the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Quantifying uncertainties of satellite NO2 superobservations for data assimilation and model evaluation
Abstract. 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, with daily global coverage for polar orbiting satellites or hourly observations from geostationary orbit. 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 resolution of the model 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 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 representativity 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 in the retrieval products where an error estimate is provided for individual observations. The various contributions to the uncertainty are analysed: from the spectral fitting, the estimate of the stratospheric contribution to the column and the air-mass factor. The method is applied to TROPOMI data but can be generalised to other trace gases such as HCHO, CO, SO2 and other instruments such as OMI, GEMS and TEMPO. The superobservations and uncertainties are tested in the ensemble Kalman filter chemical data assimilation system developed by JAMSTEC. 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.
- Preprint
(8971 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 05 Jun 2024)
-
RC1: 'Comment on egusphere-2024-632', Anonymous Referee #1, 26 Apr 2024
reply
Review comments on 10.5194/egusphere-2024-632
Summary:
This study investigated the superobservation methodology for satellite observations, especially for chemical tracers. The paper discussed how to construct superobservations and appropriately set their uncertainty. The authors took several aspects into account and visualized their contributions to the resulting superobservations. They also performed data assimilation experiments and showed that their superobservation methodology resulted in improved forecasts compared to simple thinning.
The paper appears to align with the scope of GMD and may have significant implications for data assimilation studies involving satellite observations. Unfortunately, certain points were not clear to me and could benefit from clarification prior to publication. In addition, the paper’s readability was somewhat challenging, possibly due to its unconventional structure. Therefore, I would like to recommend Major revisions.
Major comments:
1. Clarifications required.
Several statements in the manuscript were unclear to me. Most of them could be my misunderstanding, but I would like to ask the authors for enhancing the clarity.
Lines 196–197: The sentence beginning with “Given the same uncertainty,” is confusing. Could this be re-written?
Lines 322–323: Unfortunately, I could not understand this sentence. A rewrite may be necessary.
Lines 510–511: This sentence was confusing. I guess this sentence compares Figures 12a and 12c, yet I could not find clear differences between these figures.
Lines 561–562: Why does an increase of covariance inflation result in a larger O–B?
Line 641: What does correlation length mean? The cutoff radius of localization?
Line 703: I am not sure why adding one improves the results. I understood that N > Neff but does this sentence mean N–Neff=1? Could you explain? Furthermore, I could not understand that “This is not consistent with the experimental data” in Fig. 8c.
2. The structure of the paper
The manuscript does not have a typical structure that contains an introduction, method, result, discussion, and summary. Although the motivation of the study should be clearly noted in the first section, section 3 explains the motivation of superobservation as well. While section 5 discussed contributions of three aspects on uncertainty, section 6 revisits the topic of uncertainty. I might misunderstand, but I would like to ask the authors to re-consider the structure of the manuscript. This could make the paper more concise.
Minor comments:
Line 92: Remove an extra “observational?”
Line 238: Does the left-hand side correspond to ds?
Line 246: Does AS mean the superkernel?
Line 299: What are Θ and Θ0?
Line 531: I would recommend explaining the experimental settings a bit more. It would be better to include what observations were assimilated, how long the assimilation window, how localizations were set, and how covariance inflation was achieved.
Line 551: The Χ2 metrics seem similar with the consistency ratio (Dowell and Wicker 2009, 10.1175/2008JTECHA1156.1). Are they the same?
Citation: https://doi.org/10.5194/egusphere-2024-632-RC1
Model code and software
Superobservation software Pieter Rijsdijk, Henk Eskes, and Miro van der Worp https://doi.org/10.5281/zenodo.10726644
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
165 | 50 | 9 | 224 | 6 | 6 |
- HTML: 165
- PDF: 50
- XML: 9
- Total: 224
- BibTeX: 6
- EndNote: 6
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1