Articles | Volume 16, issue 3
https://doi.org/10.5194/gmd-16-1009-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-1009-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
09 Feb 2023
Development and technical paper |
| 09 Feb 2023
| 09 Feb 2023
Application of a satellite-retrieved sheltering parameterization (v1.0) for dust event simulation with WRF-Chem v4.1
Sandra L. LeGrand
CORRESPONDING AUTHOR
U.S. Army Engineer Research and Development Center, Geospatial Research Laboratory, Alexandria, Virginia, USA
Department of Geography, University of California, Los Angeles, California, USA
Theodore W. Letcher
U.S. Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire, USA
Gregory S. Okin
Department of Geography, University of California, Los Angeles, California, USA
Nicholas P. Webb
USDA-ARS Jornada Experimental Range, Las Cruces, New Mexico, USA
Alex R. Gallagher
U.S. Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire, USA
Saroj Dhital
USDA-ARS Jornada Experimental Range, Las Cruces, New Mexico, USA
Taylor S. Hodgdon
U.S. Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire, USA
Nancy P. Ziegler
U.S. Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire, USA
Michelle L. Michaels
U.S. Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire, USA
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EGUsphere, https://doi.org/10.5194/egusphere-2025-3512, https://doi.org/10.5194/egusphere-2025-3512, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Spectroscopy measurements show that the absorbance of dust in the far-infrared up to 25 μm is comparable in intensity to that in the mid-infrared (3–15μm) suggesting its relevance for dust direct radiative effect. Data evidence different absorption signatures for high and low/mid latitude dust, due to differences in mineralogical composition. These differences could be used to characterise the mineralogy and differentiate the origin of airborne dust based on infrared remote sensing observations.
Danny M. Leung, Jasper F. Kok, Longlei Li, Gregory S. Okin, Catherine Prigent, Martina Klose, Carlos Pérez García-Pando, Laurent Menut, Natalie M. Mahowald, David M. Lawrence, and Marcelo Chamecki
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Desert dust modeling is important for understanding climate change, as dust regulates the atmosphere's greenhouse effect and radiation. This study formulates and proposes a more physical and realistic desert dust emission scheme for global and regional climate models. By considering more aeolian processes in our emission scheme, our simulations match better against dust observations than existing schemes. We believe this work is vital in improving dust representation in climate models.
Theodore Letcher, Julie Parno, Zoe Courville, Lauren Farnsworth, and Jason Olivier
The Cryosphere, 16, 4343–4361, https://doi.org/10.5194/tc-16-4343-2022, https://doi.org/10.5194/tc-16-4343-2022, 2022
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We present a radiative transfer model that uses ray tracing to determine optical properties from computer-generated 3D renderings of snow resolved at the microscale and to simulate snow spectral reflection and transmission for visible and near-infrared light. We expand ray-tracing techniques applied to sub-1 cm3 snow samples to model an entire snowpack column. The model is able to reproduce known snow surface optical properties, and simulations compare well against field observations.
Mark Hennen, Adrian Chappell, Nicholas Webb, Kerstin Schepanski, Matthew Baddock, Frank Eckardt, Tarek Kandakji, Jeff Lee, Mohamad Nobakht, and Johanna von Holdt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-423, https://doi.org/10.5194/gmd-2021-423, 2022
Revised manuscript not accepted
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We use 90,000 dust point source observations (DPS), identified in satellite imagery across 9 global dryland environments to develop a novel dust emission model performance assessment. We evaluate the albedo-based dust emission model (AEM), which agrees with dust emission observations, or lack of emission 71 % of the time. Modelled dust occurs 27 % of the time with no observation, caused mostly by the incorrect assumption of infinite sediment supply and lack of dynamic dust entrainment thresholds.
Huilin Huang, Yongkang Xue, Ye Liu, Fang Li, and Gregory S. Okin
Geosci. Model Dev., 14, 7639–7657, https://doi.org/10.5194/gmd-14-7639-2021, https://doi.org/10.5194/gmd-14-7639-2021, 2021
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This study applies a fire-coupled dynamic vegetation model to quantify fire impact at monthly to annual scales. We find fire reduces grass cover by 4–8 % annually for widespread areas in south African savanna and reduces tree cover by 1 % at the periphery of tropical Congolese rainforest. The grass cover reduction peaks at the beginning of the rainy season, which quickly diminishes before the next fire season. In contrast, the reduction of tree cover is irreversible within one growing season.
Adrian Chappell, Nicholas Webb, Mark Hennen, Charles Zender, Philippe Ciais, Kerstin Schepanski, Brandon Edwards, Nancy Ziegler, Sandra Jones, Yves Balkanski, Daniel Tong, John Leys, Stephan Heidenreich, Robert Hynes, David Fuchs, Zhenzhong Zeng, Marie Ekström, Matthew Baddock, Jeffrey Lee, and Tarek Kandakji
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-337, https://doi.org/10.5194/gmd-2021-337, 2021
Revised manuscript not accepted
Short summary
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Dust emissions influence global climate while simultaneously reducing the productive potential and resilience of landscapes to climate stressors, together impacting food security and human health. Our results indicate that tuning dust emission models to dust in the atmosphere has hidden dust emission modelling weaknesses and its poor performance. Our new approach will reduce uncertainty and driven by prognostic albedo improve Earth System Models of aerosol effects on future environmental change.
