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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MDPI AG
2022-12-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/14/1/32 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T13:39:32Z |
| format | Article |
| id | doaj.art-9d293e1706d241b18092d810ac8ae50b |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-09T13:39:32Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-9d293e1706d241b18092d810ac8ae50b2023-11-30T21:08:26ZengMDPI AGAtmosphere2073-44332022-12-011413210.3390/atmos14010032Application of Optimal Interpolation to Spatially and Temporally Sparse Observations of Aerosol Optical DepthNatallia Miatselskaya0Gennadi Milinevsky1Andrey Bril2Anatoly Chaikovsky3Alexander Miskevich4Yuliia Yukhymchuk5Institute of Physics, National Academy of Sciences of Belarus, 220072 Minsk, BelarusDepartment for Atmospheric Optics and Instrumentation, Main Astronomical Observatory, 03143 Kyiv, UkraineInstitute of Physics, National Academy of Sciences of Belarus, 220072 Minsk, BelarusInstitute of Physics, National Academy of Sciences of Belarus, 220072 Minsk, BelarusInstitute of Physics, National Academy of Sciences of Belarus, 220072 Minsk, BelarusDepartment for Atmospheric Optics and Instrumentation, Main Astronomical Observatory, 03143 Kyiv, UkraineAerosol 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.https://www.mdpi.com/2073-4433/14/1/32data assimilationoptimal interpolationaerosol optical depthAERONETchemical transport model GEOS-Chem |
| spellingShingle | 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 Atmosphere data assimilation optimal interpolation aerosol optical depth AERONET chemical transport model GEOS-Chem |
| title | 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_short | 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 |
| topic | data assimilation optimal interpolation aerosol optical depth AERONET chemical transport model GEOS-Chem |
| url | https://www.mdpi.com/2073-4433/14/1/32 |
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