Anna M. Michalak
Organization:
Carnegie Institution for Science
Stanford University
Email:
Business Phone:
Work:
(650) 353-4247
Business Address:
Department of Global Ecology
260 Panama Street
Stanford, CA 94305
United StatesWebsite:
First Author Publications:
- Michalak, A. M., N. A. Randazzo, and F. Chevallier (2017), Diagnostic methods for atmospheric inversions of long-lived greenhouse gases, Atmos. Chem. Phys., 17, 7405-7421, doi:10.5194/acp-17-7405-2017.
- Michalak, A. M. (2013), Atmospheric observations and inverse modeling approaches for identifying geographical sources and sinks of carbon.
- Michalak, A. M. (2008), Technical Note: Adapting a fixed-lag Kalman smoother to a geostatistical atmospheric inversion framework, Atmos. Chem. Phys., 8, 6789-6799, doi:10.5194/acp-8-6789-2008.
Co-Authored Publications:
- Miller, S. M., et al. (2018), Characterizing biospheric carbon balance using CO2 observations from the OCO-2 satellite, Atmos. Chem. Phys., 18, 6785-6799, doi:10.5194/acp-18-6785-2018.
- Tadic, J. M., et al. (2017), Elliptic Cylinder Airborne Sampling and Geostatistical Mass Balance Approach for Quantifying Local Greenhouse Gas Emissions, Environ. Sci. Technol., 51, 10012-10021, doi:10.1021/acs.est.7b03100.
- Hammerling, D. M., et al. (2015), Detectability of CO2 flux signals by a space-based lidar mission, J. Geophys. Res., 120, 1794-1807, doi:10.1002/2014JD022483.
- Chatterjee, A., et al. (2013), Background error covariance estimation for atmospheric CO2 data assimilation, J. Geophys. Res., 118, 10140-10154, doi:10.1002/jgrd.50654.
- Schwalm, C. R., et al. (2013), Sensitivity of inferred climate model skill to evaluation decisions: a case study using CMIP5 evapotranspiration, Environmental Research Letters, 8, doi:10.1088/1748-9326/8/2/024028.
- Shiga, Y. P., et al. (2013), In-situ CO2 monitoring network evaluation and design: A criterion based on atmospheric CO2 variability, J. Geophys. Res., 118, 2007-2018, doi:10.1002/jgrd.50168.
- Yadav, V., and A. M. Michalak (2013), Improving computational efficiency in large linear inverse problems: an example from carbon dioxide flux estimation, Geosci. Model Dev., 6, 583-590, doi:10.5194/gmd-6-583-2013.
- Yadav, V., K. L. Mueller, and A. M. Michalak (2013), A backward elimination discrete optimization algorithm for model selection in spatio-temporal regression models, Environmental Modelling & Software, 42, 88-98.
- Chatterjee, A., et al. (2012), Toward reliable ensemble Kalman filter estimates of CO2 fluxes, J. Geophys. Res., 117, D22306, doi:10.1029/2012JD018176.
- Gourdji, S. M., et al. (2012), North American CO2 exchange: inter-comparison of modeled estimates with results from a fine-scale atmospheric inversion, Biogeosciences, 9, 457-475, doi:10.5194/bg-9-457-2012.
- Hammerling, D. M., et al. (2012), Global CO2 distributions over land from the Greenhouse Gases Observing Satellite (GOSAT), Geophys. Res. Lett., 39, L08804, doi:10.1029/2012GL051203.
- Hammerling, D. M., A. M. Michalak, and S. R. Kawa (2012), Mapping of CO2 at high spatiotemporal resolution using satellite observations: Global distributions from OCO-2, J. Geophys. Res., 117, D06306, doi:10.1029/2011JD017015.
- Huntzinger, D. N., et al. (2012), North American Carbon Program (NACP) regional interim synthesis: Terrestrial biospheric model intercomparison, Ecological Modelling, 232, 144-157, doi:10.1016/j.ecolmodel.2012.02.004.
- Erickson, T. A., A. M. Michalak, and J. C. Lin (2011), “A data system for visualizing 4-D atmospheric CO2 models and data”, OSGeo Journal, 8, 37-47.
- Huntzinger, D. N., et al. (2011), A systematic approach for comparing modeled biospheric carbon fluxes across regional scales, Biogeosciences, 8, 1579-1593, doi:10.5194/bg-8-1579-2011.
- Huntzinger, D. N., et al. (2011), The utility of continuous atmospheric measurements for identifying biospheric CO2 flux variability, J. Geophys. Res., 116, D06110, doi:10.1029/2010JD015048.
- Chatterjee, A., et al. (2010), A geostatistical data fusion technique for merging remote sensing and ground‐based observations of aerosol optical thickness, J. Geophys. Res., 115, D20207, doi:10.1029/2009JD013765.
- Gourdji, S. M., et al. (2010), Regional-scale geostatistical inverse modeling of North American CO2 fluxes: a synthetic data study, Atmos. Chem. Phys., 10, 6151-6167, doi:10.5194/acp-10-6151-2010.
- Mueller, K. L., et al. (2010), Attributing the variability of eddy‐covariance CO2 flux measurements across temporal scales using geostatistical regression for a mixed northern hardwood forest, Global Biogeochem. Cycles, 24, GB3023, doi:10.1029/2009GB003642.
- Yadav, V., et al. (2010), A geostatistical synthesis study of factors affecting gross primary productivity in various ecosystems of North America, Biogeosciences, 7, 2655-2671, doi:10.5194/bg-7-2655-2010.
- Alkhaled, A. A., A. M. Michalak, and S. R. Kawa (2008), Using CO2 spatial variability to quantify representation errors of satellite CO2 retrievals, Geophys. Res. Lett., 35, L16813, doi:10.1029/2008GL034528.
- Alkhaled, A. A., et al. (2008), A global evaluation of the regional spatial variability of column integrated CO2 distributions, J. Geophys. Res., 113, D20303, doi:10.1029/2007JD009693.
- Gourdji, S. M., et al. (2008), Global monthly averaged CO2 fluxes recovered using a geostatistical inverse modeling approach: 2. Results including auxiliary environmental data, J. Geophys. Res., 113, D21115, doi:10.1029/2007JD009733.
- Mueller, K. L., S. M. Gourdji, and A. M. Michalak (2008), Global monthly averaged CO2 fluxes recovered using a geostatistical inverse modeling approach: 1. Results using atmospheric measurements, J. Geophys. Res., 113, D21114, doi:10.1029/2007JD009734.
- Miller, C. E., et al. (2007), Precision requirements for space-based XCO2 data, J. Geophys. Res., 112, D10314, doi:10.1029/2006JD007659.