Three‐dimensional constrained variational analysis: Approach and application to analysis of atmospheric diabatic heating and derivative fields during an ARM SGP intensive observational period

Tang, Shuaiqi; Zhang, Minghua

doi:10.1002/2015jd023621

Cited by 12 publications

(31 citation statements)

References 34 publications

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“…The upper-level heating and drying, lowerlevel cooling and moistening indicate that there are precipitating stratiform clouds in the upper level and evaporation of precipitation underneath. The negative Q 1 − Q 2 − Q rad in the lower level and the positive Q 1 − Q 2 − Q rad in the upper level are seen in both the COS and BOS case, which indicates lower-level divergence of h and upper-level convergence of h due to moist convective processes, consistent with Tang and Zhang (2015). The lower-level positive Q 1 −Q 2 −Q rad in the afternoon is mainly contributed by the vertical convergence of moisture by sub-grid eddies, similar to that in the LOS case.…”

Section: Cos (20 -21 February 2014)supporting

confidence: 63%

“…This local circulation and the horizontal inhomogeneity of largescale vertical velocity, heating and moistening could be better studied using high-resolution 3-D gridded large-scale forcing data from the 3-D constrained variational analysis recently developed by Tang and Zhang (2015) and Tang et al (2016). This will be the subject of a future study.…”

Section: Summary and Discussionmentioning

confidence: 99%

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Large-scale vertical velocity, diabatic heating and drying profiles associated with seasonal and diurnal variations of convective systems observed in the GoAmazon2014/5 experiment

et al. 2016

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Abstract. This study describes the characteristics of largescale vertical velocity, apparent heating source (Q 1 ) and apparent moisture sink (Q 2 ) profiles associated with seasonal and diurnal variations of convective systems observed during the two intensive operational periods (IOPs) that were conducted from 15 February to 26 March 2014 (wet season) and from 1 September to 10 October 2014 (dry season) near Manaus, Brazil, during the Green Ocean Amazon (GoAmazon2014/5) experiment. The derived large-scale fields have large diurnal variations according to convective activity in the GoAmazon region and the morning profiles show distinct differences between the dry and wet seasons. In the wet season, propagating convective systems originating far from the GoAmazon region are often seen in the early morning, while in the dry season they are rarely observed. Afternoon convective systems due to solar heating are frequently seen in both seasons. Accordingly, in the morning, there is strong upward motion and associated heating and drying throughout the entire troposphere in the wet season, which is limited to lower levels in the dry season. In the afternoon, both seasons exhibit weak heating and strong moistening in the boundary layer related to the vertical convergence of eddy fluxes.A set of case studies of three typical types of convective systems occurring in Amazonia -i.e., locally occurring systems, coastal-occurring systems and basin-occurring systems -is also conducted to investigate the variability of the large-scale environment with different types of convective systems.

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Section: Cos (20 -21 February 2014)supporting

confidence: 63%

Section: Summary and Discussionmentioning

confidence: 99%

Large-scale vertical velocity, diabatic heating and drying profiles associated with seasonal and diurnal variations of convective systems observed in the GoAmazon2014/5 experiment

et al. 2016

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“…The ability to separate the physics parameterization schemes from the dynamics is very useful, as most stochastic schemes assume that model error is due only to the parameterized physics. The usefulness of SCM forcing data that includes an estimate of subgrid variability has been demonstrated by Tang and Zhang () and Tang et al (), who consider subgrid variability in forcing fields derived from measurements at the Atmospheric Radiation Measurement Southern Great Plains site. In particular, they highlight the utility of such data sets for deriving and evaluating scale‐aware parameterization schemes, since the SCM forcing files can be specified across a range of resolutions (Tang et al, ).…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

“…New sophisticated methods have been developed to reduce errors in the large‐scale forcing (Zhang & Lin, ; Zhang et al, ), though the accuracy of the data can be limited by missing data and scale aliasing in the measurements. In addition, recent advances have seen the development of a three‐dimensional constrained variational analysis approach (Tang & Zhang, ), which enables the estimation of fine‐resolution SCM forcing data sets from observational data. The new technique enables the estimation of atmospheric profiles for a number of adjacent columns around suitable observational sites.…”

