11 citations as recorded by crossref.

1. Using atmospheric trace gas vertical profiles to evaluate model fluxes: a case study of Arctic-CAP observations and GEOS simulations for the ABoVE domain C. Sweeney et al. 10.5194/acp-22-6347-2022
2. Computationally efficient methods for large-scale atmospheric inverse modeling T. Cho et al. 10.5194/gmd-15-5547-2022
3. Fundamentals of data assimilation applied to biogeochemistry P. Rayner et al. 10.5194/acp-19-13911-2019
4. A global synthesis inversion analysis of recent variability in CO<sub>2</sub> fluxes using GOSAT and in situ observations J. Wang et al. 10.5194/acp-18-11097-2018
5. Effect of Data Assimilation Parameters on The Optimized Surface CO2 Flux in Asia H. Kim et al. 10.1007/s13143-017-0049-9
6. Comparison between the Local Ensemble Transform Kalman Filter (LETKF) and 4D‐Var in atmospheric CO2 flux inversion with the Goddard Earth Observing System‐Chem model and the observation impact diagnostics from the LETKF J. Liu et al. 10.1002/2016JD025100
7. Geostatistical inverse modeling with very large datasets: an example from the Orbiting Carbon Observatory 2 (OCO-2) satellite S. Miller et al. 10.5194/gmd-13-1771-2020
8. Impacts of Horizontal Resolution on Global Data Assimilation of Satellite Measurements for Tropospheric Chemistry Analysis T. Sekiya et al. 10.1029/2020MS002180
9. Comparing the CarbonTracker and TM5-4DVar data assimilation systems for CO<sub>2</sub> surface flux inversions A. Babenhauserheide et al. 10.5194/acp-15-9747-2015
10. Length Scale Analyses of Background Error Covariances for EnKF and EnSRF Data Assimilation S. Park et al. 10.3390/atmos13020160
11. Global and regional carbon budget for 2015–2020 inferred from OCO-2 based on an ensemble Kalman filter coupled with GEOS-Chem Y. Kong et al. 10.5194/acp-22-10769-2022