Comparison of Hybrid Ensemble/4DVar and 4DVar within the NAVDAS-AR Data Assimilation Framework

Kuhl, David D.; Rosmond, Thomas E.; Bishop, Craig H.; McLay, Justin; Baker, Nancy L.

doi:10.1175/mwr-d-12-00182.1

Cited by 127 publications

(122 citation statements)

References 30 publications

Supporting

Mentioning

119

Contrasting

Order By: Relevance

“…This is also consistent with the results obtained in an idealized context with a low-dimensional toy model by Fairbairn et al (2013). A similar comparison of 4D-Var using either simple static background-error covariances or ensemble covariances by Kuhl et al (2013) also showed significant forecast improvements from using the ensemble covariances.…”

Section: Buehner Et Al: Four-dimensional Ensemble-variational Datsupporting

confidence: 89%

Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction

Buehner

Morneau

Charette

2013

Nonlin. Processes Geophys.

117

View full text Add to dashboard Cite

Abstract. The goal of this study is to evaluate a version of the ensemble-variational data assimilation approach (EnVar) for possible replacement of 4D-Var at Environment Canada for global deterministic weather prediction. This implementation of EnVar relies on 4-D ensemble covariances, obtained from an ensemble Kalman filter, that are combined in a vertically dependent weighted average with simple static covariances. Verification results are presented from a set of data assimilation experiments over two separate 6-week periods that used assimilated observations and model configuration very similar to the currently operational system. To help interpret the comparison of EnVar versus 4D-Var, additional experiments using 3D-Var and a version of EnVar with only 3-D ensemble covariances are also evaluated. To improve the rate of convergence for all approaches evaluated (including EnVar), an estimate of the cost function Hessian generated by the quasi-Newton minimization algorithm is cycled from one analysis to the next.Analyses from EnVar (with 4-D ensemble covariances) nearly always produce improved, and never degraded, forecasts when compared with 3D-Var. Comparisons with 4D-Var show that forecasts from EnVar analyses have either similar or better scores in the troposphere of the tropics and the winter extra-tropical region. However, in the summer extratropical region the medium-range forecasts from EnVar have either similar or worse scores than 4D-Var in the troposphere. In contrast, the 6 h forecasts from EnVar are significantly better than 4D-Var relative to radiosonde observations for both periods and in all regions. The use of 4-D versus 3-D ensemble covariances only results in small improvements in forecast quality. By contrast, the improvements from using 4D-Var versus 3D-Var are much larger. Measurement of the fit of the background and analyzed states to the observations suggests that EnVar and 4D-Var can both make better use of observations distributed over time than 3D-Var. In summary, the results from this study suggest that the EnVar approach is a viable alternative to 4D-Var, especially when the simplicity and computational efficiency of EnVar are considered. Additional research is required to understand the seasonal dependence of the difference in forecast quality between EnVar and 4D-Var in the extra-tropics.

show abstract

Section: Buehner Et Al: Four-dimensional Ensemble-variational Datsupporting

confidence: 89%

Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction

Buehner

Morneau

Charette

2013

Nonlin. Processes Geophys.

117

View full text Add to dashboard Cite

show abstract

“…Another approach to enhancing the tracerwind interaction within 4D-Var is to blend the static covariance with a flow-dependent ensemble covariance. This hybrid 4D-Var method is becoming increasingly popular at operational NWP centers (Buehner et al, 2010;Bonavita et al, 2012;Clayton et al, 2013;Kuhl et al, 2013;Kleist and Ide, 2015). We are developing a hybrid system within the SWM framework to study tracer-wind interaction, which we plan to present in a follow-up paper.…”

Section: Introductionmentioning

confidence: 99%

Wind extraction potential from ensemble Kalman filter assimilation of stratospheric ozone using a global shallow water model

2015

View full text Add to dashboard Cite

Abstract. The feasibility of extracting wind information from stratospheric ozone observations is tested using ensemble Kalman filter (EnKF) data assimilation (DA) and a global shallow water model that includes advection of an ozone-like tracer. Simulated observations are created from a truth run (TR) that resembles the Northern Hemisphere winter stratosphere with a polar vortex disturbed by planetaryscale wave forcing. Ozone observations mimic sampling of a polar-orbiting satellite, while geopotential height observations are randomly placed in space and time. EnKF experiments are performed assimilating ozone, height, or both, over a 10-day period. The DA is also implemented using two different pairs of flow variables: zonal and meridional wind (EnKF-uv) and stream function and velocity potential (EnKF-ψχ ). Each experiment is tuned for optimal localization length, while the ensemble spread is adaptively inflated using the TR. The experiments are evaluated using the maximum wind extraction potential (WEP). Ozone only assimilation improves winds (WEP = 46 % for EnKF-uv, and 58 % for EnKF-ψχ ), but suffers from spurious gravity wave generation. Application of nonlinear normal mode initialization (NMI) greatly reduces the unwanted imbalance and increases the WEP for EnKF-uv (84 %) and EnKF-ψχ (81 %). Assimilation of only height observations also improved the winds (WEP = 60 % for EnKF-uv, and 69 % for EnKF-ψχ ), with much less imbalance compared to the ozone experiment. The assimilation of both height and ozone performed the best, with WEP increasing to ∼ 87 % (∼ 90 % with NMI) for both EnKF-uv and EnKF-ψχ , demonstrating that wind extraction from ozone assimilation can be beneficial even in a data-rich environment. Ozone assimilation particularly improves the tropical winds, which are not well constrained by height observations due to lack of geostrophy.

show abstract

“…This hybrid 4DVar method is becoming increasingly popular at operational NWP centers (Buehner et al, 2010;Bonavita et al, 2012;Clayton et al, 2013;Kuhl et al, 2013;Kleist and Ide, 2015). In this paper, we extend our previous work by examining O 3 -wind interactions using a hybrid 4DVar system within the SWM framework.…”

Section: Introductionmentioning

confidence: 74%

Hybrid ensemble 4DVar assimilation of stratospheric ozone using a global shallow water model

2016

Self Cite

View full text Add to dashboard Cite

Abstract. Wind extraction from stratospheric ozone (O 3 ) assimilation is examined using a hybrid ensemble 4-D variational assimilation (4DVar) shallow water model (SWM) system coupled to the tracer advection equation. Stratospheric radiance observations are simulated using global observations of the SWM fluid height (Z), while O 3 observations represent sampling by a typical polar-orbiting satellite. Four ensemble sizes were examined (25, 50, 100, and 1518 members), with the largest ensemble equal to the number of dynamical state variables. The optimal length scale for ensemble localization was found by tuning an ensemble Kalman filter (EnKF). This scale was then used for localizing the ensemble covariances that were blended with conventional covariances in the hybrid 4DVar experiments. Both optimal length scale and optimal blending coefficient increase with ensemble size, with optimal blending coefficients varying from 0.2-0.5 for small ensembles to 0.5-1.0 for large ensembles. The hybrid system outperforms conventional 4DVar for all ensemble sizes, while for large ensembles the hybrid produces similar results to the offline EnKF. Assimilating O 3 in addition to Z benefits the winds in the hybrid system, with the fractional improvement in global vector wind increasing from ∼ 35 % with 25 and 50 members to ∼ 50 % with 1518 members. For the smallest ensembles (25 and 50 members), the hybrid 4DVar assimilation improves the zonal wind analysis over conventional 4DVar in the Northern Hemisphere (winter-like) region and also at the Equator, where Z observations alone have difficulty constraining winds due to lack of geostrophy. For larger ensembles (100 and 1518 members), the hybrid system results in both zonal and meridional wind error reductions, relative to 4DVar, across the globe.

show abstract

Comparison of Hybrid Ensemble/4DVar and 4DVar within the NAVDAS-AR Data Assimilation Framework

Cited by 127 publications

References 30 publications

Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction

Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction

Wind extraction potential from ensemble Kalman filter assimilation of stratospheric ozone using a global shallow water model

Hybrid ensemble 4DVar assimilation of stratospheric ozone using a global shallow water model

Contact Info

Product

Resources

About