Articles | Volume 16, issue 14
https://doi.org/10.5194/gmd-16-4265-2023
https://doi.org/10.5194/gmd-16-4265-2023
Model evaluation paper
 | 
27 Jul 2023
Model evaluation paper |  | 27 Jul 2023

Variability and combination as an ensemble of mineral dust forecasts during the 2021 CADDIWA experiment using the WRF 3.7.1 and CHIMERE v2020r3 models

Laurent Menut

Related authors

Representing improved tropospheric ozone distribution by including lightning NOx emissions in CHIMERE
Sanhita Ghosh, Arineh Cholakian, Sylvain Mailler, and Laurent Menut
EGUsphere, https://doi.org/10.5194/egusphere-2024-3087,https://doi.org/10.5194/egusphere-2024-3087, 2024
Short summary
Air quality modeling intercomparison and multiscale ensemble chain for Latin America
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024,https://doi.org/10.5194/gmd-17-7467-2024, 2024
Short summary
New explicit formulae for the settling speed of prolate spheroids in the atmosphere: theoretical background and implementation in AerSett v2.0.2
Sylvain Mailler, Sotirios Mallios, Arineh Cholakian, Vassilis Amiridis, Laurent Menut, and Romain Pennel
Geosci. Model Dev., 17, 5641–5655, https://doi.org/10.5194/gmd-17-5641-2024,https://doi.org/10.5194/gmd-17-5641-2024, 2024
Short summary
An improved version of the piecewise parabolic method advection scheme: description and performance assessment in a bidimensional test case with stiff chemistry in toyCTM v1.0.1
Sylvain Mailler, Romain Pennel, Laurent Menut, and Arineh Cholakian
Geosci. Model Dev., 16, 7509–7526, https://doi.org/10.5194/gmd-16-7509-2023,https://doi.org/10.5194/gmd-16-7509-2023, 2023
Short summary
Impact of the Guinea coast upwelling on atmospheric dynamics, precipitation and pollutant transport over southern West Africa
Gaëlle de Coëtlogon, Adrien Deroubaix, Cyrille Flamant, Laurent Menut, and Marco Gaetani
Atmos. Chem. Phys., 23, 15507–15521, https://doi.org/10.5194/acp-23-15507-2023,https://doi.org/10.5194/acp-23-15507-2023, 2023
Short summary

Related subject area

Atmospheric sciences
Simulation of the heat mitigation potential of unsealing measures in cities by parameterizing grass grid pavers for urban microclimate modelling with ENVI-met (V5)
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025,https://doi.org/10.5194/gmd-18-141-2025, 2025
Short summary
AI-NAOS: an AI-based nonspherical aerosol optical scheme for the chemical weather model GRAPES_Meso5.1/CUACE
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025,https://doi.org/10.5194/gmd-18-117-2025, 2025
Short summary
Orbital-Radar v1.0.0: a tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025,https://doi.org/10.5194/gmd-18-101-2025, 2025
Short summary
The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1)
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025,https://doi.org/10.5194/gmd-18-1-2025, 2025
Short summary
Modeling of polycyclic aromatic hydrocarbons (PAHs) from global to regional scales: model development (IAP-AACM_PAH v1.0) and investigation of health risks in 2013 and 2018 in China
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024,https://doi.org/10.5194/gmd-17-8885-2024, 2024
Short summary

Cited articles

Alfaro, S. C. and Gomes, L.: Modeling mineral aerosol production by wind erosion: Emission intensities and aerosol size distribution in source areas, J. Geophys. Res., 106, 18075–18084, 2001. a
Atger, F.: The skill of ensemble prediction systems, Mon. Weather Rev, 127, 1941–1953, 1999. a
Benedetti, A., Reid, J. S., Knippertz, P., Marsham, J. H., Di Giuseppe, F., Rémy, S., Basart, S., Boucher, O., Brooks, I. M., Menut, L., Mona, L., Laj, P., Pappalardo, G., Wiedensohler, A., Baklanov, A., Brooks, M., Colarco, P. R., Cuevas, E., da Silva, A., Escribano, J., Flemming, J., Huneeus, N., Jorba, O., Kazadzis, S., Kinne, S., Popp, T., Quinn, P. K., Sekiyama, T. T., Tanaka, T., and Terradellas, E.: Status and future of numerical atmospheric aerosol prediction with a focus on data requirements, Atmos. Chem. Phys., 18, 10615–10643, https://doi.org/10.5194/acp-18-10615-2018, 2018. a
Bowler, N. E., Arribas, A., and Mylne, K. R.: The Benefits of Multianalysis and Poor Man's Ensembles, Mon. Weather Rev., 136, 4113–4129, https://doi.org/10.1175/2008MWR2381.1, 2008. a
Buizza, R., Richardson, D. S., and Palmer, T. N.: Benefits of increased resolution in the ECMWF ensemble system and comparison with poor-man's ensembles, Q. J. Roy. Meteor. Soc., 129, 1269–1288, https://doi.org/10.1256/qj.02.92, 2003. a
Download
Short summary
This study analyzes forecasts that were made in 2021 to help trigger measurements during the CADDIWA experiment. The WRF and CHIMERE models were run each day, and the first goal is to quantify the variability of the forecast as a function of forecast leads and forecast location. The possibility of using the different leads as an ensemble is also tested. For some locations, the correlation scores are better with this approach. This could be tested on operational forecast chains in the future.