68 citations as recorded by crossref.

1. Identifying chemical aerosol signatures using optical suborbital observations: how much can optical properties tell us about aerosol composition? M. Kacenelenbogen et al. https://doi.org/10.5194/acp-22-3713-2022
2. Profiling of Saharan dust from the Caribbean to western Africa – Part 2: Shipborne lidar measurements versus forecasts A. Ansmann et al. https://doi.org/10.5194/acp-17-14987-2017
3. Assessment of severe aerosol events from NASA MODIS and VIIRS aerosol products for data assimilation and climate continuity A. Gumber et al. https://doi.org/10.5194/amt-16-2547-2023
4. Status and future of numerical atmospheric aerosol prediction with a focus on data requirements A. Benedetti et al. https://doi.org/10.5194/acp-18-10615-2018
5. Multi-sectoral impact assessment of an extreme African dust episode in the Eastern Mediterranean in March 2018 A. Monteiro et al. https://doi.org/10.1016/j.scitotenv.2022.156861
6. JEDI‐Based Three‐Dimensional Ensemble‐Variational Data Assimilation System for Global Aerosol Forecasting at NCEP B. Huang et al. https://doi.org/10.1029/2022MS003232
7. Challenges and Opportunities in Atmospheric Dust Emission, Chemistry, and Transport D. Richter & T. Gill https://doi.org/10.1175/BAMS-D-18-0007.1
8. Development and evaluation of the Aerosol Forecast Member in the National Center for Environment Prediction (NCEP)'s Global Ensemble Forecast System (GEFS-Aerosols v1) L. Zhang et al. https://doi.org/10.5194/gmd-15-5337-2022
9. A multi-model evaluation of aerosols over South Asia: common problems and possible causes X. Pan et al. https://doi.org/10.5194/acp-15-5903-2015
10. An Evaluation of the CHIMERE Chemistry Transport Model to Simulate Dust Outbreaks across the Northern Hemisphere in March 2014 B. Bessagnet et al. https://doi.org/10.3390/atmos8120251
11. WRF prediction of two winter season Saharan dust events using PM10 concentrations: Boundary versus initial conditions G. Jenkins & A. Diokhane https://doi.org/10.1016/j.atmosenv.2017.08.010
12. Development of an Ozone Monitoring Instrument (OMI) aerosol index (AI) data assimilation scheme for aerosol modeling over bright surfaces – a step toward direct radiance assimilation in the UV spectrum J. Zhang et al. https://doi.org/10.5194/gmd-14-27-2021
13. Absorbing aerosols over Asia – an inter-model and model-observation comparison study using CAM5.3-Oslo L. Frey et al. https://doi.org/10.1080/16000889.2021.1909815
14. Factors That Modulate Properties of Primary Marine Aerosol Generated From Ambient Seawater on Ships at Sea W. Keene et al. https://doi.org/10.1002/2017JD026872
15. Assessment of NAAPS-RA performance in Maritime Southeast Asia during CAMP2Ex E. Edwards et al. https://doi.org/10.5194/acp-22-12961-2022
16. Development and Evaluation of a North America Ensemble Wildfire Air Quality Forecast: Initial Application to the 2020 Western United States “Gigafire” P. Makkaroon et al. https://doi.org/10.1029/2022JD037298
17. Intercomparison of aerosol optical depths from four reanalyses and their multi-reanalysis consensus P. Xian et al. https://doi.org/10.5194/acp-24-6385-2024
18. Using GEOS-5 forecast products to represent aerosol optical depth in operational day-ahead solar irradiance forecasts for the southwest United States P. Bunn et al. https://doi.org/10.1063/5.0020785
19. Exploring the first aerosol indirect effect over Southeast Asia using a 10-year collocated MODIS, CALIOP, and model dataset A. Ross et al. https://doi.org/10.5194/acp-18-12747-2018
20. Sensitivity of the WRF-Chem (V3.6.1) model to different dust emission parametrisation: assessment in the broader Mediterranean region E. Flaounas et al. https://doi.org/10.5194/gmd-10-2925-2017
21. Observation of Wildfire Smoke Particles That Accumulated Secondary Aerosol during Long-Range Transport J. Lee et al. https://doi.org/10.1021/acsearthspacechem.5c00172
22. Monitoring multiple satellite aerosol optical depth (AOD) products within the Copernicus Atmosphere Monitoring Service (CAMS) data assimilation system S. Garrigues et al. https://doi.org/10.5194/acp-22-14657-2022
