OWEMES 2006, Civitavecchia, Italy Jens Tambke, Lueder von Bremen, *Jörg-Olaf Wolff ForWind and *ICBM, Carl von Ossietzky University Oldenburg John A. T.

Slides:



Advertisements
Similar presentations
Wind: Energy measurement and analysis services in FMI
Advertisements

Parametrization of surface fluxes: Outline
Section 2: The Planetary Boundary Layer
OpenFOAM for Air Quality Ernst Meijer and Ivo Kalkman First Dutch OpenFOAM Seminar Delft, 4 november 2010.
A drag parameterization for extreme wind speeds that leads to improved hurricane simulations Gerrit Burgers Niels Zweers Vladimir Makin Hans de Vries EMS.
Session 2, Unit 3 Atmospheric Thermodynamics
THE PARAMETERIZATION OF STABLE BOUNDARY LAYERS BASED ON CASES-99 Zbigniew Sorbjan Marquette University, Milwaukee Zbigniew Sorbjan Marquette University,
Skyler Goldman, Meteorology, DMES RELATIONSHIP BETWEEN ROUGHNESS LENGTH, STATIC STABILITY, AND DRAG COEFFICIENT IN A DUNE ENVIRONMENT.
Direct numerical simulation study of a turbulent stably stratified air flow above the wavy water surface. O. A. Druzhinin, Y. I. Troitskaya Institute of.
Turbulence and mixing in estuaries
ADMS ADMS 3.3 Modelling Summary of Model Features.
Atmospheric Analysis Lecture 3.
0.1m 10 m 1 km Roughness Layer Surface Layer Planetary Boundary Layer Troposphere Stratosphere height The Atmospheric (or Planetary) Boundary Layer is.
Internal Gravity Waves and Turbulence Closure Model for SBL Sergej Zilitinkevich Division of Atmospheric Sciences, Department of Physical Sciences University.
D A C B z = 20m z=4m Homework Problem A cylindrical vessel of height H = 20 m is filled with water of density to a height of 4m. What is the pressure at:
Comparison of Eddy Covariance Results By Wendy Couch, Rob Aves and Larissa Reames.
Training course: boundary layer II Similarity theory: Outline Goals, Buckingham Pi Theorem and examples Surface layer (Monin Obukhov) similarity Asymptotic.
Ekman Transport Ekman transport is the direct wind driven transport of seawater Boundary layer process Steady balance among the wind stress, vertical eddy.
KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association INSTITUTE OF METEOROLOGY AND CLIMATE RESEARCH,
Wind Driven Circulation I: Planetary boundary Layer near the sea surface.
The Air-Sea Momentum Exchange R.W. Stewart; 1973 Dahai Jeong - AMP.
Monin-Obukhoff Similarity Theory
KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association INSTITUTE OF METEOROLOGY AND CLIMATE RESEARCH,
Evaporation Slides prepared by Daene C. McKinney and Venkatesh Merwade
A control algorithm for attaining stationary statistics in LES of thermally stratified wind-turbine array boundary layers Adrian Sescu * and Charles Meneveau.
Xin Xi. 1946: Obukhov Length, as a universal length scale for exchange processes in surface layer. 1954: Monin-Obukhov Similarity Theory, as a starting.
Effects of Upwind Roughness Changes and Impacts on Hub-Height Winds Peter A. Taylor 1,2, Wensong Weng 1 and James R. Salmon 2 1 Centre for Research in.
Prediction of Atlantic Tropical Cyclones with the Advanced Hurricane WRF (AHW) Model Jimy Dudhia Wei Wang James Done Chris Davis MMM Division, NCAR Jimy.
Understanding the USEPA’s AERMOD Modeling System for Environmental Managers Ashok Kumar Abhilash Vijayan Kanwar Siddharth Bhardwaj University of Toledo.
Integration of the German Offshore Wind Power Potential into the Electricity Supply System B. Lange, Ü. Cali, R. Jursa, F. Schlögl, M. Wolff, K. Rohrig.
KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association INSTITUTE OF METEOROLOGY AND CLIMATE RESEARCH,
Observational and theoretical investigations of turbulent structures generated by low-Intensity prescribed fires in forested environments X. Bian, W. Heilman,
Observations and Models of Boundary-Layer Processes Over Complex Terrain What is the planetary boundary layer (PBL)? What are the effects of irregular.
WINDTEST Kaiser-Wilhelm-Koog GmbH EWEC 2007 Session DT2 Slide No. 1 Comparison of the ZephIR Wind-LiDAR to Classical Cup Measurements On- and Offshore.
Modeling the upper ocean response to Hurricane Igor Zhimin Ma 1, Guoqi Han 2, Brad deYoung 1 1 Memorial University 2 Fisheries and Oceans Canada.
50 Years of the Monin-Obukhov Similarity Theory Thomas Foken University of Bayreuth, Bayreuth, Germany.
Meteorology & Air Pollution Dr. Wesam Al Madhoun.
11th EMS meeting Sep 2011, Gustiness parameterization in the atmospheric boundary layer Irene Suomi Timo Vihma Sven-Erik Gryning.
Estimating the Optimal Location of a New Wind Farm based on Geospatial Information System Data Dec Chungwook Sim.
1 Equations of Motion Buoyancy Ekman and Inertial Motion September 17.
OWEMES 2006, Civitavecchia, Italy Accuracy of Short-Term Predictions for 25 GW Offshore Wind Power in Germany Jens Tambke, L. v. Bremen, N. Saleck, U.
Atmospheric boundary layers and turbulence I Wind loading and structural response Lecture 6 Dr. J.D. Holmes.
Analysis of Turbulence Development in the Morning
Far Shore Wind Climate Modelling Background Far shore wind conditions are expected to be favorable for wind energy power production due to increased mean.
References Conclusions Approach NORSEWIND – ARRAY OF WIND LIDARS AND METEOROLOGICAL MASTS OFFSHORE Charlotte Hasager (1) F P Detlef Stein (2) Saskia Hagemann.
R. A. Brown 2003 U. Concepci Ó n. High Winds Study - Motivation UW PBL Model says U 10 > 35 m/s Composite Storms show high winds Buoy limits:
A New Theoretical Basis for Describing Low Turbulence Wind Turbine Performance Peter Stuart PCWG Meeting – 26 June 2015.
CITES 2005, Novosibirsk Modeling and Simulation of Global Structure of Urban Boundary Layer Kurbatskiy A. F. Institute of Theoretical and Applied Mechanics.
Bulk Parameterizations for Wind Stress and Heat Fluxes (Chou 1993; Chou et al. 2003) Outlines: Eddy correlation (covariance) method Eddy correlation (covariance)
Due to the financial impact of statements derived from wind atlases, their verification is of high importance. Different wind atlases – reanalysis data.
Observed Structure of the Atmospheric Boundary Layer
Parameterizations of the Sea-Spray Effects
Scales of Motion, Reynolds averaging September 22.
Processes in the Planetary Boundary Layer
What is the Planetary Boundary Layer? The PBL is defined by the presence of turbulent mixing that couples the air to the underlying surface on a time scale.
HIRLAM coupled to the ocean wave model WAM. Verification and improvements in forecast skill. Morten Ødegaard Køltzow, Øyvind Sætra and Ana Carrasco. The.
A revised formulation of the COSMO surface-to-atmosphere transfer scheme Matthias Raschendorfer COSMO Offenbach 2009 Matthias Raschendorfer.
Wind Driven Circulation I: Ekman Layer. Scaling of the horizontal components  (accuracy, 1% ~ 1‰) Rossby Number Vertical Ekman Number R o and E v are.
Modeling of heat and mass transfer during gas adsorption by aerosol particles in air pollution plumes T. Elperin1, A. Fominykh1, I. Katra2, and B. Krasovitov1.
Development of the two-equation second-order turbulence-convection model (dry version): analytical formulation, single-column numerical results, and.
Monin-Obukhoff Similarity Theory
Similarity theory 1. Buckingham Pi Theorem and examples
The β-spiral Determining absolute velocity from density field
Mark A. Bourassa and Qi Shi
Chengcheng Chen, Tingting Jiang, Genyong Wu
Reducing Uncertainty of Near-shore wind resource Estimates (RUNE) using wind lidars and mesoscale models EMS 2015, Sofia, Bulgaria, Coastal meteorology.
A hybrid model for the wind profile
PURPOSE OF AIR QUALITY MODELING Policy Analysis
Meteorology & Air Pollution Dr. Wesam Al Madhoun
Eric TROMEUR, Sophie PUYGRENIER, Stéphane SANQUER
Presentation transcript:

OWEMES 2006, Civitavecchia, Italy Jens Tambke, Lueder von Bremen, *Jörg-Olaf Wolff ForWind and *ICBM, Carl von Ossietzky University Oldenburg John A. T. Bye Physical Oceanography, The University of Melbourne, Australia Bernhard Lange ISET, Universität Kassel, Germany Lorenzo Claveri Finnish Meteorological Institute, Helsinki, Finland Francesco Durante, DEWI German Wind Energy Institute, Wilhelmshaven, Germany Modelling of Wind Fields above the North Sea Horns Reef FiNO1

Jens Tambke, University of Oldenburg / Slide 2 Overview  Offshore Wind Speed Profiles  Influence of Thermal Stratification at Horns Rev and FiNO1  Accuracy of Numerical Weather Analysis at FiNO1  New Model: Inertially Coupled Wind Profiles (ICWP)

Jens Tambke, University of Oldenburg / Slide 3 log(Height z) Speed u(z) neutral unstablestable Classical Profiles:  Logarithmic Profiles + Monin-Obukhov-Formula  Charnock Relation for variable Roughness © Elsam A/S

Jens Tambke, University of Oldenburg / Slide 4 Measurements at Horns Rev Cup anemometers at 4 heights Temperature at 3 heights (T) Investigated period: 10/2001– 4/2002 German Weather Service DWD at 10m, 34m, 110m - 15 m - 62 m - 45 m - 30 m (T) 55 m - (T) -4 m - (T) 13 m - Numerical Weather Analysis © Elsam A/S

