Fourth order, conservative discretization of horizontal Euler equations in the COSMO model and regional climate simulations

Ogaja, Jack; Will, Andreas

doi:10.1127/metz/2016/0645

Cited by 3 publications

(7 citation statements)

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“…As shown by Ogaja and Will (2016), the HOS dynamics and numberics, also available in COSMO-CLM 5.0-HOS 1.6, has an effective model resolution close to the theoretical 2∆λ resolution. It can thus be expcted to represent the Lake Breeze of Müritz Lake using (0.01 • , 80L) grid and HOS dynamics and numerics.…”

Section: Introductionmentioning

confidence: 57%

Lake Breeze of Small Lakes in North-East Germany in COSMO-CLM simulations with non-diffusive dynamics and numerics

Will

Gupta

Trachte

2023

Preprint

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We present results of analysis of simulated Lake Breeze (LB) and Land-Lake Wind (LLW) at M¨uritz Lake (100km2) and Lausitz Ostsee (22km2) in North East Germany in a series of 1 km Regional Climate Simulations (RCS) with COSMO-CLM 5.0-HOS over North-East Germany in 2006 and 2010 and discuss the requirements of modelling. We demonstrate the ability of the model to simulate LB and LLW in a physically consistent way. Best resuls are obtained for non-diffusive dynamics and numerics, no convection parameterisation and oppropriate turbulence parameterisation simulates LB and LLW at highest efficiency with respect to forcing. Higher surface heat flux or weaker horizontal and/or vertical turbulence parameterisation decrease the efficiency of generation of the phenomena. Numerical diffusion disturbes the dynamics and reduces the intensity and frequency of LB and LLW by approximately 50%. Each day of the year is associated with Small Scale Variability (SV) or Ekman Spiral (ES) regime (70-85% of days), dominated by processes independent on the land-lake contrast, or Convecitve Instability regime (15-30% of the days), characterised by relatively high ∆rΘ ≥ 10◦ and ∆rT2m > 2K values. Lake Breeze (LB) and Land-Lake Wind (LLW) regimes are found at lake locations only at 50% of days in Convective Instability regime The LLW regime (3-5% of days) at M¨uritz Lake exhibits the theoretically expected typical 10m wind direction change ∆rΘ ≃ 90◦ and 2m temperature difference between land and lake of 6.9 K. We found a limitation of ∆rT2m ∼ −0.2vg for geostrophic wind vg ≥ 6m/s and an exponential increase of ∆rΘ ∼ exp(∆rT2m) with 2m temperature difference in all regimes.

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Section: Introductionmentioning

confidence: 57%

Lake Breeze of Small Lakes in North-East Germany in COSMO-CLM simulations with non-diffusive dynamics and numerics

Will

Gupta

Trachte

2023

Preprint

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“…Although the difference in the numerical resolution between INCA (1 km grid spacing) and CCLM (3 km grid spacing) is only a factor of 3, CCLM is not able to resolve small-scale wind patterns. This occurs for multiple reasons: (1) due to the third-order advection scheme with its horizontal diffusion damping, the effective resolution in CCLM is several times coarser than the numeric grid spacing (Ogaja and Will, 2016); (2) the orography is smoothed as well, so that individual mountain ridge and valley structures are removed. For example, the mountain peak of the Hochtor with its 2396 m elevation at the center of the WegenerNet JBT region is lowered by about 500 m in the CCLM model.…”

Section: Discussionmentioning

confidence: 99%

“…The intercomparison of the CCLM dynamical fields with the INCA analysis fields for thermally induced wind events reflects the disadvantage of smoothed terrain, which is caused by the limited effective resolution being several times coarser than the 3 km × 3 km grid spacing of the CCLM as already noted above. Improvements can be expected from the latest developments in the numerical core of CCLM by Ogaja and Will (2016), which have enabled an improvement of the effective resolution by a factor of 2 by introducing a fourth-order advection scheme that allows us to circumvent the horizontal diffusion damping.…”

Section: Discussionmentioning

confidence: 99%

“…Related to the CCLM dynamical modeling, a verification of CCLM-generated higher-resolution 1 km × 1 km wind fields and the application of the new fourth-order advection scheme from Ogaja and Will (2016) in a convectionpermitting configuration would also be a promising issue for further investigations of how this may improve the modeling of wind patterns in a complex terrain.…”

