Data‐Driven Model Uncertainty Estimation in Hydrologic Data Assimilation

[1]  G. Evensen Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[2]  W. Gilks,et al.  Following a moving target—Monte Carlo inference for dynamic Bayesian models , 2001 .

[3]  Lucy Marshall,et al.  Detecting non-stationary hydrologic model parameters in a paired catchment system using data assimilation , 2016 .

[4]  T. Miyoshi The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter , 2011 .

[5]  Jeffrey L. Anderson,et al.  An adaptive covariance inflation error correction algorithm for ensemble filters , 2007 .

[6]  A. Doucet,et al.  A Tutorial on Particle Filtering and Smoothing: Fifteen years later , 2008 .

[7]  Wade T. Crow,et al.  An adaptive ensemble Kalman filter for soil moisture data assimilation , 2007 .

[8]  Daniel S. Wilks,et al.  On the Reliability of the Rank Histogram , 2011 .

[9]  Hamid Moradkhani,et al.  Toward a reliable prediction of seasonal forecast uncertainty: Addressing model and initial condition uncertainty with ensemble data assimilation and Sequential Bayesian Combination , 2014 .

[10]  Gift Dumedah,et al.  Assessing model state and forecasts variation in hydrologic data assimilation , 2014 .

[11]  Michael Dowd,et al.  Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo , 2007 .

[12]  Wade T. Crow,et al.  An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting , 2014 .

[13]  H. Gupta,et al.  Correcting the mathematical structure of a hydrological model via Bayesian data assimilation , 2011 .

[14]  Arlindo da Silva,et al.  Data assimilation in the presence of forecast bias , 1998 .

[15]  Z. Toth,et al.  Short-Term Dynamics of Model Errors , 2002 .

[16]  M. Macconi,et al.  Parallel initial-value algorithms for singularly perturbed boundary-value problems , 1992 .

[17]  Dimitri Solomatine,et al.  A novel method to estimate model uncertainty using machine learning techniques , 2009 .

[18]  T. Hamill Interpretation of Rank Histograms for Verifying Ensemble Forecasts , 2001 .

[19]  Frank Dellaert,et al.  An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets , 2004, ECCV.

[20]  Jeffrey S. Racine,et al.  Cross-Validation and the Estimation of Conditional Probability Densities , 2004 .

[21]  Sujay V. Kumar,et al.  Role of forcing uncertainty and background model error characterization in snow data assimilation , 2017 .

[22]  Demetris Koutsoyiannis,et al.  Estimating the Uncertainty of Hydrological Predictions through Data-Driven Resampling Techniques , 2015 .

[23]  B. Bates,et al.  A Markov Chain Monte Carlo Scheme for parameter estimation and inference in conceptual rainfall‐runoff modeling , 2001 .

[24]  George Kuczera,et al.  Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors , 2017 .

[25]  Keith Beven,et al.  Multi-period and multi-criteria model conditioning to reduce prediction uncertainty in an application of TOPMODEL within the GLUE framework , 2007 .

[26]  Harrie-Jan Hendricks Franssen,et al.  Operational real‐time modeling with ensemble Kalman filter of variably saturated subsurface flow including stream‐aquifer interaction and parameter updating , 2011 .

[27]  Ashish Sharma,et al.  Bayesian calibration and uncertainty analysis of hydrological models: A comparison of adaptive Metropolis and sequential Monte Carlo samplers , 2011 .

[28]  G. Lannoy,et al.  The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter , 2011 .

[29]  V. Haverd,et al.  CSIRO Marine and Atmospheric Research Component: Final Report for Phase 3 , 2008 .

[30]  R. Mehra On the identification of variances and adaptive Kalman filtering , 1970 .

[31]  Sebastian Reich,et al.  Assimilating data into scientific models: An optimal coupling perspective , 2015 .

[32]  Kaj Madsen,et al.  Methods for Non-Linear Least Squares Problems (2nd ed.) , 2004 .

[33]  A. H. Murphy,et al.  A Sample Skill Score for Probability Forecasts , 1974 .

[34]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[35]  Hongxiang Yan,et al.  Combined assimilation of streamflow and satellite soil moisture with the particle filter and geostatistical modeling , 2016 .

[36]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[37]  Takemasa Miyoshi,et al.  The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real Observations , 2012, Pure and Applied Geophysics.