Yan Yu, Olga V. Kalashnikova, Michael J. Garay, Huikyo Lee, Myungje Choi, Gregory S. Okin, John E. Yorks, James R. Campbell, and Jared Marquis
Atmos. Chem. Phys., 21, 1427–1447, https://doi.org/10.5194/acp-21-1427-2021, https://doi.org/10.5194/acp-21-1427-2021, 2021
Short summary
Short summary
Given the current uncertainties in the simulated diurnal variability of global dust mobilization and concentration, observational characterization of the variations in dust mobilization and concentration will provide a valuable benchmark for evaluating and constraining such model simulations. The current study investigates the diurnal cycle of dust loading across the global tropics, subtropics, and mid-latitudes by analyzing aerosol observations from the International Space Station.
Cited articles
Adams, D. K. and Comrie, A. C.: The North American Monsoon, B. Am. Meteorol. Soc., 78, 2197–2213, https://doi.org/10.1175/1520-0477(1997)078<2197:TNAM>2.0.CO;2, 1997. a
Aragnou, E., Watt, S., Duc, H. N., Cheeseman, C., Riley, M., Leys, J., White, S., Salter, D., Azzi, M., Chang, L. T.-C., Morgan, G., and Hannigan, I.: Dust transport from inland Australia and its impact on air quality and health on the eastern coast of Australia during the February 2019 dust storm, Atmosphere, 12, 141, https://doi.org/10.3390/atmos12020141, 2021. a
Asadov, Kh. G. and Kerimov, N. I.: On the necessity of correction of the methodology for calculating aerosol flux from the Earth’s surface to the atmosphere using the NDVI index, Fundamental and Applied Climatology, 3, 92–101, https://doi.org/10.21513/2410-8758-2019-3-92-101, 2019 (in Russian). a
Benjamin, S. G., Weygandt, S. S., Brown, J. M., Hu, M., Alexander, C. R., Smirnova, T. G., Olson, J. B., James, E. P., Dowell, D. C., Grell, G. A., Lin, H., Peckham, S. E., Smith, T. L., Moninger, W. R., Kenyon, J. S., and Manikin, G. S.: A North American hourly assimilation and model forecast cycle: The Rapid Refresh, Mon. Weather Rev., 144, 1669–1694, https://doi.org/10.1175/MWR-D-15-0242.1, 2016. a
Bukowski, J. and van den Heever, S. C.: The impact of land surface properties on haboobs and dust lofting, J. Atmos. Sci., 79, 3195–3218, https://doi.org/10.1175/JAS-D-22-0001.1, 2022. a
Cakmur, R. V., Miller, R. L., Perlwitz, J., Geogdzhayev, I. V., Ginoux, P., Koch, D., Kohfeld, K. E., Tegen, I., and Zender, C. S.: Constraining the magnitude of the global dust cycle by minimizing the difference between a model and observations, J. Geophys. Res., 111, D06207, https://doi.org/10.1029/2005JD005791, 2006. a
Chappell, A., Van Pelt, S., Zobeck, T., and Dong, Z.: Estimating aerodynamic resistance of rough surfaces using angular reflectance, Remote Sens. Environ., 114, 1462–1470, https://doi.org/10.1016/j.rse.2010.01.025, 2010. a
Chin, M., Rood, R. B., Lin, S.-J., Müller, J.-F., and Thompson, A. M.: Atmospheric sulfur cycle simulated in the global model GOCART: Model description and global properties, J. Geophys. Res.-Atmos., 105, 24671–24687, https://doi.org/10.1029/2000JD900384, 2000. a
Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., Hughes, J., Jones, C. D., Joshi, M., Liddicoat, S., Martin, G., O'Connor, F., Rae, J., Senior, C., Sitch, S., Totterdell, I., Wiltshire, A., and Woodward, S.: Development and evaluation of an Earth-System model – HadGEM2, Geosci. Model Dev., 4, 1051–1075, https://doi.org/10.5194/gmd-4-1051-2011, 2011. a
Cremades, P. G., Fernández, R. P., Allende, D. G., Mulena, G. C., and Puliafito, S. E.: High resolution satellite derived erodibility factors for WRF/Chem windblown dust simulations in Argentina, Atmósfera, 30, 11–25, https://doi.org/10.20937/ATM.2017.30.01.02, 2017. a
Darmenova, K., Sokolik, I. N., Shao, Y., Marticorena, B., and Bergametti, G.: Development of a physically based dust emission module within the Weather Research and Forecasting (WRF) model: Assessment of dust emission parameterizations and input parameters for source regions in Central and East Asia, J. Geophys. Res., 114, D14201, https://doi.org/10.1029/2008JD011236, 2009. a, b
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res.-Atmos., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003. a
