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) multimodel 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 the ICAPMME...

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Main Authors: Pradhan, Yaswant, Kipling, Zak, Sampson, Charles R., Carcia-Pando, Carlos Perez, Eck, Thomas F., Bhattacharjee, Partha, Rubin, Juli I., Wang, Jun, Brooks, Malcolm E., Kouznetsov, Rostislav, Westphal, Douglas L., Asencio, Nicole, Yumimoto, Keiya, Ades, Melanie, Hyer, Edward J., Colarco, Peter R., Benedetti, Angela, Jorba, Oriol, Zhang, Jianglong, Xian, Peng, Mikhail, Sofiev, Guth, Jonathan, Tanaka, Taichu, da Silva, Arlindo M., Basart, Sara, Reid, Jeffrey S.
Format: Other/Unknown Material
Language:unknown
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/2060/20190000702
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spelling ftnasantrs:oai:casi.ntrs.nasa.gov:20190000702 2023-05-15T13:06:24+02:00 Current State of the Global Operational Aerosol Multi-Model Ensemble: An Update from the International Cooperative for Aerosol Prediction (ICAP) Pradhan, Yaswant Kipling, Zak Sampson, Charles R. Carcia-Pando, Carlos Perez Eck, Thomas F. Bhattacharjee, Partha Rubin, Juli I. Wang, Jun Brooks, Malcolm E. Kouznetsov, Rostislav Westphal, Douglas L. Asencio, Nicole Yumimoto, Keiya Ades, Melanie Hyer, Edward J. Colarco, Peter R. Benedetti, Angela Jorba, Oriol Zhang, Jianglong Xian, Peng Mikhail, Sofiev Guth, Jonathan Tanaka, Taichu da Silva, Arlindo M. Basart, Sara Reid, Jeffrey S. Unclassified, Unlimited, Publicly available February 4, 2019 application/pdf http://hdl.handle.net/2060/20190000702 unknown Document ID: 20190000702 http://hdl.handle.net/2060/20190000702 Copyright, Use by or on behalf of the U.S. Government permitted CASI Geophysics GSFC-E-DAA-TN65271 Quarterly Journal of the Royal Meteorological Society (ISSN 0035-9009) (e-ISSN 1477-870X); 145; S1; 176-209 2019 ftnasantrs 2020-02-15T23:47:37Z Since the first International Cooperative for Aerosol Prediction (ICAP) multimodel 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 the ICAPMME over 20122017, with a focus on the June 2016May 2017 time period. Evaluated with ground based Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and data assimilation quality Moderate Resolution Imaging Spectroradiometer (MODIS) retrieval products, the ICAPMME 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 ICAPMME study. Further, over the years, the performance of ICAPMME is relatively stable and reliable compared to more variability in the individual models. The extent to which the AOD forecast error of the ICAPMME 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. ICAPMME performance in terms of modal AOD RMSEs of the 21 regionally representative sites over 20122017 suggests a general tendency for model improvements in finemode AOD, especially over Asia. No significant improvement in coarsemode AOD is found overall for this time period. Other/Unknown Material Aerosol Robotic Network NASA Technical Reports Server (NTRS)
institution Open Polar
collection NASA Technical Reports Server (NTRS)
op_collection_id ftnasantrs
language unknown
topic Geophysics
spellingShingle Geophysics
Pradhan, Yaswant
Kipling, Zak
Sampson, Charles R.
Carcia-Pando, Carlos Perez
Eck, Thomas F.
Bhattacharjee, Partha
Rubin, Juli I.
Wang, Jun
Brooks, Malcolm E.
Kouznetsov, Rostislav
Westphal, Douglas L.
Asencio, Nicole
Yumimoto, Keiya
Ades, Melanie
Hyer, Edward J.
Colarco, Peter R.
Benedetti, Angela
Jorba, Oriol
Zhang, Jianglong
Xian, Peng
Mikhail, Sofiev
Guth, Jonathan
Tanaka, Taichu
da Silva, Arlindo M.
Basart, Sara
Reid, Jeffrey S.
Current State of the Global Operational Aerosol Multi-Model Ensemble: An Update from the International Cooperative for Aerosol Prediction (ICAP)
topic_facet Geophysics
description Since the first International Cooperative for Aerosol Prediction (ICAP) multimodel 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 the ICAPMME over 20122017, with a focus on the June 2016May 2017 time period. Evaluated with ground based Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and data assimilation quality Moderate Resolution Imaging Spectroradiometer (MODIS) retrieval products, the ICAPMME 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 ICAPMME study. Further, over the years, the performance of ICAPMME is relatively stable and reliable compared to more variability in the individual models. The extent to which the AOD forecast error of the ICAPMME 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. ICAPMME performance in terms of modal AOD RMSEs of the 21 regionally representative sites over 20122017 suggests a general tendency for model improvements in finemode AOD, especially over Asia. No significant improvement in coarsemode AOD is found overall for this time period.
format Other/Unknown Material
author Pradhan, Yaswant
Kipling, Zak
Sampson, Charles R.
Carcia-Pando, Carlos Perez
Eck, Thomas F.
Bhattacharjee, Partha
Rubin, Juli I.
Wang, Jun
Brooks, Malcolm E.
Kouznetsov, Rostislav
Westphal, Douglas L.
Asencio, Nicole
Yumimoto, Keiya
Ades, Melanie
Hyer, Edward J.
Colarco, Peter R.
Benedetti, Angela
Jorba, Oriol
Zhang, Jianglong
Xian, Peng
Mikhail, Sofiev
Guth, Jonathan
Tanaka, Taichu
da Silva, Arlindo M.
Basart, Sara
Reid, Jeffrey S.
author_facet Pradhan, Yaswant
Kipling, Zak
Sampson, Charles R.
Carcia-Pando, Carlos Perez
Eck, Thomas F.
Bhattacharjee, Partha
Rubin, Juli I.
Wang, Jun
Brooks, Malcolm E.
Kouznetsov, Rostislav
Westphal, Douglas L.
Asencio, Nicole
Yumimoto, Keiya
Ades, Melanie
Hyer, Edward J.
Colarco, Peter R.
Benedetti, Angela
Jorba, Oriol
Zhang, Jianglong
Xian, Peng
Mikhail, Sofiev
Guth, Jonathan
Tanaka, Taichu
da Silva, Arlindo M.
Basart, Sara
Reid, Jeffrey S.
author_sort Pradhan, Yaswant
title 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_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_sort current state of the global operational aerosol multi-model ensemble: an update from the international cooperative for aerosol prediction (icap)
publishDate 2019
url http://hdl.handle.net/2060/20190000702
op_coverage Unclassified, Unlimited, Publicly available
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_source CASI
op_relation Document ID: 20190000702
http://hdl.handle.net/2060/20190000702
op_rights Copyright, Use by or on behalf of the U.S. Government permitted
_version_ 1766004072152825856