Investigating the role of prior and observation error correlations in improving a model forecast of forest carbon balance using Four-dimensional Variational data assimilation
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N. Nichols | S. Dance | T. Quaife | J. Morison | E. Casella | M. Wilkinson | A. Lawless | Ewan Pinnington | E. Pinnington
[1] V. Shutyaev,et al. Sensitivity analysis with respect to observations in variational data assimilation for parameter estimation , 2018, Nonlinear Processes in Geophysics.
[2] Massimo Bonavita,et al. The evolution of the ECMWF hybrid data assimilation system , 2016 .
[3] Peter Bauer,et al. The quiet revolution of numerical weather prediction , 2015, Nature.
[4] E. Dufrene,et al. Joint assimilation of eddy covariance flux measurements and FAPAR products over temperate forests within a process‐oriented biosphere model , 2015 .
[5] D. Baldocchi,et al. Does day and night sampling reduce spurious correlation between canopy photosynthesis and ecosystem respiration , 2015 .
[6] A. Bloom,et al. Using a data-assimilation system to assess the influence of fire on simulated carbon fluxes and plant traits for the Australian continent , 2015 .
[7] Matthew J. Smith,et al. Predictability of the terrestrial carbon cycle , 2015, Global change biology.
[8] J. Eyre,et al. Accounting for correlated error in the assimilation of high‐resolution sounder data , 2014 .
[9] R. Monson,et al. Joint data assimilation of satellite reflectance and net ecosystem exchange data constrains ecosystem carbon fluxes at a high-elevation subalpine forest , 2014 .
[10] A. Anthony Bloom,et al. Constraining ecosystem carbon dynamics in a data-limited world: integrating ecological "common sense" in a model-data fusion framework , 2014 .
[11] Michael C. Dietze,et al. The role of data assimilation in predictive ecology , 2014 .
[12] Nancy Nichols,et al. Representativity error for temperature and humidity using the Met Office high‐resolution model † , 2014 .
[13] Corinne Le Quéré,et al. Carbon and Other Biogeochemical Cycles , 2014 .
[14] R. Giering,et al. The BETHY/JSBACH Carbon Cycle Data Assimilation System: experiences and challenges , 2013 .
[15] Lutz Lehmann,et al. Algorithmic differentiation in Python with AlgoPy , 2013, J. Comput. Sci..
[16] A. Lawless. Variational data assimilation for very large environmental problems , 2013 .
[17] A. Lorenc,et al. Operational implementation of a hybrid ensemble/4D‐Var global data assimilation system at the Met Office , 2013 .
[18] Nancy Nichols,et al. Data assimilation with correlated observation errors: experiments with a 1-D shallow water model , 2013 .
[19] N. Nichols,et al. A regularization of the carbon cycle data-fusion problem , 2013 .
[20] Roland Potthast,et al. Nonlinear error dynamics for cycled data assimilation methods , 2013 .
[21] J. Morison,et al. Inter-annual variation of carbon uptake by a plantation oak woodland in south-eastern England , 2012 .
[22] A. Desai,et al. A primer for data assimilation with ecological models using Markov Chain Monte Carlo (MCMC) , 2011, Oecologia.
[23] Nancy Nichols,et al. A hybrid data assimilation scheme for model parameter estimation: Application to morphodynamic modelling , 2011 .
[24] P. Ciais,et al. Seasonal patterns of CO2 fluxes in Amazon forests: Fusion of eddy covariance data and the ORCHIDEE model , 2011 .
[25] J. Thepaut,et al. The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .
[26] L1‐regularisation for ill‐posed problems in variational data assimilation , 2010 .
[27] E. Davidson,et al. Estimating parameters of a forest ecosystem C model with measurements of stocks and fluxes as joint constraints , 2010, Oecologia.
[28] J. Gove,et al. The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data , 2009 .
[29] Nancy Nichols,et al. Variational data assimilation for parameter estimation: application to a simple morphodynamic model , 2009 .
[30] R. Bannister. A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics , 2008 .
[31] Ross N. Bannister,et al. A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances , 2008 .
[32] Markus Reichstein,et al. Influences of observation errors in eddy flux data on inverse model parameter estimation , 2008 .
[33] Philip Lewis,et al. Assimilating canopy reflectance data into an ecosystem model with an Ensemble Kalman Filter , 2008 .
[34] D. Baldocchi. ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems , 2008 .
[35] Markus Reichstein,et al. Statistical properties of random CO2 flux measurement uncertainty inferred from model residuals , 2008 .
[36] T. Vesala,et al. Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation , 2006 .
[37] Y. Trémolet. Accounting for an imperfect model in 4D‐Var , 2006 .
[38] Paul Poli,et al. Diagnosis of observation, background and analysis‐error statistics in observation space , 2005 .
[39] Andrew C. Lorenc,et al. Why does 4D‐Var beat 3D‐Var? , 2005 .
[40] R. Giering,et al. Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS) , 2005 .
[41] I. C. Prentice,et al. A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system , 2005 .
[42] Ernst Linder,et al. Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations , 2005 .
[43] B. Law,et al. An improved analysis of forest carbon dynamics using data assimilation , 2005 .
[44] S. B. Healy,et al. Use of discrete Fourier transforms in the 1D‐Var retrieval problem , 2005 .
[45] B O B B,et al. Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations , 2005 .
[46] Eugenia Kalnay,et al. Atmospheric Modeling, Data Assimilation and Predictability , 2002 .
[47] Antonio Donato Nobre,et al. Acclimation of photosynthetic capacity to irradiance in tree canopies in relation to leaf nitrogen concentration and leaf mass per unit area , 2002 .
[48] K. Taylor. Summarizing multiple aspects of model performance in a single diagram , 2001 .
[49] Eric Jones,et al. SciPy: Open Source Scientific Tools for Python , 2001 .
[50] Heikki Järvinen,et al. Variational assimilation of time sequences of surface observations with serially correlated errors , 1999 .
[51] Stephen J. Wright,et al. Numerical Optimization , 2018, Fundamental Statistical Inference.
[52] Ionel Michael Navon,et al. Practical and theoretical aspects of adjoint parameter estimation and identifiability in meteorology and oceanography , 1998 .
[53] E. Rastetter,et al. PREDICTING GROSS PRIMARY PRODUCTIVITY IN TERRESTRIAL ECOSYSTEMS , 1997 .
[54] J. Renaud,et al. Automatic differentiation in robust optimization , 1996 .
[55] Yong Li,et al. Four-Dimensional Variational Data Assimilation Experiments with a Multilevel Semi-Lagrangian Semi-Implicit General Circulation Model , 1994 .
[56] J. Derber,et al. Variational Data Assimilation with an Adiabatic Version of the NMC Spectral Model , 1992 .
[57] R. Daley. The Effect of Serially Correlated Observation and Model Error on Atmospheric Data Assimilation , 1992 .