Evaluating the Impact of Assimilating Aerosol Optical Depth Observations on Dust Forecasts Over North Africa and the East Atlantic Using Different Data Assimilation Methods
This study evaluates the impact of assimilating moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data using different data assimilation (DA) methods on dust analyses and forecasts over North Africa and tropical North Atlantic. To do so, seven experiments are conducte...
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ftcdlib:oai:escholarship.org:ark:/13030/qt05r2t10c 2024-04-21T08:07:59+00:00 Evaluating the Impact of Assimilating Aerosol Optical Depth Observations on Dust Forecasts Over North Africa and the East Atlantic Using Different Data Assimilation Methods Choi, Yonghan Chen, Shu‐Hua Huang, Chu‐Chun Earl, Kenneth Chen, Chih‐Ying Schwartz, Craig S Matsui, Toshihisa e2019ms001890 2020-04-01 application/pdf https://escholarship.org/uc/item/05r2t10c unknown eScholarship, University of California qt05r2t10c https://escholarship.org/uc/item/05r2t10c public Journal of Advances in Modeling Earth Systems, vol 12, iss 4 Earth Sciences Atmospheric Sciences GSI Sahara Desert aerosol assimilation aerosol optical depth deep blue AOD dust model Geoinformatics article 2020 ftcdlib 2024-03-27T15:24:33Z This study evaluates the impact of assimilating moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data using different data assimilation (DA) methods on dust analyses and forecasts over North Africa and tropical North Atlantic. To do so, seven experiments are conducted using the Weather Research and Forecasting dust model and the Gridpoint Statistical Interpolation analysis system. Six of these experiments differ in whether or not AOD observations are assimilated and the DA method used, the latter of which includes the three-dimensional variational (3D-Var), ensemble square root filter (EnSRF), and hybrid methods. The seventh experiment, which allows us to assess the impact of assimilating deep blue AOD data, assimilates only dark target AOD data using the hybrid method. The assimilation of MODIS AOD data clearly improves AOD analyses and forecasts up to 48hr in length. Results also show that assimilating deep blue data has a primarily positive effect on AOD analyses and forecasts over and downstream of the major North African source regions. Without assimilating deep blue data (assimilating dark target only), AOD assimilation only improves AOD forecasts for up to 30hr. Of the three DA methods examined, the hybrid and EnSRF methods produce better AOD analyses and forecasts than the 3D-Var method does. Despite the clear benefit of AOD assimilation for AOD analyses and forecasts, the lack of information regarding the vertical distribution of aerosols in AOD data means that AOD assimilation has very little positive effect on analyzed or forecasted vertical profiles of backscatter. Article in Journal/Newspaper North Atlantic University of California: eScholarship |
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Open Polar |
collection |
University of California: eScholarship |
op_collection_id |
ftcdlib |
language |
unknown |
topic |
Earth Sciences Atmospheric Sciences GSI Sahara Desert aerosol assimilation aerosol optical depth deep blue AOD dust model Geoinformatics |
spellingShingle |
Earth Sciences Atmospheric Sciences GSI Sahara Desert aerosol assimilation aerosol optical depth deep blue AOD dust model Geoinformatics Choi, Yonghan Chen, Shu‐Hua Huang, Chu‐Chun Earl, Kenneth Chen, Chih‐Ying Schwartz, Craig S Matsui, Toshihisa Evaluating the Impact of Assimilating Aerosol Optical Depth Observations on Dust Forecasts Over North Africa and the East Atlantic Using Different Data Assimilation Methods |
topic_facet |
Earth Sciences Atmospheric Sciences GSI Sahara Desert aerosol assimilation aerosol optical depth deep blue AOD dust model Geoinformatics |
description |
This study evaluates the impact of assimilating moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data using different data assimilation (DA) methods on dust analyses and forecasts over North Africa and tropical North Atlantic. To do so, seven experiments are conducted using the Weather Research and Forecasting dust model and the Gridpoint Statistical Interpolation analysis system. Six of these experiments differ in whether or not AOD observations are assimilated and the DA method used, the latter of which includes the three-dimensional variational (3D-Var), ensemble square root filter (EnSRF), and hybrid methods. The seventh experiment, which allows us to assess the impact of assimilating deep blue AOD data, assimilates only dark target AOD data using the hybrid method. The assimilation of MODIS AOD data clearly improves AOD analyses and forecasts up to 48hr in length. Results also show that assimilating deep blue data has a primarily positive effect on AOD analyses and forecasts over and downstream of the major North African source regions. Without assimilating deep blue data (assimilating dark target only), AOD assimilation only improves AOD forecasts for up to 30hr. Of the three DA methods examined, the hybrid and EnSRF methods produce better AOD analyses and forecasts than the 3D-Var method does. Despite the clear benefit of AOD assimilation for AOD analyses and forecasts, the lack of information regarding the vertical distribution of aerosols in AOD data means that AOD assimilation has very little positive effect on analyzed or forecasted vertical profiles of backscatter. |
format |
Article in Journal/Newspaper |
author |
Choi, Yonghan Chen, Shu‐Hua Huang, Chu‐Chun Earl, Kenneth Chen, Chih‐Ying Schwartz, Craig S Matsui, Toshihisa |
author_facet |
Choi, Yonghan Chen, Shu‐Hua Huang, Chu‐Chun Earl, Kenneth Chen, Chih‐Ying Schwartz, Craig S Matsui, Toshihisa |
author_sort |
Choi, Yonghan |
title |
Evaluating the Impact of Assimilating Aerosol Optical Depth Observations on Dust Forecasts Over North Africa and the East Atlantic Using Different Data Assimilation Methods |
title_short |
Evaluating the Impact of Assimilating Aerosol Optical Depth Observations on Dust Forecasts Over North Africa and the East Atlantic Using Different Data Assimilation Methods |
title_full |
Evaluating the Impact of Assimilating Aerosol Optical Depth Observations on Dust Forecasts Over North Africa and the East Atlantic Using Different Data Assimilation Methods |
title_fullStr |
Evaluating the Impact of Assimilating Aerosol Optical Depth Observations on Dust Forecasts Over North Africa and the East Atlantic Using Different Data Assimilation Methods |
title_full_unstemmed |
Evaluating the Impact of Assimilating Aerosol Optical Depth Observations on Dust Forecasts Over North Africa and the East Atlantic Using Different Data Assimilation Methods |
title_sort |
evaluating the impact of assimilating aerosol optical depth observations on dust forecasts over north africa and the east atlantic using different data assimilation methods |
publisher |
eScholarship, University of California |
publishDate |
2020 |
url |
https://escholarship.org/uc/item/05r2t10c |
op_coverage |
e2019ms001890 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Journal of Advances in Modeling Earth Systems, vol 12, iss 4 |
op_relation |
qt05r2t10c https://escholarship.org/uc/item/05r2t10c |
op_rights |
public |
_version_ |
1796948125993140224 |