- Invited
- Access by Xinjiang University
Confronting Grand Challenges in environmental fluid mechanics
Phys. Rev. Fluids 6, 020501 – Published 8 February, 2021
DOI: https://doi.org/10.1103/PhysRevFluids.6.020501
Abstract
Environmental fluid mechanics underlies a wealth of natural, industrial, and, by extension, societal challenges. In the coming decades, as we strive towards a more sustainable planet, there are a wide range of Grand Challenge problems that need to be tackled, ranging from fundamental advances in understanding and modeling of stratified turbulence and consequent mixing, to applied studies of pollution transport in the ocean, atmosphere, and urban environments. A workshop was organized in the Les Houches School of Physics in France in January 2019 with the objective of gathering leading figures in the field to produce a road map for the scientific community. Five subject areas were addressed: multiphase flow, stratified flow, ocean transport, atmospheric and urban transport, and weather and climate prediction. This article summarizes the discussions and outcomes of the meeting, with the intent of providing a resource for the community going forward.
Physics Subject Headings (PhySH)
Article Text
References (196)
- https://www.un.org/sustainabledevelopment/.
- Invited talks recorded during the conference are available at http://perso.ens-lyon.fr/thierry.dauxois/GrandChallensges/speakers.html.
- G. Voet, J. B. Girton, and M. H. Alford, Pathways, volume transport, and mixing of abyssal water in the Samoan Passage, J. Phys. Oceanogr. 45, 562 (2015).
- G. Novelli, C. Guigand, C. Cousin, E. Ryan, N. Laxague, H. Dai, B. Haus, and T. Özgökmen, A biodegradable surface drifter for ocean sampling on a massive scale, J. Atmos. Oceanic Technol. 34, 2509 (2017).
- J. Sousa and C. Gorlé, Computational urban flow predictions with Bayesian inference: Validation with field data, Build. Environ. 154, 13 (2018).
- J. Song, S. Fan, L. Lin, W. Mottet, H. Woodward, M. G. Davies Wykes, R. Arcucci, D. Xiao, J.-E. Debay, H. ApSimon, E. Aristodemou, D. Birch, M. Carpentieri, F. Fang, M. Herzog, G. R. Hunt, R. L. Jones, C. Pain, D. Pavlidis, A. G. Robins, C. A. Short, and P. F. Linden, Natural ventilation in cities: the implications of fluid mechanics, Build. Res. Inf. 46, 809 (2018).
- S. Poulain and L. Bourouiba, Biosurfactants Change the Thinning of Contaminated Bubbles at Bacteria-Laden Water Interfaces, Phys. Rev. Lett. 121, 204502 (2018).
- S. Poulain and L. Bourouiba, Disease transmission via drops and bubbles, Phys. Today 72(5), 70 (2019).
- N. Mingotti and A. W. Woods, On the transport of heavy particles through an upward displacement-ventilated space, J. Fluid Mech. 772, 478 (2015).
- N. Mingotti, D. Grogino, G. Dello Ioio, M. Curran, K. Barbour, M. Taveira, J. Rudman, C. S. Haworth, R. A. Floto, and A W. Woods, The impact of hospital ward ventilation on airborne pathogen exposure, Am. J. Res. Crit. Care Med. (2020), doi:10.1164/rccm.202009-3634LE.
- N. Mingotti, R. Wood, C. Noakes, and A. W. Woods, The mixing of airborne contaminants by the repeated passage of people along a corridor, J. Fluid Mech. 903, A52 (2020).
- P. F. Linden, The fluid mechanics of natural ventilation, Annu. Rev. Fluid Mech. 31, 201 (1999).
- C. Gladstone and A. W. Woods, On buoyancy-driven natural ventilation of a room with a heated floor, J. Fluid Mech. 441, 293 (2001).
- Y. Yang, R. Verzicco, and D. Lohse, Scaling laws and flow structures of double diffusive convection in the finger regime, J. Fluid Mech. 802, 667 (2016).
- A. J. Rzeznik, G. Flierl, and T. Peacock, Model investigations of discharge plumes generated by deep-sea nodule mining operations, Ocean Eng. 172, 684 (2019).