Evans, S., Ginoux, P., Malyshev, S., and Shevliakova, E.: Climate‐vegetation interaction and amplification of Australian dust variability, Geophys. Res. Lett., 43, 11823–11830, https://doi.org/10.1002/2016GL071016, 2016. a, b, c
Fast, J. D., Gustafson, W. I., Easter, R. C., Zaveri, R. A., Barnard, J. C., Chapman, E. G., Grell, G. A., and Peckham, S. E.: Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model, J. Geophys. Res., 111, D21305, https://doi.org/10.1029/2005JD006721, 2006. a, b
Fécan, F., Marticorena, B., and Bergametti, G.: Parametrization of the increase of the aeolian erosion threshold wind friction velocity due to soil moisture for arid and semi-arid areas, Ann. Geophys., 17, 149–157, https://doi.org/10.1007/s00585-999-0149-7, 1999. a
Fountoukis, C., Ackermann, L., Ayoub, M. A., Gladich, I., Hoehn, R. D., and Skillern, A.: Impact of atmospheric dust emission schemes on dust production and concentration over the Arabian Peninsula, Model. Earth Syst. Environ., 2, 115, https://doi.org/10.1007/s40808-016-0181-z, 2016. a
Francis, D., Nelli, N., Fonseca, R., Weston, M., Flamant, C., and Cherif, C.: The dust load and radiative impact associated with the June 2020 historical Saharan dust storm, Atmos. Environ., 268, 118808, https://doi.org/10.1016/j.atmosenv.2021.118808, 2022. a
Freitas, S. R., Longo, K. M., Alonso, M. F., Pirre, M., Marecal, V., Grell, G., Stockler, R., Mello, R. F., and Sánchez Gácita, M.: PREP-CHEM-SRC – 1.0: a preprocessor of trace gas and aerosol emission fields for regional and global atmospheric chemistry models, Geosci. Model Dev., 4, 419–433, https://doi.org/10.5194/gmd-4-419-2011, 2011. a
Fu, L.-T.: Comparisons suggest more efforts are required to parameterize wind flow around shrub vegetation elements for predicting aeolian flux, Sci. Rep., 9, 3841, https://doi.org/10.1038/s41598-019-40491-z, 2019. a
Gallagher, A. R., LeGrand, S. L., Hodgdon, T. S., and Letcher, T. W.: Simulating environmental conditions for southwest United States convective dust storms using the Weather Research and Forecasting model v4.1, ERDC TR-22-11, U.S. Army Engineer Research and Development Center, Hanover, New Hampshire, USA, https://doi.org/10.21079/11681/44963, 2022. a, b, c, d, e, f, g, h, i
Gillette, D. A.: Fine particulate emissions due to wind erosion, Trans. Am. Soc. Agric. Eng., 20, 890–897, https://doi.org/10.13031/2013.35670, 1977. a
Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O., and Lin, S.-J.: Sources and distributions of dust aerosols simulated with the GOCART model, J. Geophys. Res., 106, 20255–20273, https://doi.org/10.1029/2000JD000053, 2001. a, b, c, d
Gong, S. L.: A parameterization of sea-salt aerosol source function for sub- and super-micron particles, Global Biogeochem. Cy., 17, 1097, https://doi.org/10.1029/2003GB002079, 2003. a
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmos. Environ., 39, 6957–6975, https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005. a, b
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover Daily L3 Global 500 m Grid, Version 6, NASA National Snow and Ice Data Center Distributed Active Archive Center Boulder, Colorado USA [data set], https://doi.org/10.5067/MODIS/MOD10A1.006, 2016. a, b
Hamzeh, N. H., Karami, S., Kaskaoutis, D. G., Tegen, I., Moradi, M., and Opp, C.: Atmospheric dynamics and numerical simulations of six frontal dust storms in the Middle East region, Atmosphere, 12, 125, https://doi.org/10.3390/atmos12010125, 2021. a
Han, J. and Pan, H.-L.: Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System, Weather Forecast., 26, 520–533, https://doi.org/10.1175/WAF-D-10-05038.1, 2011. a
Hyde, P., Mahalov, A., and Li, J.: Simulating the meteorology and PM10 concentrations in Arizona dust storms using the Weather Research and Forecasting model with Chemistry (Wrf-Chem), J. Air Waste Manage., 68, 177–195, https://doi.org/10.1080/10962247.2017.1357662, 2018. a, b, c
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008. a
Ito, A. and Kok, J. F.: Do dust emissions from sparsely vegetated regions dominate atmospheric iron supply to the Southern Ocean?, J. Geophys. Res.-Atmos., 122, 3987–4002, https://doi.org/10.1002/2016JD025939, 2017. a, b, c
Iowa Environmental Mesonet: Documentation on IEM generated NEXRAD Mosaics, N0R (4bit) Reflectivity, https://mesonet.agron.iastate.edu/docs/nexrad_mosaic/, last access: 2 February 2023.
