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A data-driven investigation of the flow around a high-rise building isperformed combining heterogeneous experimental samples and RANS CFD. Thecoupling is performed using techniques based on the Ensemble Kalman Filter(EnKF), including advanced manipulations such as localization and inflation.The augmented state estimation obtained via EnKF has also been employed toimprove the predictive features of the model via an optimization of the fivefree global model constant of the $\mathcal{K}-\varepsilon$ turbulence modelused to close the equations. The optimized values are very far from theclassical values prescribed as general recommendations and implemented incodes, but also different from other data-driven analyses reported in theliterature. The results obtained with this new optimized parametric descriptionshow a global improvement for both the velocity field and the pressure field.In addition, some topological improvement for the flow organization areobserved downstream, far from the location of the sensors.

Augmented state estimation of urban settings using intrusive sequential Data Assimilation