The impact of using assimilated Aeolus wind data on regional WRF-Chem dust simulations

Abstract. Land–atmosphere interactions govern the process of dust emission and transport. An accurate depiction of these physical processes within numerical weather prediction models allows for better estimating the spatial and temporal distribution of the dust burden and the characterisation of source and recipient areas. In the presented study, the ECMWF-IFS (European Centre for Medium-Range Weather Forecast – Integrated Forecasting System) outputs, produced with and without the assimilation of Aeolus quality-assured Rayleigh–clear and Mie–cloudy horizontal line-of-sight wind profiles, are used as initial or boundary conditions in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to simulate 2-month periods in the spring and autumn of 2020, focusing on a case study in October. The experiments have been performed over the broader eastern Mediterranean and Middle East (EMME) region, which is frequently subjected to dust transport, as it encompasses some of the most active erodible dust sources. Aerosol- and dust-related model outputs (extinction coefficient, optical depth and concentrations) are qualitatively and quantitatively evaluated against ground- and satellite-based observations. Ground-based columnar and vertically resolved aerosol optical properties are acquired through AERONET sun photometers and PollyXT lidar, while near-surface concentrations are taken from EMEP. Satellite-derived vertical dust and columnar aerosol optical properties are acquired through LIVAS (LIdar climatology of Vertical Aerosol Structure) and MIDAS (ModIs Dust AeroSol), respectively. Overall, in cases of either high or low aerosol loadings, the model predictive skill is improved when WRF-Chem simulations are initialised with the meteorological fields of Aeolus wind profiles assimilated by the IFS. The improvement varies in space and time, with the most significant impact observed during the autumn months in the study region. Comparison with observation datasets saw a remarkable improvement in columnar aerosol optical depths, vertically resolved dust mass concentrations and near-surface particulate concentrations in the assimilated run against the control run. Reductions in model biases, either positive or negative, and an increase in the correlation between simulated and observed values was achieved for October 2020.

[1]  L. Isaksen,et al.  The impact of Aeolus wind retrievals on ECMWF global weather forecasts , 2021, Quarterly Journal of the Royal Meteorological Society.

[2]  V. Amiridis,et al.  Quantification of the dust optical depth across spatiotemporal scales with the MIDAS global dataset (2003–2017) , 2021, Atmospheric Chemistry and Physics.

[3]  A. Kazantzidis,et al.  15-year variability of desert dust optical depth on global and regional scales , 2021, Atmospheric Chemistry and Physics.

[4]  J. Lelieveld,et al.  Winter AOD trend changes over the Eastern Mediterranean and Middle East region , 2021, International Journal of Climatology.

[5]  V. Amiridis,et al.  ModIs Dust AeroSol (MIDAS): a global fine-resolution dust optical depth data set , 2021 .

[6]  Xiaoyan Ma,et al.  Dust emission and transport in Northwest China: WRF-Chem simulation and comparisons with multi-sensor observations , 2020 .

[7]  R. Engelmann,et al.  Validation of Aeolus wind products above the Atlantic Ocean , 2020, Atmospheric Measurement Techniques.

[8]  O. Reitebuch,et al.  Intercomparison of wind observations from the European Space Agency's Aeolus satellite mission and the ALADIN Airborne Demonstrator , 2020 .

[9]  Kanike Raghavendra Kumar,et al.  Evaluation of dust extinction and vertical profiles simulated by WRF-Chem with CALIPSO and AERONET over North Africa , 2020 .

[10]  G. Stenchikov,et al.  Assessment of natural and anthropogenic aerosol air pollution in the Middle East using MERRA-2, CAMS data assimilation products, and high-resolution WRF-Chem model simulations , 2020, Atmospheric Chemistry and Physics.

[11]  Alexander Smirnov,et al.  The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2 , 2020, Atmospheric Measurement Techniques.

[12]  O. Reitebuch,et al.  First validation of Aeolus wind observations by airborne Doppler wind lidar measurements , 2020, Atmospheric Measurement Techniques.

[13]  Ahmed Amine Hachicha,et al.  Impact of dust on the performance of solar photovoltaic (PV) systems under United Arab Emirates weather conditions , 2019, Renewable Energy.

[14]  J. Roering,et al.  The potential influence of dust flux and chemical weathering on hillslope morphology: Convex soil-mantled carbonate hillslopes in the Eastern Mediterranean , 2019, Geomorphology.

[15]  A. Ansmann,et al.  Dust mass, cloud condensation nuclei, and ice-nucleating particle profiling with polarization lidar: updated POLIPHON conversion factors from global AERONET analysis , 2019, Atmospheric Measurement Techniques.

