Quantifying uncertainties in satellite NO<sub>2</sub> superobservations for data assimilation and model evaluation
<p>Satellite observations of tropospheric trace gases and aerosols are evolving rapidly. Recently launched instruments provide increasingly higher spatial resolutions, with footprint diameters in the range of 2–8 km and with daily global coverage for polar orbiting satellites or hourly observa...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-01-01
|
| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/18/483/2025/gmd-18-483-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832583591101464576 |
|---|---|
| author | P. Rijsdijk P. Rijsdijk P. Rijsdijk H. Eskes A. Dingemans A. Dingemans K. F. Boersma K. F. Boersma T. Sekiya K. Miyazaki S. Houweling S. Houweling |
| author_facet | P. Rijsdijk P. Rijsdijk P. Rijsdijk H. Eskes A. Dingemans A. Dingemans K. F. Boersma K. F. Boersma T. Sekiya K. Miyazaki S. Houweling S. Houweling |
| author_sort | P. Rijsdijk |
| collection | DOAJ |
| description | <p>Satellite observations of tropospheric trace gases and aerosols are evolving rapidly. Recently launched instruments provide increasingly higher spatial resolutions, with footprint diameters in the range of 2–8 km and with daily global coverage for polar orbiting satellites or hourly observations from geostationary orbits. Often the modelling system has a lower spatial resolution than the satellites used, with a model grid size in the range of 10–100 km. When the resolution mismatch is not properly bridged, the final analysis based on the satellite data may be degraded. Superobservations are averages of individual observations matching the model's resolution and are functional to reduce the data load on the assimilation system. In this paper, we discuss the construction of superobservations, their kernels, and uncertainty estimates. The methodology is applied to nitrogen dioxide tropospheric column measurements of the TROPOspheric Monitoring Instrument (TROPOMI) instrument on the Sentinel-5P satellite. In particular, the construction of realistic uncertainties for the superobservations is non-trivial and crucial to obtaining close-to-optimal data assimilation results. We present a detailed methodology to account for the representation error when satellite observations are missing due to, e.g., cloudiness. Furthermore, we account for systematic errors in the retrievals leading to error correlations between nearby individual observations contributing to one superobservation. Correlation information is typically missing from the retrieval products, where an error estimate is provided for individual observations. The various contributions to the uncertainty are analysed from the spectral fitting and the estimate of the stratospheric contribution to the column and the air mass factor for which we find a typical correlation length of 32 km. The method is applied to TROPOMI data but can be generalized to other trace gases such as <span class="inline-formula">HCHO</span>, <span class="inline-formula">CO</span>, and <span class="inline-formula">SO<sub>2</sub></span> and other instruments such as the Ozone Monitoring Instrument (OMI), the Geostationary Environment Monitoring Spectrometer (GEMS), and the Tropospheric Emissions: Monitoring of POllution (TEMPO) instrument. The superobservations and uncertainties are tested in the Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation ensemble Kalman filter system. These are shown to improve forecasts compared to thinning or compared to assuming fully correlated or uncorrelated uncertainties within the superobservation. The use of realistic superobservations within model comparisons and data assimilation in this way aids the quantification of air pollution distributions, emissions, and their impact on climate.</p> |
| format | Article |
| id | doaj-art-56d63a71a4334e1b9858d73d91cdb456 |
| institution | Kabale University |
| issn | 1991-959X 1991-9603 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj-art-56d63a71a4334e1b9858d73d91cdb4562025-01-28T09:38:08ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032025-01-011848350910.5194/gmd-18-483-2025Quantifying uncertainties in satellite NO<sub>2</sub> superobservations for data assimilation and model evaluationP. Rijsdijk0P. Rijsdijk1P. Rijsdijk2H. Eskes3A. Dingemans4A. Dingemans5K. F. Boersma6K. F. Boersma7T. Sekiya8K. Miyazaki9S. Houweling10S. Houweling11SRON Netherlands Institute for Space Research, Leiden, the NetherlandsSatellite Observations department, Royal Netherlands Meteorological Institute, De Bilt, the NetherlandsDepartment of Earth Sciences, Vrije Universiteit, Amsterdam, the NetherlandsSatellite Observations department, Royal Netherlands Meteorological Institute, De Bilt, the NetherlandsSRON Netherlands Institute for Space Research, Leiden, the Netherlandscurrently at: Koninklijke Luchmacht, Breda, the NetherlandsSatellite Observations department, Royal Netherlands Meteorological Institute, De Bilt, the NetherlandsMeteorology and Air Quality group, Wageningen University, Wageningen, the NetherlandsJapan Agency for Marine-Earth Science and Technology, Yokohama, JapanJet Propulsion Laboratory/California Institute for Technology, Pasadena, California, USASRON Netherlands Institute for Space Research, Leiden, the NetherlandsDepartment of Earth Sciences, Vrije Universiteit, Amsterdam, the Netherlands<p>Satellite