Potential of an ensemble Kalman smoother for stratospheric
                        chemical-dynamical data assimilation

Milewski, Thomas; Bourqui, M. S.

doi:10.3402/tellusa.v65i0.18541

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…However the linear dependence assumption between the MPs and OPs is likely to be challenged by the forecast model and/or by the observation operator which are highly non-linear. To a large extent, the 4DEnVar assimilation methods are very similar to the Ensemble Kalman Smoother (Evensen and van Leeuwen, 2000;Harlim and Hunt, 2007;Milewski and Bourqui, 2013). As the theoretical foundation for many 4DEnVar assimilation methods, the assumption of the linear dependence between the MPs and OPs has often been questioned.…”

Section: Introductionmentioning

confidence: 99%

A non-linear least squares enhanced POD-4DVar algorithm for data assimilation

Tian

Feng

2015

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T This paper presents a novel non-linear least squares enhanced proper orthogonal decomposition (POD)-based 4DVar algorithm (referred as NLS-4DVar) for the non-linear ensemble-based 4DVar. In the algorithm, the GaussÁNewton iterative method is employed to handle the non-quadratic non-linearity of the 4DVar cost function while the overall structure of the algorithm still resembles the original POD-4DVar algorithm. It is proved that the original POD-4DVar algorithm is a special case of the proposed NLS-4DVar algorithm under the assumption of the linear relationship between the model perturbations (MPs) and the simulated observation perturbations (OPs). Under the assumption it is also shown that the solution of POD-4DVar algorithm coincides with the solution of the proposed NLS-4DVar algorithm. On the contrary, if the linear relationship assumption is dropped, the solution of the POD-4DVar algorithm is only the first iteration of the proposed NLS-4DVar algorithm. As a result, our analysis provides an explanation for the degraded and inaccurate performance of the POD-4DVar algorithm when the underlying forecast model or (and) the observation operator is strongly non-linear. The potential merits and advantages of the proposed NLS-4DVar are demonstrated by a group of Observing System Simulation Experiments with Advanced Research WRF (ARW) using accumulated rainfall-observations.

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Section: Introductionmentioning

confidence: 99%

A non-linear least squares enhanced POD-4DVar algorithm for data assimilation

Tian

Feng

2015

Tellus A: Dynamic Meteorology and Oceanography

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“…Like the above-mentioned method, it is apparent that a fixed-lag EnKS with lag N repeatedly corrects the dust emissions with observations from that time step up to N subsequent time steps. The EnKS solves an analysis equation very similar to that of EnKF but with time lags between the analysis and innovations (observations minus forecast) [20]. To efficiently assimilate the hourly aerosol observations from the next-generation geostationary satellite Himawari-8 [21], we apply a four-dimensional local ensemble transform Kalman filter (4D-LETKF) to solve the Kalman equations [22].…”

Section: Methodsmentioning

confidence: 99%

Inverting the East Asian Dust Emission Fluxes Using the Ensemble Kalman Smoother and Himawari-8 AODs: A Case Study with WRF-Chem v3.5.1

et al. 2019

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We present the inversions (back-calculations or optimizations) of dust emissions for a severe winter dust event over East Asia in November 2016. The inversion system based on a fixed-lag ensemble Kalman smoother is newly implemented in the Weather Research and Forecasting model and is coupled with Chemistry (WRF-Chem). The assimilated observations are the hourly aerosol optical depths (AODs) from the next-generation geostationary satellite Himawari-8. The posterior total dust emissions (2.59 Tg) for this event are 3.8 times higher than the priori total dust emissions (0.68 Tg) during 25–27 November 2016. The net result is that the simulated aerosol horizontal and vertical distributions are both in better agreement with the assimilated Himawari-8 observations and independent observations from the ground-based AErosol RObotic NETwork (AERONET), the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The developed emission inversion approach, combined with the geostationary satellite observations, can be very helpful for properly estimating the Asian dust emissions.

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“…Using an ensemble Kalman filter and an intermediate-complexity model, Milewski and Bourqui [48] demonstrated that information about the ozone-wind cross-covariance is essential in constraining dynamical fields when ozone only is assimilated. Moreover they showed that a further reduction in error can be obtained with an ensemble Kalman smoother [49]. In a series of studies using 4D-Var and the ensemble Kalman filter, Allen et al [50][51][52] showed that poorly-specified observation error could lead to an increase in RMS wind error, that observational coverage is important such that wind extraction could be improved if several chemical tracers were used, and that the balance between wind and temperature could be offset by the wind recovery from tracer measurements.…”

Section: Introductionmentioning

confidence: 99%

Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part II: Weak and Strong Coupling

et al. 2019

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We examine data assimilation coupling between meteorology and chemistry in the stratosphere from both weak and strong coupling strategies. The study was performed with the Canadian operational weather prediction Global Environmental Multiscale (GEM) model coupled online with the photochemical stratospheric chemistry model developed at the Belgian Institute for Space Aeronomy, described in Part I. Here, the Canadian Meteorological Centre’s operational variational assimilation system was extended to include errors of chemical variables and cross-covariances between meteorological and chemical variables in a 3D-Var configuration, and we added the adjoint of tracer advection in the 4D-Var configuration. Our results show that the assimilation of limb sounding observations from the MIPAS instrument on board Envisat can be used to anchor the AMSU-A radiance bias correction scheme. Additionally, the added value of limb sounding temperature observations on meteorology and transport is shown to be significant. Weak coupling data assimilation with ozone–radiation interaction is shown to give comparable results on meteorology whether a simplified linearized or comprehensive ozone chemistry scheme is used. Strong coupling data assimilation, using static error cross-covariances between ozone and temperature in a 3D-Var context, produced inconclusive results with the approximations we used. We have also conducted the assimilation of long-lived species observations using 4D-Var to infer winds. Our results showed the added value of assimilating several long-lived species, and an improvement in the zonal wind in the Tropics within the troposphere and lower stratosphere. 4D-Var assimilation also induced a correction of zonal wind in the surf zone and a temperature bias in the lower tropical stratosphere.

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Potential of an ensemble Kalman smoother for stratospheric chemical-dynamical data assimilation

Cited by 7 publications

References 36 publications

A non-linear least squares enhanced POD-4DVar algorithm for data assimilation

A non-linear least squares enhanced POD-4DVar algorithm for data assimilation

Inverting the East Asian Dust Emission Fluxes Using the Ensemble Kalman Smoother and Himawari-8 AODs: A Case Study with WRF-Chem v3.5.1

Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part II: Weak and Strong Coupling

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