We are interested in real-time estimation and short-term forecasting of 3D fluid flows, using limited computational resources. This is possible through the coupling between data, numerical simulations and sparse fluid flow measurements. Here, the term data refers to numerical simulation outputs. To achieve these ambitious goals, synthetic (i.e. simulated) data and intrusive Reduced Order Model (ROM) drastically reduce the problem dimensionality – typically from 10^7 to 10. Unfortunately, even with corrections, the accumulated errors of these surrogate models increase rapidly over time due to the chaotic and intermittent nature of fluid mechanics. Therefore, deterministic predictions are hardly possible outside the learning time interval. Data assimilation (DA) can alleviate these problems by (i) providing a set of simulations covering probable futures (without increasing the computational cost) and (ii) constraining these online simulations with measurements. We addressed this Uncertainty Quantification (UQ) problem (i) with a multi-scale physically-based stochastic parameterization called "Location uncertainty models" (LUM) [1-4] and new statistical estimators based on stochastic calculus, signal processing and physics [4]. The deterministic ROM coefficients are obtained by a Galerkin projection whereas the correlations of the noises are estimated from the residual velocity, the physical model structure, and the evolution of the resolved modes. We solved problem (ii) with a particle filter [5]. Whether we consider UQ [4] or DA [5] applications, our method greatly exceeds the state of the art, for ROM degrees of freedom smaller than 10 and moderately turbulent 3D flows (Reynolds number up to 300). Measurements at a single location in space and the usual sequential importance resampling algorithm are sufficient for a successful DA. For large number of measurement locations (O(10^4 )), the inference problem becomes overdetermined, the requirement for a dynamic ROM for flow estimation is less apparent, and the particle filtering algorithm needs to be adapted to prevent filter degeneracy. Tempering and non-Gaussian jittering [6, 7] have been shown to provide efficient solutions in this case. References: Mémin, E. (2014). Fluid flow dynamics under location uncertainty. Geophysical & Astrophysical Fluid Dynamics, 108(2), 119-146. Resseguier, V., Mémin, E., & Chapron, B. (2017). Geophysical flows under location uncertainty, Part I Random transport and general models. Geophysical & Astrophysical Fluid Dynamics, 111(3), 149-176. Resseguier, V., Li, L., Jouan, G., Dérian, P., Mémin, E., & Chapron, B. (2021). New trends in ensemble forecast strategy: uncertainty quantification for coarse-grid computational fluid dynamics. Archives of Computational Methods in Engineering, 28(1), 215-261. Resseguier, V., Picard, A. M., Mémin, E., & Chapron, B. (2021). Quantifying truncation-related uncertainties in unsteady fluid dynamics reduced order models. SIAM/ASA Journal on Uncertainty Quantification, 9(3), 1152-1183. Resseguier, V., Ladvig, M., & Heitz, D. (2022). Real-time estimation and prediction of unsteady flows using reduced-order models coupled with few measurements. Journal of Computational Physics, 471, 111631. N. Kantas, A. Beskos, A. Jasra, Sequential monte carlo methods for high-dimensional inverse problems: A case study for the navier–stokes equations., SIAM/ASA Journal on Uncertainty Quantification 2.1 (2014) 464–489. C. Cotter, D. Crisan, D. D. Holm, W. Pan, I. Shevchenko, A particle filter for stochastic advection by lie transport: A case study for the damped and forced incompressible two-dimensional euler equation, SIAM/ASA Journal on Uncertainty Quantification 8 (4) (2020) 1446–1492.