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 (COMBLE) provided a comprehensive set of ground-based in-situ and remote sensing observations of MCAOs at a coastal location in northern Norway. We determine MCAO periods that unambiguously exhibit open or closed MCC. Individual cells observed with a profiling Ka-band radar are identified using a water segmentation method. Using self-organizing maps (SOMs), these cells are then objectively classified based on the variability in their vertical structure. 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 spectral width, larger amounts of liquid water, lower 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. Such convection, when weakening, becomes virtually indistinguishable from the more intense stratiform precipitation cores in closed MCC. Non-precipitating stratiform cores have weak vertical drafts and are almost exclusively found during closed MCC periods. Convection is observed only occasionally in the closed MCC environment.

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 surface fuel load. 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, to isolate the fuel loading needed to produce fire-generated flows and plume rise comparable to NEXRAD 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.

This study introduces the firebrand spotting parameterization implemented in WRF-Fire and applies it to the Marshall Fire, Colorado (2021) to demonstrate that, without fire spotting, wind-driven fire simulations cannot accurately represent the fire behavior. Spotting can be a dominant fire spread mechanism in wind-driven events, particularly those that occur in the wildland-urban interface (WUI), such as the Marshall Fire. To simulate these fires, the model’s ability to spot is critical, in that it accelerates the rate of spread and enables the fire to spread over streams and urban features such as highways. The firebrand spotting parameterization was implemented in WRF-Fire to improve simulations of wind-driven fires in a fire-atmosphere coupled system. In the parameterization, particles are generated with a set of fixed firebrand properties, from locations vertically aligned with the fire front. Firebrands are transported using a Lagrangian framework and firebrand physics is represented by a burnout (combustion) parameterization. Fire spots may occur when firebrands land on unburned grid points. The parameterization components are illustrated through idealized simulations and its application is demonstrated through simulations of a devastating real case - the Marshall Fire (Colorado, 2021). The simulations were verified using time of arrival and contingency table metrics. Our metrics show that when fire spots were included in the simulations, fire rate of spread and burn area consistently improved.

A document by Maria Frediani. Click on the document to view its contents.

Marine cold-air outbreaks, or CAOs, are airmass transformations whereby relatively cold boundary layer (BL) air is transported over relatively warm water. Such convectively-driven conditions are rather ubiquitous in the high-latitudes, occurring most frequently during the winter and spring. To more deeply understand BL and cloud properties during CAO conditions, the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) took place from late 2019 into early 2020. During COMBLE, the U.S. Department of Energy (DOE) first Atmospheric Radiation Measurement Mobile Facility (AMF1) was deployed to Andenes, Norway, far downstream (~1000 km) from the Arctic pack ice. This study examines the two most intense CAOs sampled at the AMF1 site. The observed BL structures are open cellular in nature with high (~3-5 km) and cold (-30 to -50 oC) cloud tops, and they often have pockets of high liquid water paths (LWPs; up to ~1000 g m-2) associated with strong updrafts and enhanced turbulence. We use a high-resolution mesoscale model to explore how well four different turbulence closure methods represent open cellular cloud properties. After applying a radar simulator to the model outputs for direct evaluation, we show that cloud top properties agree well with AMF1 observations (within ~10%), but radar reflectivity and LWP agreement is more variable. The eddy-diffusivity/mass-flux approach produces the deepest cloud layer and therefore the largest and most coherent cellular structures. Our results suggest that the turbulent Prandtl number may play an important role for the simulated BL and cloud properties.

The Arctic is marked by deep intrusions of warm, moist air, alternating with outbreaks of cold air down to lower latitudes. The typical vertical structure of clouds and precipitation during these two synoptic weather extremes is examined at a coastal site at 69°N in Norway. The Norwegian Sea is a corridor for warm-air intrusions (WAIs) and frequently witnesses cold-air outbreaks (CAOs). This study uses data from profiling radar, lidar, and microwave radiometer, radiosondes and other probes that were collected during the Cold air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) between 1 December 2019 and 31 May 2020. Marine CAOs are defined in terms of thermal instability relative to the sea surface temperature, and warm-air intrusions in terms of stratification of moist static energy between the surface and 850 hPa. Cloud structures in CAOs are convective, driven by strong surface heat fluxes over a long fetch of open water, with cloud tops between 2-4 km. The mostly open-cellular convection may contain substantial ice and produce intermittent moderate precipitation at the observational site, notwithstanding the low precipitable water vapor. In contrast, WAIs are marked by high values of precipitable water vapor and integrated vapor transport. WAI clouds are stratiform, with cloud tops often exceeding 6 km, sometimes layered, and generally producing persistent precipitation that can be heavier than in CAOs.

On 30 December 2021, the Marshall Fire devastated the Boulder, Colorado region. The fire initiated in fine fuels in open space just southeast of Boulder and spread rapidly due to the strong, downslope winds that penetrated into the Boulder Foothills. Despite the increasing occurrence of wildland-urban interface (WUI) disasters, many questions remain about how fires progress through vegetation and the built environment. To help answer these questions for the Marshall Fire, we use a coupled fire-atmosphere model and Doppler on Wheels (DOW) observations to study the fire’s progression as well as examine the physical drivers of its spread. Evaluation of the model using the DOW suggests that the model is able to capture general characteristics of the flow field; however, it does not produce as robust of a hydraulic jump as the one observed. Our results highlight limitations of the model that should be addressed for successful WUI simulations.

A fire-spotting parameterization was developed for the WRF-Fire component of the WRF model version 4.0.1. The parameterization uses a Lagrangian particle transport framework and is coupled to the fire component of the WRF-ARW model as an independent Fortran module. When fires are active, the fire-spotting module identifies areas at risk of fire spotting by modeling transport and physical processes of individual firebrands released from fire locations. Firebrands are released at varying heights, from locations with higher emission potential, defined as a function of fire rate of spread and fuel load. Firebrands are transported with the atmospheric flow, and physical properties (temperature, mass, and terminal velocity) are updated at the default model timestep. The particles may either burnout before settling or deposit at a grid point when carried below a specified height threshold. The number and spatial distribution of deposited firebrands correspond to the flow-dependent risk component of new fire ignitions due to fire spotting. The flow-dependent component is then combined with the risk associated with local fuel properties (load and moisture) to yield the fire spotting spatial likelihood. In this presentation, the fire-spotting parameterization is assessed through a qualitative analysis of wildfires in Colorado. Uncertainties in fire ignition observations, used to initialize fires in the WRF-Fire model, often limit the ability to accurately model fire area, which in turn controls the firebrands’ emission location. Limited spotting observations are also a challenge to an objective verification of the module skill. We expect that the most recent remote sensing products will improve the representation of surface properties and accuracy of ignition parameters for WRF-Fire, which will directly transfer to the fire-spotting module capability. Direct enhancements to the parameterization may be incorporated into the module as laboratory experiments and field campaigns provide data to improve our ability to model firebrands’ initial properties (e.g. firebrand size and ejection height) and physical processes (burnout and terminal velocity).