Application of a Satellite-Retrieved Sheltering Parameterization (v1.0) for Dust Event Simulation with WRF-Chem v4.1

. Roughness features (e.g., rocks, vegetation, furrows, etc.) 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 5 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) 10 dust emission module and conducted a sensitivity study on simulated PM 10 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 southwest 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 15 drag parameterization to the AFWA dust module markedly improved the overall magnitude and spatial pattern of simulated dust conditions for this event. Simulated PM 10 values near the leading edge of the storm substantially decreased in magnitude (e.g., maximum PM 10 values reduced from 17,151 µ g m -3 to 8,539 µ g m -3 ), bringing the simulated results into alignment with the observed PM 10 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 20 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 where surface roughness is not expected to change and for landscapes where roughness is constant over years to months. Though this alter-1 native 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 25 improved in vegetated drylands by calculating the dust emission ﬂux with surface wind friction speed from a drag partition treatment. dust event using ASOS data accessed through the Meteorological Assimilation Data Ingest System (MADIS) portal (https://madis.ncep.noaa.gov, last access: 12 November 2021). We also compared simulated results against observed concentrations of particulate matter up to 280 10 µ m in diameter (PM 10 ) using hourly PM 10 in situ measurements obtained from the U.S. Environmental Protection Agency’s Air Quality System database (retrieved via an application programming interface at https://www.epa.gov/outdoor-air-quality-data, last access: 1 July 2020). We did not consult additional satellite-retrieved products like false color dust-enhanced imagery and aerosol optical depth to supplement our assessment due to cloud obscuration over the area of interest. To evaluate simulated storm structure, location, and timing, we analyzed observed Next Generation Weather Radar (NEXRAD) 285 composite imagery (available at https://www.ncdc.noaa.gov/nexradinv/, last access: 1 July 2020) to qualitatively compare and verify the location, structure, and intensity of the simulated convective storms. The radar composite images comprise a blend of radar reﬂectivity from individual locations with overlapping coverage to generate a cohesive radar map across the continental United States. This mosaic approach has the advantage of ﬁlling in blockages from individual radar sites with neighboring ones, providing a more complete snapshot of convective activity across the radar network coverage area. 290 km). ALT4 conﬁguration a viable modeling solution for users in the effects of land management land cover change on sediment transport and air quality and may be limited in its use to short term atmospheric forecasting applications. It appears that drag partition treatments effectively suppressed the broadscale erroneous dust emissions in southern California and southwestern Arizona inherent to the CTRL conﬁguration without restricting the dust emissions that generated the Phoenix haboob. However, the relatively low PM 10 concentrations produced by the ALT1 simulation suggest that the S parameter was masking the absence of drag representation in the original AFWA code. Although the model performance for this case study is arguably better with the drag partition when S = 1, this may not be the case for all regions. Further investigation on the role of the S parameter in areas with more heterogeneous sediment supply is necessary to resolve this issue. better sheltering-effect representation on dust emissions. This code adaptation could take the form of a formal drag partition treatment or a wind speed-tuning approach if the roughness element coverage of the dust-producing areas associated with the model domain is somewhat homogenous and the simulation is run over a short enough period that the surface roughness conﬁguration does not change. We 555 note, however, that we cannot generalize model performance from a single case study event. Instead, we view this study as a preliminary demonstration of capability and motivation to further explore the use of drag partitioning in dust transport models to account for roughness effects on the dust entrainment process. Additional case studies and seasonal analyses are necessary to determine if the Chappell and Webb (2016) drag partition method can reliably improve dust simulations used for various weather prediction, land management, and climate change modeling applications.