observations of tropospheric trace gases and aerosols are evolving rapidly. Recently launched instruments provide increasingly higher spatial resolutions, with footprint diameters in the range of 2–8 km and with daily global coverage for polar orbiting satellites or hourly observations from geostationary orbits. Often the modelling system has a lower spatial resolution than the satellites used, with a model grid size in the range of 10–100 km. When the resolution mismatch is not properly bridged, the final analysis based on the satellite data may be degraded. Superobservations are averages of individual observations matching the model's resolution and are functional to reduce the data load on the assimilation system. In this paper, we discuss the construction of superobservations, their kernels, and uncertainty estimates. The methodology is applied to nitrogen dioxide tropospheric column measurements of the TROPOspheric Monitoring Instrument (TROPOMI) instrument on the Sentinel-5P satellite. In particular, the construction of realistic uncertainties for the superobservations is non-trivial and crucial to obtaining close-to-optimal data assimilation results. We present a detailed methodology to account for the representation error when satellite observations are missing due to, e.g., cloudiness. Furthermore, we account for systematic errors in the retrievals leading to error correlations between nearby individual observations contributing to one superobservation. Correlation information is typically missing from the retrieval products, where an error estimate is provided for individual observations. The various contributions to the uncertainty are analysed from the spectral fitting and the estimate of the stratospheric contribution to the column and the air mass factor for which we find a typical correlation length of 32 km. The method is applied to TROPOMI data but can be generalized to other trace gases such as <span class="inline-formula">HCHO</span>, <span class="inline-formula">CO</span>, and <span class="inline-formula">SO<sub>2</sub></span> and other instruments such as the Ozone Monitoring Instrument (OMI), the Geostationary Environment Monitoring Spectrometer (GEMS), and the Tropospheric Emissions: Monitoring of POllution (TEMPO) instrument. The superobservations and uncertainties are tested in the Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation ensemble Kalman filter system. These are shown to improve forecasts compared to thinning or compared to assuming fully correlated or uncorrelated uncertainties within the superobservation. The use of realistic superobservations within model comparisons and data assimilation in this way aids the quantification of air pollution distributions, emissions, and their impact on climate.</p>https://gmd.copernicus.org/articles/18/483/2025/gmd-18-483-2025.pdf |
| spellingShingle | P. Rijsdijk P. Rijsdijk P. Rijsdijk H. Eskes A. Dingemans A. Dingemans K. F. Boersma K. F. Boersma T. Sekiya K. Miyazaki S. Houweling S. Houweling Quantifying uncertainties in satellite NO<sub>2</sub> superobservations for data assimilation and model evaluation Geoscientific Model Development |
| title | Quantifying uncertainties in satellite NO<sub>2</sub> superobservations for data assimilation and model evaluation |
| title_full | Quantifying uncertainties in satellite NO<sub>2</sub> superobservations for data assimilation and model evaluation |
| title_fullStr | Quantifying uncertainties in satellite NO<sub>2</sub> superobservations for data assimilation and model evaluation |
| title_full_unstemmed | Quantifying uncertainties in satellite NO<sub>2</sub> superobservations for data assimilation and model evaluation |
| title_short | Quantifying uncertainties in satellite NO<sub>2</sub> superobservations for data assimilation and model evaluation |
| title_sort | quantifying uncertainties in satellite no sub 2 sub superobservations for data assimilation and model evaluation |
| url | https://gmd.copernicus.org/articles/18/483/2025/gmd-18-483-2025.pdf |
| work_keys_str_mv | AT prijsdijk quantifyinguncertaintiesinsatellitenosub2subsuperobservationsfordataassimilationandmodelevaluation AT prijsdijk quantifyinguncertaintiesinsatellitenosub2subsuperobservationsfordataassimilationandmodelevaluation AT prijsdijk quantifyinguncertaintiesinsatellitenosub2subsuperobservationsfordataassimilationandmodelevaluation AT heskes quantifyinguncertaintiesinsatellitenosub2subsuperobservationsfordataassimilationandmodelevaluation AT adingemans quantifyinguncertaintiesinsatellitenosub2subsuperobservationsfordataassimilationandmodelevaluation AT adingemans quantifyinguncertaintiesinsatellitenosub2subsuperobservationsfordataassimilationandmodelevaluation AT kfboersma quantifyinguncertaintiesinsatellitenosub2subsuperobservationsfordataassimilationandmodelevaluation AT kfboersma quantifyinguncertaintiesinsatellitenosub2subsuperobservationsfordataassimilationandmodelevaluation AT tsekiya quantifyinguncertaintiesinsatellitenosub2subsuperobservationsfordataassimilationandmodelevaluation AT kmiyazaki quantifyinguncertaintiesinsatellitenosub2subsuperobservationsfordataassimilationandmodelevaluation AT shouweling quantifyinguncertaintiesinsatellitenosub2subsuperobservationsfordataassimilationandmodelevaluation AT shouweling quantifyinguncertaintiesinsatellitenosub2subsuperobservationsfordataassimilationandmodelevaluation |