Japan Geoscience Union Meeting 2015

Presentation information

International Session (Oral)

Symbol A (Atmospheric and Hydrospheric Sciences) » A-CG Complex & General

[A-CG09] Satellite Earth Environment Observation

Thu. May 28, 2015 2:15 PM - 4:00 PM 301B (3F)

Convener:*Riko Oki(Japan Aerospace Exploration Agency), Tadahiro Hayasaka(Graduate School of Science, Tohoku University), Kaoru Sato(Department of Earth and Planetary Science, Graduate School of Science, The University of Tokyo), Masaki Satoh(Atmosphere and Ocean Research Institute, The University of Tokyo), Nobuhiro Takahashi(National Institute of Information and Communications Technology), Yoshiaki HONDA(Center for Environmental Remote Sensing, Chiba University), Kenlo Nasahara(Faculty of Life and Environmental Sciences, University of Tsukuba), Takashi Nakajima(Tokai University, School of Information Science & Technology, Dept. of Human & Information Science), Taikan Oki(Institute of Industrial Science, The University of Tokyo), Tatsuya Yokota(National Institute for Environmental Studies), Yukari Takayabu(Atmosphere and Ocean Research Institute, the University of Tokyo), Hiroshi Murakami(Earth Observation Research Center, Japan Aerospace Exploration Agency), Hajime Okamoto(Kyushu University), Chair:Hiroshi Murakami(Earth Observation Research Center, Japan Aerospace Exploration Agency)

3:45 PM - 4:00 PM

[ACG09-31] On the optimal design for a global high-resolution surface CO_{2} flux inversion model

*Shamil MAKSYUTOV1, Tomohiro ODA2, Rajesh JANARDANAN1, Alexey YAREMCHUK3, Johannes W. KAISER4, Akihiko ITO1, Dmitry BELIKOV5, Ruslan ZHURAVLEV6, Alexander GANSHIN6, Vinu VALSALA7 (1.NIES, Tsukuba, Japan, 2.USRA/GSFC, MD, USA, 3.N. Andreev Acoustics Inst., Moscow, Russia, 4.MPI for Chemistry, Mainz, Germany, 5.NIPR, Tokyo and NIES, Tsukuba, Japan, 6.Central Aerological Observatory, Dolgoprudny, Russia, 7.Indian Institute for Tropical Meteorology, Pune, India)

Keywords:remote sensing, carbon dioxide, data assimilation, inverse modeling

We devised an iterative inversion framework that is optimally designed for estimating surface CO2 fluxes at a high spatial resolution using a Lagrangian-Eulerian coupled tracer transport model and atmospheric CO2 data collected by the global reference in-situ network and satellite observations. In our inverse system, the Lagrangian particle dispersion model FLEXPART was coupled to the Eulerian atmospheric tracer transport model (NIES-TM) and an adjoint of the coupled model was derived. Weekly corrections to given prior fluxes are calculated at a spatial resolution of the meteorology driver of FLEXPART (0.1 degrees) via iterative optimization. The hourly terrestrial biosphere fluxes are simulated with the VISIT model using the CFSR reanalysis. Ocean fluxes are calculated using a 4D-Var assimilation system based on the surface pCO2 observations. Fossil fuel (ODIAC) and biomass burning (GFAS v1.1) emissions are given at original model resolutions (0.1 degree), while terrestrial biosphere and ocean fluxes are interpolated from a coarser resolution. Flux response functions (footprints) for observations are first simulated with FLEXPART. The precalculated flux response functions are used in forward and adjoint runs of the coupled transport model. We apply Lanczos process to obtain truncated singular value decomposition of the scaled tracer transport operator. The square root of covariance matrix for surface fluxes is constructed by implementing two algorithms. The first algorithm uses a lookup table to store precalculated covariance matrix elements implemented as Gaussian function of a great circle distance between grid points. This algorithm appears to produce highly accurate results but it is becoming computationally expensive when implemented at a high spatial resolution. The second (faster) algorithm, which is based on an implicit diffusion operator with a directional splitting in latitude, longitude and time, was also tested. The covariance operator of the second algorithm approximates Matear function rather than Gaussian, the analysis of its singular value spectra and the singular vectors however shows that the resulting covariance matrix has very similar properties to Gaussian covariance matrix. In our method, the prior and posterior flux uncertainties are evaluated using singular vectors of scaled tracer transport operator. The weekly flux uncertainties at a resolution of 0.1 degree and flux uncertainty reduction due to assimilating single shot GOSAT XCO2 data were estimated for a period of one year. We demonstrated that our application of a coupled tracer transport model in adjoint-based assimilation system provides an efficient way to increase spatial resolution of the inverse model.