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Characteristics of precipitating convection in the UM at Δ x ≈200m-2km

Characteristics of precipitating convection in the UM at Δ x ≈200m-2km. Bob Plant 1 a nd Emilie Carter 2 , Peter Clark 1 , Heather Guy 2 , Carol Haliwell 2 , Kirsty Hanley 2 , Robin Hogan 1,3 ,Humphrey Lean 2 , Thorwald Stein 1 ,Mark Weeks 2

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Characteristics of precipitating convection in the UM at Δ x ≈200m-2km

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  1. Characteristics of precipitating convection in the UM at Δx≈200m-2km Bob Plant1 and Emilie Carter2, Peter Clark1, Heather Guy2, Carol Haliwell2, Kirsty Hanley2, Robin Hogan1,3,Humphrey Lean2, Thorwald Stein1,Mark Weeks2 1University of Reading 2 Met Office 3ECMWF RMetS NCAS Conference, 6-8 July2016, Challenges in using high resolution numerical weather prediction to forecast high impact weather in the atmospheric boundary layer

  2. 1:3.33 aspect ratio, 25/08/12 Robin Hogan

  3. Example of US forecast 00 UTC 17th May 2015 • Hazardous weather testbed • Despite biases in representation of convective elements, good forecasting tool • Both qualitatively and improved skill according to FSS Mark Weeks

  4. Example of US forecast 2km UM Radar 00 UTC 17th May 2015 • Hazardous weather testbed • Despite biases in representation of convective elements, good forecasting tool • Both qualitatively and improved skill according to FSS Mark Weeks

  5. Explicit to Grey Zone to Fully Parameterized • Grey zone for deep convectionwhen Δx≈ cell size • Model can produce cells on near-grid scales • But don’t expect them to be well represented • Probably needs some form of parameterization, but often better without one than with a conventional parameterization • Grey zone for boundary layer turbulence for Δx≈ h • Model can produce BL overturning structures on near-grid scales • But don’t expect them to be well represented • Needs a turbulence parameterization, but neither a 1D NWP nor a 3D LES parameterization is well suited • Resolutions in this talk range from explicit to grey for deep convection and from grey to fully parameterized for boundary layer turbulence

  6. Convection-permitting models (e.g. UKV) struggle with timing and characteristics of convective storms Example, 07/08/11 Rainfall radar (Nimrod) 1.5 km forecast model (UKV) • By eye, UKV does not have enough small storms in this case • Too much heavy rain, not enough light

  7. Cell sizes Average over strong cases Average over shower cases 200-m model best • Storms are identified using an area threshold of 10 km2 and a rain rate threshold of 4 mm/hr. • 40 days of convective weather in 2011-2012 over southern England. • 1.5km model is expected to be poorly resolved for the small storms!

  8. Average cell size in the UKV UKV around twice the size of radar cells

  9. Rain rate distributions Average over strong cases • UKV struggles to get the largest rain rates • Improves for stronger cases at higher resolutions • In general, model cells seem to be not variable enough

  10. Treatment of turbulence • The UM boundary-layer parameterization is based on semi-empirical vertical mixing profiles in a range of boundary layer types, with horizontal mixing based on Smagorinsky-Lilly • The 500m and 200m models use the full 3D Smagorinsky-Lilly scheme • The subgrid eddy-viscosity, is a mixing length, Δ is the horizontal gridlength, and cs is a constant • Here we vary l such that it is equivalent between the different resolution models

  11. Mixing length effects, 500m model Example shower case Example strong case, 25/08/12 • 500m-model run with a mixing length of 300m, 100m (standard) and 40m. • The mixing length plays a key role in determining the number of small storms.

  12. Mixing length effects, 500m model Example shower case Example strong case, 25/08/12 • By altering Smagorinsky mixing length, we can produce storm morphologies similar to a higher resolution simulation with the equivalent mixing length • i.e., the turbulent mixing formulation is a (the?) leading control on convective cell characteristics • 500m-model run with a mixing length of 300m, 100m (standard) and 40m. • The mixing length plays a key role in determining the number of small storms.

  13. Convective cell life cycle Cumulative fraction of total rainfall • UKV does not get enough rainfall from short-lived storms. • 200m and 100m simulations lack long-lived events (more break-ups and mergers). • 200m with increased mixing length behaves like a lower resolution run

  14. Conclusions • The highest resolutions of current NWP run at resolutions for which the dynamics produces convective clouds, but not with many grid points • UKV has not enough light rain, too much heavier rain, not enough heaviest. • UKV has not enough small cells (under-resolved) and not enough large ones • Consistent across different models in different regions • Improved to some degree by resolution • Higher resolutions give more small cells and possibly too many, so appearing fragmented • All of the above is sensitive to mixing formulation!

  15. East Africa • Again too few large cells and too many small. • 1.5km better than 4.4km but now too fragmented (like the higher res ones in UK) cases. Chloe Eagle

  16. 21/07/10, Δx=500m better sea breeze convergence line Rob Warren

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