Accounting for non‐uniform static stability in orographic drag parametrization

Wells, Helen; Brown, Andrew R.

doi:10.1002/qj.407

Cited by 19 publications

(22 citation statements)

References 11 publications

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“…Allaerts and Meyers [15,21] showed that the upward displacement of the boundary layer by flow deceleration in the wind farm excites gravity waves, supporting the suggestion from Smith [49]. Reinecke and Durran [50], and Vosper et al [51] demonstrated that a Froude number could be used to characterize the blocking behavior of flow over mountains and the onset of upwind mountain stagnation. Using a similar approach, the Froude number:…”

Section: Wind Farm Induction Regionsupporting

confidence: 50%

“…is the reduced gravity taking into account the stability variation between the ground and a depth scale D h , and z i is the height above which the stability is independent of height and is prescribed with a fixed Γ. Vosper et al [51] demonstrated that the thermal stability over a mountain has a significant effect on the gravity-wave-induced flow blockage, and the depth scale D h = z i + U N could account for such stability effect on mountain flows. The Fr of a flow is the ratio of the flow speed to the speed of the induced gravity waves.…”

Section: Wind Farm Induction Regionmentioning

confidence: 99%

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Flow Adjustment Inside and Around Large Finite-Size Wind Farms

Porté‐Agel

2017

Energies

117

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Abstract:In this study, large-eddy simulations are performed to investigate the flow inside and around large finite-size wind farms in conventionally-neutral atmospheric boundary layers. Special emphasis is placed on characterizing the different farm-induced flow regions, including the induction, entrance and development, fully-developed, exit and farm wake regions. The wind farms extend 20 km in the streamwise direction and comprise 36 wind turbine rows arranged in aligned and staggered configurations. Results show that, under weak free-atmosphere stratification (Γ = 1 K/km), the flow inside and above both wind farms, and thus the turbine power, do not reach the fully-developed regime even though the farm length is two orders of magnitude larger than the boundary layer height. In that case, the wind farm induction region, affected by flow blockage, extends upwind about 0.8 km and leads to a power reduction of 1.3% and 3% at the first row of turbines for the aligned and staggered layouts, respectively. The wind farm wake leads to velocity deficits at hub height of around 3.5% at a downwind distance of 10 km for both farm layouts. Under stronger stratification (Γ = 5 K/km), the vertical deflection of the subcritical flow induced by the wind farm at its entrance and exit regions triggers standing gravity waves whose effects propagate upwind. They, in turn, induce a large decelerating induction region upwind of the farm leading edge, and an accelerating exit region upwind of the trailing edge, both extending about 7 km. As a result, the turbine power output in the entrance region decreases more than 35% with respect to the weakly stratified case. It increases downwind as the flow adjusts, reaching the fully-developed regime only for the staggered layout at a distance of about 8.5 km from the farm edge. The flow acceleration in the exit region leads to an increase of the turbine power with downwind distance in that region, and a relatively fast (compared with the weakly stratified case) recovery of the farm wake, which attains its inflow hub height speed at a downwind distance of 5 km.

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Section: Wind Farm Induction Regionsupporting

confidence: 50%

Section: Wind Farm Induction Regionmentioning

confidence: 99%

Flow Adjustment Inside and Around Large Finite-Size Wind Farms

Porté‐Agel

2017

Energies

117

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“…Changes in the atmospheric component between the HadGEM2 and HadGEM3 model families include the ENDGame dynamical core (Wood et al, 2014), the inclusion of a prognostic cloud and condensate scheme (PC2; Wilson et al, 2008), increased convective entrainment and detrainment, a new orographic gravity wave drag (GWD) representation (Vosper et al, 2009), and numerous other changes (see Walters et al, 2011Walters et al, , 2014Walters et al, , 2017. In addition, the vertical resolution has been increased and the model lid extended from 40 to 85 km.…”

