×

zbMATH — the first resource for mathematics

Bayesian calibration of the community land model using surrogates. (English) Zbl 1323.86032

MSC:
86A32 Geostatistics
62F15 Bayesian inference
62F25 Parametric tolerance and confidence regions
62F86 Parametric inference and fuzziness
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] J. D. Annan, J. C. Hargreaves, N. R. Edwards, and R. Marsh, Parameter estimation in an intermediate complexity Earth system model using an ensemble Kalman filter, Ocean Model., 8 (2005), pp. 135–154.
[2] M. Aubinet, T. Vesala, and D. Papale, eds.), Eddy Covariance: A Practical Guide to Measurement and Data Analysis, Springer Atmospheric Sciences, Springer-Verlag, Berlin, 2012.
[3] S. D. Babacan, R. Molina, and A. K. Katsaggelos, Bayesian compressive sensing using Laplace priors, IEEE Trans. Image Process., 19 (2010), pp. 53–63. · Zbl 1371.94480
[4] A. G. Barr, D. M. Ricciuto, K. Schaefer, A. Richardson, D. Agarwal, P. E. Thornton, K. Davis, B. Jackson, R. B. Cook, D. Y. Hollinger, C. van Ingen, B. Amiro, A. Andrews, M. A. Arain, D. Baldocchi, T. A. Black, P. Bolstad, P. Curtis, A. Desai, D. Dragoni, L. Flanagan, L. Gu, G. Katul, B. E. Law, P. Lafleur, H. Margolis, R. Matamala, T. Meyers, H. McCaughey, R. Monson, J. W. Munger, W. Oechel, R. Oren, N. Roulet, M. Torn, and S. Verm, NACP Site: Tower Meteorology, Flux Observations with Uncertainty, and Ancillary Data, data set available online from Oak Ridge National Laboratory Distributed Active Archive Center, 2013, http://daac.ornl.gov/NACP/guides/NACP_Site_Tower_Met_and_Flux_v2.html.
[5] S. Brooks and A. Gelman, General methods for monitoring convergence of iterative simulations, J. Comput. Graph. Statist., 7 (1998), pp. 434–445.
[6] R. H. Byrd, P. Lu, J. Nocedal, and C. Zhu, A limited memory algorithm for bound constrained optimization, SIAM J. Sci. Comput., 16 (1995), pp. 1190–1208. · Zbl 0836.65080
[7] B. J. Cosby, G. M. Hornberger, R. B. Clapp, and T. R. Ginn, A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils, Water Resources Res., 20 (1984), pp. 682–690.
[8] R. V. Craiu, J. Rosenthal, and C. Yang, Learn from thy neighbor: Parallel-chain and regional adaptive MCMC, J. Amer. Statist. Assoc., 104 (2009), pp. 1454–1466. · Zbl 1205.65028
[9] W. N. Edeling, P. Cinnella, R. P. Dwight, and H. Bijl, Bayesian estimates of parameter variability in the k-\(ϵ\) turbulence model, J. Comput. Phys., 258 (2013), pp. 73–94. · Zbl 1349.76110
[10] M. Emory, R. Pecnik, and G. Iaccarino, Modeling structural uncertainties in Reynolds-averaged computations of shock/boundary layer interactions, in Proceedings of the 49th AIAA Aerospace Sciences Meeting, 2011, AIAA Paper 2011-479.
[11] G. Evensen, Data Assimilation : The Ensemble Kalman Filter, Springer, New York, 2007. · Zbl 1157.86001
[12] A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin, Model checking and improvement, in Bayesian Data Analysis, Chapman and Hall/ CRC Press, London, 2004, pp. 157–196.
