Improving CFD atmospheric simulations at local scale for wind resource assessment using the iterative ensemble Kalman smoother

Accurate wind fields simulated by CFD models are necessary for many environmental and safety micro-meteorological applications, such as wind resource assessment. Atmospheric simulations at local scale are largely determined by boundary conditions (BCs), which are generally provided by outputs of mesoscale models (e.g., WRF). In order to improve the accuracy of the BCs, especially in the lowest levels, data assimilation methods might be used to take available observations into account. Data assimilation methods have generally been developed for larger scale meteorology and deal with initial conditions. Among the existing methods, the iterative ensemble Kalman smoother (IEnKS) has been chosen and adapted to micro-meteorology by taking BCs into account. In the present study, we assess the ability of the adapted IEnKS to improve wind simulations over a very complex topography in a context of wind resource assessment, by assimilating a few in situ observations. The IEnKS is tested with the CFD model Code Saturne in 2D and 3D using both twin experiments and real observations. We propose a method to determine the first estimate of the BCs and to construct the associated background error covariance matrix, from the statistical analysis of three years of WRF simulations. The IEnKS is proved to greatly reduce the error and the uncertainty of the BCs and thus of the simulated wind field over the small-scale domain. As a consequence, the wind resource estimate is also much more accurate. Highlights • This article provides a framework to perform ensemble variational data assimilation of in situ observations to improve local scale simulations with a CFD model. • The iterative ensemble Kalman smoother is adapted to correct boundary conditions and is tested with twin experiments and real data experiments in 2D and 3D with a CFD model over very complex topography. • The adapted IEnKS is proved to enhance the accuracy of boundary conditions and local scale simulations in operationally affordable conditions.

[1]  O. Probst,et al.  State of the Art and Trends in Wind Resource Assessment , 2010 .

[2]  C. Gorlé,et al.  Improving urban flow predictions through data assimilation , 2018 .

[3]  S. E. Haupt,et al.  A preliminary study of assimilating numerical weather prediction data into computational fluid dynamics models for wind prediction , 2011 .

[4]  Luís Frölén Ribeiro,et al.  Linear and nonlinear models in wind resource assessment and wind turbine micro-siting in complex terrain , 2008 .

[5]  Susumu Shimada,et al.  Accuracy of the Wind Speed Profile in the Lower PBL as Simulated by the WRF Model , 2011 .

[6]  Jake Badger,et al.  Wind Resource Estimation—An Overview , 2003 .

[7]  G. Powers,et al.  A Description of the Advanced Research WRF Version 3 , 2008 .

[8]  L. Adolphe,et al.  Cross indicator analysis between wind energy potential and urban morphology , 2017 .

[9]  Dean S. Oliver,et al.  An Iterative EnKF for Strongly Nonlinear Systems , 2012 .

[10]  Ana Estanqueiro,et al.  A new methodology for urban wind resource assessment , 2016 .

[11]  X. Zou,et al.  Introduction to Adjoint Techniques and the MM5 Adjoint Modeling System. , 1997 .

[12]  Jorge Nocedal,et al.  A Limited Memory Algorithm for Bound Constrained Optimization , 1995, SIAM J. Sci. Comput..

[13]  F. L. Dimet,et al.  Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects , 1986 .

[14]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[15]  R. Daley.  Atmospheric Data Analysis , 1991 .

[16]  P. Courtier,et al.  Variational Data Assimilation with a Semi-Lagrangian Semi-implicit Global Shallow-Water Equation Model and Its Adjoint , 1993 .

[17]  Pierre Sagaut,et al.  Data assimilation-based reconstruction of urban pollutant release characteristics , 2017 .

[18]  Marc Bocquet,et al.  Data Assimilation: Methods, Algorithms, and Applications , 2016 .

[19]  B. Carissimo,et al.  Downscaling wind energy resource from mesoscale to microscale model and data assimilating field measurements , 2014 .

[20]  Marc Bocquet,et al.  Combining inflation-free and iterative ensemble Kalman filters for strongly nonlinear systems , 2012 .

[21]  Eugenia Kalnay,et al.  Atmospheric Modeling, Data Assimilation and Predictability , 2002 .

[22]  Xinyang Jin,et al.  New inflow boundary conditions for modelling the neutral equilibrium atmospheric boundary layer in computational wind engineering , 2009 .

[23]  F. Archambeau,et al.  Code Saturne: A Finite Volume Code for the computation of turbulent incompressible flows - Industrial Applications , 2004 .

[24]  Hiroshi Kato,et al.  A data assimilation methodology for reconstructing turbulent flows around aircraft , 2015, J. Comput. Phys..

[25]  Cécile L. Defforge,et al.  Data assimilation at local scale to improve CFD simulations of atmospheric dispersion: application to 1D shallow-water equations and method comparisons , 2018 .

[26]  Shigeru Obayashi,et al.  Assimilation Experiment of Lidar Measurements for Wake Turbulence , 2008 .

[27]  P. Courtier,et al.  Variational assimilation of meteorological observations with the direct and adjoint shallow-water equations , 1990 .

[28]  Alexander Kalmikov,et al.  Wind power resource assessment in complex urban environments: MIT campus case-study using CFD Analysis , 2010 .

[29]  Otto Weiler,et al.  CFD simulation of wind flow over natural complex terrain : case study with validation by field measurements for Ria de Ferrol, Galicia, Spain , 2015 .

[30]  Dean S. Oliver,et al.  An Iterative Ensemble Kalman Filter for Multiphase Fluid Flow Data Assimilation , 2007 .

[31]  Bert Blocken,et al.  50 years of Computational Wind Engineering: Past, present and future , 2014 .

[32]  Michael Schatzmann,et al.  Recommendations on the use of CFD in wind engineering , 2004 .

[33]  Marc Bocquet,et al.  An iterative ensemble Kalman smoother , 2014 .

[34]  C. Gorlé,et al.  Uncertainty quantification for microscale CFD simulations based on input from mesoscale codes , 2018 .

[35]  E. Peterson,et al.  On the Use of Power Laws for Estimates of Wind Power Potential , 1978 .

[36]  Q. Xiao,et al.  An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test , 2008 .