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Current state of the global operational aerosol multi-model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP).
Xian, Peng; Reid, Jeffrey S; Hyer, Edward J; Sampson, Charles R; Rubin, Juli I; Ades, Melanie; Asencio, Nicole; Basart, Sara; Benedetti, Angela; Bhattacharjee, Partha S; Brooks, Malcolm E; Colarco, Peter R; da Silva, Arlindo M; Eck, Tom F; Guth, Jonathan; Jorba, Oriol; Kouznetsov, Rostislav; Kipling, Zak; Sofiev, Mikhail; Perez Garcia-Pando, Carlos; Pradhan, Yaswant; Tanaka, Taichu; Wang, Jun; Westphal, Douglas L; Yumimoto, Keiya; Zhang, Jianglong.
Afiliación
  • Xian P; Marine Meteorology Division Naval Research Laboratory Monterey California.
  • Reid JS; Marine Meteorology Division Naval Research Laboratory Monterey California.
  • Hyer EJ; Marine Meteorology Division Naval Research Laboratory Monterey California.
  • Sampson CR; Marine Meteorology Division Naval Research Laboratory Monterey California.
  • Rubin JI; Remote Sensing Division Naval Research Laboratory Washington District of Columbia.
  • Ades M; European Centre for Medium-Range Weather Forecasts Reading UK.
  • Asencio N; Météo-France, UMR3589 Toulouse France.
  • Basart S; Earth Sciences Department Barcelona Supercomputing Center Barcelona Spain.
  • Benedetti A; European Centre for Medium-Range Weather Forecasts Reading UK.
  • Bhattacharjee PS; I.M. System Group at NOAA/NCEP/EMC College Park Maryland.
  • Brooks ME; NOAA NCEP College Park Maryland.
  • Colarco PR; Met Office Exeter UK.
  • da Silva AM; NASA Goddard Space Flight Center Greenbelt Maryland.
  • Eck TF; NASA Goddard Space Flight Center Greenbelt Maryland.
  • Guth J; NASA Goddard Space Flight Center Greenbelt Maryland.
  • Jorba O; Météo-France, UMR3589 Toulouse France.
  • Kouznetsov R; Earth Sciences Department Barcelona Supercomputing Center Barcelona Spain.
  • Kipling Z; Atmospheric Composition Unit Finnish Meteorological Institute Helsinki Finland.
  • Sofiev M; Obukhov Institute for Atmospheric Physics Moscow Russia.
  • Perez Garcia-Pando C; European Centre for Medium-Range Weather Forecasts Reading UK.
  • Pradhan Y; Atmospheric Composition Unit Finnish Meteorological Institute Helsinki Finland.
  • Tanaka T; Earth Sciences Department Barcelona Supercomputing Center Barcelona Spain.
  • Wang J; Met Office Exeter UK.
  • Westphal DL; Atmospheric Environment and Applied Meteorology Research Department Meteorological Research Institute, Japan Meteorological Agency Tsukuba Japan.
  • Yumimoto K; I.M. System Group at NOAA/NCEP/EMC College Park Maryland.
  • Zhang J; NOAA NCEP College Park Maryland.
Q J R Meteorol Soc ; 145(Suppl 1): 176-209, 2019 Sep.
Article en En | MEDLINE | ID: mdl-31787783
Since the first International Cooperative for Aerosol Prediction (ICAP) multi-model ensemble (MME) study, the number of ICAP global operational aerosol models has increased from five to nine. An update of the current ICAP status is provided, along with an evaluation of the performance of ICAP-MME over 2012-2017, with a focus on June 2016-May 2017. Evaluated with ground-based Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and data assimilation quality MODerate-resolution Imaging Spectroradiometer (MODIS) retrieval products, the ICAP-MME AOD consensus remains the overall top-scoring and most consistent performer among all models in terms of root-mean-square error (RMSE), bias and correlation for total, fine- and coarse-mode AODs as well as dust AOD; this is similar to the first ICAP-MME study. Further, over the years, the performance of ICAP-MME is relatively stable and reliable compared to more variability in the individual models. The extent to which the AOD forecast error of ICAP-MME can be predicted is also examined. Leading predictors are found to be the consensus mean and spread. Regression models of absolute forecast errors were built for AOD forecasts of different lengths for potential applications. ICAP-MME performance in terms of modal AOD RMSEs of the 21 regionally representative sites over 2012-2017 suggests a general tendency for model improvements in fine-mode AOD, especially over Asia. No significant improvement in coarse-mode AOD is found overall for this time period.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Q J R Meteorol Soc Año: 2019 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Q J R Meteorol Soc Año: 2019 Tipo del documento: Article