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.

Accurate representation of heterogeneous surface layer processes is essential for numerical weather prediction (NWP) with sub-kilometer grid spacing. NWP models such as the Weather Research and Forecasting (WRF) Model generally use second-moment turbulent models for parameterizing the planetary boundary layer (PBL). The most common parameterizations follow Mellor–Yamada and account for the vertical turbulent mixing only; that is, standard PBL parameterizations are one-dimensional (1DPBL). The horizontal diffusion of momentum is parameterized based on Smagorinsky’s model for numerical stability. Although the combination of 1DPBL and 2D Smagorinsky parameterizations is successful at coarse grid resolutions (e.g., grid-size dx ∼ 12–2 km), it does not represent well the effect of horizontal turbulence as gridcell size decreases (<1 km). To reconcile the representation of vertical and horizontal turbulent mixing, a full three-dimensional PBL scheme (3DPBL) based on the Mellor–Yamada model was implemented in WRF. The 3DPBL uses the horizontal and vertical turbulent fluxes diagnosed from the flow gradients to handle the turbulent mixing. These gradients cannot be directly calculated near the surface. Therefore, the 3DPBL parameterization is coupled herein to a second-order diagnostic model of the three-dimensional turbulent fluxes in the surface layer. Several adjustments to the original Mellor–Yamada model, including a modified length scale, were introduced to capture flow anisotropy and dependence on stability conditions. The results are compared against data from the Wind Forecast Improvement Project 2 (WFIP2) for different weather regimes and using different grid resolutions to examine stability and scale dependency.

Smoke from wildfires or burning biomass directly affects air quality and weather through modulating cloud microphysics and radiation. A simple wildfire emission coupling of black carbon (BC) and organic carbon (OC) with microphysics was implemented using the Weather Research and Forecasting model's fire module. A set of large‐eddy simulations inspired by unique surface and upper atmospheric observations from the 2021 Santa Coloma de Queralt Fire (Spain) were conducted to investigate the influence of background conditions and interactions between atmospheric and fire processes such as fire smoke, ambient moisture, and latent heat release on the formation and evolution of pyroconvective clouds. While the microphysical impact of BC and OC emissions on the dynamics of fire behavior is minimal on short time scales (<6 hr), their presence increased the cloud water content and decreased the rain rates in our case study. In our case study, atmospheric moisture played an important role in the formation and development of pyroconvective clouds, which in turn enhanced the surface winds (8%) and fire spread rate (25%). The influence of fuel moisture on the pyroconvective cloud formation is smaller when compared with the atmospheric moisture content. A better representation of cloud processes can improve the mesoscale forecasts, which is important for better fire behavior modeling.

This study examines the implications of Tropical Montane Forests (TMFs) loss on orographic precipitation in the Eastern slopes of the Andes (EADS). The focus is on moist processes for synoptic regimes associated with significant EADS precipitation: (1) monsoon rainfall for weak and strong South America Low-Level Jet (LLJ) conditions and (2) heavy rainfall associated with cold air intrusions (CAI) in the dry season. High-resolution simulations using the Weather Research and Forecasting (WRF) model were conducted for realistic and modified land-cover resulting from the conversion of TMFs to savanna. The deforestation scenarios result in 50–100% decrease (up to ∼400 J kg^–1) in Convective Available Potential Energy (CAPE) spatially organized by land-cover change along the EADS. Analysis of the differences in simulated frequency distributions of rainfall intensity shows robust daytime increases in light rainfall (<2 mm h^–1) and decreases in moderate rainfall rates (2–10 mm h^–1) in the altitudinal band 500–2,000 m where orographic enhancement is dominant. Whereas there are negligible changes in the spatial patterns of precipitation and hydrologic response for monsoon conditions, rainfall accumulations decrease for all cases, and the precipitation maxima shift downslope into the Amazon lowlands. Changes in rainfall amount and intensity result in runoff decreases of 5–10% at the event-scale for the CAI case. Sensitivity simulations for lower initial soil moisture conditions indicate a strong positive feedback of forest loss to hydrologic drought along the EADS foothills in the austral spring when CAIs play a key role in the tropical EADS dry season hydrometeorology.

Previous work showed that atmospheric model simulations exhibit different scaling behavior of vertically averaged horizontal wind (u,v) and moisture (q) in the mesoscales for convective (spectral slopes β~−5/3) and nonconvective (β~−11/5) conditions. Here, the focus is on the transient behavior of horizontal scaling in the vertical during the evolution of extreme orographic precipitation storms in middle (Appalachians, <2,500m) and high mountains (Andes). The results show that β exhibits a strong diurnal cycle switching between convective and nonconvective behavior following the space‐time evolution of atmospheric stability in the lower troposphere (below 700 hPa) depending on latitude, topography, landform, and the synoptic environment. Anomalous flattening of the wind and moisture spectra (i.e., spectral saturation, ∣β ∣ < 5/3) at high wavenumbers and up to 200 hPa is tied to convective activity, where and when strong vertical motions develop, corresponding to an abrupt directional switch from horizontal energy transfer to vertical energy transfer including latent heating release and parameterized microphysical processes. In the small mesoscales (<50 km), β~ − 5/3 at all times up to 200 hPa with nighttime steepening (β~−11/5) below the orographic envelope where cold air pools form at low elevations and vertical motion weakens in the Appalachians. In the Andes, the scaling behavior exhibits a stronger diurnal cycle at low levels (below 700 hPa) with significant shoaling between tropical and high latitudes. Blocking and strong modification of regional circulations result in nighttime anisotropy at midlevels on the altitudinal profile along the North‐South topographic divide.

Accurate wildfire spread modeling critically depends on the representation of wind dynamics, which vary with terrain, land cover characteristics, and height above ground. Many fire spread models are often coupled with coarse atmospheric grids that cannot explicitly resolve the vertical variation of wind near flame heights. Rothermel’s fire spread model, a widely used parameterization, relies on midflame wind speed to calculate the fire rate of spread. In coupled fire atmosphere models such as the Community Fire Behavior Model (CFBM), users are required to specify the midflame height before running a fire spread simulation. This study evaluates the use of logarithmic interpolation wind adjustment factors (WAF) for improving midflame wind speed estimates, which are critical for the Rothermel model. We compare the fixed wind height approach that is currently used in CFBM with WAF-derived winds for unsheltered and sheltered surface fire spread. For the first time in this context, these simulations are validated against satellite and ground-based observations of fire perimeters. The results show that WAF implementation improves fire perimeter predictions for both grass and canopy fires while reducing the overestimation of fire spread. Moreover, this approach solely depends on the fuel bed depth and estimation of canopy density, enhancing operational efficiency by eliminating the need for users to specify a wind height for simulations.