Karumuri, R. K., Kunchala, R. K., Attada, R., Dasari, H. P., and Hoteit, I.: Seasonal simulations of summer aerosol optical depth over the Arabian Peninsula using WRF‐Chem: Validation, climatology, and variability, Int. J. Climatol., 42, 2901–2922, https://doi.org/10.1002/joc.7396, 2022. a
Kim, D., Chin, M., Bian, H., Tan, Q., Brown, M. E., Zheng, T., You, R., Diehl, T., Ginoux, P., and Kucsera, T.: The effect of the dynamic surface bareness on dust source function, emission, and distribution, J. Geophys. Res.-Atmos., 118, 871–886, https://doi.org/10.1029/2012JD017907, 2013. a, b
Kim, K., Kim, S., Choi, M., Kim, M., Kim, J., Shin, I., Kim, J., Chung, C., Yeo, H., Kim, S., Joo, S. J., McKeen, S. A., and Zhang, L.: Modeling Asian dust storms using WRF‐Chem during the DRAGON‐Asia Field Campaign in April 2012, J. Geophys. Res.-Atmos., 126, e2021JD034793, https://doi.org/10.1029/2021JD034793, 2021. a
King, J., Nickling, W. G., and Gillies, J. A.: Representation of vegetation and other nonerodible elements in aeolian shear stress partitioning models for predicting transport threshold, J. Geophys. Res.-Earth, 110, F04015, https://doi.org/10.1029/2004JF000281, 2005. a
Kok, J. F.: A scaling theory for the size distribution of emitted dust aerosols suggests climate models underestimate the size of the global dust cycle, P. Natl. Acad. Sci. USA, 108, 1016–1021, https://doi.org/10.1073/pnas.1014798108, 2011. a
Kok, J. F., Parteli, E. J. R., Michaels, T. I., and Karam, D. B.: The physics of wind-blown sand and dust, Rep. Prog. Phys., 75, 106901, https://doi.org/10.1088/0034-4885/75/10/106901, 2012. a, b
Kok, J. F., Albani, S., Mahowald, N. M., and Ward, D. S.: An improved dust emission model – Part 2: Evaluation in the Community Earth System Model, with implications for the use of dust source functions, Atmos. Chem. Phys., 14, 13043–13061, https://doi.org/10.5194/acp-14-13043-2014, 2014. a
Koven, C. D. and Fung, I.: Identifying global dust source areas using high-resolution land surface form, J. Geophys. Res., 113, D22204, https://doi.org/10.1029/2008JD010195, 2008. a
Kuchera, E. L., Rentschler, S. A., Creighton, G. A., and Rugg, S. A.: A review of operational ensemble forecasting efforts in the United States Air Force, Atmosphere, 12, 677, https://doi.org/10.3390/atmos12060677, 2021. a
Letcher, T. W. and LeGrand, S. L.: A comparison of simulated dust produced by three dust-emission schemes in WRF-Chem: Case study assessment, ERDC/CRREL TR-18-13, U.S. Army Engineer Research and Development Center, Hanover, New Hampshire, USA, https://doi.org/10.21079/11681/28868, 2018. a
Letcher, T. W., LeGrand, S. L., and Michaels, M. L.: WRF-Chem-v4.1-AFWA-Drag-Partition-Modifications: Full model code release (bug-fix release) (v1.1.1), Zenodo [code], https://doi.org/10.5281/zenodo.7447886, 2022. a, b
Li, J., Okin, G. S., Herrick, J. E., Belnap, J., Miller, M. E., Vest, K., and Draut, A. E.: Evaluation of a new model of aeolian transport in the presence of vegetation, J. Geophys. Res.-Earth, 118, 288–306, https://doi.org/10.1002/jgrf.20040, 2013. a
Ma, S., Zhang, X., Gao, C., Tong, D. Q., Xiu, A., Wu, G., Cao, X., Huang, L., Zhao, H., Zhang, S., Ibarra-Espinosa, S., Wang, X., Li, X., and Dan, M.: Multimodel simulations of a springtime dust storm over northeastern China: implications of an evaluation of four commonly used air quality models (CMAQ v5.2.1, CAMx v6.50, CHIMERE v2017r4, and WRF-Chem v3.9.1), Geosci. Model Dev., 12, 4603–4625, https://doi.org/10.5194/gmd-12-4603-2019, 2019. a
Marshall, J. K.: Drag measurements in roughness arrays of varying density and distribution, Agric. Meteorol., 8, 269–292, https://doi.org/10.1016/0002-1571(71)90116-6, 1971. a, b, c
Marticorena, B. and Bergametti, G.: Modeling the atmospheric dust cycle: 1. Design of a soil-derived dust emission scheme, J. Geophys. Res., 100, 16415, https://doi.org/10.1029/95JD00690, 1995. a, b, c
