Results of the GABLS3 diurnal-cycle benchmark for wind energy applications

Transcription

1 Results of the GABLS3 diurnal-cycle benchmark for wind energy applications Javier Sanz Rodrigo Wake Conference 2017 Visby, 1 June 2017

2 GABLS 3: Boundary-layer characteristics (Bosveld et al., 2014) Cabauw (Netherlands) 30 km Stationary synoptic Clear skies No fog Substantial LLJ 6 years 9 days night of 1 2 July 2006 Bosveld et al. (2014) The third GABLS Intercomparison case for evaluation studies of Boundary-Layer Models Part A: Case Selection and Set-Up. Boundary Layer Meteorol 152:

3 GABLS3 revisited for Wind Energy ABL models New European Wind Atlas (NEWA) context Add 1 st -order physics to the flow model Include thermal stratification vs neutral Transient model vs steady-steate Include large-scale tendencies vs idealized forcing Avoid adding physical complexity if this is not justified by meaningful improved performance No humidity equation just momentum and energy No heat transfer by radiation or phase changes just diffusion and advection No land-surface modeling just MOST Focus on wind energy quantities of interest (QoIs) Rotor heights ( m): S hub, WD hub, REWS, etc Metrics Cycle-integrated errors, relevant for intended use (AEP) Quantify performance vs observations and vs reference model

4 Simulations

5 WRF Sensitivity Analysis WRF-YSU (ref): ERA Interim 9 > 3 > 1 km, 46 vertical levels 24 hr spin-up YSU Tendencies from d02 (3 km) L av = 3x3 = 9 km t av = 60 min (rolling average) Sensitivity analysis Input data: GFS, ERA Interim PBL scheme: MYJ, MYNN, QNSE, TEMF, YSU

6 WRF Sensitivity Analysis: Horizontal wind fields at 80 m z = 80 m

7 WRF Sensitivity Analysis: time-series at 80 m

8 WRF Sensitivity Analysis: vertical profiles at midnight

9 Momentum Budget at Mesoscale: Tendencies Meso 1 U 1 U U U 1 u ' w' U V W V Vg fc t fc x y z fc z 1 V 1 V V V 1 v ' w' U V W U U g fc t fc x y z fc z Tendency Advection Coriolis Horizontal Pressure Gradient Turbulence Coriolis parameter: f c 2 sin Utend Uadv Ucor U pg U pbl

10 Mesoscale Tendencies from WRF WRF-YSU ERA Interim 9 > 3 > 1 km 24 hr spin-up Tendencies from d02 (3 km) L av = 3x3 = 9 km t av = 60 min (rolling average) Utend Uadv Ucor U pg U pbl D = 160 m z hub = 120 m

11 Mesoscale Tendencies from WRF WRF-YSU ERA Interim 9 > 3 > 1 km 24 hr spin-up Tendencies from d02 (3 km) L av = 3x3 = 9 km t av = 60 min (rolling average) Vtend Vadv Vcor Vpg Vpbl D = 160 m z hub = 120 m

12 SCM with Large-Scale Tendencies and Data Assimilation Mesoscale forcing Solved at microscale Data assimilation at microscale 1 U 1 uw Uadv V U pg U f t f z c 1 V 1 vw Vad v U Vp g V f t f z c t adv w z nud c c nud nud No humidity No heat transfer by radiation or phase changes nud z obs f c nud τ nud = 10min : 1hr ω = 1 within measurements M-O at the surface based on mesoscale inputs 2 * 2 2 * w' ' 0 lg h ; * z0t L0 u

13 1.5 th -order k-ε model (Sogachev et al. 2012) Eddy-viscosity from k-ε: Prognostic equations for k and ε: Length-scale limiter (MY74): m k m K C l k C k Km k P B t z k z * Km C 1 P C 2 C 3 B t k z z 0 12 zk dz 0 lmax C ; C k dz Constants: C ε1 = 1.52, C ε2 = 1.833, σ k = 2.95, σ ε = 2.95 and C μ = 0.03 * C C ( C C ) l C ( C C ) B l m max B 2 C ( C C ) lm lmax if Ri C 2 1 C 2 C 1l m lmax if Ri 0

14 Vertical Profiles at Midnight (LLJ)

15 Time-Height Fields Good consistency among all the models at capturing mesoscale forcing Best results on mean wind speed obtained from WRF ensemble Spread of microscale models of about the same size as that observed in the WRF ensemble Differences in TKE fields

16 Wind Energy Quantities of Interest Hub-height wind speed and direction S hub, WD hub Rotor Equivalent Wind Speed REWS z D = 160 m i+1 REWS A S cos A i z i z obs = [40, 80, 140, 200] z hub = 120 m A i i i i β i : veer angle with respect to hubheight wind direction Wind speed shear, α, and direction veer ψ S i z Shub zhub

17 Wind Energy Quantities of Interest

18 Cycle-Aggregrated Metrics 1 N pred obs i 1 MAE N

19 Conclusions Consistent flow fields of microscale models (RANS and LES) using tendencies from mesoscale Spread is significant, of similar magnitude in microscale models (using the same input) than that observed in the WRF sensitivity analysis Ensemble approach leads to the best predictions of mean wind speed fields Assessment is limited to the particular conditions of the diurnal cycle What shall be the impact of meso-micro in wind resource assessment applications?

20 Follow-up: NEWA Meso-Micro Challenge Determine the applicability range of meso-micro AEP methodologies for the NEWA validation domain Cabauw (flat onshore) Fino-1 (offshore) Alaiz (complex terrain) + additional sites as data comes in (Perdigao, Hornamossen, Kassel, etc) Launched in May 2017 Exploitation of NEWA s open-access experimental database and modelchain

21 Acknowledgements

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