[13] W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, Markov Chain Monte Carlo in Practice, Chapman and Hall, London, 1996. · Zbl 0832.00018
[14] T. Gneiting, F. Balabdaoui, and A. E. Raftery, Probabilistic forecasts, calibration and sharpness, J. Roy. Statist. Soc. Ser. B, 69 (2007), pp. 243–268. · Zbl 1120.62074
[15] T. Gneiting and A. E. Raftery, Strictly proper scoring rules, prediction, and estimation, J. Amer. Statist. Assoc., 102 (2007), pp. 359–378. · Zbl 1284.62093
[16] M. Gohler, J. Mai, and M. Cuntz, Use of eigendecomposition in a parameter sensitivity analysis of the community land model, J. Geophys. Res. Biogeosci., 118 (2013), pp. 904–921.
[17] L. Gu, W. J. Massman, R. Leuning, S. G. Pallardy, T. Meyers, P. J. Hanson, J. S. Riggs, K. P. Hosman, and B. Yang, The fundamental equation of eddy covariance and its application in flux measurements, Agricult. Forest Meteorol., 152 (2012), pp. 135–148.
[18] L. Gu, T. Meyers, S. G. Pallardy, P. J. Hanson, B. Yang, M. Heuer, K. P. Hosman, J. S. Riggs, D. Sluss, and S. D. Wullschleger, Direct and indirect effects of atmospheric conditions and soil moisture on surface energy partitioning revealed by a prolonged drought at a temperate forest site, J. Geophys. Res., 111 (2006), \newblock D16102.
[19] H. Haario, M. Laine, A. Mira, and E. Saksman, DRAM-Efficient adaptive MCMC, Stat. Comput., 16 (2006), pp. 339–354.
[20] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer, New York, 2009. · Zbl 1273.62005
[21] Z. Hou, M. Huang, L. R. Leung, G. Lin, and D. M. Ricciuto, Sensitivity of surface flux simulations to hydrologic parameters based on an uncertainty quantification framework applied to the community land model, J. Geophys. Res., 117 (2012), \newblock D15108.
[22] M. Huang, Z. Hou, L. R. Leung, Y. Ke, Y. Liu, Z. Fang, and Y. Sun, Uncertainty analysis of runoff simulations and parameter identifiability in the community land model—Evidence from MOPEX basins, J. Hydrometeorol., 14 (2013), pp. 1754–1772.
[23] J. W. Hurrell, M. M. Holland, P. R. Gent, S. Ghan, J. E. Kay, P. J. Kushner, J.-F. Lamarque, W. G. Large, D. Lawrence, K. Lindsay, W. H. Lipscomb, M. C. Long, N. Mahowald, D. R. Marsh, R. B. Neale, P. Rasch, S. Vavrus, M. Vertenstein, D. Bader, W. D. Collins, J. J. Hack, J. Kiehl, and S. Marshall, The Community Earth System Model: A framework for collaborative research, Bull. Amer. Meteorol. Soc., 94 (2013), pp. 1339–1360.
[24] L. Ingber, Very fast simulated annealing, Math. Comput. Model., 12 (1989), pp. 967–973. · Zbl 0681.90091
[25] C. Jackson, M. K. Sen, and P. L. Stoffa, An efficient stochastic Bayesian approach to optimal parameter and uncertainty estimation for climate model predictions, J. Climate, 17 (2004), pp. 2828–2841.
[26] H. Järvinen, P. Räisänen, M. Laine, J. Tamminen, A. Lin, E. Oja, A. Solonen, and H. Haario, Estimation of ECHAM5 climate model closure parameters with adaptive MCMC, Atmos. Chem. Phys., 10 (2010), pp. 9993–10002.
[27] J.-C. Jouhaud, P. Sagaut, B. Enaux, and J. Laurenceau, Sensitivity analysis and multiobjective optimization for LES numerical parameters, J. Fluid Engrg., 130 (2008), 021401.
[28] M. C. Kennedy and A. O’hagan, Bayesian calibration of computer models (with discussion), J. Roy. Statist. Soc. B, 63 (2001), pp. 425–464. · Zbl 1007.62021
[29] J. Laurenceau and P. Sagaut, Building efficient response surfaces of aerodynamic functions with kriging and cokriging, AIAA J., 46 (2008), pp. 498–507.
