scholarly journals Nonlinear Conditional Model Bias Estimation for Data Assimilation

2021 ◽  
Vol 20 (1) ◽  
pp. 299-332
Author(s):  
Jason A. Otkin ◽  
Roland W. E. Potthast ◽  
Amos S. Lawless
Ocean Science ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 123-144 ◽  
Author(s):  
Jiping Xie ◽  
Laurent Bertino ◽  
François Counillon ◽  
Knut A. Lisæter ◽  
Pavel Sakov

Abstract. Long dynamical atmospheric reanalyses are widely used for climate studies, but data-assimilative reanalyses of ocean and sea ice in the Arctic are less common. TOPAZ4 is a coupled ocean and sea ice data assimilation system for the North Atlantic and the Arctic that is based on the HYCOM ocean model and the ensemble Kalman filter data assimilation method using 100 dynamical members. A 23-year reanalysis has been completed for the period 1991–2013 and is the multi-year physical product in the Copernicus Marine Environment Monitoring Service (CMEMS) Arctic Marine Forecasting Center (ARC MFC). This study presents its quantitative quality assessment, compared to both assimilated and unassimilated observations available in the whole Arctic region, in order to document the strengths and weaknesses of the system for potential users. It is found that TOPAZ4 performs well with respect to near-surface ocean variables, but some limitations appear in the interior of the ocean and for ice thickness, where observations are sparse. In the course of the reanalysis, the skills of the system are improving as the observation network becomes denser, in particular during the International Polar Year. The online bias estimation successfully maintains a low bias in our system. In addition, statistics of the reduced centered random variables (RCRVs) confirm the reliability of the ensemble for most of the assimilated variables. Occasional discontinuities of these statistics are caused by the changes of the input data sets or the data assimilation settings, but the statistics remain otherwise stable throughout the reanalysis, regardless of the density of observations. Furthermore, no data type is severely less dispersed than the others, even though the lack of consistently reprocessed observation time series at the beginning of the reanalysis has proven challenging.


2016 ◽  
Author(s):  
Jiping Xie ◽  
Laurent Bertino ◽  
Francois Counillon ◽  
Knut A. Lisæter ◽  
Pavel Sakov

Abstract. Long dynamical atmospheric reanalyses are widely used for climate studies, but data assimilative reanalyses of the Arctic ocean and sea ice are less common. TOPAZ4 is a coupled ocean and sea ice data assimilation system for the North Atlantic and the Arctic that is based on the HYCOM ocean model and the Ensemble Kalman Filter data assimilation method using 100 dynamical members. A 23-years reanalysis has been completed for the period 1991–2013. This study presents its quantitative quality assessment, compared to both assimilated and unassimilated observations available in the whole Arctic region in order to document the strengths and weaknesses of the system for potential users. It is found that TOPAZ4 performs well with respect to near surface ocean variables, but some limitations appear in the interior of the ocean and for ice thickness, where observations are sparse. In the course of the reanalysis, the skills of the system are improving as the observation network becomes denser, in particular during the International Polar Year. The online bias estimation successfully maintains a low bias in our system.


2017 ◽  
Vol 145 (7) ◽  
pp. 2683-2696 ◽  
Author(s):  
Raquel Lorente-Plazas ◽  
Joshua P. Hacker

In numerical weather prediction and in reanalysis, robust approaches for observation bias correction are necessary to approach optimal data assimilation. The success of bias correction can be limited by model errors. Here, simultaneous estimation of observation and model biases, and the model state for an analysis, is explored with ensemble data assimilation and a simple model. The approach is based on parameter estimation using an augmented state in an ensemble adjustment Kalman filter. The observation biases are modeled with a linear term added to the forward operator. A bias is introduced in the forcing term of the model, leading to a model with complex errors that can be used in imperfect-model assimilation experiments. Under a range of model forcing biases and observation biases, accurate observation bias estimation and correction are possible when the model forcing bias is simultaneously estimated and corrected. In the presence of both model error and observation biases, estimating one and ignoring the other harms the assimilation more than not estimating any errors at all, because the biases are not correctly attributed. Neglecting a large model forcing bias while estimating observation biases results in filter divergence; the observation bias parameter absorbs the model forcing bias, and recursively and incorrectly increases the increments. Neglecting observation bias results in suboptimal assimilation, but the model forcing bias parameter estimate remains stable because the model dynamics ensure covariance between the parameter and the model state.


