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  • 1
    Language: English
    In: Climate dynamics, 2020-07, Vol.55 (1-2), p.215-234
    Subject(s): Weather ; Technology application ; Models ; Analysis
    ISSN: 0930-7575
    E-ISSN: 1432-0894
    Source: Alma/SFX Local Collection
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  • 2
    Language: English
    In: Journal of climate, 2020-03-01, Vol.33 (5), p.1915-1933
    Description: AbstractThe “gray zone” of convection is defined as the range of horizontal grid-space resolutions at which convective processes are partially but not fully resolved explicitly by the model dynamics (typically estimated from a few kilometers to a few hundred meters). The representation of convection at these scales is challenging, as both parameterizing convective processes or relying on the model dynamics to resolve them might cause systematic model biases. Here, a regional climate model over a large European domain is used to study model biases when either using parameterizations of deep and shallow convection or representing convection explicitly. For this purpose, year-long simulations at horizontal resolutions between 50- and 2.2-km grid spacing are performed and evaluated with datasets of precipitation, surface temperature, and top-of-the-atmosphere radiation over Europe. While simulations with parameterized convection seem more favorable than using explicit convection at around 50-km resolution, at higher resolutions (grid spacing ≤ 25 km) models tend to perform similarly or even better for certain model skills when deep convection is turned off. At these finer scales, the representation of deep convection has a larger effect in model performance than changes in resolution when looking at hourly precipitation statistics and the representation of the diurnal cycle, especially over nonorographic regions. The shortwave net radiative balance at the top of the atmosphere is the variable most strongly affected by resolution changes, due to the better representation of cloud dynamical processes at higher resolutions. These results suggest that an explicit representation of convection may be beneficial in representing some aspects of climate over Europe at much coarser resolutions than previously thought, thereby reducing some of the uncertainties derived from parameterizing deep convection.
    ISSN: 0894-8755
    E-ISSN: 1520-0442
    Source: Academic Search Ultimate
    Source: Alma/SFX Local Collection
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  • 3
    Language: English
    In: Monthly weather review, 2020-06-01, Vol.148 (6), p.2391-2410
    Description: Abstract Currently, major efforts are under way to refine the horizontal resolution of weather and climate models to kilometer-scale grid spacing (Δx). Besides refining the representation of the atmospheric dynamics and enabling the use of explicit convection, this will also provide higher resolution in the representation of orography. This study investigates the influence of these resolution increments on the simulation of orographic moist convection. Nine days of fair-weather thermally driven flow over the Alps are analyzed. Two sets of simulations with the COSMO model are compared, each consisting of three runs at Δx of 4.4, 2.2, and 1.1 km: one set using a fixed representation of orography at a resolution of 8.8 km, and one with varying representation at the resolution of the computational mesh. The spatial distribution of precipitation during daytime is only marginally affected by the orographic details, but nighttime convection to the south of the Alps—triggered by cold-air outflow from the valleys—is very sensitive to orography and precipitation is enhanced if more detailed orography is provided. During daytime, the onset of precipitation is delayed. The amplitude of the diurnal cycle of precipitation is reduced, even though more moisture converges toward the Alpine region during the afternoon. The hereby accumulated moisture sustains precipitation during the evening and nighttime over the surrounding plains. For these differences, the effects of changes in orographic detail are more important than changes in grid spacing. In addition, the individual convective cells are weaker, but their number increases with higher resolved orography.
    ISSN: 0027-0644
    E-ISSN: 1520-0493
    Source: Academic Search Ultimate
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  • 4
    Language: English
    In: Journal of the atmospheric sciences, 2016-10-01, Vol.73 (10), p.4021-4041
    Description: Abstract On summertime fair-weather days, thermally driven wind systems play an important role in determining the initiation of convection and the occurrence of localized precipitation episodes over mountainous terrain. This study compares the mechanisms of convection initiation and precipitation development within a thermally driven flow over an idealized double-ridge system in large-eddy (LESs) and convection-resolving (CRM) simulations. First, LES at a horizontal grid spacing of 200 m is employed to analyze the developing circulations and associated clouds and precipitation. Second, CRM simulations at horizontal grid length of 1 km are conducted to evaluate the performance of a kilometer-scale model in reproducing the discussed mechanisms. Mass convergence and a weaker inhibition over the two ridges flanking the valley combine with water vapor advection by upslope winds to initiate deep convection. In the CRM simulations, the spatial distribution of clouds and precipitation is generally well captured. However, if the mountains are high enough to force the thermally driven flow into an elevated mixed layer, the transition to deep convection occurs faster, precipitation is generated earlier, and surface rainfall rates are higher compared to the LES. Vertical turbulent fluxes remain largely unresolved in the CRM simulations and are underestimated by the model, leading to stronger upslope winds and increased horizontal moisture advection toward the mountain summits. The choice of the turbulence scheme and the employment of a shallow convection parameterization in the CRM simulations change the strength of the upslope winds, thereby influencing the simulated timing and intensity of convective precipitation.
