Current state of the global operational aerosol multi-model ensemble : An update from the International Cooperative for Aerosol Prediction (ICAP)
© 2019 The Authors. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:145 |
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| Enthalten in: |
Quarterly journal of the Royal Meteorological Society. Royal Meteorological Society (Great Britain) - 145(2019), Suppl 1 vom: 26. Sept., Seite 176-209 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xian, Peng [VerfasserIn] |
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| Links: |
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| Themen: |
Aerosol |
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| Anmerkungen: |
Date Revised 19.10.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1002/qj.3497 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 100 | 1 | |a Xian, Peng |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Current state of the global operational aerosol multi-model ensemble |b An update from the International Cooperative for Aerosol Prediction (ICAP) |
| 264 | 1 | |c 2019 | |
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| 500 | |a Date Revised 19.10.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2019 The Authors. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a aerosol | |
| 650 | 4 | |a aerosol forecast | |
| 650 | 4 | |a aerosol modelling | |
| 650 | 4 | |a ensemble | |
| 650 | 4 | |a global aerosol model | |
| 650 | 4 | |a multi‐model ensemble | |
| 650 | 4 | |a operational aerosol forecast | |
| 650 | 4 | |a probabilistic forecast | |
| 700 | 1 | |a Reid, Jeffrey S |e verfasserin |4 aut | |
| 700 | 1 | |a Hyer, Edward J |e verfasserin |4 aut | |
| 700 | 1 | |a Sampson, Charles R |e verfasserin |4 aut | |
| 700 | 1 | |a Rubin, Juli I |e verfasserin |4 aut | |
| 700 | 1 | |a Ades, Melanie |e verfasserin |4 aut | |
| 700 | 1 | |a Asencio, Nicole |e verfasserin |4 aut | |
| 700 | 1 | |a Basart, Sara |e verfasserin |4 aut | |
| 700 | 1 | |a Benedetti, Angela |e verfasserin |4 aut | |
| 700 | 1 | |a Bhattacharjee, Partha S |e verfasserin |4 aut | |
| 700 | 1 | |a Brooks, Malcolm E |e verfasserin |4 aut | |
| 700 | 1 | |a Colarco, Peter R |e verfasserin |4 aut | |
| 700 | 1 | |a da Silva, Arlindo M |e verfasserin |4 aut | |
| 700 | 1 | |a Eck, Tom F |e verfasserin |4 aut | |
| 700 | 1 | |a Guth, Jonathan |e verfasserin |4 aut | |
| 700 | 1 | |a Jorba, Oriol |e verfasserin |4 aut | |
| 700 | 1 | |a Kouznetsov, Rostislav |e verfasserin |4 aut | |
| 700 | 1 | |a Kipling, Zak |e verfasserin |4 aut | |
| 700 | 1 | |a Sofiev, Mikhail |e verfasserin |4 aut | |
| 700 | 1 | |a Perez Garcia-Pando, Carlos |e verfasserin |4 aut | |
| 700 | 1 | |a Pradhan, Yaswant |e verfasserin |4 aut | |
| 700 | 1 | |a Tanaka, Taichu |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Westphal, Douglas L |e verfasserin |4 aut | |
| 700 | 1 | |a Yumimoto, Keiya |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Jianglong |e verfasserin |4 aut | |
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