2021 ◽  
Vol 13 (4) ◽  
pp. 673
Author(s):  
Xiaolei Zou

With the rapid advances and abundant observations from Chinese Fengyun-3 (FY-3) meteorological satellites, it is of great interest to summarize a decade of quality assessments of FY-3 observations. The topics covered are noise characterization, bias estimation, striping noise detection and mitigation of striping noise, radio frequency interference detection, geolocation accuracy estimation and improvement, data assimilation cloud detection and quality control for observations from the MicroWave Temperature Sounder (MWTS), the MicroWave Humidity Sounder (MWHS), the MicroWave Radiation Imager (MWRI) and the Hyperspectral Infrared Atmospheric Sounder (HIRAS) instruments on board FY-3A/B/C/D. Whether and how much FY-3 data assimilation could improve the numerical weather forecast skill strongly depends on how well the FY-3 data characteristics and errors listed above are known. This review article shall contribute to promoting internal and national usages of FY-3 observations for weather and climate studies.


2020 ◽  
Author(s):  
jiangyu li ◽  
shaoqing zhang

<p>High-quality wave prediction with a numerical wave model is of societal value. To initialize the wave model, wave data assimilation (WDA) is necessary to combine the model and observations. Due to inaccurate wind forcing, imperfect numerical schemes, and approximated physical processes, a wave model is always biased in relation to the real world. In this study, two assimilation systems are first developed using two nearly independent wave models; then, “perfect” and “biased” assimilation frameworks based on the two assimilation systems are designed to reveal the uncertainties of WDA. A series of “biased” assimilation experiments is conducted to systematically examine the adverse impact of initial condition, boundary forcing, and model bias on WDA, then model bias play a strongest role among them . A statistical approach based on the results from multiple assimilation systems is explored to carry out bias correction, by which the final wave analysis is significantly improved with the merits of individual assimilation systems. The framework with multiple assimilation systems provides an effective platform to improve wave analyses and predictions and help identify model deficits, thereby improving the model.</p>


2020 ◽  
pp. 1-51
Author(s):  
Sara C. Sanchez ◽  
Gregory J. Hakim ◽  
Casey P. Saenger

AbstractScientific understanding of low-frequency tropical Pacific variability, especially responses to perturbations in radiative forcing, suffers from short observational records, sparse proxy networks, and bias in model simulations. Here, we combine the strengths of proxies and models through coral-based paleoclimate data assimilation. We combine coral archives (δ18O, Sr/Ca) with the dynamics, spatial teleconnections, and intervariable relationships of the CMIP5/PMIP3 Past1000 experiments using the Last Millennium Reanalysis data assimilation framework. This analysis creates skillful reconstructions of tropical Pacific temperatures over the observational era. However, during the period of intense volcanism in the early 19th century, southwestern Pacific corals produce El Niño Southern Oscillation (ENSO) reconstructions that are of opposite sign from those from eastern Pacific corals and tree ring records. We systematically evaluate the source of this discrepancy using 1) single-proxy experiments, 2) varied proxy system models (PSMs), and 3) diverse covariance patterns from the Past1000 simulations. We find that individual proxy records and coral PSMs do not significantly contribute to the discrepancy. However, following major eruptions, the southwestern Pacific corals locally record more persistent cold anomalies than found in the Past1000 experiments and canonical ENSO teleconnections to the southwest Pacific strongly control the reconstruction response. Furthermore, using covariance patterns independent of ENSO yield reconstructions consistent with coral archives across the Pacific. These results show that model bias can strongly affect how proxy information is processed in paleoclimate data assimilation. As we illustrate here, model bias influences the magnitude and persistence of the response of the tropical Pacific to volcanic eruptions.


2020 ◽  
Vol 34 (2) ◽  
pp. 400-412
Author(s):  
Yu Xia ◽  
Jing Chen ◽  
Xiefei Zhi ◽  
Lianglyu Chen ◽  
Yang Zhao ◽  
...  

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