Mayaud, J. and Webb, N.: Vegetation in drylands: Effects on wind flow and aeolian sediment transport, Land, 6, 64, https://doi.org/10.3390/land6030064, 2017. a
Mesbahzadeh, T., Salajeghe, A., Sardoo, F. S., Zehtabian, G., Ranjbar, A., Marcello Miglietta, M., Karami, S., and Krakauer, N. Y.: Spatial-temporal variation characteristics of vertical dust flux simulated by WRF-Chem model with GOCART and AFWA dust emission schemes (Case study: Central Plateau of Iran), Appl. Sci., 10, 4536, https://doi.org/10.3390/app10134536, 2020. a
Michaels, M. L., Letcher, T. W., LeGrand, S. L., Webb, N. P., and Putnam, J. B.: Implementation of an albedo-based drag partition into the WRF-Chem v4.1 AFWA dust emission module, ERDC/CRREL TR-22-2, U.S. Army Engineer Research and Development Center, Hanover, New Hampshire, USA, https://doi.org/10.21079/11681/42782, 2022. a, b, c
Miller, P. W., Williams, M., and Mote, T.: Modeled atmospheric optical and thermodynamic responses to an exceptional Trans‐Atlantic dust outbreak, J. Geophys. Res.-Atmos., 126, e2020JD032909, https://doi.org/10.1029/2020JD032909, 2021. a
Mohebbi, A., Green, G. T., Akbariyeh, S., Yu, F., Russo, B. J., and Smaglik, E. J.: Development of dust storm modeling for use in freeway safety and operations management: An Arizona case study, Transp. Res. Rec. J. Transp. Res. Board, 2673, 175–187, https://doi.org/10.1177/0361198119839978, 2019. a
Mohebbi, A., Yu, F., Cai, S., Akbariyeh, S., and Smaglik, E. J.: Spatial study of particulate matter distribution, based on climatic indicators during major dust storms in the State of Arizona, Front. Earth Sci., 15, 133–150, https://doi.org/10.1007/s11707-020-0814-4, 2020. a
Nabavi, S. O., Haimberger, L., and Samimi, C.: Sensitivity of WRF-chem predictions to dust source function specification in West Asia, Aeolian Res., 24, 115–131, https://doi.org/10.1016/j.aeolia.2016.12.005, 2017. a
Nakanishi, M. and Niino, H.: An improved Mellor–Yamada Level-3 Model with condensation physics: Its design and verification, Boundary Layer Meteorol., 112, 1–31, https://doi.org/10.1023/B:BOUN.0000020164.04146.98, 2004. a, b
National Center for Atmospheric Research (NCAR): WRF Version 4.1, Github [code],
https://github.com/wrf-model/WRF/releases/tag/v4.1 (last access: 1 June 2020), 2019. a
Nguyen, H. D., Riley, M., Leys, J., and Salter, D.: Dust storm event of February 2019 in Central and East Coast of Australia and evidence of long-range transport to New Zealand and Antarctica, Atmosphere, 10, 653, https://doi.org/10.3390/atmos10110653, 2019. a
Nikfal, A., Raadatabadi, A., and Sehatkashani, S.: Investigation of dust schemes in the model WRF/Chem, J. Air Pollut. Health, 3, 1–8, 2018. a
NOAA National Centers for Environmental Information: Global Surface Hourly [ASOS Hourly Data: 2014] [data set], NOAA National Centers for Environmental Information, https://www.ncei.noaa.gov/data/global-hourly/archive/csv/2014.tar.gz (last access: 2 February 2023), 2001.
NOAA National Centers for Environmental Information: Rapid Refresh/Rapid Update Cycle, https://www.ncei.noaa.gov/products/weather-climate-models/rapid-refresh-update, last access: 1 February 2023a.
NOAA National Centers for Environmental Information: North American Mesoscale Forecast System, https://www.ncei.noaa.gov/products/weather-climate-models/north-american-mesoscale, last access: 1 February 2023b.
NOAA National Weather Service U.S. Federal Aviation Administration, U.S. Department of Defense, and NOAA National Centers for Environmental Information: 1-Minute Page 1 Surface Weather Observations from the Automated Surface Observing Systems (ASOS) Network, [July 2014] [data set], NOAA National Centers for Environmental Information, NCEI DSI 6405_02, https://www.ncei.noaa.gov/data/automated-surface-observing-system-one-minute-pg1/access/2014/07/ (last access: 12 November 2021), 2005.
