We analyze observed and simulated winds and gusts occurring before, during, and immediately after the ignition of the Thomas fire of December 2017. This fire started in Ventura county during a record-long Santa Ana wind event from two closely located but independent ignitions and grew to become (briefly) the largest by area burned in modern California history. Observations placed wind gusts as high as 35 m/s within 40 km of the ignition sites, but stations much closer to them reported much lower speeds. Our analysis of these records indicate these low wind reports (especially from cooperative “CWOP” stations) are neither reliable nor representative of conditions at the fire origin sites. Model simulations verified against available better quality observations indicate downslope wind conditions existed that placed the fastest winds on the lee slope locations where the fires are suspected to have started. A crude gust estimate suggests winds as fast as 32 m/s occurred at the time of the first fire origin, with higher speeds attained later.

While numerical weather prediction models have made considerable progress regarding forecast skill, less attention has been paid to the planetary boundary layer. This study leverages High-Resolution Rapid Refresh (HRRR) forecasts on native levels, 1-s radiosonde data, and (primarily airport) surface observations across the conterminous United States. We construct temporally- and spatially-averaged composites of wind speed and potential temperature in the lowest 1 km for selected months to identify systematic errors in both forecasts and observations in this critical layer. We find near-surface temperature and wind speed predictions to be skillful although wind biases were negatively correlated with observed speed and temperature biases revealed a robust relationship with station elevation. Above ≈250 m above ground level, below which radiosonde wind data were apparently contaminated by processing, biases were small for wind speed and potential temperature at the analysis time (which incorporates sonde data) but became substantial by the 24-h forecast. Wind biases were positive through the layer for both 00 and 12 UTC, and morning potential temperature profiles were marked by excessively steep lapse rates that persisted across seasons and (again) exaggerated at higher elevation sites. While the source or cause of these systematic errors are not fully understood, this analysis highlights areas for potential model improvement and the need for a continued and accessible archive of the data that make analyses like this possible.

Abstract. Roughness features (e.g., rocks, vegetation, furrows) that shelter or attenuate wind flow over the soil surface can considerably affect the magnitude and spatial distribution of sediment transport in active aeolian environments. Existing dust and sediment transport models often rely on vegetation attributes derived from static land use datasets or remotely sensed greenness indicators to incorporate sheltering effects on simulated particle mobilization. However, these overly simplistic approaches do not represent the three-dimensional nature or spatiotemporal changes of roughness element sheltering. They also ignore the sheltering contribution of non-vegetation roughness features and photosynthetically inactive (i.e., brown) vegetation common to dryland environments. Here, we explore the use of a novel albedo-based sheltering parameterization in a dust transport modeling application of the Weather Research and Forecasting model with Chemistry (WRF-Chem). The albedo method estimates sheltering effects on surface wind friction speeds and dust entrainment from the shadows cast by subgrid-scale roughness elements. For this study, we applied the albedo-derived drag partition to the Air Force Weather Agency (AFWA) dust emission module and conducted a sensitivity study on simulated PM10 concentrations using the Georgia Institute of Technology–Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model as implemented in WRF-Chem v4.1. Our analysis focused on a convective dust event case study from 3–4 July 2014 for the southwestern United States desert region discussed by other published works. Previous studies have found that WRF-Chem simulations grossly overestimated the dust transport associated with this event. Our results show that removing the default erodibility map and adding the drag parameterization to the AFWA dust module markedly improved the overall magnitude and spatial pattern of simulated dust conditions for this event. Simulated PM10 values near the leading edge of the storm substantially decreased in magnitude (e.g., maximum PM10 values were reduced from 17 151 to 8539 µg m−3), bringing the simulated results into alignment with the observed PM10 measurements. Furthermore, the addition of the drag partition restricted the erroneous widespread dust emission of the original model configuration. We also show that similar model improvements can be achieved by replacing the wind friction speed parameter in the original dust emission module with globally scaled surface wind speeds, suggesting that a well-tuned constant could be used as a substitute for the albedo-based product for short-duration simulations in which surface roughness is not expected to change and for landscapes wherein roughness is constant over years to months. Though this alternative scaling method requires less processing, knowing how to best tune the model winds a priori could be a considerable challenge. Overall, our results demonstrate how dust transport simulation and forecasting with the AFWA dust module can be improved in vegetated drylands by calculating the dust emission flux with surface wind friction speed from a drag partition treatment.