Search

During marine cold‐air outbreaks (MCAOs), when cold polar air moves over warmer ocean, a well‐recognized cloud pattern develops, with open or closed mesoscale cellular convection (MCC) at larger fetch over open water. The Cold‐Air Outbreaks in the Marine Boundary Layer Experiment provided a comprehensive set of ground‐based in situ and remote sensing observations of MCAOs at a coastal location in northern Norway. MCAO periods that unambiguously exhibit open or closed MCC are determined. Individual cells observed with a profiling Ka‐band radar are identified using a watershed segmentation method. Using self‐organizing maps (SOMs), these cells are then objectively classified based on the variability in their vertical structure. The SOM nodes contain some information about the location of the cell transect relative to the center of the MCC. This adds classification noise, requiring numerous cell transects to isolate cell dynamical information. The SOM‐based classification shows that comparatively intense convection occurs only in open MCC. This convection undergoes an apparent lifecycle. Developing cells are associated with stronger updrafts, large spectrum width, larger amounts of liquid water, lower surface precipitation rates, and lower cloud tops than mature and weakening cells. The weakening of these cells is associated with the development of precipitation‐induced cold pools. The SOM classification also reveals less intense convection, with a similar lifecycle. More stratiform vertical cloud structures with weak vertical motions are common during closed MCC periods and are separated into precipitating and non‐precipitating stratiform cores. Convection is observed only occasionally in the closed MCC environment. During marine cold‐air outbreaks a characteristic cloud field develops over the open ocean. At large fetch, this cloud field is characterized by open and closed mesoscale cellular convection (MCC). The Cold‐Air Outbreaks in the Marine Boundary Layer Experiment in northern Norway provided comprehensive observations of mixed‐phase MCC using ground‐based and remote sensing instruments. Distinct open or closed MCC periods are identified using a vertically pointing cloud radar. Within these periods individual cells are identified. Using a machine learning algorithm, these cells are objectively classified based on their vertical structure as observed by the radar. The classification reveals that intense convection primarily occurs in open MCC, displaying a lifecycle with developing cells characterized by strong updrafts, substantial liquid water, lower precipitation rates, and lower cloud tops compared to mature and weakening cells. Weakening cells are associated with precipitation‐induced cold pools. Less intense convection with a similar lifecycle is observed during open and closed MCC. Most frequently during closed MCC, stratiform cells with weak vertical motions are observed, some of them without precipitation reaching the surface. The vertical cloud structure of the precipitating stratiform cells is very similar to weakening convective cells. Cloud cells in marine cold‐air outbreaks are objectively identified and classified using a profiling mm‐wave radarThe classification reveals that open‐cellular clouds undergo a convective lifecycle with distinct characteristics at each lifecycle stageClosed‐cellular clouds contain occasional convection but generally have more stratiform characteristics Cloud cells in marine cold‐air outbreaks are objectively identified and classified using a profiling mm‐wave radar The classification reveals that open‐cellular clouds undergo a convective lifecycle with distinct characteristics at each lifecycle stage Closed‐cellular clouds contain occasional convection but generally have more stratiform characteristics

Coupled fire‐atmosphere models often struggle to simulate important fire processes like fire generated flows, deep flaming fronts, extreme updrafts, and stratospheric smoke injection during large wildfires. This study uses the coupled fire‐atmosphere model, WRF‐Fire, to examine the sensitivities of some of these phenomena to the modeled total fuel load and its consumption. Specifically, the 2020 Bear Fire and 2021 Caldor Fire in California's Sierra Nevada are simulated using three fuel loading scenarios (1X, 4X, and 8X LANDFIRE derived surface fuel), while controlling the fire rate of spread using observations. This approach helps isolate the fuel loading and consumption needed to produce fire‐generated winds and plume rise comparable to radar observations of these events. Increasing fuel loads and corresponding fire residence time in WRF‐Fire leads to deep plumes in excess of 10 km, strong vertical velocities of 40–45 m s−1, and combustion fronts several kilometers in width (in the along wind direction). These results indicate that LANDFIRE‐based surface fuel loads in WRF‐Fire likely under‐represent fuel loading, having significant implications for simulating landscape‐scale wildfire processes, associated impacts on spread, and fire‐atmosphere feedbacks. Coupled fire‐atmosphere models poorly depict fire‐generated winds and deep smoke plumes during extreme wildfires. In this study, the 2020 Bear Fire and 2021 Caldor Fire in California are simulated under various fuel scenarios. The simulations show that fuel characteristics used in the fire‐atmosphere model under‐represent observed conditions and thus, produce inadequate fire‐generated winds and plume characteristics. When the modeled fuels are augmented to match proxy observations of fuel characteristics, simulated fire‐atmosphere feedbacks better resemble fire generated winds and deep convective plumes seen in radar observations. The results of these simulations will help inform future improvements to coupled fire‐atmosphere models to better simulate large wildland fires. Coupled fire‐atmosphere models struggle to simulate critical fire‐generated winds and plume rise during large wildland firesDeficient fire‐generated winds are linked to inadequate fuel loads and burnout timescale in the modelAdjustment of the fuel characteristics results in more realistic simulated plumes and fire‐generated winds Coupled fire‐atmosphere models struggle to simulate critical fire‐generated winds and plume rise during large wildland fires Deficient fire‐generated winds are linked to inadequate fuel loads and burnout timescale in the model Adjustment of the fuel characteristics results in more realistic simulated plumes and fire‐generated winds