Okin, G. S.: Dependence of wind erosion and dust emission on surface heterogeneity: Stochastic modeling, J. Geophys. Res., 110, D11208, https://doi.org/10.1029/2004JD005288, 2005. a, b
Okin, G. S.: A new model of wind erosion in the presence of vegetation, J. Geophys. Res., 113, F02S10, https://doi.org/10.1029/2007JF000758, 2008. a, b
Okin, G. S.: The contribution of brown vegetation to vegetation dynamics, 91, 743–755, https://doi.org/10.1890/09-0302.1, 2010. a
Parajuli, S. P. and Zender, C. S.: Projected changes in dust emissions and regional air quality due to the shrinking Salton Sea, Aeolian Res., 33, 82–92, https://doi.org/10.1016/j.aeolia.2018.05.004, 2018. a, b
Parajuli, S. P., Stenchikov, G. L., Ukhov, A., and Kim, H.: Dust emission modeling using a new high‐resolution dust source function in WRF‐Chem with implications for air quality, J. Geophys. Res.-Atmos., 124, 10109–10133, https://doi.org/10.1029/2019JD030248, 2019. a
Parajuli, S. P., Stenchikov, G. L., Ukhov, A., Shevchenko, I., Dubovik, O., and Lopatin, A.: Aerosol vertical distribution and interactions with land/sea breezes over the eastern coast of the Red Sea from lidar data and high-resolution WRF-Chem simulations, Atmos. Chem. Phys., 20, 16089–16116, https://doi.org/10.5194/acp-20-16089-2020, 2020. a
Peckham, S. E., Grell, G., McKeen, S. A., Ahmadov, R., Wong, K. Y., Barth, M., Pfister, G., Wiedinmyer, C., Fast, J. D., Gustafson, W. I., Ghan, S. J., Zaveri, R., Easter, R. C., Barnard, J., Chapman, E., Hewson, M., Schmitz, R., Salzmann, M., Beck, V., and Freitas, S. R.: WRF-Chem version 3.8.1 user’s guide, NOAA Technical Memorandum OAR GSD-48, 83 pp., https://doi.org/10.7289/V5/TM-OAR-GSD-48, 2017. a
Péré, J.-C., Rivellini, L., Crumeyrolle, S., Chiapello, I., Minvielle, F., Thieuleux, F., Choël, M., and Popovici, I.: Simulation of African dust properties and radiative effects during the 2015 SHADOW campaign in Senegal, Atmos. Res., 199, 14–28, https://doi.org/10.1016/j.atmosres.2017.07.027, 2018. a
Pierre, C., Bergametti, G., Marticorena, B., Kergoat, L., Mougin, E., and Hiernaux, P.: Comparing drag partition schemes over a herbaceous Sahelian rangeland: Drag partitions comparison in Sahel, J. Geophys. Res.-Earth, 119, 2291–2313, https://doi.org/10.1002/2014JF003177, 2014. a
Raupach, M. and Lu, H.: Representation of land-surface processes in aeolian transport models, Environ. Modell. Softw., 19, 93–112, https://doi.org/10.1016/S1364-8152(03)00113-0, 2004. a
Raupach, M. R.: Drag and drag partition on rough surfaces, Bound.-Lay. Meteorol., 60, 375–395, https://doi.org/10.1007/BF00155203, 1992. a
Raupach, M. R., Gillette, D. A., and Leys, J. F.: The effect of roughness elements on wind erosion threshold, J. Geophys. Res.-Atmos., 98, 3023–3029, https://doi.org/10.1029/92JD01922, 1993. a, b, c
Rizza, U., Anabor, V., Mangia, C., Miglietta, M. M., Degrazia, G. A., and Passerini, G.: WRF-Chem simulation of a Saharan dust outbreakover the Mediterranean regions, Ciência e Natura, 38, 330–336, https://doi.org/10.5902/2179460X20249, 2016. a
Rizza, U., Kandler, K., Eknayan, M., Passerini, G., Mancinelli, E., Virgili, S., Morichetti, M., Nolle, M., Eleftheriadis, K., Vasilatou, V., and Ielpo, P.: Investigation of an intense dust outbreak in the Mediterranean using XMed-Dry Network, multiplatform observations, and numerical modeling, Appl. Sci., 11, 1566, https://doi.org/10.3390/app11041566, 2021. a
Saidou Chaibou, A. A., Ma, X., Kumar, K. R., Jia, H., Tang, Y., and Sha, T.: Evaluation of dust extinction and vertical profiles simulated by WRF-Chem with CALIPSO and AERONET over North Africa, J. Atmos. Sol.-Terr. Phy., 199, 105213, https://doi.org/10.1016/j.jastp.2020.105213, 2020. a
Saleeby, S. M., van den Heever, S. C., Bukowski, J., Walker, A. L., Solbrig, J. E., Atwood, S. A., Bian, Q., Kreidenweis, S. M., Wang, Y., Wang, J., and Miller, S. D.: The influence of simulated surface dust lofting and atmospheric loading on radiative forcing, Atmos. Chem. Phys., 19, 10279–10301, https://doi.org/10.5194/acp-19-10279-2019, 2019. a