[16]  Albert Ansmann,et al.  Retrieval of ice-nucleating particle concentrations from lidar observations and comparison with UAV in situ measurements , 2019, Atmospheric Chemistry and Physics.

[17]  Z. Zeng,et al.  Constraining the vertical distribution of coastal dust aerosol using OCO-2 O2 A-band measurements , 2019, Remote Sensing of Environment.

[18]  Guolong Zhang,et al.  Sensitivity of simulating a dust storm over Central Asia to different dust schemes using the WRF-Chem model , 2019, Atmospheric Environment.

[19]  Jasper R. Lewis,et al.  Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements , 2019, Atmospheric Measurement Techniques.

[20]  A. Chédin,et al.  Detection of IASI dust AOD trends over Sahara: How many years of data required? , 2018, Atmospheric Research.

[21]  Silas C. Michaelides,et al.  The Implementation of a Mineral Dust Wet Deposition Scheme in the GOCART-AFWA Module of the WRF Model , 2018, Remote. Sens..

[22]  G. Passerini,et al.  Sensitivity of WRF-Chem model to land surface schemes: assessment in a severe dust outbreak episode in the Central Mediterranean (Apulia Region). , 2018 .

[23]  Sara Basart,et al.  Direct radiative effects during intense Mediterranean desert dust outbreaks , 2017, Atmospheric Chemistry and Physics.

[24]  George K. Georgiou,et al.  Air quality modelling in the summer over the eastern Mediterranean using WRF-Chem: chemistry and aerosol mechanism intercomparison , 2017, Atmospheric Chemistry and Physics.

[25]  Eleni Marinou,et al.  Nine-year spatial and temporal evolution of desert dust aerosols over South and East Asia as revealed by CALIOP , 2017 .

[26]  Albert Ansmann,et al.  Three-dimensional evolution of Saharan dust transport towards Europe based on a 9-year EARLINET-optimized CALIPSO dataset , 2017 .

[27]  A. Ansmann,et al.  Potential of polarization/Raman lidar to separate fine dust, coarse dust, maritime, and anthropogenic aerosol profiles , 2017 .

[28]  Dominik Brunner,et al.  The Lagrangian particle dispersion model FLEXPART version 10.4 , 2017, Geoscientific Model Development.

[29]  L. Haimberger,et al.  Sensitivity of WRF-chem predictions to dust source function specification in West Asia , 2017 .

[30]  Konstantinos Lagouvardos,et al.  Sensitivity of the WRF-Chem (V3.6.1) model to different dust emission parametrisation: Assessment in the broader Mediterranean region , 2017 .

[31]  N. Middleton,et al.  Desert dust hazards: A global review , 2017 .

[32]  Manu Mehta,et al.  Recent global aerosol optical depth variations and trends — A comparative study using MODIS and MISR level 3 datasets , 2016 .

[33]  L. Haimberger,et al.  Climatology of dust distribution over West Asia from homogenized remote sensing data , 2016 .

[34]  Georgiy L. Stenchikov,et al.  Aerosol optical depth trend over the Middle East , 2016 .

[35]  Ulla Wandinger,et al.  EARLINET Single Calculus Chain - overview on methodology and strategy , 2015 .

[36]  Sara Basart,et al.  Mediterranean intense desert dust outbreaks and their verticalstructure based on remote sensing data , 2015 .

[37]  Mark A. Cane,et al.  Climate change in the Fertile Crescent and implications of the recent Syrian drought , 2015, Proceedings of the National Academy of Sciences.

[38]  N. Mahowald,et al.  An improved dust emission model – Part 2: Evaluation in the Community Earth System Model, with implications for the use of dust source functions , 2014 .

[39]  A. Pozzer,et al.  AOD trends during 2001–2010 from observations and model simulations , 2014 .

[40]  V. Freudenthaler,et al.  EARLINET: towards an advanced sustainable European aerosol lidar network , 2014 .

[41]  F. Châtel The Role of Drought and Climate Change in the Syrian Uprising: Untangling the Triggers of the Revolution , 2014 .

[42]  A. Ansmann,et al.  Optimizing CALIPSO Saharan dust retrievals , 2013 .

[43]  Xavier Querol,et al.  The regime of intense desert dust episodes in the Mediterranean based on contemporary satellite observations and ground measurements , 2013 .

[44]  L. Remer,et al.  The Collection 6 MODIS aerosol products over land and ocean , 2013 .

[45]  Gerhard Wotawa,et al.  The Lagrangian particle dispersion model FLEXPART-WRF version 3.1 , 2013 .

