Importance of interpolation and coincidence errors in data fusion

Ceccherini, Simone; Carli, B.; Tirelli, Cecilia; Zoppetti, Nicola; Bianco, Samuele Del; Cortesi, Ugo; Kujanpää, Jukka; Dragani, Rossana

doi:10.5194/amt-11-1009-2018

Cited by 17 publications

(32 citation statements)

References 29 publications

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“…Furthermore, other assumptions and inaccuracies in the observation operator (which transforms the model state into the observation space) also contribute to the representativeness error. Ceccherini et al (2018) showed the importance of interpolation and coincidence errors for retrievals on different vertical grids in data fusion. For example, the difference in the 5 layers between the retrieval grid and the MOCAGE CTM may easily lead to regridding (interpolation) errors that may make it difficult to assimilate stratospheric (high concentration) and tropospheric ozone (low concentration) together.…”

Section: Satellite Observations Error Covariance Matrixmentioning

confidence: 99%

Benefit of ozone observations from Sentinel-5P and future Sentinel-4 missions on tropospheric composition

Quesada-Ruiz¹,

Attié²,

Lahoz³

et al. 2019

Preprint

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Abstract. We present an observing simulated system experiment (OSSE) dedicated to evaluate the potential added value from the Sentinel-4 and the Sentinel-5P observations on tropospheric ozone composition. For this purpose, the ozone data of Sentinel-4 (Ultraviolet Visible Near-infrared) and Sentinel-5P (TROPOspheric Monitoring Instrument) onboard a geostationary (GEO) and a low Earth orbit (LEO) platform, respectively, has been simulated for the summer 2003. To ensure the robustness of the results, the OSSE has been configured with conservative assumptions. We simulate the reality by combining two chemistry transport models (CTMs): the Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) and the Transport Model version 5 (TM5). The assimilation system is based on a different CTM, the MOdèle de Chimie Atmosphérique à Grande Echelle (MOCAGE), combined with the 3D variational technique. The background error covariance matrix does not evolve in time and its variance is proportional to the field values. The simulated data are formed of six eigenvectors to minimize the size of the dataset by removing the noise-dominated part of the observations. The results show that the satellite data clearly bring direct added value around 200 hPa for the whole assimilation period and for the whole European domain, while a likely indirect added value is identified but not for the whole period and domain at 500 hPa, and to a lower extent at 700 hPa. In addition, the ozone added value from Sentinel-5P (LEO) appears close to that from Sentinel-4 (GEO) in the free troposphere (200–500 hPa) in our OSSE.

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Section: Satellite Observations Error Covariance Matrixmentioning

confidence: 99%

Benefit of ozone observations from Sentinel-5P and future Sentinel-4 missions on tropospheric composition

Quesada-Ruiz¹,

Attié²,

Lahoz³

et al. 2019

Preprint

Self Cite

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show abstract

“…Carried out in the context of several satellite validation studies (for Sentinel-5P, the European Space Agency's Climate Change Initiative, and the Satellite Application Facility on Atmospheric Composition Monitoring) and considering the exploration of advanced data fusion methods (Cortesi et al, 2018), with a view to harmonize practices across satellite missions and Earth Observation domains, this work is meant to provide an overview of existing approaches that allow estimating and potentially (partially) correcting for these observational differences in quantitative data comparisons. The uncertainties that are tied to these differences, as typically expressed in terms of covariance matrices, as a result are also (partially) removed from the uncertainty budget of the data comparison.…”

Section: Introductionmentioning

confidence: 99%

Harmonization and comparison of vertically resolved atmospheric state observations: methods, effects, and uncertainty budget

et al. 2019

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Abstract. Many applications of atmospheric composition and climate data involve the comparison or combination of vertically resolved atmospheric state variables. Calculating differences and combining data require harmonization of data representations in terms of physical quantities and vertical sampling at least. If one or both datasets result from a retrieval process, knowledge of prior information and averaging kernel matrices in principle allows retrieval differences to be accounted for as well. Spatiotemporal mismatch of the sensed air masses and its contribution to the data discrepancies can be estimated with chemistry transport modeling support. In this work an overview of harmonization or matching operations for atmospheric profile observations is provided. The effect of these manipulations on the information content of the original data and on the uncertainty budget of data comparisons is examined and discussed.

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“…The CDF method was proved (Ceccherini, 2016) to be equivalent to the measurement space solution data fusion method (Ceccherini et al, 2009) and the latter was successfully applied to the data fusion of MIPAS-ENVISAT and IASI-METOP measurements (Ceccherini et al, 2010a, b) and of MIPAS-STR and MARSCHALS measurements (Cortesi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Conversely, as highlighted in Ceccherini et al (2018), the CDF provides poor results when applied to inconsistent measurements. Three causes of inconsistency are possible:…”

Section: Introductionmentioning

confidence: 99%

The cost function of the data fusion process and its application

et al. 2019

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Abstract. When the complete data fusion method is used to fuse inconsistent measurements, it is necessary to add to the measurement covariance matrix of each fusing profile a covariance matrix that takes into account the inconsistencies. A realistic estimate of these inconsistency covariance matrices is required for effectual fused products. We evaluate the possibility of assisting the estimate of the inconsistency covariance matrices using the value of the cost function minimized in the complete data fusion. The analytical expressions of expected value and variance of the cost function are derived. Modelling the inconsistency covariance matrix with one parameter, we determine the value of the parameter that makes the reduced cost function equal to its expected value and use the variance to assign an error to this determination. The quality of the inconsistency covariance matrix determined in this way is tested for simulated measurements of ozone profiles obtained in the thermal infrared in the framework of the Sentinel-4 mission of the Copernicus programme. As expected, the method requires sufficient statistics and poor results are obtained when a small number of profiles are being fused together, but very good results are obtained when the fusion involves a large number of profiles.

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Importance of interpolation and coincidence errors in data fusion

Cited by 17 publications

References 29 publications

Benefit of ozone observations from Sentinel-5P and future Sentinel-4 missions on tropospheric composition

Benefit of ozone observations from Sentinel-5P and future Sentinel-4 missions on tropospheric composition

Harmonization and comparison of vertically resolved atmospheric state observations: methods, effects, and uncertainty budget

The cost function of the data fusion process and its application

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