Current state of the global operational aerosol multi-model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP).

aerosol aerosol forecast aerosol modelling ensemble global aerosol model multi‐model ensemble operational aerosol forecast probabilistic forecast

Journal

Quarterly journal of the Royal Meteorological Society. Royal Meteorological Society (Great Britain)
ISSN: 0035-9009
Titre abrégé: Q J R Meteorol Soc
Pays: England
ID NLM: 101661952

Informations de publication

Date de publication:
Sep 2019
Historique:
received: 30 04 2018
revised: 08 11 2018
accepted: 24 01 2019
entrez: 3 12 2019
pubmed: 4 12 2019
medline: 4 12 2019
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 31787783
doi: 10.1002/qj.3497
pii: QJ3497
pmc: PMC6876662
doi:

Types de publication

Journal Article

Langues

eng

Pagination

176-209

Informations de copyright

© 2019 The Authors. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.

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Auteurs

Peng Xian (P)

Marine Meteorology Division Naval Research Laboratory Monterey California.

Jeffrey S Reid (JS)

Marine Meteorology Division Naval Research Laboratory Monterey California.

Edward J Hyer (EJ)

Marine Meteorology Division Naval Research Laboratory Monterey California.

Charles R Sampson (CR)

Marine Meteorology Division Naval Research Laboratory Monterey California.

Juli I Rubin (JI)

Remote Sensing Division Naval Research Laboratory Washington District of Columbia.

Melanie Ades (M)

European Centre for Medium-Range Weather Forecasts Reading UK.

Nicole Asencio (N)

Météo-France, UMR3589 Toulouse France.

Sara Basart (S)

Earth Sciences Department Barcelona Supercomputing Center Barcelona Spain.

Angela Benedetti (A)

European Centre for Medium-Range Weather Forecasts Reading UK.

Partha S Bhattacharjee (PS)

I.M. System Group at NOAA/NCEP/EMC College Park Maryland.
NOAA NCEP College Park Maryland.

Malcolm E Brooks (ME)

Met Office Exeter UK.

Peter R Colarco (PR)

NASA Goddard Space Flight Center Greenbelt Maryland.

Arlindo M da Silva (AM)

NASA Goddard Space Flight Center Greenbelt Maryland.

Tom F Eck (TF)

NASA Goddard Space Flight Center Greenbelt Maryland.

Jonathan Guth (J)

Météo-France, UMR3589 Toulouse France.

Oriol Jorba (O)

Earth Sciences Department Barcelona Supercomputing Center Barcelona Spain.

Rostislav Kouznetsov (R)

Atmospheric Composition Unit Finnish Meteorological Institute Helsinki Finland.
Obukhov Institute for Atmospheric Physics Moscow Russia.

Zak Kipling (Z)

European Centre for Medium-Range Weather Forecasts Reading UK.

Mikhail Sofiev (M)

Atmospheric Composition Unit Finnish Meteorological Institute Helsinki Finland.

Carlos Perez Garcia-Pando (C)

Earth Sciences Department Barcelona Supercomputing Center Barcelona Spain.

Yaswant Pradhan (Y)

Met Office Exeter UK.

Taichu Tanaka (T)

Atmospheric Environment and Applied Meteorology Research Department Meteorological Research Institute, Japan Meteorological Agency Tsukuba Japan.

Jun Wang (J)

I.M. System Group at NOAA/NCEP/EMC College Park Maryland.
NOAA NCEP College Park Maryland.

Douglas L Westphal (DL)

Marine Meteorology Division Naval Research Laboratory Monterey California.

Keiya Yumimoto (K)

Atmospheric Environment and Applied Meteorology Research Department Meteorological Research Institute, Japan Meteorological Agency Tsukuba Japan.
Research Institute for Applied Mechanics, Kyushu University Fukuoka Japan.

Jianglong Zhang (J)

Department of Atmospheric Sciences University of North Dakota Grand Forks North Dakota.

Classifications MeSH