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

Peng Xian, Jeffrey S. Reid, Edward J. Hyer, Charles R. Sampson, Juli I. Rubin, Melanie Ades, Nicole Asencio, Sara Basart, Angela Benedetti, Partha S. Bhattacharjee, Malcolm E. Brooks, Peter R. Colarco, Arlindo M. da Silva, Tom F. Eck, Jonathan Guth, Oriol Jorba, Rostislav Kouznetsov, Zak Kipling, Mikhail Sofiev, Carlos Perez Garcia-Pando & 6 others Yaswant Pradhan, Taichu Tanaka, Jun Wang, Douglas L. Westphal, Keiya Yumimoto, Jianglong Zhang

研究成果: ジャーナルへの寄稿記事

2 引用 (Scopus)

抄録

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.

元の言語英語
ジャーナルQuarterly Journal of the Royal Meteorological Society
DOI
出版物ステータス出版済み - 1 1 2019

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aerosol
prediction
optical depth
data assimilation
MODIS
dust

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

これを引用

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.

:: Quarterly Journal of the Royal Meteorological Society, 01.01.2019.

研究成果: ジャーナルへの寄稿記事

Xian, P, Reid, JS, Hyer, EJ, Sampson, CR, Rubin, JI, Ades, M, Asencio, N, Basart, S, Benedetti, A, Bhattacharjee, PS, Brooks, ME, Colarco, PR, da Silva, AM, Eck, TF, Guth, J, Jorba, O, Kouznetsov, R, Kipling, Z, Sofiev, M, Perez Garcia-Pando, C, Pradhan, Y, Tanaka, T, Wang, J, Westphal, DL, Yumimoto, K & Zhang, J 2019, 'Current state of the global operational aerosol multi-model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP)' Quarterly Journal of the Royal Meteorological Society. https://doi.org/10.1002/qj.3497
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). :: Quarterly Journal of the Royal Meteorological Society. 2019.
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title = "Current state of the global operational aerosol multi-model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP)",
abstract = "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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T1 - Current state of the global operational aerosol multi-model ensemble

T2 - An update from the International Cooperative for Aerosol Prediction (ICAP)

AU - Xian, Peng

AU - Reid, Jeffrey S.

AU - Hyer, Edward J.

AU - Sampson, Charles R.

AU - Rubin, Juli I.

AU - Ades, Melanie

AU - Asencio, Nicole

AU - Basart, Sara

AU - Benedetti, Angela

AU - Bhattacharjee, Partha S.

AU - Brooks, Malcolm E.

AU - Colarco, Peter R.

AU - da Silva, Arlindo M.

AU - Eck, Tom F.

AU - Guth, Jonathan

AU - Jorba, Oriol

AU - Kouznetsov, Rostislav

AU - Kipling, Zak

AU - Sofiev, Mikhail

AU - Perez Garcia-Pando, Carlos

AU - Pradhan, Yaswant

AU - Tanaka, Taichu

AU - Wang, Jun

AU - Westphal, Douglas L.

AU - Yumimoto, Keiya

AU - Zhang, Jianglong

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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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