Preprints
https://doi.org/10.5194/egusphere-2026-2127
https://doi.org/10.5194/egusphere-2026-2127
22 Apr 2026
 | 22 Apr 2026
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Impact of high-resolution soil erodibility datasets on dust simulations in WRF-Chem with the GOCART scheme

Leandro C. Segado-Moreno, Juan Pedro Montávez, Ginés Garnés-Morales, Eloisa Raluy-López, Pedro Jiménez-Guerrero, and Rajesh Kumar

Abstract. Mineral dust is a major atmospheric aerosol influencing climate, air quality, and human health through radiative and microphysical processes. The Iberian Peninsula is frequently affected by North African dust intrusions, leading to episodic PM10 exceedances that challenge air quality forecasting. However, accurate representation of dust emissions remains limited by uncertainties in soil erodibility, land surface properties, and meteorological forcing.

This study evaluates the impact of two high-resolution soil erodibility datasets on dust simulations using WRF-Chem with the GOCART scheme. The first dataset, EROD-HR, integrates fine-resolution topography (GMTED2010) to improve dust source representation at 0.0625 ° (~5 km) globally and 1 km over the Iberian Peninsula. The second dataset, SOILHD, further refines dust source characterization by incorporating high-resolution soil texture (sand, silt, clay fractions) and removing misclassified bare soil areas, reaching 1 km global resolution. Both datasets aim to better capture spatial heterogeneity of dust sources in semi-arid environments.

Simulations are conducted for five dust episodes between 2022 and 2025, covering local and long-range transport conditions. Model performance is evaluated against PM10 observations from the SINQLAIR network in the Region of Murcia. Results show improved representation of dust emissions, with better agreement in magnitude and timing of PM10 peaks at inland stations. Improvements are more limited at coastal and anthropogenically influenced sites, although statistical metrics (correlation, bias, RMSE) indicate consistent gains.

Overall, high-resolution erodibility datasets enhance WRF-Chem dust simulations by reducing biases and improving variability representation, highlighting the importance of detailed land-surface information for regional dust forecasting systems.

Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Leandro C. Segado-Moreno, Juan Pedro Montávez, Ginés Garnés-Morales, Eloisa Raluy-López, Pedro Jiménez-Guerrero, and Rajesh Kumar

Status: open (until 03 Jun 2026)

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Leandro C. Segado-Moreno, Juan Pedro Montávez, Ginés Garnés-Morales, Eloisa Raluy-López, Pedro Jiménez-Guerrero, and Rajesh Kumar
Leandro C. Segado-Moreno, Juan Pedro Montávez, Ginés Garnés-Morales, Eloisa Raluy-López, Pedro Jiménez-Guerrero, and Rajesh Kumar
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Latest update: 22 Apr 2026
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Short summary
This study focuses on improving models that simulate how mineral dust is emitted and transported in the atmosphere. We created two new, more detailed maps of soils and terrain to better represent where dust is generated. As a case study, we use dust events coming from North Africa affecting Spain. Results show that the improved data helps the model match observed air pollution better, especially during dust episodes in inland areas.
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