edu.ucar.opensky::articles:13007 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-26T03:52:08.355541 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d73x87jg Toward reliable ensemble Kalman filter estimates of CO₂ fluxes 2012-11-27T00:00:00Z publication https://n2t.org/ark:/85065/d73x87jg https://doi.org/10.1029/2012JD018176 Chatterjee, Abhishek author Michalak, Anna M. author Anderson, Jeffrey L. author Mueller, K. author Yadav, V. author UCAR/NCAR - Library publisher The use of ensemble filters for estimating sources and sinks of carbon dioxide (CO₂) is becoming increasingly common, because they provide a relatively computationally efficient framework for assimilating high-density observations of CO₂. Their applicability for estimating fluxes at high-resolutions and the equivalence of their estimates to those from more traditional "batch" inversion methods have not been demonstrated, however. In this study, we introduce a Geostatistical Ensemble Square Root Filter (GEnSRF) as a prototypical filter and examine its performance using a synthetic data study over North America at a high spatial (1° × 1°) and temporal (3-hourly) resolution. The ensemble performance, both in terms of estimates and associated uncertainties, is benchmarked against a batch inverse modeling setup in order to isolate and quantify the degradation in the estimates due to the numerical approximations and parameter choices in the ensemble filter. The examined case studies demonstrate that adopting state-of-the-art covariance inflation and localization schemes is a necessary but not sufficient condition for ensuring good filter performance, as defined by its ability to yield reliable flux estimates and uncertainties across a range of resolutions. Observational density is found to be another critical factor for stabilizing the ensemble performance, which is attributed to the lack of a dynamical model for evolving the ensemble between assimilation times. This and other results point to key differences between the applicability of ensemble approaches to carbon cycle science relative to its use in meteorological applications where these tools were originally developed. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright 2012 American Geophysical Union. None eng geoscientificInformation