Schaaf, C. and Wang, Z.: MODIS/Terra+Aqua BRDF/Albedo Model Parameters Daily L3 Global – 500 m V061, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD43A1.061, 2021a. a, b
Schaaf, C. and Wang, Z.: MODIS/Terra+Aqua BRDF/Albedo Daily L3 Global – 500 m V061, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD43A3.061, 2021b. a, b
Shao, Y., Nickling, W., Bergametti, G., Butler, H., Chappell, A., Findlater, P., Gillies, J., Ishizuka, M., Klose, M., Kok, J. F., Leys, J., Lu, H., Marticorena, B., McTainsh, G., McKenna-Neuman, C., Okin, G. S., Strong, C., and Webb, N.: A tribute to Michael R. Raupach for contributions to aeolian fluid dynamics, Aeolian Res., 19, 37–54, https://doi.org/10.1016/j.aeolia.2015.09.004, 2015. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A description of the Advanced Research WRF Model version 4, NCAR Technical Notes; NCAR/TN-556+STR, UCAR/NCAR, https://doi.org/10.5065/1DFH-6P97, 2019. a, b
Solomos, S., Kalivitis, N., Mihalopoulos, N., Amiridis, V., Kouvarakis, G., Gkikas, A., Binietoglou, I., Tsekeri, A., Kazadzis, S., Kottas, M., Pradhan, Y., Proestakis, E., Nastos, P., and Marenco, F.: From tropospheric folding to Khamsin and Foehn winds: How atmospheric dynamics advanced a record-breaking dust episode in Crete, Atmosphere, 9, 240, https://doi.org/10.3390/atmos9070240, 2018. a
Solomos, S., Abuelgasim, A., Spyrou, C., Binietoglou, I., and Nickovic, S.: Development of a dynamic dust source map for NMME-DREAM v1.0 model based on MODIS Normalized Difference Vegetation Index (NDVI) over the Arabian Peninsula, Geosci. Model Dev., 12, 979–988, https://doi.org/10.5194/gmd-12-979-2019, 2019. a
Spyrou, C., Solomos, S., Bartsotas, N. S., Douvis, K. C., and Nickovic, S.: Development of a dust source map for WRF-Chem model based on MODIS NDVI, Atmosphere, 13, 868, https://doi.org/10.3390/atmos13060868, 2022. a, b
Stull, R. B. (Ed.): An Introduction to Boundary Layer Meteorology, Springer Netherlands, Dordrecht, https://doi.org/10.1007/978-94-009-3027-8, 1988. a
Su, L. and Fung, J. C. H.: Sensitivities of WRF-Chem to dust emission schemes and land surface properties in simulating dust cycles during springtime over East Asia, J. Geophys. Res.-Atmos., 120, 11215–11230, https://doi.org/10.1002/2015JD023446, 2015. a
Tegen, I., Harrison, S. P., Kohfeld, K., Prentice, I. C., Coe, M., and Heimann, M.: Impact of vegetation and preferential source areas on global dust aerosol: Results from a model study, J. Geophys. Res.-Atmos., 107, 4576, https://doi.org/10.1029/2001JD000963, 2002. a, b, c, d
Teixeira, J. C., Carvalho, A. C., Tuccella, P., Curci, G., and Rocha, A.: WRF-chem sensitivity to vertical resolution during a saharan dust event, Phys. Chem. Earth Parts ABC, 94, 188–195, https://doi.org/10.1016/j.pce.2015.04.002, 2016. a
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1, 2008. a
Tong, D., Lee, P., Tang, Y., Baker, B., Campbell, P. C., Saylor, R., Chai, T., Lamsal, L. N., Krotkov, N. A., Li, C., and Kondragunta, S.: Advancing National Air Quality Forecasts through Emission Data Assimilation, 100th American Meteorological Society Annual Meeting, Boston, Massachusetts, USA, 15 January 2020, AMS, https://ams.confex.com/ams/2020Annual/meetingapp.cgi/Paper/366256 (last access: 1 March 2022), 2020. a
Tsarpalis, K., Papadopoulos, A., Mihalopoulos, N., Spyrou, C., Michaelides, S., and Katsafados, P.: The implementation of a mineral dust wet deposition scheme in the GOCART-AFWA module of the WRF model, Remote Sens., 10, 1595, https://doi.org/10.3390/rs10101595, 2018. a
Tsarpalis, K., Katsafados, P., Papadopoulos, A., and Mihalopoulos, N.: Assessing desert dust indirect effects on cloud microphysics through a cloud nucleation scheme: A case study over the Western Mediterranean, Remote Sens., 12, 3473, https://doi.org/10.3390/rs12213473, 2020. a
US Environmental Protection Agency: Pre-Generated Data File: Particulates, PM10 Mass (81102), 2014 [data set] https://aqs.epa.gov/aqsweb/airdata/hourly_81102_2014.zip (last access: 30 January 2023), 2022.