    Subject(s): Climatology ; Usage ; Cumulus clouds ; Research ; Eddies ; Simulation methods ; Boundary layer
    ISSN: 0022-4928
    E-ISSN: 1520-0469
    Source: Academic Search Ultimate
    Source: Alma/SFX Local Collection
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  • 5
    Language: English
    In: Quarterly journal of the Royal Meteorological Society, 2019-04, Vol.145 (721), p.1427-1443
    Description: Convection‐resolving models (CRMs) are established as a solid framework to simulate moist convection in both numerical weather prediction and regional‐scale climate projections. However, capturing the different scales of the governing processes is challenging. Previous studies have shown that the size and properties of individual clouds and updraughts do not converge until horizontal grid spacings (Δx) of O (100 m). We refer to this as structural convergence. On the other hand, a few recent studies have demonstrated that domain‐averaged and integrated tendencies related to a large ensemble of convective cells converge at the kilometre scale. We refer to this as bulk convergence. This study investigates both the bulk convergence of the mean diurnal cycle and spatial distribution of precipitation, clouds and convective transport, and structural convergence of cloud‐scale statistics in real‐case convection‐resolving simulations. Two nine‐day episodes of quasi‐periodic diurnal moist convection are simulated at Δx = 8.8, 4.4, 2.2, 1.1 km and 550 m over the Alps and over Central Germany to compare the results in the presence and in the absence of a mesoscale orographic forcing. Results reveal that bulk convergence is systematically achieved in both episodes for the spatial distribution of the analysed quantities. For their mean diurnal cycle, bulk convergence is generally observed in simulations over the Alps, but not over Central Germany, indicating that the presence of a mesoscale orographic forcing reduces the resolution sensitivity of the bulk flow properties. Structural convergence is confirmed to be not yet fully achieved at the kilometre scale. In particular, the size and strength of the simulated convective updraughts and the size of the smallest clouds are largely determined by Δx. This study investigates both the bulk convergence of the mean diurnal cycle and spatial distribution of precipitation, clouds and convective transport, and structural convergence of cloud‐scale statistics in real‐case convection‐resolving simulations over flat and mountainous terrain. Bulk convergence is systematically obtained for the spatial distribution of the analyzed variables. For their mean diurnal cycle, bulk convergence is attained only in the presence of a mesoscale orographic forcing. Structural convergence is not yet achieved at the kilometre scale.
    Subject(s): convection‐resolving models ; grey zone of convection ; convergence ; resolution sensitivity
    ISSN: 0035-9009
    E-ISSN: 1477-870X
    Source: Alma/SFX Local Collection
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  • 6
    Language: English
    In: Bulletin of the American Meteorological Society, 2020-05-01, Vol.101 (5), p.E567-E587
    Description: Abstract Currently major efforts are underway toward refining the horizontal resolution (or grid spacing) of climate models to about 1 km, using both global and regional climate models (GCMs and RCMs). Several groups have succeeded in conducting kilometer-scale multiweek GCM simulations and decadelong continental-scale RCM simulations. There is the well-founded hope that this increase in resolution represents a quantum jump in climate modeling, as it enables replacing the parameterization of moist convection by an explicit treatment. It is expected that this will improve the simulation of the water cycle and extreme events and reduce uncertainties in climate change projections. While kilometer-scale resolution is commonly employed in limited-area numerical weather prediction, enabling it on global scales for extended climate simulations requires a concerted effort. In this paper, we exploit an RCM that runs entirely on graphics processing units (GPUs) and show examples that highlight the prospects of this approach. A particular challenge addressed in this paper relates to the growth in output volumes. It is argued that the data avalanche of high-resolution simulations will make it impractical or impossible to store the data. Rather, repeating the simulation and conducting online analysis will become more efficient. A prototype of this methodology is presented. It makes use of a bit-reproducible model version that ensures reproducible simulations across hardware architectures, in conjunction with a data virtualization layer as a common interface for output analyses. An assessment of the potential of these novel approaches will be provided.
    ISSN: 0003-0007
    E-ISSN: 1520-0477
    Source: Academic Search Ultimate
    Source: Alma/SFX Local Collection
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