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An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO₂ Retrievals.

ABSTRACT: A growing constellation of satellites is providing near-global coverage of column-averaged CO₂ observations. Launched in 2019, NASA's OCO-3 instrument is set to provide XCO₂ observations at a high spatial and temporal resolution for regional domains (100 × 100 km). The atmospheric column version of the Stochastic Time-Inverted Lagrangian Transport (X-STILT) model is an established method of determining the influence of upwind sources on column measurements of the atmosphere, providing a means of analysis for current OCO-3 observations and future space-based column-observing missions. However, OCO-3 is expected to provide hundreds of soundings per targeted observation, straining this already computationally intensive technique. This work proposes a novel scheme to be used with the X-STILT model to generate upwind influence footprints with less computational expense. The method uses X-STILT generated influence footprints from a key subset of OCO-3 soundings. A nonlinear weighted averaging is applied to these footprints to construct additional footprints for the remaining soundings. The effects of subset selection, meteorological data, and topography are investigated for two test sites: Los Angeles, California, and Salt Lake City, Utah. The computational time required to model the source sensitivities for OCO-3 interpretation was reduced by 62% and 78% with errors smaller than other previously acknowledged uncertainties in the modeling system (OCO-3 retrieval error, atmospheric transport error, prior emissions error, etc.). Limitations and future applications for future CO₂ missions are also discussed.

SUBMITTER: Roten D

PROVIDER: S-EPMC8047910 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Lagrangian particle dispersion models (LPDMs) are frequently used for regional-scale inversions of greenhouse gas emissions. However, the turbulence parameterizations used in these models were developed for coarse resolution grids, hence, when moving to the kilometre-scale the validity of these descriptions should be questioned. Here, we analyze the influence of the turbulence parameterization employed in the LPDM FLEXPART-COSMO model. Comparisons of the turbulence kinetic energy between the turbulence schemes of FLEXPART-COSMO and the underlying Eulerian model COSMO suggest that the dispersion in FLEXPART-COSMO suffers from a double-counting of turbulent elements when run at a high resolution of 1×1km2 . Such turbulent elements are represented in both COSMO, by the resolved grid-scale winds, and FLEXPART, by its stochastic parameterizations. Therefore, we developed a new parametrization for the variations of the winds and the Lagrangian time scales in FLEXPART in order to harmonize the amount of turbulence present in both models. In a case study for a power plant plume, the new scheme results in improved plume representation when compared with in situ flight observations and with a tracer transported in COSMO. Further in-depth validation of the LPDM against methane observations at a tall tower site in Switzerland shows that the model's ability to predict the observed tracer variability and concentration at different heights above ground is considerably enhanced using the updated turbulence description. The high-resolution simulations result in a more realistic and pronounced diurnal cycle of the tracer concentration peaks and overall improved correlation with observations when compared to previously used coarser resolution simulations (at 7 km × 7 km). Our results indicate that the stochastic turbulence schemes of LPDMs, developed in the past for coarse resolution models, should be revisited to include a resolution dependency and resolve only the part of the turbulence spectrum that is a subgrid process at each different mesh size. Although our new scheme is specific to COSMO simulations at 1×1km2 resolution, the methodology for deriving the scheme can easily be applied to different resolutions and other regional models.Supplementary informationThe online version contains supplementary material available at 10.1007/s10546-022-00728-3.

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An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO₂ Retrievals.

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An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO2 Retrievals.

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An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO₂ Retrievals.