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

Allen, Douglas R.; Hoppel, K. W.; Kuhl, David D.

doi:10.5194/acp-15-5835-2015

Cited by 15 publications

(21 citation statements)

References 42 publications

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“…Andersson et al , ; Daley, ; Riish???ojgaard?? ?, ; Peuch et al , ; Allen et al , , ). In the absence of moist processes or chemistry, these are atmospheric tracers, and in the presence of a tracer gradient, winds can be inferred using the adjoint of the continuity equation.…”

Section: Development Of All‐sky Assimilationmentioning

confidence: 99%

The growing impact of satellite observations sensitive to humidity, cloud and precipitation

Geer

Baordo

Bormann

et al. 2017

Quart J Royal Meteoro Soc

147

174

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Ten years ago, humidity observations were thought to give little benefit to global weather forecasts. Nowadays, at the European Centre for Medium‐range Weather Forecasts, satellite microwave radiances sensitive to humidity, cloud and precipitation provide 20% of short‐range forecast impact, as measured by adjoint‐based forecast sensitivity diagnostics. This makes them one of the most important sources of data and equivalent in impact to microwave temperature sounding observations. Forecasts of dynamical quantities, and precipitation, are improved out to at least day 6. This article reviews the impact of and the science behind these data. It is not straightforward to assimilate cloud and precipitation‐affected observations when the intrinsic predictability of cloud and precipitation features is limited. Assimilation systems must be able to operate in the presence of all‐pervasive cloud and precipitation ‘mislocation’ errors. However, by assimilating these observations using the ‘all‐sky’ approach, and supported by advances in data assimilation and forecast modelling, modern data assimilation systems can infer the dynamical state of the atmosphere, not just from traditional temperature‐related observations, but from observations of humidity, cloud and precipitation.

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Section: Development Of All‐sky Assimilationmentioning

confidence: 99%

The growing impact of satellite observations sensitive to humidity, cloud and precipitation

Geer

Baordo

Bormann

et al. 2017

Quart J Royal Meteoro Soc

147

174

View full text Add to dashboard Cite

show abstract

“…Various studies have examined tracer-wind interactions within a variety of data assimilation (DA) systems including extended Kalman filter (EKF) (Daley, 1995(Daley, , 1996, 4-D variational assimilation (4D-Var) (Andersson et al, 1994;Riishøjgaard, 1996;Peuch et al, 2000;Andersson et al, 2007;Peubey and McNally, Published by Copernicus Publications on behalf of the European Geosciences Union. 3000 D. R. Allen et al: Extraction of wind and temperature information from ozone assimilation 2009; Semane et al, 2009;Han and McNally, 2010;Dragani and McNally, 2013;Allen et al, 2013Allen et al, , 2014, ensemble Kalman filter (EnKF) (Milewski and Bourqui, 2011;Allen et al, 2015), and hybrid 4D-Var (Allen et al, 2016). Initial 1-D and 2-D investigations by Daley (1995Daley ( , 1996 and Riishøjgaard (1996), and 3-D investigations by Peuch et al (2000), Semane et al (2009), and Allen et al (2013) showed that coupling the tracer continuity equation with the dynamical equations could allow wind information to be extracted from tracer observations in either 4D-Var or EKF.…”

Section: Introductionmentioning

confidence: 99%

“…These studies illustrated the potential of tracer assimilation to influence winds but also highlighted limitations on this process from observation quality and sampling, inadequate tracer modeling, and geophysical variability. Further theoretical studies by Allen et al (2014Allen et al ( , 2015Allen et al ( , 2016 examined the tracer-wind mechanisms in the shallow water model framework using a hierarchy of DA systems: 4D-Var, EnKF, and hybrid 4D-Var. Additional work by Milewski and Bourqui (2011) examined the assimilation of ozone in the EnKF framework using a 3-D data assimilation system, highlighting the propagation of information from ozone to wind via background error covariances.…”

Section: Introductionmentioning

confidence: 99%

Extraction of wind and temperature information from hybrid 4D-Var assimilation of stratospheric ozone using NAVGEM