Uzan, L., Egert, S., and Alpert, P.: Ceilometer evaluation of the eastern Mediterranean summer boundary layer height – first study of two Israeli sites, Atmos. Meas. Tech., 9, 4387–4398, https://doi.org/10.5194/amt-9-4387-2016, 2016. a
Vukovic, A., Vujadinovic, M., Pejanovic, G., Andric, J., Kumjian, M. R., Djurdjevic, V., Dacic, M., Prasad, A. K., El-Askary, H. M., Paris, B. C., Petkovic, S., Nickovic, S., and Sprigg, W. A.: Numerical simulation of “an American haboob”, Atmos. Chem. Phys., 14, 3211–3230, https://doi.org/10.5194/acp-14-3211-2014, 2014. a, b, c
Walker, A. L., Liu, M., Miller, S. D., Richardson, K. A., and Westphal, D. L.: Development of a dust source database for mesoscale forecasting in southwest Asia, J. Geophys. Res., 114, D18207, https://doi.org/10.1029/2008JD011541, 2009. a
Walter, B., Gromke, C., and Lehning, M.: Shear-stress partitioning in live plant canopies and modifications to Raupach’s model, Bound.-Lay. Meteorol., 144, 217–241, https://doi.org/10.1007/s10546-012-9719-4, 2012. a
Webb, N. P. and Pierre, C.: Quantifying anthropogenic dust emissions, Earth's Future, 6, 286–295, https://doi.org/10.1002/2017EF000766, 2018. a
Webb, N. P., Herrick, J. E., and Duniway, M. C.: Ecological site-based assessments of wind and water erosion: informing accelerated soil erosion management in rangelands, Ecol. Appl, 24, 1405–1420, https://doi.org/10.1890/13-1175.1, 2014a. a
Webb, N. P., Okin, G. S., and Brown, S.: The effect of roughness elements on wind erosion: The importance of surface shear stress distribution, J. Geophys. Res.-Atmos., 119, 6066–6084, https://doi.org/10.1002/2014JD021491, 2014b. a, b
Webb, N. P., Chappell, A., LeGrand, S. L., Ziegler, N. P., and Edwards, B. L.: A note on the use of drag partition in aeolian transport models, Aeolian Res., 42, 100560, https://doi.org/10.1016/j.aeolia.2019.100560, 2020. a, b
Woodward, S.: Modeling the atmospheric life cycle and radiative impact of mineral dust in the Hadley Centre climate model, J. Geophys. Res.-Atmos., 106, 18155–18166, https://doi.org/10.1029/2000JD900795, 2001. a
World Meteorological Organization (WMO): Manual on Codes Volume I.1 Annex II to the WMO Technical Regulations Part A – Alphanumeric Codes, (WMO No.306, Part I.1 Part A), 2011 edition, updated in 2019, 480 pp., ISBN 978-92-63-10306-2, Geneva, Switzerland, https://library.wmo.int/doc_num.php?explnum_id=10235 (last access: 25 January 2021), 2019. a
Xu, X., Wang, J., Wang, Y., Henze, D. K., Zhang, L., Grell, G. A., McKeen, S. A., and Wielicki, B. A.: Sense size-dependent dust loading and emission from space using reflected solar and infrared spectral measurements: An observation system simulation experiment, J. Geophys. Res.-Atmos., 122, 8233–8254, https://doi.org/10.1002/2017JD026677, 2017. a
Yu, M. and Yang, C.: Improving the non-hydrostatic numerical dust model by integrating soil moisture and greenness vegetation fraction data with different spatiotemporal resolutions, PLOS One, 11, e0165616, https://doi.org/10.1371/journal.pone.0165616, 2016. a
Yu, M., Wu, B., Yan, N., Xing, Q., and Zhu, W.: A method for estimating the aerodynamic roughness length with NDVI and BRDF signatures using multi-temporal Proba-V data, Remote Sens., 9, 6, https://doi.org/10.3390/rs9010006, 2016. a
Yuan, T., Chen, S., Huang, J., Zhang, X., Luo, Y., Ma, X., and Zhang, G.: Sensitivity of simulating a dust storm over Central Asia to different dust schemes using the WRF-Chem model, Atmos. Environ., 207, 16–29, https://doi.org/10.1016/j.atmosenv.2019.03.014, 2019.
a
Zhang, L., Grell, G. A., McKeen, S. A., Ahmadov, R., Froyd, K. D., and Murphy, D.: Inline coupling of simple and complex chemistry modules within the global weather forecast model FIM (FIM-Chem v1), Geosci. Model Dev., 15, 467–491, https://doi.org/10.5194/gmd-15-467-2022, 2022. a
Zhao, J., Ma, X., Wu, S., and Sha, T.: Dust emission and transport in Northwest China: WRF-Chem simulation and comparisons with multi-sensor observations, Atmos. Res., 241, 104978, https://doi.org/10.1016/j.atmosres.2020.104978, 2020. a
Zhou, M., Zhang, L., Chen, D., Gu, Y., Fu, T.-M., Gao, M., Zhao, Y., Lu, X., and Zhao, B.: The impact of aerosol–radiation interactions on the effectiveness of emission control measures, Environ. Res. Lett., 14, 024002, https://doi.org/10.1088/1748-9326/aaf27d, 2019. a
Ziegler, N. P., Webb, N. P., Chappell, A., and LeGrand, S. L.: Scale invariance of albedo‐based wind friction velocity, J. Geophys. Res.-Atmos., 125, e2019JD031978, https://doi.org/10.1029/2019JD031978, 2020. a, b, c
Zobeck, T. M., Sterk, G., Funk, R., Rajot, J. L., Stout, J. E., and Van Pelt, R. S.: Measurement and data analysis methods for field-scale wind erosion studies and model validation, Earth Surf. Proc. Land., 28, 1163–1188, https://doi.org/10.1002/esp.1033, 2003. a
Short summary
Ground cover affects dust emissions by reducing wind flow over the immediate soil surface. This study reviews a method for estimating ground cover effects on wind erosion from satellite-detected terrain shadows. We conducted a case study for a US dust event using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Adding the shadow-based method for ground cover effects markedly improved simulated results and may lead to better dust modeling outcomes in vegetated drylands.
Ground cover affects dust emissions by reducing wind flow over the immediate soil surface. This...