[30] D. M. Lawrence, K. W. Oleson, M. G. Flanner, P. E. Thornton, S. C. Swenson, P. J. Lawrence, X. Zeng, Z.-L. Yang, S. Lewis, K. Sakaguchi, G. B. Bonan, and A. G. Slater, Parameterization improvements and functional and structural advances in version 4 of the community land model, J. Adv. Model. Earth Systems, 3 (2011), M03001.
[31] H. Lei, M. Huang, L. R. Leung, D. Yang, X. Shi, J. Mao, D. J. Hayes, C. R. Schwalm, Y. Wei, and S. Liu, Sensitivity of global terrestrial gross primary production to hydrologic states simulated by the community land model using two runoff parameterizations, J. Adv. Model. Earth Systems, 6 (2014), pp. 658–679.
[32] G. Leng, M. Huang, Q. Tang, H. Gao, and L. R. Leung, Modeling the effects of groundwater-fed irrigation on terrestrial hydrology over the conterminous united states, J Hydrometeorol, 15 (2014), pp. 957–972.
[33] Y. Q. Luo, J. T. Randerson, G. Abramowitz, C. Bacour, E. Blyth, N. Carvalhais, P. Ciais, D. Dalmonech, J. B. Fisher, R. Fisher, P. Friedlingstein, K. Hibbard, F. Hoffman, D. Huntzinger, C. D. Jones, C. Koven, D. Lawrence, D. J. li, M. Mahecha, S. L. Niu, R. Norby, S. L. Piao, X. Qi, P. Peylin, I. C. Prentice, W. Riley, M. Reichstein, C. Schwalm, Y. P. Wang, J. Y. Xia, S. Zaehle, and X. H. Zhou, A framework for benchmarking land models, Biogeosci., 9 (2012), pp. 3857–3874.
[34] G.-Y. Niu, Z.-L. Yang, R. E. Dickinson, and L. E. Gulden, A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models, J. Geophys. Res., 111 (2005), D211106.
[35] G.-Y. Niu, Z.-L. Yang, R. E. Dickinson, L. E. Gulden, and H. Su, Development of a simple groundwater model for use in climate models and evaluation with gravity recovery and climate experiment data, J. Geophys. Res., 112 (2007), D07103.
[36] K. W. Oleson, D. M. Lawrence, G. B. Bonan, M. G. Flanner, E. Kluzek, P. J. Lawrence, S. Levis, S. C. Swenson, and P. E. Thornton, Technical Description of Version 4.0 of the Community Land Model (CLM), 2010; available online at http://www.cesm.ucar.edu/models/cesm1.2/clm/CLM4_Tech_Note.pdf.
[37] V. R. N. Pauwels, N. E. C. Verhoest, G. J. M. De Lannoy, V. Guissard, C. Lacau, and P. Defoumy, Optimization of a coupled hydrology-crop growth model through the assimilation of observed soil moisture and leaf area index values using an ensemble Kalman filter, Water Resources Res., 43 (2007), \newblock W04421.
[38] R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2012.
[39] A. Raftery and S. M. Lewis, Implementing MCMC, in Markov Chain Monte Carlo in Practice, W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, eds., Chapman and Hall, London, 1996, pp. 115–130.
[40] C. E. Rasmussen and C. K. I Williams, Gaussian Process for Machine Learning, MIT Press, Cambridge, MA, 2006. · Zbl 1177.68165
[41] J. Ray, Z. Hou, M. Huang, and L. Swiler, Bayesian Calibration of the Community Land Model Using Surrogates, SAND Report SAND2014-0867, Sandia National Laboratories, Livermore, CA, 2013. \newblock · Zbl 1323.86032
[42] W. J. Riley, S. C. Biraud, M. S. Torn, M. L. Fischer, D. P. Billesbach, and J. A. Berry, Regional CO\(_2\) and latent heat surface fluxes in the Southern Great Plains: Measurements, modeling and scaling, J. Geophys. Res. Biogeosci., 114 (2009), \newblock G04009.