2018

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Extraction of wind and temperature information from stratospheric ozone assimilation is examined within the context of the Navy Global Environmental Model (NAVGEM) hybrid 4-D variational assimilation (4D-Var) data assimilation (DA) system. Ozone can improve the wind and temperature through two different DA mechanisms: (1) through the "flow-of-the-day" ensemble background error covariance that is blended together with the static background error covariance and (2) via the ozone continuity equation in the tangent linear model and adjoint used for minimizing the cost function. All experiments assimilate actual conventional data in order to maintain a similar realistic troposphere. In the stratosphere, the experiments assimilate simulated ozone and/or radiance observations in various combinations. The simulated observations are constructed for a case study based on a 16-day cycling truth experiment (TE), which is an analysis with no stratospheric observations. The impact of ozone on the analysis is evaluated by comparing the experiments to the TE for the last 6 days, allowing for a 10-day spin-up. Ozone assimilation benefits the wind and temperature when data are of sufficient quality and frequency. For example, assimilation of perfect (no applied error) global hourly ozone data constrains the stratospheric wind and temperature to within ∼ 2 m s −1 and ∼ 1 K. This demonstrates that there is dynamical information in the ozone distribution that can potentially be used to improve the stratosphere. This is particularly important for the tropics, where radiance observations have difficulty constraining wind due to breakdown of geostrophic balance. Global ozone assimilation provides the largest benefit when the hybrid blending coefficient is an intermediate value (0.5 was used in this study), rather than 0.0 (no ensemble background error covariance) or 1.0 (no static background er-ror covariance), which is consistent with other hybrid DA studies. When perfect global ozone is assimilated in addition to radiance observations, wind and temperature error decreases of up to ∼ 3 m s −1 and ∼ 1 K occur in the tropical upper stratosphere. Assimilation of noisy global ozone (2 % errors applied) results in error reductions of ∼ 1 m s −1 and ∼ 0.5 K in the tropics and slightly increased temperature errors in the Northern Hemisphere polar region. Reduction of the ozone sampling frequency also reduces the benefit of ozone throughout the stratosphere, with noisy polarorbiting data having only minor impacts on wind and temperature when assimilated with radiances. An examination of ensemble cross-correlations between ozone and other variables shows that a single ozone observation behaves like a potential vorticity (PV) "charge", or a monopole of PV, with rotation about a vertical axis and vertically oriented temperature dipole. Further understanding of this relationship may help in designing observation systems that would optimize the impact of ozone on the dynamics.

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“…In an effort to understand the problem in more detail, we previously developed a shallow water model (SWM) test case representing Northern Hemisphere (NH) winter stratosphere conditions. Assimilation experiments using both 4DVar (Allen et al, 2014;hereinafter A14) and EnKF (Allen et al, 2015; hereinafter A15) not only showed that tracer assimilation is useful for wind extraction but also raised issues such as sensitivity to measurement errors, localization, and choice of DA state variables, as well as the problem of imbalance.…”

Section: Introductionmentioning

confidence: 99%

“…The tracer-wind relationship has been examined with a variety of DA systems including the extended Kalman filter (EKF; Daley, 1995Daley, , 1996, 4-D variational assimilation (4DVar; Riishøjgaard, 1996;Peuch et al, 2000;Andersson et al, 2007;Peubey and McNally, 2009;Semane et al, 2009;Han and McNally, 2010;Dragani and McNally, 2013;Allen et al, 2013Allen et al, , 2014, and ensemble Kalman filter (EnKF; Milewski and Bourqui, 2011;Allen et al, 2015). While idealized studies have shown strong potential for wind extraction from tracer assimilation, attempts to assimilate O 3 using realistic numerical weather prediction (NWP) systems have produced mixed results (see Allen et al, 2015, for a discussion). In an effort to understand the problem in more detail, we previously developed a shallow water model (SWM) test case representing Northern Hemisphere (NH) winter stratosphere conditions.…”

Section: Introductionmentioning

confidence: 99%

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

Cited by 15 publications

References 42 publications

The growing impact of satellite observations sensitive to humidity, cloud and precipitation

The growing impact of satellite observations sensitive to humidity, cloud and precipitation

Extraction of wind and temperature information from hybrid 4D-Var assimilation of stratospheric ozone using NAVGEM

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

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