[46]  Guy P. Brasseur,et al.  WRF-Chem simulations of a typical pre-monsoon dust storm in northern India: influences on aerosol optical properties and radiation budget , 2013 .

[47]  K. Schepanski,et al.  The role of deep convection and nocturnal low-level jets for dust emission in summertime West Africa: Estimates from convection-permitting simulations , 2013, Journal of geophysical research. Atmospheres : JGR.

[48]  C. Willmott,et al.  A refined index of model performance , 2012 .

[49]  L. Emmons,et al.  The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions , 2012 .

[50]  Kerstin Schepanski,et al.  Comparing two years of Saharan dust source activation obtained by regional modelling and satellite observations , 2012 .

[51]  M. Todd,et al.  Model Simulations of Complex Dust Emissions over the Sahara during the West African Monsoon Onset , 2012 .

[52]  X. Querol,et al.  African dust source regions for observed dust outbreaks over the Subtropical Eastern North Atlantic region, above 25°N , 2012 .

[53]  Sara Basart,et al.  Atmospheric dust modeling from meso to global scales with the online NMMB/BSC-Dust model – Part 2: Experimental campaigns in Northern Africa , 2011, Atmospheric Chemistry and Physics.

[54]  Michael Schulz,et al.  Global dust model intercomparison in AeroCom phase I , 2011 .

[55]  Sally A. McFarlane,et al.  The spatial distribution of mineral dust and its shortwave radiative forcing over North Africa: modeling sensitivities to dust emissions and aerosol size treatments , 2010 .

[56]  J. Barnard,et al.  Technical Note: Evaluation of the WRF-Chem "aerosol chemical to aerosol optical properties" module using data from the MILAGRO campaign , 2010 .

[57]  J. Baldasano,et al.  Aerosol characterization in Northern Africa, Northeastern Atlantic, Mediterranean Basin and Middle East from direct-sun AERONET observations , 2009 .

[58]  M. Sivakumar,et al.  Impacts of sand and dust storms on agriculture and potential agricultural applications of a SDSWS , 2009 .

[59]  Victoria E. Cachorro,et al.  Aerosol optical depth and Ångström exponent climatology at El Arenosillo AERONET site (Huelva, Spain) , 2007 .

[60]  D. Hatzidimitriou,et al.  Aerosol physical and optical properties in the Eastern Mediterranean Basin, Crete, from Aerosol Robotic Network data , 2006 .

[61]  Richard Washington,et al.  North African dust emissions and transport , 2006 .

[62]  Andreas H. Fink,et al.  Synoptic and dynamic aspects of an extreme springtime Saharan dust outbreak , 2006 .

[63]  Georg A. Grell,et al.  Fully coupled “online” chemistry within the WRF model , 2005 .

[64]  A. Stohl,et al.  Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2 , 2005 .

[65]  Christos Zerefos,et al.  Measurements of Saharan dust aerosols over the Eastern Mediterranean using elastic backscatter-Raman lidar, spectrophotometric and satellite observations in the frame of the EARLINET project , 2005 .

[66]  B. Katsoulis,et al.  Relation between sensible and latent heat fluxes in the Mediterranean and precipitation in the Greek area during winter , 2004 .

[67]  R. Trigo,et al.  Climate impact of the European winter blocking episodes from the NCEP/NCAR Reanalyses , 2004 .

[68]  Pinhas Alpert,et al.  Vertical distribution of Saharan dust based on 2.5-year model predictions , 2004 .

[69]  J. Lelieveld,et al.  Global Air Pollution Crossroads over the Mediterranean , 2002, Science.

[70]  C. Anagnostopoulou,et al.  A 40‐year climatological study of relative vorticity distribution over the Mediterranean , 2001 .

[71]  Nick Middleton,et al.  Saharan dust: sources and trajectories , 2001 .

[72]  J. Prospero Long-range transport of mineral dust in the global atmosphere: impact of African dust on the environment of the southeastern United States. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[73]  Anders Ångström,et al.  On the Atmospheric Transmission of Sun Radiation and on Dust in the Air , 1929 .

[74]  M. Shinoda,et al.  Regional Characteristics of Recent Dust Occurrence and Its Controlling Factors in East Asia , 2016 .

[75]  Josef Gasteiger,et al.  On the visibility of airborne volcanic ash and mineral dust from the pilot’s perspective in flight , 2012 .

[76]  H. Barker,et al.  Accounting for subgrid‐scale cloud variability in a multi‐layer 1d solar radiative transfer algorithm , 1999 .