參考文獻 |
曾忠一 (2006),大氣科學中的反問題 (上)、(下)。國立編譯館,1288頁。
張時禹等人 (2007),整合性中尺度環境評估報告子計畫五: 排放模式及直接耦合氣象與空氣品質模式。NSC-97-2752-M-008-006-PAE。
劉遵賢等人 (2002),台灣空氣品質模式Taiwan Air Quality Model (TAQM) 操作使用手冊(Version 1.11),33頁。
連國淵 (2009),颱風路徑與結構同化研究-系集卡爾曼濾波器。國立台灣大學大氣科學研究所論文,87頁。
高晟傑 (2012),利用WRF-LETKF同化系統探討掩星折射率觀測對於強降水事件預報之影響。國立中央大學大氣物理所碩士論文,70頁。
林冠任 (2012),改善區域系及卡爾曼濾波器在颱風同化及預報中的spin-up問題-2008年颱風辛樂克個案研究。國立中央大學大氣物理所碩士論文,68頁。
Anderson, J. L. (2001), An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903.
Anderson, J. L., and S. L. Anderson (1999), A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev., 127, 2741–2758.
Baker, D. F., S. C. Doney, and D. S. Schimel (2006), Variational data assimilation for atmospheric CO2, Tellus, Ser. B, 58, 359–365.
Baker, D. F., H. Bösch, S. C. Doney, D. O’Brien, and D. S. Schimel (2010), Carbon source/sink information provided by column CO2 measurements from the Orbiting Carbon Observatory, Atmos. Chem. Phys., 10, 4145–4165, doi:10.5194/acp-10-4145-2010.
Barker, D. M., W. Huang, Y-R. Guo, A. J. Bourgeois, and Q. N. Xiao (2004), A Three-Dimensional Variational Data Assimilation System for MM5: Implementation and Initial Results. Mon. Weather Rev., 132, 897–914.
Bishop, C.H., B. J. Etherton, S. J. Majumdar (2001), Adaptive sampling with the ensemble transform Kalman filter. part I: Theoretical aspects, Mon. Weather Rev., 129, 420–436.
Burgers, G., P. J. van Leeuwen, and G. Evensen (1998), Analysis scheme in the ensemble Kalman Filter, Mon. Weather Rev., 126, 1719–1724.
Chevallier, F., M. Fisher, P. Peylin, S. Serrar, P. Bousquet, F.-M. Bréon, A. Chédin, and P. Ciais, 2005: Inferring CO2 sources and sinks from satellite observations: Method and application to TOVS data , J. Geophys. Res., 110, D24309, doi:10.1029/2005JD006390.
Chevallier, F., F.‐M. Bréon, and P. J. Rayner (2007), Contribution of the Orbiting Carbon Observatory to the estimation of CO2 sources and sinks: Theoretical study in a variational data assimilation framework, J. Geophys. Res., 112, D09307, doi:10.1029/2006JD007375.
Chevallier, F., L. Feng, H. Bösch, P. I. Palmer, and P. J. Rayner (2010), On the impact of transport model errors for the estimation of CO2 surface fluxes from GOSAT observations, Geophys. Res. Lett., 37, L21803, doi:10.1029/2010GL044652.
Crisp, D., et al. (2004), The Obiting Carbon Observatory (OCO) mission, Adv. Space Res., 34, 700–709, doi:10.1016/j.asr.2003.08.062.
Dudhia, J., (1989), Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model, J. Atmos. Sci., 46, 3077–3107.
Engelen, R. J., S. Serrar, and F. Chevallier (2009), Four‐dimensional data assimilation of atmospheric CO2 using AIRS observations, J. Geophys. Res., 114, D03303, doi:10.1029/2008JD010739.
Evensen, G. (1994), Sequential data assimilation with a non-linear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 143–10 162.
Evensen, G. (2003), The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynamics, 53, 343–367,
doi: 10.1007/s10236-003-0036-9.
Feng, L., P. I. Palmer, H. Bosch, and S. Dance (2009), Estimating surface CO2 fluxes from space‐borne CO2 dry air mole fraction observations using an ensemble Kalman filter, Atmos. Chem. Phys., 9, D03303, 2619–2633.
Gaspari, G., and S. E. Cohn (1999), Construction of correlation functions
in two and three dimensions, Q. J. R. Meteorol. Soc., 125, 723–757,
doi:10.1002/qj.49712555417.
GLOBALVIEW‐CO2 (2011), Cooperative Atmospheric Data Integration Project: Carbon Dioxide [CD‐ROM], http://www.esrl.noaa.gov/gmd/ccgg/globalview/, NOAA ESRL, Boulder, Colo.
Gurney, K. R., et al. (2004), Transcom 3 inversion intercomparison: Model mean results for the estimation of seasonal carbon sources and sinks, Global Biogeochem. Cycles, 18, GB1010, doi:10.1029/2003GB002111.
Hamill, T. M., J. S. Whitaker, and C. Snyder (2001), Distance‐dependent filtering of background error covariance estimates in an ensemble Kalman filter, Mon. Weather Rev.,129,2776–2790, doi:10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2.
Hong, S.-Y., and Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318–2341.