23. Arctic spring and summertime aerosol optical depth baseline from long-term observations and model reanalyses – Part 2: Statistics of extreme AOD events, and implications for the impact of regional biomass burning processes P. Xian et al. https://doi.org/10.5194/acp-22-9949-2022
24. The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP – Part 1: Model descriptions J. Wang et al. https://doi.org/10.5194/gmd-11-2315-2018
25. Review of surface particulate monitoring of dust events using geostationary satellite remote sensing M. Sowden et al. https://doi.org/10.1016/j.atmosenv.2018.04.020
26. Use of the CALIOP vertical feature mask for evaluating global aerosol models E. Nowottnick et al. https://doi.org/10.5194/amt-8-3647-2015
27. Mineral dust cycle in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) Version 2.0 M. Klose et al. https://doi.org/10.5194/gmd-14-6403-2021
28. Revisiting the relationship between Atlantic dust and tropical cyclone activity using aerosol optical depth reanalyses: 2003–2018 P. Xian et al. https://doi.org/10.5194/acp-20-15357-2020
29. Particulate matter concentrations derived from airborne high spectral resolution lidar measurements using machine learning regression R. Ferrare et al. https://doi.org/10.5194/amt-18-7735-2025
30. A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions R. Sokhi et al. https://doi.org/10.1016/j.envint.2021.106818
31. Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting J. Rubin et al. https://doi.org/10.5194/acp-16-3927-2016
32. Simulation of long-term direct aerosol radiative forcing over the arctic within the framework of the iAREA project K. Markowicz et al. https://doi.org/10.1016/j.atmosenv.2020.117882
33. An 11-year global gridded aerosol optical thickness reanalysis (v1.0) for atmospheric and climate sciences P. Lynch et al. https://doi.org/10.5194/gmd-9-1489-2016
34. Description and evaluation of the tropospheric aerosol scheme in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS-AER, cycle 45R1) S. Rémy et al. https://doi.org/10.5194/gmd-12-4627-2019
35. Statistical Evaluation of the Temperature Forecast Error in the Lower‐Level Troposphere on Short‐Range Timescales Induced by Aerosol Variability A. Yamagami et al. https://doi.org/10.1029/2022JD036595
36. Simulations of the effect of intensive biomass burning in July 2015 on Arctic radiative budget K. Markowicz et al. https://doi.org/10.1016/j.atmosenv.2017.10.015
37. Development and basic evaluation of a prognostic aerosol scheme (v1) in the CNRM Climate Model CNRM-CM6 M. Michou et al. https://doi.org/10.5194/gmd-8-501-2015
38. Nighttime smoke aerosol optical depth over U.S. rural areas: First retrieval from VIIRS moonlight observations M. Zhou et al. https://doi.org/10.1016/j.rse.2021.112717
39. Ground‐based High Spectral Resolution Lidar observation of aerosol vertical distribution in the summertime Southeast United States J. Reid et al. https://doi.org/10.1002/2016JD025798
40. Global aerosol simulations using NICAM.16 on a 14 km grid spacing for a climate study: improved and remaining issues relative to a lower-resolution model D. Goto et al. https://doi.org/10.5194/gmd-13-3731-2020
41. Arctic spring and summertime aerosol optical depth baseline from long-term observations and model reanalyses – Part 1: Climatology and trend P. Xian et al. https://doi.org/10.5194/acp-22-9915-2022
42. AERONET Remotely Sensed Measurements and Retrievals of Biomass Burning Aerosol Optical Properties During the 2015 Indonesian Burning Season T. Eck et al. https://doi.org/10.1029/2018JD030182
43. JRAero: the Japanese Reanalysis for Aerosol v1.0 K. Yumimoto et al. https://doi.org/10.5194/gmd-10-3225-2017
44. A bulk-mass-modeling-based method for retrieving particulate matter pollution using CALIOP observations T. Toth et al. https://doi.org/10.5194/amt-12-1739-2019
45. A Coupled Evaluation of Operational MODIS and Model Aerosol Products for Maritime Environments Using Sun Photometry: Evaluation of the Fine and Coarse Mode J. Reid et al. https://doi.org/10.3390/rs14132978