- M. H. Di Benedetto, J. R. Koseff, and N. T. Ouellette, Orientation dynamics of non-spherical particles under surface gravity waves, Phys. Rev. Fluids 4, 034301 (2019).
- M. Robbe-Saule, C. Morize, R. Henaff, Y. Bertho, A. Sauret, and P. Gondret, Experimental investigation of tsunami waves generated by granular collapse into water, J. Fluid Mech. 907, A11 (2021).
- B. Vowinckel, J. Withers, P. Luzzatto-Fegiz, and E. Meiburg, Settling of cohesive sediment: particle-resolved simulations, J. Fluid Mech. 858, 5 (2019).
- P. D. Dueben and P. Bauer, Challenges and design choices for global weather and climate models based on machine learning, Geosci. Model Dev. 11, 3999 (2018).
- K. L. Johnson, Contact Mechanics (Cambridge University Press, Cambridge, 1985).
- P. Jop, Y. Forterre, and O. Pouliquen, A constitutive law for dense granular flows, Nature (London) 441, 727 (2006).
- K. Kamrin and G. Koval, Nonlocal Constitutive Relation for Steady Granular Flow, Phys. Rev. Lett. 108, 178301 (2012).
- M. Bouzid, M. Trulsson, P. Claudin, E. Clement, and B. Andreotti, Nonlocal Rheology of Granular Flows Across Yield Conditions, Phys. Rev. Lett. 111, 238301 (2013).
- J. M. N. T. Gray and A. N. Edwards, A depth-averaged -rheology for shallow granular free-surface flows, J. Fluid Mech. 755, 503 (2014).
- O. Pouliquen, J. Delour, and S. B. Savage, Fingering in granular flows, Nature (London) 386, 816 (1997).
- M. L. Hunt and N. M. Vriend, Booming sand dunes, Annu. Rev. Earth Planet Sci. 38, 281 (2010).
- H. J. Van Gerner, M. A. van der Hoef, D. van der Meer, and K. van der Weele, Interplay of air and sand: Faraday heaping unravelled, Phys. Rev. E 76, 051305 (2007).
- S. Weis and M. Schroeter, Analyzing x-ray tomographies of granular packings, Rev. Sci. Instrum. 88, 051809 (2017).
- D. J. Parker, Positron emission particle tracking and its application to granular media, Rev. Sci. Instrum. 88, 051803 (2017).
- A. L. Thomas and N. M. Vriend, Photoelastic study of dense granular free-surface flows, Phys. Rev. E 100, 012902 (2019).
- A. L. Thomas, Z. Tang, K. E. Daniels, N. M. Vriend, Force fluctuations at the transition from quasistatic to inertial granular flow, Soft Matter 15, 8532 (2019).
- C. M. Dundas, A. S. McEwen, S. Diniega, C. J. Hansen, S. Byrne, and J. N. McElwaine, The formation of gullies on Mars today, in Martian Gullies and Their Earth Analogues, edited by S. J. Conway, T. de Haas, T. N. Harrison, P. A. Carling, and J. Carrivick, Special Publications Vol. 467 (Geological Society, London, 2017).
- A. S. McEwen, E. M. Eliason, J. W. Bergstrom, N. T. Bridges, C. J. Hansen, W. A. Delamere, J. A. Grant, V. C. Gulick, K. E. Herkenhoff, L. Keszthelyi et al., Mars reconnaissance orbiter's high resolution imaging science experiment (HiRISE), J. Geophys. Res. 112, E05S02 (2007).
- E. Meiburg and B. Kneller, Turbidity currents and their deposits, Annu. Rev. Fluid Mech. 42, 135 (2010).
- F. Necker, C. Haertel, L. Kleiser, and E. Meiburg, High-resolution simulations of particle-driven gravity currents, Int. J. Multiphase Flow 28, 279 (2002).
- M. I. Cantero, S. Balachandar, A. Cantelli, C. Pirmez, and G. Parker, Turbidity current with a roof: Direct numerical simulation of self-stratified turbulent channel flow driven by suspended sediment, J. Geophys. Res. 114, C03008 (2009).