[43] J. Sacks, W. J. Welch, T. J. Mitchell, and H. P. Wynn, Design and analysis of computer experiments, Statist. Sci., 4 (1989), pp. 409–435. · Zbl 0955.62619
[44] T. Santer, B. Williams, and W. Notz, The Design and Analysis of Computer Experiments, Springer, New York, 2003.
[45] K. Sargsyan, C. Safta, H. N. Najm, B. J. Debusschere, D. Ricciuto, and P. Thornton, Dimensionality reduction for complex models via Bayesian compressive sensing, Int. J. Uncertain. Quantif., 4 (2014), pp. 63–93.
[46] T. W. Simpson, V. Toropov, V. Balabanov, and F. A. C. Viana, Design and analysis of computer experiments in multidisciplinary optimization: A review of how far we have come or not, in Proceedings of the 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Victoria, BC, Canada, 2008, paper 2008-5802.
[47] W. C. Skamarock and J. B. Klemp, A time-split nonhydrostatic atmospheric model for weather research and forecasting applications, J. Comput. Phys., 227 (2008), pp. 3465–3485. · Zbl 1132.86312
[48] K. Soetaert and T. Petzoldt, Inverse modeling, sensitivity and Monte Carlo in R using package FME, J. Statist. Softw., 33 (2010), pp. 1–28.
[49] A. Solonen, P. Ollinaho, M. Laine, H. Haario, J. Tamminen, and H. Järvinen, Efficient MCMC for climate model parameter estimation: Parallel adaptive chains and early rejection, Bayesian Anal., 7 (2012), pp. 715–736. · Zbl 1330.60091
[50] C. B. Storlie and J. C. Helton, Multiple predictor smoothing methods for sensitivity analysis: Description of techniques, Reliability Engrg. System Safety, 94 (2008), pp. 28–54.
[51] C. B. Storlie, L. P. Swiler, J. C. Helton, and C. J. Sallaberry, Implementation and evaluation of non-parametric regression procedures for sensitivity analysis of computationally demanding models, Reliability Engrg. System Safety, 94 (2009), pp. 1735–1763.
[52] Y. Sun, Z. Hou, M. Huang, F. Tian, and L. Ruby Leung, Inverse modeling of hydrologic parameters using surface flux and runoff observations in the community land model, Hydrol. Earth System Sci., 17 (2013), pp. 4995–5011.
[53] A. E. Suyker and S. B. Verma, Evapotranspiration of irrigated and rainfed maize-soybean cropping systems, Agricultural Forest Meteorol., 149 (2009), pp. 443–452.
[54] L. Tomassini, P. Reichert, R. Knutti, T. F. Stocker, and M. E. Borsuk, Robust Bayesian uncertainty analysis of climate system properties using Markov chain Monte Carlo methods, J. Climate, 20 (2007), pp. 1239–1254.
[55] W. N. Venables and B. D. Ripley, Modern Applied Statistics in S, Springer-Verlag, New York, 2002. · Zbl 1006.62003
[56] B. Yang, Y. Qian, G. Lin, L. R. Leung, P. J. Rasch, G. J. Zhang, S. A. McFarlane, C. Zhao, Y. Zhang, H. Wang, M. Wang, and X. Liu, Uncertainty quantification and parameter tuning in the CAM5 Zhang-Mcfarlane convection scheme and impact of improved convection on the global circulation and climate, J. Geophys. Res. Atmospheres, 118 (2013), pp. 395–415.
[57] B. Yang, Y. Qian, G. Lin, R. Leung, and Y. Zhang, Some issues in uncertainty quantification and parameter tuning: A case study of convective parameterization in the WRF regional climate model, Atmospher. Chem. Phys., 12 (2012), pp. 2409–2427.
[58] X. Zeng, B. A. Drewniak, and E. M. Constantinescu, Calibration of the crop model in the community land model, Geosci. Model Devel. Discuss., 6 (2013), pp. 379–398.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.