Accounting for Transport Error in Inversions: An Urban Synthetic Data Experiment
| Autor: | Subhomoy Ghosh, James R. Whetstone, K. L. Mueller, Kuldeep R. Prasad |
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| Rok vydání: | 2021 |
| Předmět: |
Informatics
Astronomy green house gases Time Series Experiments error covariance Stochastic Phenomena Remote Sensing and Electromagnetic Processes Parametric statistics QE1-996.5 Ionospheric Propagation Fourier Analysis Nonlinear Geophysics Uncertainty Geology Covariance regularization Oceanography: General Shock Waves Spatial Modeling Ensemble Transform Kalman Filter Probability Distributions Heavy and Fat‐tailed Uncertainty Quantification Mathematical Geophysics Algorithm Research Article Persistence Memory Correlations Clustering QB1-991 Temporal Analysis and Representation Context (language use) Wavelet Transform Environmental Science (miscellaneous) Spatial Analysis and Representation Synthetic data Radio Science Greenhouse Gases inversion Paleoceanography Extreme Events Inverse Theory Robustness (computer science) Solitons and Solitary Waves Time Series Analysis Ionosphere Spatial Analysis Stochastic Processes Electromagnetics Uncertainty Assessment Inversion (meteorology) Kalman filter Spectral Analysis Nonlinear Waves Shock Waves Solitons Space Plasma Physics General Earth and Planetary Sciences Environmental science Errors-in-variables models Computational Geophysics Wave Propagation Hydrology Scaling: Spatial and Temporal Natural Hazards |
| Zdroj: | Earth and Space Science (Hoboken, N.j.) Earth and Space Science, Vol 8, Iss 7, Pp n/a-n/a (2021) |
| ISSN: | 2333-5084 |
| DOI: | 10.1029/2020ea001272 |
| Popis: | We present and discuss the use of a high‐dimensional computational method for atmospheric inversions that incorporates the space‐time structure of transport and dispersion errors. In urban environments, transport and dispersion errors are largely the result of our inability to capture the true underlying transport of greenhouse gas (GHG) emissions to observational sites. Motivated by the impact of transport model error on estimates of fluxes of GHGs using in situ tower‐based mole‐fraction observations, we specifically address the need to characterize transport error structures in high‐resolution large‐scale inversion models. We do this using parametric covariance functions combined with shrinkage‐based regularization methods within an Ensemble Transform Kalman Filter inversion setup. We devise a synthetic data experiment to compare the impact of transport and dispersion error component of the model‐data mismatch covariance choices on flux retrievals and study the robustness of the method with respect to fewer observational constraints. We demonstrate the analysis in the context of inferring CO2 fluxes starting with a hypothesized prior in the Washington D.C. /Baltimore area constrained by a synthetic set of tower‐based CO2 measurements within an observing system simulation experiment framework. This study demonstrates the ability of these simple covariance structures to substantially improve the estimation of fluxes over standard covariance models in flux estimation from urban regions. Key Points Urban‐scale transport errors are correlated in space and time, which should be included in atmospheric inversions that estimate greenhouse gas emissionsIn a synthetic data study, multiple methods to characterize correlated errors with model‐data covariance matrices demonstrate a better performanceOverall, dynamically adaptive covariance structures perform better than standard parametric models in both recovering total fluxes and their spatial distributions |
| Databáze: | OpenAIRE |
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