Operational assimilation of ozone‐sensitive infrared radiances at ECMWF

Dragani, Rossana; McNally, A. P.

doi:10.1002/qj.2106

Cited by 41 publications

(49 citation statements)

References 26 publications

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“…This is particularly the case for the assimilation of TCO 3 . An example of a background error profile and vertical correlation matrix obtained from Dragani and McNally (2013) is given in Fig. 3.…”

Section: The Data Assimilation Systemmentioning

confidence: 99%

A comparative analysis of UV nadir-backscatter and infrared limb-emission ozone data assimilation

Dragani

2016

Atmos. Chem. Phys.

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Abstract. This paper presents a comparative assessment of ultraviolet nadir-backscatter and infrared limb-emission ozone profile assimilation. The Meteorological Operational Satellite A (MetOp-A) Global Ozone Monitoring Experiment 2 (GOME-2) nadir and the ENVISAT Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) limb profiles, generated by the ozone consortium of the European Space Agency Climate Change Initiative (ESA O3-CCI), were individually added to a reference set of ozone observations and assimilated in the European Centre for MediumRange Weather Forecasts (ECMWF) data assimilation system (DAS). The two sets of resulting analyses were compared with that from a control experiment, only constrained by the reference dataset, and independent, unassimilated observations.Comparisons with independent observations show that both datasets improve the stratospheric ozone distribution. The changes inferred by the limb-based observations are more localized and, in places, more important than those implied by the nadir profiles, albeit they have a much lower number of observations. A small degradation (up to 0.25 mg kg −1 for GOME-2 and 0.5 mg kg −1 for MIPAS in the mass mixing ratio) is found in the tropics between 20 and 30 hPa. In the lowermost troposphere below its vertical coverage, the limb data are found to be able to modify the ozone distribution with changes as large as 60 %. Comparisons of the ozone analyses with sonde data show that at those levels the assimilation of GOME-2 leads to about 1 Dobson Unit (DU) smaller root mean square error (RMSE) than that of MIPAS. However, the assimilation of MIPAS can still improve the quality of the ozone analyses and -with a reduction in the RMSE of up to about 2 DU -outperform the control experiment thanks to its synergistic assimilation with totalcolumn ozone data within the DAS.High vertical resolution ozone profile observations are essential to accurately monitor and forecast ozone concentrations in a DAS. This study demonstrates the potential and limitations of each dataset and instrument type, as well as the need for a balanced future availability of nadir and limb sensors and long-term plans for limb-viewing instruments.

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Section: The Data Assimilation Systemmentioning

confidence: 99%

A comparative analysis of UV nadir-backscatter and infrared limb-emission ozone data assimilation

Dragani

2016

Atmos. Chem. Phys.

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“…It is unclear at this point whether tracer observations from current satellites are of sufficient accuracy and frequency to benefit wind analyses in an operational setting; a potential source of operational ozone profiles is the OMPS on the NASA Suomi National Polar-orbiting Partnership (NPP) mission (Flynn et al, 2009), launched 28 October 2011. In addition, ozone-sensitive infrared radiances can potentially provide dynamical information via tracer-wind coupling in NWP systems (Dragani and McNally, 2013). In this study we will examine some of the limitations of wind extraction from ozone assimilation.…”

Section: R Allen Et Al: Limitations Of Wind Extraction From 4d-vmentioning

confidence: 99%

Limitations of wind extraction from 4D-Var assimilation of ozone

et al. 2013

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Time-dependent variational data assimilation allows the possibility of extracting wind information from observations of ozone or other trace gases. Since trace gas observations are not available at sufficient resolution for deriving feature-track winds, they must be combined with model background information to produce an analysis. If done with time-dependent variational assimilation, wind information may be extracted via the adjoint of the linearized tracer continuity equation. This paper presents idealized experiments that illustrate the mechanics of tracer–wind extraction and demonstrate some of the limitations of this procedure. We first examine tracer–wind extraction using a simple one-dimensional advection equation. The analytic solution for a single trace gas observation is discussed along with numerical solutions for multiple observations. The limitations of tracer–wind extraction are then explored using highly idealized ozone experiments performed with a development version of the Navy Global Environmental Model (NAVGEM) in which globally distributed hourly stratospheric ozone profiles are assimilated in a single 6 h update cycle in January 2009. Starting with perfect background ozone conditions, but imperfect dynamical conditions, ozone errors develop over the 6 h background window. Wind increments are introduced in the analysis in order to reduce the differences between background ozone and ozone observations. For "perfect" observations (unbiased and no random error), this results in root-mean-square (RMS) vector wind error reductions of up to ~4 m s⁻¹ in the winter hemisphere and tropics. Wind extraction is more difficult in the summer hemisphere due to weak ozone gradients and smaller background wind errors. The limitations of wind extraction are also explored for observations with imposed random errors and for limited sampling patterns. As expected, the amount of wind information extracted degrades as observation errors or data voids increase. In the case of poorly specified observation error covariances, assimilation of ozone data with imposed errors may result in increased RMS wind error, since the assimilation is constrained too tightly to the noisy observations

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“…Peubey and McNally (2009) isolated the mechanisms whereby geostationary clear-sky radiances can impact the wind analyses in 4D-Var and showed that the dominant factor involves adjustment of the wind field to match observed humidity features (the so-called "tracer advection effect"). However, attempts to assimilate stratospheric ozone using 4D-Var algorithms and the resultant dynamical coupling have previously resulted in problems in operational numerical weather prediction (NWP) (Han and McNally, 2010;Dragani and McNally, 2013). These assimilation challenges led Allen et al (2014) to re-examine the stratospheric tracer-wind problem at a more fundamental level using 4D-Var assimilation studies with a shallow water model (SWM) coupled to the tracer continuity equation.…”

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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Operational assimilation of ozone‐sensitive infrared radiances at ECMWF

Cited by 41 publications

References 26 publications

A comparative analysis of UV nadir-backscatter and infrared limb-emission ozone data assimilation

A comparative analysis of UV nadir-backscatter and infrared limb-emission ozone data assimilation

Limitations of wind extraction from 4D-Var assimilation of ozone

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

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