Application of Optimal Interpolation to Spatially and Temporally Sparse Observations of Aerosol Optical Depth
Aerosol optical depth (AOD) is one of the basic characteristics of atmospheric aerosol. A global ground-based network of sun and sky photometers, the Aerosol Robotic Network (AERONET) provides AOD data with low uncertainty. However, AERONET observations are sparse in space and time. To improve data...
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ftmdpi:oai:mdpi.com:/2073-4433/14/1/32/ 2023-08-20T03:59:11+02:00 Application of Optimal Interpolation to Spatially and Temporally Sparse Observations of Aerosol Optical Depth Natallia Miatselskaya Gennadi Milinevsky Andrey Bril Anatoly Chaikovsky Alexander Miskevich Yuliia Yukhymchuk agris 2022-12-24 application/pdf https://doi.org/10.3390/atmos14010032 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/atmos14010032 https://creativecommons.org/licenses/by/4.0/ Atmosphere; Volume 14; Issue 1; Pages: 32 data assimilation optimal interpolation aerosol optical depth AERONET chemical transport model GEOS-Chem Text 2022 ftmdpi https://doi.org/10.3390/atmos14010032 2023-08-01T07:57:30Z Aerosol optical depth (AOD) is one of the basic characteristics of atmospheric aerosol. A global ground-based network of sun and sky photometers, the Aerosol Robotic Network (AERONET) provides AOD data with low uncertainty. However, AERONET observations are sparse in space and time. To improve data density, we merged AERONET observations with a GEOS-Chem chemical transport model prediction using an optimal interpolation (OI) method. According to OI, we estimated AOD as a linear combination of observational data and a model forecast, with weighting coefficients chosen to minimize a mean-square error in the calculation, assuming a negligible error of AERONET AOD observations. To obtain weight coefficients, we used correlations between model errors in different grid points. In contrast with classical OI, where only spatial correlations are considered, we developed the spatial-temporal optimal interpolation (STOI) technique for atmospheric applications with the use of spatial and temporal correlation functions. Using STOI, we obtained estimates of the daily mean AOD distribution over Europe. To validate the results, we compared daily mean AOD estimated by STOI with independent AERONET observations for two months and three sites. Compared with the GEOS-Chem model results, the averaged reduction of the root-mean-square error of the AOD estimate based on the STOI method is about 25%. The study shows that STOI provides a significant improvement in AOD estimates. Text Aerosol Robotic Network MDPI Open Access Publishing Atmosphere 14 1 32 |
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MDPI Open Access Publishing |
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English |
topic |
data assimilation optimal interpolation aerosol optical depth AERONET chemical transport model GEOS-Chem |
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data assimilation optimal interpolation aerosol optical depth AERONET chemical transport model GEOS-Chem Natallia Miatselskaya Gennadi Milinevsky Andrey Bril Anatoly Chaikovsky Alexander Miskevich Yuliia Yukhymchuk Application of Optimal Interpolation to Spatially and Temporally Sparse Observations of Aerosol Optical Depth |
topic_facet |
data assimilation optimal interpolation aerosol optical depth AERONET chemical transport model GEOS-Chem |
description |
Aerosol optical depth (AOD) is one of the basic characteristics of atmospheric aerosol. A global ground-based network of sun and sky photometers, the Aerosol Robotic Network (AERONET) provides AOD data with low uncertainty. However, AERONET observations are sparse in space and time. To improve data density, we merged AERONET observations with a GEOS-Chem chemical transport model prediction using an optimal interpolation (OI) method. According to OI, we estimated AOD as a linear combination of observational data and a model forecast, with weighting coefficients chosen to minimize a mean-square error in the calculation, assuming a negligible error of AERONET AOD observations. To obtain weight coefficients, we used correlations between model errors in different grid points. In contrast with classical OI, where only spatial correlations are considered, we developed the spatial-temporal optimal interpolation (STOI) technique for atmospheric applications with the use of spatial and temporal correlation functions. Using STOI, we obtained estimates of the daily mean AOD distribution over Europe. To validate the results, we compared daily mean AOD estimated by STOI with independent AERONET observations for two months and three sites. Compared with the GEOS-Chem model results, the averaged reduction of the root-mean-square error of the AOD estimate based on the STOI method is about 25%. The study shows that STOI provides a significant improvement in AOD estimates. |
format |
Text |
author |
Natallia Miatselskaya Gennadi Milinevsky Andrey Bril Anatoly Chaikovsky Alexander Miskevich Yuliia Yukhymchuk |
author_facet |
Natallia Miatselskaya Gennadi Milinevsky Andrey Bril Anatoly Chaikovsky Alexander Miskevich Yuliia Yukhymchuk |
author_sort |
Natallia Miatselskaya |
title |
Application of Optimal Interpolation to Spatially and Temporally Sparse Observations of Aerosol Optical Depth |
title_short |
Application of Optimal Interpolation to Spatially and Temporally Sparse Observations of Aerosol Optical Depth |
title_full |
Application of Optimal Interpolation to Spatially and Temporally Sparse Observations of Aerosol Optical Depth |
title_fullStr |
Application of Optimal Interpolation to Spatially and Temporally Sparse Observations of Aerosol Optical Depth |
title_full_unstemmed |
Application of Optimal Interpolation to Spatially and Temporally Sparse Observations of Aerosol Optical Depth |
title_sort |
application of optimal interpolation to spatially and temporally sparse observations of aerosol optical depth |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/atmos14010032 |
op_coverage |
agris |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Atmosphere; Volume 14; Issue 1; Pages: 32 |
op_relation |
https://dx.doi.org/10.3390/atmos14010032 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/atmos14010032 |
container_title |
Atmosphere |
container_volume |
14 |
container_issue |
1 |
container_start_page |
32 |
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1774719180057083904 |