- X. Yu, T. Hsu, and S. Balachandar, Convective instability in sedimentation: 3-D numerical study, J. Geophys. Res. 119, 8141 (2014).
- P. Burns and E. Meiburg, Sediment-laden fresh water above salt water: Nonlinear simulations, J. Fluid Mech. 762, 156 (2015).
- A. Alsinan, E. Meiburg, and P. Garaud, A settling-driven instability in two-component, stably stratified fluids, J. Fluid Mech. 816, 243 (2017).
- J. F. Reali, P. Garaud, A. Alsinan, and E. Meiburg, Layer formation in sedimentary fingering convection, J. Fluid Mech. 816, 268 (2017).
- J. F. Richardson and W. N. Zaki, The sedimentation of a suspension of uniform spheres under conditions of viscous flow, Chem. Eng. Sci. 3, 65 (1954).
- S. Te Slaa, D. S. van Maren, Q. He, and J. C. Winterwerp, Hindered settling of silt, J. Hydraul. Eng. 141, 04015020 (2015).
- J. T. Jenkins and C. Zhang, Kinetic theory for identical, frictional, nearly elastic spheres, Phys. Fluids 14, 1228 (2002).
- F. Boyer, E. Guazzelli, and O. Pouliquen, Unifying Suspension and Granular Rheology, Phys. Rev. Lett. 107, 188301 (2011).
- A. Shields, Anwendung der Aenhlichkeitsmechanik und Turbulenzforschung auf die Geschiebebewegung, Dissertation, Preuß. Versuchsanst. f. Wasserbau, Berlin, 1936.
- M. H. Garcia and G. Parker, Entrainment of bed sediment into suspension, J. Hydraul. Eng. 117, 414 (1991).
- D. Frank, D. Foster, I. M. Sou, J. Calantoni, and P. Chou, Lagrangian measurements of incipient motion in oscillatory flows, J. Geophys. Res. Oceans 120, 244 (2015).
- S. Balachandar and J. K. Eaton, Turbulent dispersed multiphase flow, Annu. Rev. Fluid Mech. 42, 111 (2010).
- R. Mittal and G. Iaccarino, Immersed boundary methods, Annu. Rev. Fluid Mech. 37, 239 (2005).
- E. Biegert, B. Vowinckel, and E. Meiburg, A collision model for grain-resolving simulations of flows over dense, mobile, polydisperse granular sediment beds, J. Comput. Phys. 340, 105 (2017).
- R. A. Shaw, Particle-turbulence interactions in atmospheric clouds, Annu. Rev. Fluid Mech. 35, 183 (2003).
- R. Ouillon, N. G. Lensky, V. Lyakhovsky, A. Arnon, and E. Meiburg, Halite precipitation from double-diffusive salt fingers in the Dead Sea: Numerical simulations, Water Resour. Res. 55, 4252 (2019).
- https://csdms.colorado.edu/wiki/Main_Page.
- D. H. Kelley and N. T. Ouellette, Emergent dynamics of laboratory insect swarms, Sci. Rep. 3, 1073 (2013).
- I. A. Houghton, J. R. Koseff, S. G. Monismith, and J. O. Dabiri, Vertically migrating swimmers generate aggregation-scale eddies in a stratified column, Nature (London) 556, 497 (2018).
- R. Ouillon, I. A. Houghton, J. O. Dabiri, and E. Meiburg, Active swimmers interacting with stratified fluids during collective vertical migration, J. Fluid Mech. 902, A23 (2020).
- N. T. Ouellette, Flowing crowds, Science 363, 27 (2019).
- S. Herminghaus, Dynamics of wet granular matter, Adv. Phys. 54, 221 (2005).
- S. Sarkar and A. Scotti, From topographic internal waves to turbulence, Annu. Rev. Fluid Mech. 49, 195 (2017).
- M. C. Gregg, E. A. D'Asaro, J. J. Riley, and E. Kunze, Mixing efficiency in the ocean, Annu. Rev. Mar. Sci. 10, 443 (2018).
- M. R. Hipsey, G. Gideon, G. B. Arhonditsis, C. C. Carey, J. A. Elliott, M. A. Frassl, J. H. Janse, L. de Mora, and B. J. Robson, A system of metrics for the assessment and improvement of aquatic ecosystem models, Environ. Model. Softw. 128, 104697 (2020).
- T. R. Osborn, Estimates of the local-rate of vertical diffusion from dissipation measurements, J. Phys. Oceanogr. 10, 83 (1980).
- A. F. Waterhouse, J. A. MacKinnon, J. D. Nash, M. H. Alford, E. Kunze, H. L. Simmons, K. L. Polzin, L. C. St Laurent, O. M. Sun, R. Pinkel et al., Global patterns of diapycnal mixing from measurements of the turbulent dissipation rate, J. Phys. Oceanogr. 44, 1854 (2014).
- T. R. Osborn and C. S. Cox, Oceanic fine structure, Geophys. Fluid Dyn. 3, 321 (1972).
- H. Salehipour, W. R. Peltier, C. B. Whalen, and J. A. MacKinnon, A. new characterization of the turbulent diapycnal diffusivities of mass and momentum in the ocean, Geophys. Res. Lett. 43, 3370 (2016).
- A. Mashayek, H. Salehipour, D. Bouffard, C. P. Caulfield, R. Ferrari, M. Nikurashin, W. R. Peltier, and W. D. Smyth, Efficiency of turbulent mixing in the abyssal ocean circulation, Geophys. Res. Lett. 44, 6296 (2017).
- G. N. Ivey, K. B. Winters, and J. R. Koseff, Density stratification, turbulence, but how much mixing? Annu. Rev. Fluid Mech. 40, 169 (2008).
- R. S. Arthur, S. K. Venayagamoorthy, J. R. Koseff, and O. B. Fringer, How we compute matters to estimates of mixing in stratified flows, J. Fluid Mech. 831, R2 (2017).
- W. D. Smyth, J. N. Moum, and D. R. Caldwell, The efficiency of mixing in turbulent patches: Inferences from direct simulations and microstructure observations, J. Phys. Oceanogr. 31, 1969 (2001).
- A. Mashayek, C. P. Caulfield, and W. R. Peltier, Time-dependent, non-monotonic mixing in stratified turbulent shear flows: Implications for oceanographic estimates of buoyancy flux, J. Fluid Mech. 736, 570 (2013).
- H. Salehipour and W. R. Peltier, Diapycnal diffusivity, turbulent Prandtl number and mixing efficiency in Boussinesq stratified turbulence, J. Fluid Mech. 775, 464 (2015).
- H. Salehipour, W. R. Peltier, and C. P. Caulfield, Turbulent mixing due to the Holmboe wave instability at high Reynolds number, J. Fluid Mech. 803, 591 (2016).
- K. B. Winters, P. N. Lombard, J. J. Riley, and E. A. D'Asaro, Available potential energy and mixing in density-stratified fluids, J. Fluid Mech. 289, 115 (1995).
- G. N. Ivey, C. E. Bluteau, and N. L. Jones, Quantifying diapycnal mixing in an energetic ocean, J. Geophys. Res. Oceans 123, 346 (2018).
- C. E. Bluteau, R. G. Lueck, G. N. Ivey, N. L. Jones, J. W. Book, and A. E. Rice, Determining mixing rates from concurrent temperature and velocity measurements, J. Atmos. Ocean. Technol. 34, 2283 (2017).
- A. Mashayek, C. P. Caulfield, and W. R. Peltier, Role of overturns in optimal mixing in stratified mixing layers, J. Fluid Mech. 826, 522 (2017).
- H. Salehipour, W. R. Peltier, and C. P. Caulfield, Self-organized criticality of turbulence in strongly stratified mixing layers, J. Fluid Mech. 856, 228 (2018).
- J. W. Miles, On the stability of heterogeneous shear flows, J. Fluid Mech. 10, 496 (1961).
- L. N. Howard, Note on a paper of John W. Miles, J. Fluid Mech. 10, 509 (1961).
- S. A. Thorpe and Z. Liu, Marginal instability?, J. Phys. Oceanogr. 39, 2373 (2009).
- W. D. Smyth, J. D. Nash, and J. N. Moum, Self-organized criticality in geophysical turbulence, Sci. Rep. 9, 3747 (2019).
- G. N. Ivey and J. Imberger, On the nature of turbulence in a stratified fluid. 1. The energetics of mixing, J. Phys. Oceanogr. 21, 650 (1991).
- A. Maffioli, G. Brethouwer, and E. Lindborg, Mixing efficiency in stratified turbulence, J. Fluid Mech. 794, R3 (2016).
- A. Garanaik and S. K. Venayagamoorthy, On the inference of the state of turbulence and mixing efficiency in stably stratified flows, J. Fluid Mech. 867, 323 (2019).
- A. Gargett, T. Osborn, and P. Nasmyth, Local isotropy and the decay of turbulence in a stratified fluid, J. Fluid Mech. 144, 231 (1984).
- L. H. Shih, J. R. Koseff, G. N. Ivey, and J. H. Ferziger, Parameterization of turbulent fluxes and scales using homogeneous sheared stably stratified turbulence simulations, J. Fluid Mech. 525, 193 (2005).
- S. G. Monismith, J. R. Koseff, and B. L. White, Mixing efficiency in the presence of stratification: When is it constant? Geophys. Res. Lett. 45, 5627 (2018).
- G. D. Portwood, S. M. de Bruyn Kops, and C. P. Caulfield, Asymptotic Dynamics of High Dynamic Range Stratified Turbulence, Phys. Rev. Lett. 122, 194504 (2019).
- T. M. Dillon, Vertical overturns—A comparison of Thorpe and Ozmidov length scales, J. Geophys. Res. Oceans 87, 9601 (1982).
- B. D. Mater, S. K. Venayagamoorthy, L. St Laurent, and J. N. Moum, Biases in Thorpe-scale estimates of turbulence dissipation. Part I: Assessments from large-scale overturns in oceanographic data, J. Phys. Oceanogr. 45, 2497 (2015).
- R. M. Holmes, J. D. Zika, and M. H. England, Diathermal heat transport in a global ocean model, J. Phys. Oceanogr. 49, 141 (2019).
- W. G. Large, J. C. McWilliams, and S. C. Doney, Oceanic vertical mixing: A. review and a model with a nonlocal boundary layer parameterization, Rev. Geophys. 32, 363 (1994).
- E. D. Zaron and J. N. Moum, A new look at Richardson number mixing schemes for equatorial ocean modeling, J. Phys. Oceanogr. 39, 2652 (2009).
- C. Viatte, C. Clerbaux, C. Maes, P. Daniel, R. Garello, S. Safieddine, F. Ardhuin et al., Air pollution and sea pollution seen from space, Surv. Geophys. 41, 1583 (2020).
- L. Brach, P. Deixonne, M.-F. Bernard, E. Durand, M.-C. Desjean, E. Perez, E. van Sebille, and A. ter Halle, Anticyclonic eddies increase accumulation of microplastic in the North Atlantic subtropical gyre, Mar. Pollut. Bull. 126, 191 (2018).
- L. C.-M. Lebreton, S. D. Greer, and J. C. Borrero, Anticyclonic eddies increase numerical modelling of floating debris in the world's oceans, Mar. Pollut. Bull. 64, 653 (2012).
- ITOPF, Oil spill tanker statistics 2019, http://www.itopf.org/knowledge-resources/data-statistics/statistics/.
- G. Ferraro, A. Bernardini, M. David, S. Meyer-Roux, O. Muellenhoff, M. Perkovic, D. Tarchi, and K. Topouzelis, Towards an operational use of space imagery for oil pollution monitoring in the Mediterranean basin: A demonstration in the Adriatic Sea, Mar. Pollut. Bull. 54, 403 (2007).
- J. B. Weiss and A. Provenzale, Transport and Mixing in Geophysical Flows (Springer, Berlin, 2008).
- G. Haller and F. J. Beron-Vera, Coherent Lagrangian vortices: The black holes of turbulence, J. Fluid Mech. 731, R4 (2013).
- M. E. Gurtin, E. Fried, and L. Anand, The Mechanics and Thermodynamics of Continua (Cambridge University Press, Cambridge, 2010).
- A. Okubo, Horizontal dispersion of floatable trajectories in the vicinity of velocity singularities such as convergencies, Deep-Sea Res. 17, 445 (1970).
- J. Weiss, The dynamics of enstrophy transfer in two-dimensional hydrodynamics, Physica D 48, 273 (1991).
- R. Drouot, Definition d'un transport associe un modele de fluide de deuxieme ordre, C. R. Acad. Sci. Paris A 282, 923 (1976).
- R. Drouot and M. Lucius, Approximation du second ordre de la loi de comportement des fluides simples. Lois classiques déduites de l'introduction d'un nouveau tenseur objectif, Arch. Mech. Stasow. 28, 189 (1976).
- G. Astarita, Objective and generally applicable criteria for flow classification, J. Non-Newtonian Fluid Mech. 6, 69 (1979).
- H. J. Lugt, The dilemma of defining a vortex, in Recent Developments in Theoretical and Experimental Fluid Mechanics, edited by U. Muller, K. G. Riesner, and B. Schmidt (Springer, Berlin, Heidelberg, 1979), p. 309.
- G. Haller, An objective definition of a vortex, J. Fluid Mech., 525, 1 (2005).
- G. Haller, Lagrangian coherent structures, Annu. Rev. Fluid Mech. 47, 137 (2015).
- A. Hadjighasem, M. Farazmand, D. Blazevski, G. Froyland, and G. Haller, A critical comparison of Lagrangian methods for coherent structure detection, Chaos 27, 053104 (2017).
- G. Haller, D. Karrasch, and F. Kogelbauer, Material barriers to diffusive and stochastic transport, Proc. Natl. Acad. Sci. U. S. A. 115, 9074 (2018).
- G. Haller, D. Karrasch, and F. Kogelbauer, Barriers to the transport of diffusive scalars in compressible flows, SIAM J. Appl. Dyn. Syst. 19, 85 (2020).
- github.com/LCSETH.
- G. Haller, S. Katsanoulis, M. Holzner, B. Frohnapfel, and D. Gatti, Objective barriers to the transport of dynamically active vector fields, J. Fluid Mech. 905, A17 (2020).
- W. Cui, W. Wang, J. Zhang, and J. Yang, Multicore structures and the splitting and merging of eddies in global oceans from satellite altimeter data, Ocean Sci. 15, 413 (2019).
- B. Pearson and B. Fox-Kemper, Log-Normal Turbulence Dissipation in Global Ocean Models, Phys. Rev. Lett. 120, 094501 (2018).
- R. T. Pierrehumbert, Tracer microstructure in the large-eddy dominated regime, Chaos Solitons Fractals 4, 1091 (1994).
- P. D. Sardeshmukh and C. Penland, Understanding the distinctively skewed and heavy tailed character of atmospheric and oceanic probability distributions, Chaos 25, 036410 (2015).
- P. C. Chu, Statistical characteristics of the global surface current speeds obtained from satellite altimetry and scatterometer data, IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 2, 27 (2009).
- Y. Ashkenazy and H. Gildor, On the probability and spatial distribution of ocean surface currents, J. Phys. Oceanogr. 41, 2295 (2011).
- Y. Hu and R. T. Pierrehumbert, The advection–diffusion problem for stratospheric flow. Part I: Concentration probability distribution function, J. Atmos. Sci. 58, 1493 (2001).
- A. A. Sepp-Neves, N. Pinardi, A. Navarra, and F. Trotta, A general methodology for beached oil spill hazard mapping, Front. Mar. Sci. 7, 65 (2020).
- N. Maximenko, J. Hafner, and P. Niiler, Pathways of marine debris derived from trajectories of Lagrangian drifters, Mar. Pollut. Bull. 65, 51 (2012).
- S. Liubartseva, G. Coppini, R. Lecci, and E. Clementi, