Assessing the impact of satellite, aircraft, and surface observations on CO2 flux estimation using an ensemble-based 4-D data assimilation system

Journal of Geophysical Research Atmospheres

Volumn 116, Issue 16, 2011, Pages

  Miyazaki, Kazuyuki ^a,b   Maki, Takashi ^c   Patra, Prabir ^a   Nakazawa, Takakiyo ^a,d  

^a JAPAN AGENCY FOR MARINE EARTH SCIENCE AND TECHNOLOGY   (Japan)

^b ROYAL NETHERLANDS METEOROLOGICAL INSTITUTE KNMI   (Netherlands)

^c METEOROLOGICAL RESEARCH INSTITUTE   (Japan)

^d TOHOKU UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ATMOSPHERIC COMPOSITION; CARBON DIOXIDE; COMPUTER SIMULATION; DATA PROCESSING; ESTIMATION; SYSTEMATIC ERRORS;

CONCENTRATION DATA; CONTINENTAL AREA; DATA ASSIMILATION; DATA ASSIMILATION SYSTEMS; DATA COVERAGE; DATA SETS; ENSEMBLE KALMAN FILTER; ERROR REDUCTION; EXTRATROPICS; FLUX ERROR; FLUX ESTIMATION; GLOBAL FLUX; GRID POINTS; HORIZONTAL RESOLUTION; LIMITING FACTORS; OBSERVATION SYSTEMS; POTENTIAL IMPACTS; PROFILE DATA; SIBERIA; SOUTH AFRICA; SOUTH AMERICA; SURFACE FLUX; SURFACE NETWORKS; SURFACE OBSERVATION; TRANSPORT MODELS;

DATA REDUCTION;

AIRCRAFT; ASSESSMENT METHOD; CARBON FLUX; CONCENTRATION (COMPOSITION); DATA SET; ERROR ANALYSIS; ESTIMATION METHOD; FLUX MEASUREMENT; FOUR-DIMENSIONAL MODELING; GOSAT; KALMAN FILTER; LIMITING FACTOR; MEASUREMENT METHOD; NUMERICAL MODEL; OBSERVATIONAL METHOD; RESOLUTION; SATELLITE IMAGERY; TROPOSPHERE;

ASIA; NORTH AMERICA; SOUTH AFRICA; SOUTH AMERICA;

EID: 80052057954     PISSN: 01480227     EISSN: None     Source Type: Journal    
DOI: 10.1029/2010JD015366     Document Type: Article

References (53)

1. Aksoy, A., F. Zhang, and J. W. Nielsen-Gammon (2006), Ensemble-based simultaneous state and parameter estimation in a two-dimensional seabreeze model, Mon. Weather Rev., 134, 2951-2970. (Pubitemid 44649932)
2. Anderson, J. L. (2001), An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884-2903. (Pubitemid 34004796)
3. Anderson, J. L. (2009), Spatially and temporally varying adaptive covariance inflation for ensemble filters, Tellus, Ser. A, 61, 72-83.
4. Bishop, C. H., and D. Hodyss (2009), Ensemble covariances adaptively localized with ECO-RAP. Part 1: Tests on simple error models, Tellus, Ser. A, 61, 84-96.
5. Brenkert, A. L. (1998), Carbon dioxide emission estimates from fossil fuel burning, hydraulic cement production and gas flaring for 1995 on a one degree grid cell basis, Rep. NDP058A, Carbon Dioxide Inf. Anal. Cent., Oak Ridge. Natl. Lab., Oak Ridge, Tenn. (Available at http://cdiac.esd. ornl.gov/ndps/ndp058a.html.)
6. Brenninkmeijer, C. A. M., et al. (2007), The CARIBIC aircraft system for detailed, long-term, global-scale measurement of trace gases and aerosol in a changing atmosphere, IGAC Newsl., 37, 2-9.
7. 2 sources and sinks: Theoretical study in a variational data assimilation framework, J. Geophys. Res., 112, D09307, doi:10.1029/2006JD007375. (Pubitemid 47009866)
8. 2 surface fluxes to be estimated from the GOSAT observations, Geophys. Res. Lett., 36, L19807, doi:10.1029/2009GL040108.
9. Connor, B. J., H. Boesch, G. Toon, B. Sen, C. Miller, and D. Crisp (2008), Orbiting Carbon Observatory: Inverse method and prospective error analysis, J. Geophys. Res., 113, D05305, doi:10.1029/2006JD008336.
10. Conway, T. J., P. P. Tans, L. S. Waterman, K. W. Thoning, D. R. Kitzis, K. A. Masarie, and N. Zhang (1994), Evidence for interannual variability of the carbon cycle from the National Ocean and Atmospheric Administration/ Climate Monitoring and Diagnostics Laboratory Global Air Sampling Network, J. Geophys. Res., 99, 22,831-22,855.
11. 2 retrievals, J. Geophys. Res., 113, D02301, doi:10.1029/2007JD008716.
12. 2 retrievals using a global transport model, Atmos. Chem. Phys., 9, 3043-3048.
13. Dee, D. P., and A. M. da Silva (1998), Data assimilation in the presence of forecast bias, Q. J. R. Meteorol. Soc., 135, 269-295. (Pubitemid 128614310)
14. 2 using AIRS observations, J. Geophys. Res., 114, D03303, doi:10.1029/2008JD010739.
15. Eskes, H., P. van Velthoven, P. Valks, and H. Kelder (2003), Assimilation of GOME total ozone satellite observations in a three-dimensional tracer transport model, Q. J. R. Meteorol. Soc., 129, 1663-1681. (Pubitemid 36568284)
16. Evensen, G. (1994), Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99, 10,143-10,162.
17. 2 dry air mole fraction observations using an ensemble Kalman filter, Atmos. Chem. Phys., 9, D03303, 2619-2633.
18. Fertig, E. J., J. Harlim, and B. R. Hunt (2007), A comparative study of 4D-VAR and a 4D ensemble Kalman filter: Perfect model simulations with Lorenz-96. Tellus, Ser. A, 59, 96-100. (Pubitemid 46111269)
19. Francey, R. J., P. P. Tans, C. E. Allison, I. G. Enting, J. W. C. White, and M. Trolier (1995), Changes in oceanic and terrestrial carbon uptake since 1982, Nature, 273, 326-330.
20. 2 (2006), Cooperative Atmospheric Data Integration Project: Carbon Dioxide [CD-ROM], http://www.esrl.noaa.gov/gmd/ ccgg/globalview/, NOAA ESRL, Boulder, Colo.
21. 2 (2009), Cooperative Atmospheric Data Integration Project: Carbon Dioxide [CD-ROM], http://www.esrl.noaa.gov/gmd/ ccgg/globalview/, NOAA ESRL, Boulder, Colo.
22. 2 sources and sinks using atmospheric transport models, Nature, 415, 626-630. (Pubitemid 34136386)
23. 2 inversion intercomparison: 1. Annual mean control results and sensitivity to transport and prior flux information, Tellus, Ser. B, 55, 555-579. (Pubitemid 36610992)
24. Hoffman, R. N., and E. Kalnay (1983), Lagged average forecasting: An alternative to Monte Carlo forecasting, Tellus, Ser. A, 35, 100-118.
25. Houtekamer, P. L., and H. L. Mitchell (2001), A sequential ensemble Kalman filter for atmospheric data assimilation, Mon. Weather Rev., 129, 123-137. (Pubitemid 32180439)
26. 2 sources and sinks using satellite measurements, Atmos. Chem. Phys. Discuss., 10, 14,737-14,769.
27. 2 surface fluxes, Atmos. Chem. Phys. Discuss., 10, 18,561-18,605.
