwave infrared imagery 52 s after the initial near-infrared heat source detected by the ground-based camera network. GOES-17 Advanced Baseline Imager Fire Detection Characteristic algorithms registered the fire 5 min after ignition. These observations represent the first documented comparative dataset between fire initiation and satellite detection, and thus provide context for GOES-16/17 wildland fire detections.

Multiple high-impact wildfire episodes on the southern Great Plains in 2021/22 provided unique opportunities to demonstrate the emerging utility of Convection-allowing Models (CAMs) in fire-weather forecasting. This short contribution article will present preliminary analyses of the deterministic Texas Tech Real Time Weather Prediction System’s Red Flag Threat Index (RFTI) compared to wildfire activity observed via the Geostationary Operational Environmental Satellite-16 during four southern Great Plains wildfire outbreaks. Visual side-by-side comparisons of model-predicted RFTI and satellite-detected wildfires will be shown in static and animated displays that demonstrate the model’s prognostic signal in depicting fire-outbreak evolution. The data analyses are supplemented with preliminary information from state forestry agencies that provide context to predicted RFTI relative to size-based categorization of observed wildfires and human casualties. In addition, use of the National Severe Storm Laboratory’s Warn-on-Forecast System to provide short-term updates on the evolution of fire-effective atmospheric features that promote new fire ignition, problematic spread, and extreme fire behavior is also demonstrated. The examples presented here suggest that CAMs serve an important role in the mesoscale prediction of dangerous wildfire conditions. With this novel use of CAMs in fire meteorology, the authors advocate for expanded availability of fire weather-specific fields and parameters in high-resolution numerical weather prediction systems that would improve wildfire forecasts and associated impact-based decision support.