Bias and data assimilation

By Dick Dee. 2005

627 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/69062b7c689e78210a76a93696c1f27a9ef95d0a

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Citations in the corpus

Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations (2017, methodology): “Nor will we dwell on the important issue of structural uncertainty—model error— other than to note that this can, in principle, be addressed through the inverse problem approach advocated here: additional unknown parameters, placed judiciously within the model to account for model error, can be lear”
Toward Elimination of the Warm Bias in Historic Radiosonde Temperature Records—Some New Results from a Comprehensive Intercomparison of Upper-Air Data (2008, background): “As long as radiosonde temperature biases cannot be estimated reliably within the dataassimilation process itself (Dee 2005), we recommend the available new adjusted radiosonde temperature data be used as input in climate data–assimilation efforts.”
Issues Regarding the Assimilation of Cloud and Precipitation Data (2007, background): “Some assumptions are therefore required since different origins of the biases require entirely different means of removal (Dee 2005).”
Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities (2012, background): “This means that great care should be taken in constructingM andH if there is belief that systematic biases or phase errors in the data or model exist (e.g., Dee, 2005; Crow and Van Loon, 2006).”
Linear Filtering of Sample Covariances for Ensemble-Based Data Assimilation. Part I: Optimality Criteria and Application to Variance Filtering and Covariance Localization (2015, background): “However, because of the huge size of state vectors in operational NWP systems (;10(8) elements), the background error covariance matrix (hereafter denoted B) cannot be easily estimated, and not even stored (Dee 2005).”
Data assimilation: making sense of Earth Observation (2014, methodology): “For weather forecasting many assimilation schemes now incorporate a bias correction, and various techniques have been developed to correct observations to remove biases (Dee, 2005).”
A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation (2018, methodology): “This was initially proposed by825 Dee et al. (1999a) and tested in Dee et al. (1999b) in the case of maximizing the innovation like-826 lihood, in Dee (2005) in a state augmentation formulation, and was adapted to a Bayesian update827 formulation in Liu et al. (2017) and in Berry and Harlim (2017).”
The ERA5 global reanalysis (2020, background): “P = 𝜕b∕𝜕𝜷 is a matrix representation of the VarBC predictors from Equation (3).”
Evaluation of the CMCC eddy‐permitting global ocean physical reanalysis system (C‐GLORS, 1982–2012) and its assimilation components (2016, background): “Biases in the forecast step of reanalysis systems may arise from a variety of sources, mainly related to OGCM parametrizations, unsteady initial conditions and propagation of biases from boundary conditions (Dee, 2005).”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, methodology): “These biases can be estimated recursively in time, along with the system's state, using an approach known as state augmentation in which the state is formally augmented with the bias term (e.g., Dee, 2005).”
Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging (2011, methodology): “Systematic errors in numerical weather prediction models can be removed using MOS, but often these errors (which we refer to as biases; see Dee 2005) vary spatially.”
Estimation of ECHAM5 climate model closure parameters with adaptive MCMC (2010, methodology): “Sequential state estimation in numerical weather prediction aims at fitting the initial condition and model parameters to prior information and to observations (e.g.,Dee, 2005).”
Introduction to the SPARC Reanalysis Intercomparison Project (S-RIP) and overview of the reanalysis systems (2016, background): “ERA-Interim Dee et al. (2011) Centre: ECMWF Coverage: January 1979 to present”
Adaptive bias correction for satellite data in a numerical weather prediction system (2007, methodology): “Results have been shown for a particular assimilation system (the ECMWF 4D-Var system), and would not necessarily be the same in a system using different tuning parameters (for example, the background error covariances).”