28. Hunt,B.R., E.Kalnay,E. J. Kostelich, E.Ott, D. J. Patil, T.Sauer, I.Szunyogh, J. A. Yorke, and A. V. Zimin (2004), Four-dimensional ensemble Kalman filtering, Tellus, Ser. A, 56, 273-277. (Pubitemid 39047840)
29. Hunt, B. R., E. J. Kostelich, and I. Szunyogh (2007), Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica D, 230, 112-126. (Pubitemid 46776911)
30. Kalnay, E. (2010), Ensemble Kalman filter: Current status and potential, in Data Assimilation: Making Sense of Observations, edited by W. A. Lahoz, B. Khattatov, and R. Ménard, pp. 69-92, Springer, Heidleberg, Germany.
31. Kang, J.-S., E. Kalnay, J. Liu, I. Fung, T. Miyoshi, and K. Ide (2011), "Variable localization" in an ensemble Kalman filter: Application to the carbon cycle data assimilation, J. Geophys. Res., 116, D09110, doi:10.1029/2010JD014673.
32. Keeling, C. D., T. P. Whorf, M. Wahlen, and J. van der Plicht (1995), Interannual extremes in the rate of rise of atmospheric carbon dioxide since 1980, Nature, 375, 666-670.
33. 2: Experimental overview and diurnal cycle results for 2002, Global Biogeochem. Cycles, 22, GB3009, doi:10.1029/2007GB003050.
34. 2 and other trace gas species using commercial airlines, J. Atmos. Oceanic Technol., 25, 1744-1754, doi:10.1175/2008JTECHA1082. 1.
35. Masarie, K. A., et al. (2001), The NOAA/CSIRO Flask-Air Intercomparison Program: A strategy for directly assessing consistency among atmospheric measurements made by independent laboratories, J. Geophys. Res., 106, 20,445-20,464.
36. 2 measurements from new flask air sampling of JAL airliner observations, Pap. Meteorol. Geophys., 59, 1-17.
37. Miyazaki, K. (2009), Performance of a local ensemble transform Kalman filter for the analysis of atmospheric circulation and distribution of long-lived tracers under idealized conditions, J. Geophys. Res., 114, D19304, doi:10.1029/2009JD011892.
38. Miyazaki, K., P. K. Patra, M. Takigawa, T. Iwasaki, and T. Nakazawa (2008), Global-scale transport of carbon dioxide in the troposphere, J. Geophys. Res., 113, D15301, doi:10.1029/2007JD009557.
39. Miyoshi, T., S. Yamane, and T. Enomoto (2007), Localizing the error covariance by physical distances within a local ensemble transform Kalman filter (LETKF), Sci. Online Lett. Atmos., 3, 89-92.
40. Numaguti, A., M. Takahashi, T. Nakajima, and A. Sumi (1995), Development of an atmospheric general circulation model, Rep. I-3, in Reports of a New Program for Creative Basic Research Studies: Studies of Global Environment Change to Asia and Pacific Regions, pp. 1-27, CCSR, Tokyo.
41. 2 and implications for carbon cycle research, J. Geophys. Res., 109, D02301, doi:10.1029/2003JD003968.
42. Ott, E., B. R. Hunt, I. Szunyogh, A. V. Zimin, E. J. Kostelich, M. Corazza, E. Kalnay, D. J. Patil, and J. A. Yorke (2004), A local ensemble Kalman filter for atmospheric data assimilation, Tellus, Ser. A, 56, 415-428. (Pubitemid 39533058)
43. 2 observations with Solar Occultation FTS for Inclined-Orbit Satellite sensor for surface source inversion, J. Geophys. Res., 108(D24), 4759, doi:10.1029/2003JD003661.
44. 2 inverse modeling, Global Biogeochem. Cycles, 19, GB4013, doi:10.1029/2004GB002257. (Pubitemid 43280676)
45. 2 measurements, Atmos. Chem. Phys. Discuss., 11, 5379-5405.
46. 2 surface fluxes from atmospheric trace gas observations, J. Geophys. Res., 110, D24304, doi:10.1029/ 2005JD006157.
47. Randerson, J. T., M. V. Thompson, T. J. Conway, I. Y. Fung, and C. B. Field (1997), The contribution of terrestrial sources and sinks to trends in the seasonal cycle of atmospheric carbon dioxide, Global Biogeochem. Cycles, 11, 535-560.
48. 2 concentration data in surface source inversions, Geophys. Res. Lett., 28, 175-178.
49. 2 near the tropopause observed by commercial aircraft, J. Geophys. Res., 113, D23301, doi:10.1029/2008JD010568.
50. 2, Science, 316, 1732-1735. (Pubitemid 46988545)
51. 2, and seasonal biological and temperature effects, Deep Sea Res., Part II, 49, 1601-1622. (Pubitemid 34565015)
52. Tong, M., and M. Xue (2008), Simultaneous estimation of microphysical parameters and atmospheric state with radar data and ensemble Kalman filter. Part II: Parameter estimation experiments, Mon. Weather Rev., 136, 1649-1668. (Pubitemid 351819019)
53. 4 retrieved from GOSAT: First preliminary results, Sci. Online Lett. Atmos., 5, 160-163, doi:10.2151/sola.2009-041.