Section: Hadgemmentioning

confidence: 99%

Process-level improvements in CMIP5 models and their impact on tropical variability, the Southern Ocean, and monsoons

et al. 2018

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Abstract. The performance of updated versions of the four earth system models (ESMs) CNRM, EC-Earth, HadGEM, and MPI-ESM is assessed in comparison to their predecessor versions used in Phase 5 of the Coupled Model Intercomparison Project. The Earth System Model Evaluation Tool (ESMValTool) is applied to evaluate selected climate phenomena in the models against observations. This is the first systematic application of the ESMValTool to assess and document the progress made during an extensive model development and improvement project. This study focuses on the South Asian monsoon (SAM) and the West African monsoon (WAM), the coupled equatorial climate, and Southern Ocean clouds and radiation, which are known to exhibit systematic biases in present-day ESMs.The analysis shows that the tropical precipitation in three out of four models is clearly improved. Two of three updated coupled models show an improved representation of tropical sea surface temperatures with one coupled model not exhibiting a double Intertropical Convergence Zone (ITCZ). Simulated cloud amounts and cloudradiation interactions are improved over the Southern Ocean. Improvements are also seen in the simulation of the SAM and WAM, although systematic biases remain in regional details and the timing of monsoon rainfall. Analysis of simulations with EC-Earth at different horizontal resolutions from T159 up to T1279 shows that the synoptic-scale variability in precipitation over the SAM and WAM regions improves with higher model resolution. The results suggest that the reasonably good agreement of modeled and observed mean WAM and SAM rainfall in lower-resolution models may be a result of unrealistic intensity distributions.

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“…We use the MetUM Global Atmosphere version 6.0 (MetUM-GA6; Walters et al, 2016;Williams et al, 2015), which is an updated version of the MetUM-GA3 (Walters et al, 2011) configuration analysed by Klingaman et al (2017), with a different dynamical core (ENDGAME; Wood et al, 2014 orographic gravity-wave drag representation (Vosper et al, 2009), and several changes to the convective parametrization (see Walters et al (2011Walters et al ( , 2016 for details). MetUM-GA6 includes a 25 % increase to the rates of mixing entrainment and detrainment for diagnosed deep convection relative to MetUM-GA3, implemented to improve the representation of tropical sub-seasonal variability following Klingaman and Woolnough (2014).…”

Section: Model Descriptionmentioning

confidence: 99%

Connecting spatial and temporal scales of tropical precipitation in observations and the MetUM-GA6

2017

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Abstract. This study analyses tropical rainfall variability (on a range of temporal and spatial scales) in a set of parallel Met Office Unified Model (MetUM) simulations at a range of horizontal resolutions, which are compared with two satellitederived rainfall datasets. We focus on the shorter scales, i.e. from the native grid and time step of the model through subdaily to seasonal, since previous studies have paid relatively little attention to sub-daily rainfall variability and how this feeds through to longer scales. We find that the behaviour of the deep convection parametrization in this model on the native grid and time step is largely independent of the grid-box size and time step length over which it operates. There is also little difference in the rainfall variability on larger/longer spatial/temporal scales. Tropical convection in the model on the native grid/time step is spatially and temporally intermittent, producing very large rainfall amounts interspersed with grid boxes/time steps of little or no rain. In contrast, switching off the deep convection parametrization, albeit at an unrealistic resolution for resolving tropical convection, results in very persistent (for limited periods), but very sporadic, rainfall. In both cases, spatial and temporal averaging smoothes out this intermittency. On the ∼ 100 km scale, for oceanic regions, the spectra of 3-hourly and daily mean rainfall in the configurations with parametrized convection agree fairly well with those from satellite-derived rainfall estimates, while at ∼ 10-day timescales the averages are overestimated, indicating a lack of intra-seasonal variability. Over tropical land the results are more varied, but the model often underestimates the daily mean rainfall (partly as a result of a poor diurnal cycle) but still lacks variability on intra-seasonal timescales. Ultimately, such work will shed light on how uncertainties in modelling small-/short-scale processes relate to uncertainty in climate change projections of rainfall distribution and variability, with a view to reducing such uncertainty through improved modelling of small-/short-scale processes.

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Accounting for non‐uniform static stability in orographic drag parametrization

Cited by 19 publications

References 11 publications

Flow Adjustment Inside and Around Large Finite-Size Wind Farms

Flow Adjustment Inside and Around Large Finite-Size Wind Farms

Process-level improvements in CMIP5 models and their impact on tropical variability, the Southern Ocean, and monsoons

Connecting spatial and temporal scales of tropical precipitation in observations and the MetUM-GA6

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