An OSSE-Based Evaluation of Hybrid Variational–Ensemble Data Assimilation for the NCEP GFS. Part II: 4DEnVar and Hybrid Variants

Kleist, Daryl; Ide, Kayo

doi:10.1175/mwr-d-13-00350.1

Cited by 156 publications

(142 citation statements)

References 53 publications

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“…variability, and this has led to significant improvements in Numerical Weather Prediction (NWP) skill (e.g. Kleist, 2012). Ensemble data analysis exploits inter-variable correlations to estimate both observed and unobserved model variables (e.g.…”

Section: Introductionmentioning

confidence: 99%

Impact of assimilation window length on diurnal features in a Mars atmospheric analysis

Zhao¹,

Greybush²,

Wilson³

et al. 2015

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T Effective simulation of diurnal variability is an important aspect of many geophysical data assimilation systems. For the Martian atmosphere, thermal tides are particularly prominent and contribute much to the Martian atmospheric circulation, dynamics and dust transport. To study the Mars diurnal variability and Mars thermal tides, the Geophysical Fluid Dynamics Laboratory Mars Global Climate Model with the 4D-local ensemble transform Kalman filter (4D-LETKF) is used to perform an analysis assimilating spacecraft temperature retrievals. We find that the use of a 'traditional' 6-hr assimilation cycle induces spurious forcing of a resonantly enhanced semi-diurnal Kelvin waves represented in both surface pressure and mid-level temperature by forming a wave 4 pattern in the diurnal averaged analysis increment that acts as a 'topographic' stationary forcing. Different assimilation window lengths in the 4D-LETKF are introduced to remove the artificially induced resonance. It is found that short assimilation window lengths not only remove the spurious resonance, but also push the migrating semi-diurnal temperature variation at 50 Pa closer to the estimated 'true' tides even in the absence of a radiatively active water ice cloud parameterisation. In order to compare the performance of different assimilation window lengths, short-term to mid-range forecasts based on the hour 00 and 12 assimilation are evaluated and compared. Results show that during Northern Hemisphere summer, it is not the assimilation window length, but the radiatively active water ice clouds that influence the model prediction. A 'diurnal bias correction' that includes bias correction fields dependent on the local time is shown to effectively reduce the forecast root mean square differences between forecasts and observations, compensate for the absence of water ice cloud parameterisation and enhance Martian atmosphere prediction. The implications of these results for data assimilation in the Earth's atmosphere are discussed.

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Section: Introductionmentioning

confidence: 99%

Impact of assimilation window length on diurnal features in a Mars atmospheric analysis

Zhao¹,

Greybush²,

Wilson³

et al. 2015

Tellus A: Dynamic Meteorology and Oceanography

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“…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

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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.

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“…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

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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.

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An OSSE-Based Evaluation of Hybrid Variational–Ensemble Data Assimilation for the NCEP GFS. Part II: 4DEnVar and Hybrid Variants

Cited by 156 publications

References 53 publications

Impact of assimilation window length on diurnal features in a Mars atmospheric analysis

Impact of assimilation window length on diurnal features in a Mars atmospheric analysis

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

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