Development of NAVDAS-AR: formulation and initial tests of the linear problem

Xu, Liang; Rosmond, Thomas E.; Daley, Roger

doi:10.1111/j.1600-0870.2005.00123.x

Cited by 48 publications

(33 citation statements)

References 17 publications

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“…2 demonstrate the potential of using tracer observations to constrain wind fields in time-dependent variational assimilation, but also illustrate many of the limitations of this approach. Extending the approach to a more realistic setting, in this section we describe wind extraction experiments that use a development version of NAVGEM with the 4D-Var assimilation algorithm NAVDAS-AR (Xu et al, 2005;Rosmond and Xu, 2006). NAVGEM is the successor to the Navy Operational Global Atmosphere Prediction System (NOGAPS) (Hogan and Rosmond, 1991).…”

Section: Methodsmentioning

confidence: 99%

“…3). These experiments use the NRL Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) 4D-Var assimilation system (Xu et al, 2005;Rosmond and Xu, 2006) with a preoperational version of the Navy Global Environmental Model (NAVGEM) semiLagrangian forecast model to assimilate idealized global ozone profiles in the stratosphere (∼4000 profiles per hour evenly sampled over the globe), with and without imposed errors. With these experiments we are able to examine the limitations of wind extraction due to geophysical conditions as well as due to observation errors and sampling patterns.…”

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

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

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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“…The development of the NRL 4D-Var system NAVDAS-AR including the nonlinear formulation, implementation of outer-loop iterations, and the ability to account for model errors is discussed in the work of Xu et al (2005) and Rosmond and Xu (2006). The analysis framework adopted in this study is of a strong-constraint 4D-Var DAS with a single outer-loop iteration.…”

Section: The Analysis Equationmentioning

confidence: 99%

“…In NAVDAS-AR, the computational steps (15) and (16) are performed using a matrix-free implementation (Xu et al, 2005;Rosmond and Xu, 2006). From (6) and (7), it is noticed that a single integration of the tangent linear model (forward sweep) from t 0 to t N is required to obtain the analysis at all intermediate time instants t k of the assimilation interval…”

Section: The Analysis Equationmentioning

confidence: 99%

“…The current work presents novel theoretical aspects of the adjoint-DAS error covariance sensitivity analysis and first applications in an operational 4D-Var data assimilation and forecast system, the Naval Research Laboratory Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR; Daley and Barker, 2001;Xu et al, 2005;Rosmond and Xu, 2006) and the Navy Operational Global Atmospheric Prediction System (NOGAPS; Hogan and Rosmond, 1991).…”

Section: Introductionmentioning

confidence: 99%

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Error covariance sensitivity and impact estimation with adjoint 4D‐Var: theoretical aspects and first applications to NAVDAS‐AR

Daescu

Langland²

2012

Quart J Royal Meteoro Soc

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The Advantages of Hybrid 4DEnVar in the Context of the Forecast Sensitivity to Initial Conditions

Song

Shin

et al. 2017

JGR Atmospheres

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Hybrid four‐dimensional ensemble variational data assimilation (hybrid 4DEnVar) is a prospective successor to three‐dimensional variational data assimilation (3DVar) in operational weather prediction centers currently developing a new weather prediction model and those that do not operate adjoint models. In experiments using real observations, hybrid 4DEnVar improved Northern Hemisphere (NH; 20°N–90°N) 500 hPa geopotential height forecasts up to 5 days in a NH summer month compared to 3DVar, with statistical significance. This result is verified against ERA‐Interim through a Monte Carlo test. By a regression analysis, the sensitivity of 5 day forecast is associated with the quality of the initial condition. The increased analysis skill for midtropospheric midlatitude temperature and subtropical moisture has the most apparent effect on forecast skill in the NH including a typhoon prediction case. Through attributing the analysis improvements by hybrid 4DEnVar separately to the ensemble background error covariance (BEC), its four‐dimensional (4‐D) extension, and climatological BEC, it is revealed that the ensemble BEC contributes to the subtropical moisture analysis, whereas the 4‐D extension does to the midtropospheric midlatitude temperature. This result implies that hourly wind‐mass correlation in 6 h analysis window is required to extract the potential of hybrid 4DEnVar for the midlatitude temperature analysis to the maximum. However, the temporal ensemble correlation, in hourly time scale, between moisture and another variable is invalid so that it could not work for improving the hybrid 4DEnVar analysis.

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Development of NAVDAS-AR: formulation and initial tests of the linear problem

Cited by 48 publications

References 17 publications

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

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

Error covariance sensitivity and impact estimation with adjoint 4D‐Var: theoretical aspects and first applications to NAVDAS‐AR

The Advantages of Hybrid 4DEnVar in the Context of the Forecast Sensitivity to Initial Conditions

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