The Pros and Cons of Diffusion, Filters and Fixers in Atmospheric General Circulation Models

Jablonowski, Christiane; Williamson, David L.

doi:10.1007/978-3-642-11640-7_13

Cited by 129 publications

(156 citation statements)

References 196 publications

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“…Finally, at the highest altitudes, the overall seasonal variation of E P that is observed with the RO sensors is reproduced by the models, but modeled E P values are smaller than those derived from RO observations by factors between 2 and 3. This is expected since the sponge layer in the ECMWF models starts strongly damping any smallscale structures above 10 hPa or ∼ 32 km (Jablonowski and Williamson, 2011; and since RO measurement noise is picking up substantially above 35 km (see Marquardt and Healy, 2005, and our analysis in Fig. 4 and corresponding discussion).…”

Section: Comparison Of Metop E P Values With Ecmwf Model Data and Gromentioning

confidence: 60%

An intercomparison of stratospheric gravity wave potential energy densities from METOP GPS radio occultation measurements and ECMWF model data

2018

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Abstract. Temperature profiles based on radio occultation (RO) measurements with the operational European METOP satellites are used to derive monthly mean global distributions of stratospheric (20-40 km) gravity wave (GW) potential energy densities (E P ) for the period July 2014-December 2016. In order to test whether the sampling and data quality of this data set is sufficient for scientific analysis, we investigate to what degree the METOP observations agree quantitatively with ECMWF operational analysis (IFS data) and reanalysis (ERA-Interim) data. A systematic comparison between corresponding monthly mean temperature fields determined for a latitude-longitude-altitude grid of 5 • by 10 • by 1 km is carried out. This yields very low systematic differences between RO and model data below 30 km (i.e., median temperature differences is between −0.2 and +0.3 K), which increases with height to yield median differences of +1.0 K at 34 km and +2.2 K at 40 km. Comparing E P values for three selected locations at which also ground-based lidar measurements are available yields excellent agreement between RO and IFS data below 35 km. ERA-Interim underestimates E P under conditions of strong local mountain wave forcing over northern Scandinavia which is apparently not resolved by the model. Above 35 km, RO values are consistently much larger than model values, which is likely caused by the model sponge layer, which damps small-scale fluctuations above ∼ 32 km altitude. Another reason is the wellknown significant increase of noise in RO measurements above 35 km. The comparison between RO and lidar data reveals very good qualitative agreement in terms of the seasonal variation of E P , but RO values are consistently smaller than lidar values by about a factor of 2. This discrepancy is likely caused by the very different sampling characteristics of RO and lidar observations. Direct comparison of the global data set of RO and model E P fields shows large correlation coefficients (0.4-1.0) with a general degradation with increasing altitude. Concerning absolute differences between observed and modeled E P values, the median difference is relatively small at all altitudes (but increasing with altitude) with an exception between 20 and 25 km, where the median difference between RO and model data is increased and the corresponding variability is also found to be very large. The reason for this is identified as an artifact of the E P algorithm: this erroneously interprets the pronounced climatological feature of the tropical tropopause inversion layer (TTIL) as GW activity, hence yielding very large E P values in this area and also large differences between model and observations. This is because the RO data show a more pronounced TTIL than IFS and ERA-Interim. We suggest a correction for this effect based on an estimate of this "artificial" E P using monthly mean zonal mean temperature profiles. This correction may be recommended for application to data sets that can only be analyzed using a vertical background determination me...

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Section: Comparison Of Metop E P Values With Ecmwf Model Data and Gromentioning

confidence: 60%

An intercomparison of stratospheric gravity wave potential energy densities from METOP GPS radio occultation measurements and ECMWF model data

2018

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“…Since the discretization can also lead to an unphysical loss of mass or energy, mass or energy fixers are also employed to replace lost mass or energy to the system. A comprehensive overview of schemes for diffusion and stabilization schemes can be found in Jablonowski and Williamson (2011). In this section, we discuss some of the diffusion and stabilization strategies employed by the DCMIP suite of dynamical cores.…”

Section: Tempestmentioning

confidence: 99%

DCMIP2016: a review of non-hydrostatic dynamical core design and intercomparison of participating models

et al. 2017

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Abstract. Atmospheric dynamical cores are a fundamental component of global atmospheric modeling systems and are responsible for capturing the dynamical behavior of the Earth's atmosphere via numerical integration of the NavierStokes equations. These systems have existed in one form or another for over half of a century, with the earliest discretizations having now evolved into a complex ecosystem of algorithms and computational strategies. In essence, no two dynamical cores are alike, and their individual successes suggest that no perfect model exists. To better understand modern dynamical cores, this paper aims to provide a comprehensive review of 11 non-hydrostatic dynamical cores, drawn from modeling centers and groups that participated in the 2016 Dynamical Core Model Intercomparison Project (DCMIP) workshop and summer school. This review includes a choice of model grid, variable placement, vertical coordinate, prognostic equations, temporal discretization, and the diffusion, stabilization, filters, and fixers employed by each system.

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“…High order CG and DG methods suffer from this limitation especially when used for solving convection dominated flows. According to [20], the method often used in operational NWP models to stabilize CG and DG is to add second or higher-order artificial diffusion (AD) to the right hand side of Eq. (3).…”

Section: Stabilizationmentioning

confidence: 99%

Efficient construction of unified continuous and discontinuous Galerkin formulations for the 3D Euler equations

Abdi

Giraldo

2016

Journal of Computational Physics

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A unified approach for the numerical solution of the 3D hyperbolic Euler equations using high order methods, namely continuous Galerkin (CG) and discontinuous Galerkin (DG) methods, is presented. First, we examine how classical CG that uses a global storage scheme can be constructed within the DG framework using constraint imposition techniques commonly used in the finite element literature. Then, we implement and test a simplified version in the Non-hydrostatic Unified Model of the Atmosphere (NUMA) for the case of explicit time integration and a diagonal mass matrix. Constructing CG within the DG framework allows CG to benefit from the desirable properties of DG such as, easier hp-refinement, better stability etc. Moreover, this representation allows for regional mixing of CG and DG depending on the flow regime in an area. The different flavors of CG and DG in the unified implementation are then tested for accuracy and performance using a suite of benchmark problems representative of cloud-resolving scale, meso-scale and global-scale atmospheric dynamics. The value of our unified approach is that we are able to show how to carry both CG and DG methods within the same code and also offer a simple recipe for modifying an existing CG code to DG and vice versa.

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The Pros and Cons of Diffusion, Filters and Fixers in Atmospheric General Circulation Models

Cited by 129 publications

References 196 publications

An intercomparison of stratospheric gravity wave potential energy densities from METOP GPS radio occultation measurements and ECMWF model data

An intercomparison of stratospheric gravity wave potential energy densities from METOP GPS radio occultation measurements and ECMWF model data

DCMIP2016: a review of non-hydrostatic dynamical core design and intercomparison of participating models

Efficient construction of unified continuous and discontinuous Galerkin formulations for the 3D Euler equations

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