Probing the ‘grey zone’ of NWP – is higher resolution always better?

Kealy, John C.

doi:10.1002/wea.3506

Cited by 8 publications

(7 citation statements)

References 9 publications

(11 reference statements)

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“…There is one concern regarding the different approaches for turbulence parametrizations. While a three-dimensional turbulence closure (Heinze et al, 2017) was employed in the hectometer-scale simulations analysed, most km-scale models are equipped with a one-dimensional planetary boundary-layer scheme (e.g., Kealy, 2019). However, both hectometer-and km-scale models suffer from similar deficiencies: the lag in timing of the diurnal cycle of convection and a relative lack of convective organization, especially late in the day (e.g., Baldauf et al, 2011;Clark et al, 2016;Rasp et al, 2018).…”

Section: Summary and Discussionmentioning

confidence: 99%

Cold‐pool‐driven convective initiation: using causal graph analysis to determine what convection‐permitting models are missing

Hirt

Craig

Schäfer

et al. 2020

Quart J Royal Meteoro Soc

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Cold-pool-driven convective initiation is investigated in high-resolution, convection-permitting simulations with a focus on the diurnal cycle and organization of convection and the sensitivity to grid size. Simulations of four different days over Germany were performed using the ICON-LEM model with grid sizes from 156 to 625 m. In these simulations, we identify cold pools, cold-pool boundaries and initiated convection. Convection is triggered much more efficiently in the vicinity of cold pools than in other regions and can provide as much as 50% of total convective initiation, in particular in the late afternoon. By comparing different model resolutions, we find that cold pools are more frequent, smaller and less intense in lower-resolution simulations. Furthermore, their gust fronts are weaker and less likely to trigger new convection. To identify how model resolution affects this triggering probability, we use a linear causal graph analysis.In doing so, we postulate a graph structure with potential causal pathways and then apply multi-linear regression accordingly. We find a dominant, systematic effect: reducing grid sizes directly reduces upward mass flux at the gust front, which causes weaker triggering probabilities. These findings are expected to be even more relevant for km-scale, numerical weather prediction models. We thus expect that a better representation of cold-pool-driven convective initiation will improve forecasts of convective precipitation. K E Y W O R D S causal effect estimation, numerical weather prediction, convection organization INTRODUCTIONConvection-permitting models have become increasingly prominent for numerical weather prediction in recent years (Baldauf et al., 2011;Clark et al., 2016). Such models have a grid size of several hectometres to a few kilometres which is sufficient to run without the use of a convection parametrization. Due to the many underlying approximations and systematic biases of convection schemes (e.g., Gentine et al., 2018), being able to simulateThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Summary and Discussionmentioning

confidence: 99%

Cold‐pool‐driven convective initiation: using causal graph analysis to determine what convection‐permitting models are missing

Hirt

Craig

Schäfer

et al. 2020

Quart J Royal Meteoro Soc

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show abstract

“…However, finer grids are more prone to the so-called double penalty problem (Rossa et al 2008;Gilleland et al 2009), where features are slightly shifted in time and/or space compared to the truth (as represented by observations or analysis), resulting in verification penalties in both space-time locations (Colle et al 2000;Mass et al 2002;Michaelides 2008). When refining a model grid it is also important to be aware of the NWP gray zone (Zheng et al 2016;Chow et al 2019;Kealy 2019;Jeworrek et al 2019): Model setup can be challenging at grid spacings that are not fine enough to fully resolve processes explicitly, yet too fine to fully parameterize them using approximating schemes. These 'gray zone' scales differ for various processes (e.g., cumulus convection, turbulent eddies, orographic effects).…”

Section: Background and Introductionmentioning

confidence: 99%

WRF Precipitation Performance and Predictability for Systematically Varied Parameterizations over Complex Terrain

Jeworrek

West

Stull

2021

Weather and Forecasting

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Physics parameterizations in the Weather Research and Forecasting (WRF) model are systematically varied to investigate precipitation forecast performance over the complex terrain of southwest British Columbia (BC). Comparing a full year of modelling data from over 100 WRF configurations to station observations reveals sensitivities of precipitation intensity, season, location, grid resolution, and accumulation window. The choice of cumulus and microphysics parameterizations is most important. The WSM5 microphysics scheme yields competitive verification scores when compared to more sophisticated and computationally expensive parameterizations. Although the cale-aware Grell-Freitas cumulus parameterization performs better for summertime convective precipitation, the conventional Kain-Fritsch parameterization better simulates wintertime frontal precipitation, which contributes to the majority of the annual precipitation in southwest BC. Finer grid spacings have lower relative biases and a more realistic spread in precipitation intensity distribution, yet higher relative standard deviations of their errors — they produce finer spatial differences and local extrema. Finer resolutions produce the best fraction of correct-to-incorrect forecasts across all precipitation intensities, whereas the coarser 27-km domain yields the highest hit rates and equitable threat scores. Verification metrics improve greatly with longer accumulation windows — hourly precipitation values are prone to double-penalty issues, while longer accumulation windows compensate for timing errors but lose information about short-term precipitation intensities. This study provides insights regarding WRF precipitation performance in complex terrain across a wide variety of configurations, using metrics important to a range of end users.

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“…Therefore, finer the horizontal resolution better it is for simulating the wind farms. However, it is not advisable to keep the WRF model horizontal resolution finer than 1 km because of the "grey-zone" limitations related with PBL schemes (Kealy, 2019). Therefore, in this study we have used a horizontal resolution of 1 km for all the simulations.…”

Section: Casesmentioning

confidence: 99%

Recovery processes in coastal wind farms under sea-breeze conditions

Gupta

Roy

2021

Adv. Geosci.

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Abstract. With the rapid growth in offshore wind energy, it is important to understand the dynamics of offshore wind farms. Most of the offshore wind farms are currently installed in coastal regions where they are often affected by sea-breezes. In this work, we quantitatively study the recovery processes for coastal wind farms under sea-breeze conditions. We use a modified Borne's method to identify sea breeze days off the west coast of India in the Arabian Sea. For the identified sea breeze days, we simulate a hypothetical wind farm covering 50×50 km2 area using the Weather Research and Forecasting (WRF) model driven by realistic initial and boundary conditions. We use three wind farm layouts with the turbines spaced 0.5, 1, and 2 km apart. The results show an interesting power generation pattern with a peak at the upwind edge and another peak at the downwind edge due to sea breeze. Wind farms affect the circulation patterns, but the effects of these modifications are very weak compared to the sea breezes. Vertical recovery is the dominant factor with more than half of the momentum extracted by wind turbines being replenished by vertical turbulent mixing. However, horizontal recovery can also play a strong role for sparsely packed wind farms. Horizontal recovery is stronger at the edges where the wind speeds are higher whereas vertical recovery is stronger in the interior of the wind farms. This is one of the first studies to examine replenishment processes in offshore wind farms under sea breeze conditions. It can play an important role in advancing our understanding wind farm-atmospheric boundary layer interactions.

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Probing the ‘grey zone’ of NWP – is higher resolution always better?

Cited by 8 publications

References 9 publications

Cold‐pool‐driven convective initiation: using causal graph analysis to determine what convection‐permitting models are missing

Cold‐pool‐driven convective initiation: using causal graph analysis to determine what convection‐permitting models are missing

WRF Precipitation Performance and Predictability for Systematically Varied Parameterizations over Complex Terrain

Recovery processes in coastal wind farms under sea-breeze conditions

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