Section: Discussionmentioning

confidence: 99%

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A spatial evaluation of high-resolution wind fields from empirical and dynamical modeling in hilly and mountainous terrain

et al. 2019

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Abstract. Empirical high-resolution surface wind fields, automatically generated by a weather diagnostic application, the WegenerNet Wind Product Generator (WPG), were intercompared with wind field analysis data from the Integrated Nowcasting through Comprehensive Analysis (INCA) system and with regional climate model wind field data from the Consortium for Small Scale Modeling Model in Climate Mode (CCLM). The INCA analysis fields are available at a horizontal grid spacing of 1 km × 1 km, whereas the CCLM fields are from simulations at a 3 km × 3 km grid. The WPG, developed by Schlager et al. (2017, 2018), generates diagnostic fields on a high-resolution grid of 100 m × 100 m, using observations from two dense meteorological station networks: the WegenerNet Feldbach Region (FBR), located in a region predominated by a hilly terrain, and its Alpine sister network, the WegenerNet Johnsbachtal (JBT), located in a mountainous region. The wind fields of these different empirical–dynamical modeling approaches were intercompared for thermally induced and strong wind events, using hourly temporal resolutions as supplied by the WPG, with the focus on evaluating spatial differences and displacements between the different datasets. For this comparison, a novel neighborhood-based spatial wind verification methodology based on fractions skill scores (FSSs) is used to estimate the modeling performances. All comparisons show an increasing FSS with increasing neighborhood size. In general, the spatial verification indicates a better statistical agreement for the hilly WegenerNet FBR than for the mountainous WegenerNet JBT. The results for the WegenerNet FBR show a better agreement between INCA and WegenerNet than between CCLM and WegenerNet wind fields, especially for large scales (neighborhoods). In particular, CCLM clearly underperforms in the case of thermally induced wind events. For the JBT region, all spatial comparisons indicate little overlap at small neighborhood sizes, and in general large biases of wind vectors occur between the regional climate model (CCLM) and analysis (INCA) fields and the diagnostic (WegenerNet) reference dataset. Furthermore, grid-point-based error measures were calculated for the same evaluation cases. The statistical agreement, estimated for the vector-mean wind speed and wind directions again show better agreement for the WegenerNet FBR than for the WegenerNet JBT region. A combined examination of all spatial and grid-point-based error measures shows that CCLM with its limited horizontal resolution of 3 km × 3 km, and hence too smoothed an orography, is not able to represent small-scale wind patterns. The results for the JBT region indicate significant biases in the INCA analysis fields, especially for strong wind speed events. Regarding the WegenerNet diagnostic wind fields, the statistics show acceptable performance in the FBR and somewhat overestimated wind speeds for strong wind speed events in the Enns valley of the JBT region.

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“…As this study is about low stratus clouds, the focus lies on the advection of the tracers. We have conducted additional tests combining the tracer‐advection scheme with a third‐order upstream advection scheme, a fourth‐order centred scheme requiring minimal explicit horizontal diffusion of the wind velocities, and a symmetric fourth‐order centred scheme which conserves kinetic energy (Ogaja and Will, 2016); all combinations result in qualitatively similar findings as when using the fifth‐order upstream scheme.…”

Section: Model Set‐upmentioning

confidence: 99%

Effects of terrain‐following vertical coordinates on simulation of stratus clouds in numerical weather prediction models

Westerhuis

Fuhrer

Bhattacharya

et al. 2020

Quart J Royal Meteoro Soc

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Many numerical weather prediction models employ terrain‐following vertical coordinates. As a consequence, over orography, flat tops of stratus clouds are intersected by the vertical coordinate surfaces. We conduct idealised two‐dimensional simulations of a stratus cloud with the COSMO model to study the effect of such sloping vertical coordinate surfaces. The evolution of the stratus cloud above a flat surface within a horizontally homogeneous atmosphere serves as a reference. During night‐time, the cloud thickens, driven by radiative cooling at the cloud top. Adding a sinusoidal perturbation to the vertical coordinate surfaces reduces the growth of the stratus cloud. With strong perturbations, the cloud starts to dissipate. The physical processes in the two simulations are identical, hence this behaviour is purely driven by numerical diffusion. The cloud is similarly thinned when sinusoidal orographic features are introduced. The reduction depends on the amplitude and wavelength of the perturbations and hills. Increasing the horizontal resolution partly mitigates the numerical diffusion. However, this is a very costly measure for an operational weather model. We suggest conducting further research on a new vertical coordinate with additional local smoothing of the orographic signal.

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Fourth order, conservative discretization of horizontal Euler equations in the COSMO model and regional climate simulations

Cited by 3 publications

References 0 publications

Lake Breeze of Small Lakes in North-East Germany in COSMO-CLM simulations with non-diffusive dynamics and numerics

Lake Breeze of Small Lakes in North-East Germany in COSMO-CLM simulations with non-diffusive dynamics and numerics

A spatial evaluation of high-resolution wind fields from empirical and dynamical modeling in hilly and mountainous terrain

Effects of terrain‐following vertical coordinates on simulation of stratus clouds in numerical weather prediction models

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