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

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 ov...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Ltd 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6876662/
https://www.ncbi.nlm.nih.gov/pubmed/31787783
http://dx.doi.org/10.1002/qj.3497
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author 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
author_facet 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
author_sort Xian, Peng
collection PubMed
description 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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spelling pubmed-68766622019-11-27 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 Q J R Meteorol Soc Special Supplement on 25 Years of Ensemble Forecasting 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. John Wiley & Sons, Ltd 2019-04-02 2019-09 /pmc/articles/PMC6876662/ /pubmed/31787783 http://dx.doi.org/10.1002/qj.3497 Text en © 2019 The Authors. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Special Supplement on 25 Years of Ensemble Forecasting
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
Current state of the global operational aerosol multi‐model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP)
title Current state of the global operational aerosol multi‐model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP)
title_full Current state of the global operational aerosol multi‐model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP)
title_fullStr Current state of the global operational aerosol multi‐model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP)
title_full_unstemmed Current state of the global operational aerosol multi‐model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP)
title_short Current state of the global operational aerosol multi‐model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP)
title_sort current state of the global operational aerosol multi‐model ensemble: an update from the international cooperative for aerosol prediction (icap)
topic Special Supplement on 25 Years of Ensemble Forecasting
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6876662/
https://www.ncbi.nlm.nih.gov/pubmed/31787783
http://dx.doi.org/10.1002/qj.3497
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