[38]  R. Ibbitt,et al.  Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model , 2007 .

[39]  Dongryeol Ryu,et al.  Correcting Unintended Perturbation Biases in Hydrologic Data Assimilation , 2009 .

[40]  V. A. Epanechnikov Non-Parametric Estimation of a Multivariate Probability Density , 1969 .

[41]  L. Feyen,et al.  Assessing parameter, precipitation, and predictive uncertainty in a distributed hydrological model using sequential data assimilation with the particle filter , 2009 .

[42]  H. Hersbach Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems , 2000 .

[43]  Ian Abramson On Bandwidth Variation in Kernel Estimates-A Square Root Law , 1982 .

[44]  O. Talagrand,et al.  Evaluation of probabilistic prediction systems for a scalar variable , 2005 .

[45]  D. P. DEE,et al.  Bias and data assimilation , 2005 .

[46]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[47]  A. Weerts,et al.  Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall‐runoff models , 2006 .

[48]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[49]  G. Blöschl,et al.  Soil moisture updating by Ensemble Kalman Filtering in real-time flood forecasting , 2008 .

[50]  Ashish Sharma,et al.  Hydrological model selection: A Bayesian alternative , 2005 .

[51]  Y. Hong,et al.  Hydrological data assimilation with the Ensemble Square-Root-Filter: Use of streamflow observations to update model states for real-time flash flood forecasting , 2013 .

[52]  W. Crow Correcting Land Surface Model Predictions for the Impact of Temporally Sparse Rainfall Rate Measurements Using an Ensemble Kalman Filter and Surface Brightness Temperature Observations , 2003 .

[53]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[54]  Seong Jin Noh,et al.  Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities , 2012 .

[55]  Jeffrey L. Anderson An Ensemble Adjustment Kalman Filter for Data Assimilation , 2001 .

[56]  Keith Beven,et al.  Multivariate seasonal period model rejection within the generalised likelihood uncertainty estimation procedure. , 2013 .

[57]  Scott A. Sisson,et al.  Efficient hydrological model parameter optimization with Sequential Monte Carlo sampling , 2012, Environ. Model. Softw..

[58]  D. Dee On-line Estimation of Error Covariance Parameters for Atmospheric Data Assimilation , 1995 .

[59]  George Kuczera,et al.  Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors , 2010 .

[60]  Jeffrey S. Racine,et al.  Nonparametric Econometrics: The np Package , 2008 .

[61]  Jeffrey L. Anderson,et al.  A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts , 1999 .

[62]  Kuolin Hsu,et al.  Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter , 2005 .

[63]  A. H. Murphy,et al.  What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting , 1993 .

[64]  Ashish Sharma,et al.  A nonparametric approach for representing interannual dependence in monthly streamflow sequences , 2002 .

[65]  Jonathan Briggs,et al.  Data assimilation for large‐scale spatio‐temporal systems using a location particle smoother , 2013 .

[66]  M. Clark,et al.  Snow Data Assimilation via an Ensemble Kalman Filter , 2006 .

[67]  C. M. DeChant,et al.  Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation , 2011 .

[68]  H. Moradkhani,et al.  Hydrologic modeling in dynamic catchments: A data assimilation approach , 2016 .

[69]  J. Whitaker,et al.  Accounting for the Error due to Unresolved Scales in Ensemble Data Assimilation: A Comparison of Different Approaches , 2005 .

[70]  Xianhong Xie,et al.  Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy , 2013 .

[71]  S. Sorooshian,et al.  Shuffled complex evolution approach for effective and efficient global minimization , 1993 .

[72]  Leonard A. Smith,et al.  Towards improving the framework for probabilistic forecast evaluation , 2015, Climatic Change.

[73]  Soroosh Sorooshian,et al.  Evolution of ensemble data assimilation for uncertainty quantification using the particle filter‐Markov chain Monte Carlo method , 2012 .

[74]  George Kuczera,et al.  Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation , 2011 .

[75]  Hongxiang Yan,et al.  Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[76]  Wade T. Crow,et al.  An improved approach for estimating observation and model error parameters in soil moisture data assimilation , 2010 .

[77]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[78]  Istvan Szunyogh,et al.  Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter , 2005, physics/0511236.