Investigating the role of prior and observation error correlations in improving a model forecast of forest carbon balance using Four-dimensional Variational data assimilation

[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 .