Section: Introductionmentioning

confidence: 99%

Forcing Single‐Column Models Using High‐Resolution Model Simulations

Christensen

Dawson

Holloway

2018

J Adv Model Earth Syst

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To use single‐column models (SCMs) as a research tool for parameterization development and process studies, the SCM must be supplied with realistic initial profiles, forcing fields, and boundary conditions. We propose a new technique for deriving these required profiles, motivated by the increase in number and scale of high‐resolution convection‐permitting simulations. We suggest that these high‐resolution simulations be coarse grained to the required resolution of an SCM, and thereby be used as a proxy for the true atmosphere. This paper describes the implementation of such a technique. We test the proposed methodology using high‐resolution data from the UK Met Office's Unified Model, with a resolution of 4 km, covering a large tropical domain. These data are coarse grained and used to drive the European Centre for Medium‐Range Weather Forecast's Integrated Forecasting System (IFS) SCM. The proposed method is evaluated by deriving IFS SCM forcing profiles from a consistent T639 IFS simulation. The SCM simulations track the global model, indicating a consistency between the estimated forcing fields and the true dynamical forcing in the global model. We demonstrate the benefits of selecting SCM forcing profiles from across a large domain, namely, robust statistics, and the ability to test the SCM over a range of boundary conditions. We also compare driving the SCM with the coarse‐grained data set to driving it using the European Centre for Medium‐Range Weather Forecast operational analysis. We conclude by highlighting the importance of understanding biases in the high‐resolution data set and suggest that our approach be used in combination with observationally derived forcing data sets.

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“…As described in Tang and Zhang [], the 3DCVA follows the general idea of 1DCVA [ Zhang and Lin , ] that the atmospheric state variables (referred to as background data hereafter) u , v , q , and s are adjusted to minimize the cost function:

I = {(u - u_{o})}^{T} B_{u}^{prefix- 1} ((), u - u_{o}) + {(v - v_{o})}^{T} B_{v}^{prefix- 1} ((), v - v_{o}) + ((), q - q_{o}) + {(s - s_{o})}^{T} B_{s}^{prefix- 1} ((), s - s_{o}),

while maintaining the column‐integrated conservation of mass, moisture, and energy simultaneously across all spatial grids in the analysis domain:

(⟨⟩, \nabla \cdot \vec{V}) = prefix- \frac{1}{g} \frac{normald P_{s}}{normald t}

\frac{\partial (⟨⟩, q)}{\partial t} + (⟨⟩, \nabla \cdot {\overset{true\to}{V}}_{q}) = E_{s} - P_{normalr normale normalc} - \frac{\partial (⟨⟩, q_{l})}{\partial t}

\frac{\partial (⟨⟩, s)}{\partial t} + (⟨⟩, \nabla \cdot \vec{V} s) = R_{normalT normalO normalA} - R_{normalS normalF normalC} + L_{v} P_{normalr normale normalc} + S H + L_{v} \frac{\partial (⟨⟩, q_{l})}{\partial t} .

…”

Section: Methods and Data Of Ensemble 3dcvamentioning

confidence: 99%

An ensemble constrained variational analysis of atmospheric forcing data and its application to evaluate clouds in CAM5

2016

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Large-scale atmospheric forcing data can greatly impact the simulations of atmospheric process models (e.g., large eddy simulations, cloud-resolving models, and single column models (SCMs)) that are used to develop physical parameterizations in global climate models. This study introduces an ensemble variationally constrained objective analysis of atmospheric large-scale forcing data and its application to evaluate the cloud biases in the Community Atmospheric Model (CAM5). Sensitivities of the variational objective analysis to background data, error covariance matrix, and constraint variables are presented to quantify the uncertainties in the large-scale forcing data and state variables. Application of the ensemble forcing in the CAM5 SCM during March 2000 intensive operational period at the Southern Great Plains (SGP) of the Atmospheric Radiation Measurement Program shows that the systematic biases in the model simulations (i.e., excessive high clouds and insufficient low clouds) cannot be explained by the uncertainty of large-scale forcing data, which points to the deficiencies of physical parameterizations. These biases are found to also exist in the global simulation of CAM5 when it is compared with satellite data over the surrounding SGP site for annual and seasonal means.

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Three‐dimensional constrained variational analysis: Approach and application to analysis of atmospheric diabatic heating and derivative fields during an ARM SGP intensive observational period

Cited by 12 publications

References 34 publications

Large-scale vertical velocity, diabatic heating and drying profiles associated with seasonal and diurnal variations of convective systems observed in the GoAmazon2014/5 experiment

Large-scale vertical velocity, diabatic heating and drying profiles associated with seasonal and diurnal variations of convective systems observed in the GoAmazon2014/5 experiment

Forcing Single‐Column Models Using High‐Resolution Model Simulations

An ensemble constrained variational analysis of atmospheric forcing data and its application to evaluate clouds in CAM5

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