Jens Tambke, University of Oldenburg / Slide 5 Wind Profiles and Predictions at Horns Rev  Different wind speed gradients! DWD-Model Observation RMSE = 15%

Jens Tambke, University of Oldenburg / Slide 6 Influence of Thermal Stratification at Horns Rev unstablestable  Binned wind speed ratios

Jens Tambke, University of Oldenburg / Slide 7 Influence of Thermal Stratification Measure for atmospheric stability: Bulk-Richardson-Number Ri b - Acceleration of Gravity - Temperature [K] - Measurement-Height - Difference of virtual potential temperature between sea-surface and height z - Wind speed at height z Calculation of Monin-Obukhov (MO) Length L: Grachev and Fairall (1997) allows calculation of Businger-Dyer stability functions at Horns Rev and FiNO1

Jens Tambke, University of Oldenburg / Slide 8 Influence of Thermal Stratification at Horns Rev unstable stable u(62m) u(15m) = 1.5 u(62m) u(15m) < 1.1

Jens Tambke, University of Oldenburg / Slide 9 Observations at FiNO1  Located in the German Bight 45km north of Borkum  Investigated period: 2004  Wind speed measurements at 33, 41, 51, 61, 71, 81, 91 and 103m height  Massive lattice mast causes strong flow distortion:  Corrections are very important

Jens Tambke, University of Oldenburg / Slide 10  Binned Wind-Speed Ratios Influence of Thermal Stratification at FiNO1 unstable stable

Jens Tambke, University of Oldenburg / Slide 11 Influence of Thermal Stratification at FiNO1 unstable stable u(51m) u(33m) = 1.15 u(51m) u(33m) < 1.05

Jens Tambke, University of Oldenburg / Slide 12 Influence of Thermal Stratification at FiNO1 unstable stable u(103m) u(33m) = 1.4 u(103m) u(33m) < 1.1

Jens Tambke, University of Oldenburg / Slide 13 Comparison of Modelled Profiles at FiNO1 RMSE(103m) = 1.4m/s MM5 (NCEP) Observation DWD analysis wind directions between 190° and 250°

Jens Tambke, University of Oldenburg / Slide 14 Accuracy of DWD Analysis at FiNO1 Observation Analysis Wind SpeedPotential Power Output  Mean Values vs. Hour of the Day, Average of 12 months, 2004 RMSE: 1.4 m/sRMSE: 13% of P(inst)

Jens Tambke, University of Oldenburg / Slide : Mean 103m Wind Speeds in the DWD Analysis Mean Potential Power Production in the German Bight 2004: 51% of the Installed Capacity

Jens Tambke, University of Oldenburg / Slide 16 New Air-Sea-Interaction Model: Inertially Coupled Wind Profiles (ICWP) 1.) Coupling of Ekman- and Log-Profile 2.) Coupling of wind and wave field Height Speed 0 z B < 20m : Matching height for speed, stress  and eddy viscosity Ekman Layer: (z) = A  u * 2 /f = const.  (z) = ρ ∂u/∂z Wave Boundary Layer: (z) =  u * z Φ (MO-Log.)  (z) =  (wave)

Jens Tambke, University of Oldenburg / Slide 17 Inertially Coupled Wind Profiles (ICWP) Similarity Assumptions: 2.) Ratio of drift velocities close to the air-sea interface 3.) Inertial Coupling Relation: a drag law with respect to the matching height z B in air and sea 1.) Ratio of eddy viscosities Ekman Spiral: Onset at z B ~ 1/29

Jens Tambke, University of Oldenburg / Slide 18 Comparison of theoretical and observed Profiles at Horns Rev ICWP Model Observation WAsP Model Input: time series of wind speed at 30m height WAsPbias = m/s ICWP bias = -0.1 m/s RMSE(62m) = 6% (3%) for wind directions between 135° and 360°

Jens Tambke, University of Oldenburg / Slide 19 Comparison of Mean Profiles at FiNO1 ICWP Observation WAsP for wind directions between 190° and 250° Model Input: time series of wind speed at 33m height WAsP bias = m/s ICWP bias = +0.1 m/s RMSE(103m) = 10% (5.5%)

Jens Tambke, University of Oldenburg / Slide 20 Comparison of Mean Profiles at FiNO1 Observation for wind directions between 190° and 250° Model Input: time series of wind speed at 33m height z0=0.2mm IEC-3

Jens Tambke, University of Oldenburg / Slide 21 Current Research: Analysis of Turbulence Intensities at FiNO1 Turbulence Intensity (σ u /u) vs. Wind Speed (u) at 103m, Jan-Dec 2004 for wind directions between 190° and 250°

Jens Tambke, University of Oldenburg / Slide 22 Conclusions 2.Observed Wind Profiles show higher wind shears above 45m height than expected 3.The ICWP-Model reproduces these higher wind shears with an Ekman-Approach 1.Thermal stratification has a crucial impact on offshore wind profiles Thank You for Your Attention! This work was funded by the EU within the Projects ANEMOS and POW’WOW.