Hollingsworth, A., et al. (2008), The Global Earth‐system Monitoring using Satellite and in‐situ data (GEMS) Project: Towards a monitoring and forecasting system for atmospheric composition, Bull. Am. Meteorol. Soc., 89, 1147–1164, doi:10.1175/2008BAMS2355.1.
Hunt, B. R., E. Kostelich, and I. Szunyogh (2007), Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica D, 230, 112–126, doi:10.1016/j.physd.2006.11.008.
Jazwinski, A. H. (1970), Stochastic Processes and Filtering Theory. Academic, New York.
Kain J. S. and J. M. Fritsch (1993), Convective parameterization for mesoscalemodels: The Kain-Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 165–170.
Kalman, R. E. (1960), A new approach to linear filtering and prediction problems, Trans. ASME, Series D, J. Basic Eng., 82, 35-45.
Kalnay E. and S-C Yang, (2010), Accelerating the spin-up of Ensemble Kalman Filtering. Q. J. R. Meteorol. Soc., 136, 1644-1651.
Kang, J.-S., E. Kalnay, J. Liu, I. Fung, T. Miyoshi, and K. Ide (2011), “Variable localization” in an ensemble Kalman filter: Application to carbon cycle data assimilation, J. Geophys. Res., 116, D09110,doi:10.1029/2010JD01467.
Kang, J.-S., E. Kalnay, T. Miyoshi, J. Liu, and I. Fung (2012), Estimation of surface carbon fluxes with an advanced data assimilation methodology, J. Geophys. Res., in press.
Li, H., E. Kalnay, and T. Miyoshi (2009), Simultaneous estimation of covariance inflation and observation errors within ensemble Kalman filter, Q. J. R. Meteorol. Soc., 135, 523–533, doi:10.1002/qj.371.
Li, J., S. Hsu , T. Liu, C. Chiang , and J. Chang, (2007), A New Direct Coupled Regional-scale Meteorology and Chemistry Model, American Geophysical Union, Fall Meeting 2007, abstract #A23C-1472.
Liu, J., I. Fung, E. Kalnay, and J.-S. Kang (2011), CO2 transport uncertainties from the uncertainties in meteorological fields, Geophys. Res. Lett., 38, L12808, doi:10.1029/2011GL047213.
Liu, J., I. Fung, E. Kalnay, J.-S. Kang, E. T. Olsen, and L. Chen (2012), Simultaneous assimilation of AIRS Xco2 and meteorological observations in a carbon climate model with an ensemble Kalman filter, J. Geophys. Res., 117, D05309.
Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, (1997), Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long-wave. J. Geophys. Res., 102 (D14), 16663–16682.
Miller, R. N., M. Ghil, and F. Gauthiez (1994), Advanced data assimilation in strongly nonlinear dynamical systems., J. Atmos. Sci., 51, 1037–1056.
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.
Miyazaki, K., T. Maki, P. Patra, and T. Nakazawa (2011), Assessing the impact of satellite, aircraft, and surface observations on CO2 flux estimation using an ensemble‐based 4‐D data assimilation system, J. Geophys. Res., 116, D16306, doi:10.1029/2010JD015366.
Miyoshi, T. (2011), The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter. Mon. Wea. Rev., 139, 1519-1535. doi:10.1175/2010MWR3570.1.
NAPAP-Report 4 (1990), The Regional acid deposition model and engineering model. Superintendent of Documents Government Printing Office, Washington, DC 20402-9325.
Peters, W., J. B. Miller, J. Whitaker, A. S. Denning, A. Hirsch, M. C. Krol, D. Zupanski, L. Bruhwiler, and P. P. Tans (2005), An ensemble data assimilation system to estimate CO2 surface fluxes from atmospheric trace gas observations, J. Geophys. Res., 110, D24304, doi:10.1029/2005JD006157.
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), Estimating the state of large spatio‐ temporally chaotic systems, Phys. Lett. A, 330, 365–370, doi:10.1016/j.physleta.2004.08.004.
Stephens, B. B., et al. (2007), Weak northern and strong tropical land carbon uptake from vertical profiles of atmospheric CO2, Science, 316, 1732–1735.
Tippett, M. K., J. L. Anderson, C. H. Bishop, T. M. Hamill, and J. S. Whitaker (2003), Ensemble square-root filters., Mon. Weather Rev., 131, 1485–1490.
Torn, R. D., G. J. Hakim, and C. Snyder (2006), Boundary Conditions for Limited-Area Ensemble Kalman Filters., Mon. Weather Rev., 134, 2490–2502.
Whitaker, J. S. and T. M. Hamill (2002), Ensemble data assimilation without perturbed observations, Mon. Weather Rev., 130, 1913–1924.
Yang, S-C, E. Kalnay and T. Miyohsi (2012), Improving EnKF spin-up for typhoon assimilation and prediction, Wea. Forecasting, 27, 878-897.
Yokota, T., H. Oguma, I. Morino, and G. Inoue (2004), A nadir looking SWIR FTS to monitor CO2 column density for Japanese GOSAT project, paper presented at 24th International Symposium on Space Technology and Science, Jpn. Soc. for Aeronaut. and Space Sci., Miyazaki, Japan. |