46. An overview of mesoscale aerosol processes, comparisons, and validation studies from DRAGON networks B. Holben et al. https://doi.org/10.5194/acp-18-655-2018
47. Estimates of Health Impacts and Radiative Forcing in Winter Haze in Eastern China through Constraints of Surface PM2.5 Predictions M. Gao et al. https://doi.org/10.1021/acs.est.6b03745
48. In situ particle sampling relationships to surface and turbulent fluxes using large eddy simulations with Lagrangian particles H. Park et al. https://doi.org/10.5194/amt-15-7171-2022
49. The implementation of NEMS GFS Aerosol Component (NGAC) Version 1.0 for global dust forecasting at NOAA/NCEP C. Lu et al. https://doi.org/10.5194/gmd-9-1905-2016
50. A global evaluation of daily to seasonal aerosol and water vapor relationships using a combination of AERONET and NAAPS reanalysis data J. Rubin et al. https://doi.org/10.5194/acp-23-4059-2023
51. Observations of the Interaction and Transport of Fine Mode Aerosols With Cloud and/or Fog in Northeast Asia From Aerosol Robotic Network and Satellite Remote Sensing T. Eck et al. https://doi.org/10.1029/2018JD028313
52. The WMO vegetation fire and smoke pollution warning advisory and assessment system (VFSP-WAS) A. Baklanov et al. https://doi.org/10.37002/biobrasil.v11i2.1738
53. The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP – Part 2: Evaluation of aerosol optical thickness P. Bhattacharjee et al. https://doi.org/10.5194/gmd-11-2333-2018
54. Decision theory‐based detection of atmospheric natural hazards from satellite imagery using the example of volcanic ash L. Western et al. https://doi.org/10.1002/qj.3230
55. Prediction of health effects of cross-border atmospheric pollutants using an aerosol forecast model K. Onishi et al. https://doi.org/10.1016/j.envint.2018.04.035
56. EUNADICS-AV early warning system dedicated to supporting aviation in the case of a crisis from natural airborne hazards and radionuclide clouds H. Brenot et al. https://doi.org/10.5194/nhess-21-3367-2021
57. Advances in air quality modeling and forecasting A. Baklanov & Y. Zhang https://doi.org/10.1016/j.glt.2020.11.001
58. An aerosol classification scheme for global simulations using the K-means machine learning method J. Li et al. https://doi.org/10.5194/gmd-15-509-2022
59. High-resolution vertical profiling of aerosols microphysical and optical properties during the 2024 LEADER UAV campaign in Nadarzyce (Poland) K. Markowicz et al. https://doi.org/10.1007/s11356-025-36887-2
60. An evaluation of the impact of aerosol particles on weather forecasts from a biomass burning aerosol event over the Midwestern United States: observational-based analysis of surface temperature J. Zhang et al. https://doi.org/10.5194/acp-16-6475-2016
61. High-resolution dust modelling over complex terrains in West Asia S. Basart et al. https://doi.org/10.1016/j.aeolia.2016.09.005
62. Supporting Weather Forecasters in Predicting and Monitoring Saharan Air Layer Dust Events as They Impact the Greater Caribbean A. Kuciauskas et al. https://doi.org/10.1175/BAMS-D-16-0212.1
63. Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0 E. Di Tomaso et al. https://doi.org/10.5194/gmd-10-1107-2017
64. Assimilation and Forecasting Experiment for Heavy Siberian Wildfire Smoke in May 2016 with Himawari-8 Aerosol Optical Thickness K. YUMIMOTO et al. https://doi.org/10.2151/jmsj.2018-035
65. Application of linear minimum variance estimation to the multi-model ensemble of atmospheric radioactive Cs-137 with observations D. Goto et al. https://doi.org/10.5194/acp-20-3589-2020
66. A global aerosol classification algorithm incorporating multiple satellite data sets of aerosol and trace gas abundances M. Penning de Vries et al. https://doi.org/10.5194/acp-15-10597-2015
67. Revealing dominant patterns of aerosol regimes in the lower troposphere and their evolution from preindustrial times to the future in global climate model simulations J. Li et al. https://doi.org/10.5194/acp-24-12727-2024
68. Current state of the global operational aerosol multi‐model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP) P. Xian et al. https://doi.org/10.1002/qj.3497