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## Mesoscale modeling of the atmosphere

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Wind Energy Modeling and Simulation - Volume 1: Atmosphere and Plant — Recommend this title to your library

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This chapter discusses the mechanisms responsible for generating atmospheric motion, scales of the various phenomena of the atmosphere relevant to modeling the wind plant, atmospheric energetics, and introduces the chaotic nature of the flow. Then the equations of motion and their discretization are presented, along with the discussion of typical boundary and initial conditions, including a discussion of data assimilation (DA) as a way to assure that the initial conditions best incorporate the observations. The author then discuss how the atmospheric models are applied to wind resource assessment and forecasting, and how the wind farm wakes are currently parameterized in the atmospheric models. For completeness, we introduce methods for assessment, postprocessing, and uncertainty quantification. As the field is rapidly evolving, we finish with discussion of where the field is likely to go in the next decade.

Chapter Contents:

• 3.1 Introduction to meteorology for wind energy modeling
• 3.1.1 Forces and the general circulation of the atmosphere
• 3.1.2 Scales and phenomena in the atmosphere
• 3.1.3 Atmospheric energetics
• 3.1.4 The chaotic nature of atmospheric flow
• 3.2 Basics of atmospheric modeling
• 3.2.1 Historical perspective
• 3.2.2 Governing equations for flows in the atmosphere
• 3.2.3 Numerical resolution requirements
• 3.2.4 Reynolds averaged Navier–Stokes simulation methodology
• 3.2.5 Discretizations
• 3.2.6 Forcing physics and parameterizations
• 3.3 Initial conditions and data assimilation
• 3.3.1 Nudging
• 3.3.2 Variational DA
• 3.3.3 Ensemble Kalman filters
• 3.3.4 EnVar and hybrid DA
• 3.4 Boundary conditions
• 3.4.1 Forcing from global models
• 3.4.2 Top boundary
• 3.4.3 Bottom boundary
• 3.4.4 Coupled models
• 3.5 Using NWP for wind power
• 3.5.1 Resource assessment
• 3.5.2 Forecasting
• 3.5.3 Turbine wake parameterization
• 3.5.4 Postprocessing
• 3.5.5 Assessment
• 3.6 Uncertainty quantification
• 3.6.1 Quantifying parametric uncertainty
• 3.6.2 Quantifying structural uncertainty—ensemble methods
• 3.6.3 Calibrating ensembles
• 3.6.4 Analog ensembles
• 3.7.1 Storm-scale prediction
• 3.7.2 Scale-aware models
• 3.7.3 Blended global/mesoscale models
• 3.7.4 Seasonal to subseasonal prediction
• 3.7.5 Regime-dependent corrections
• 3.8 Summary and conclusions
• References

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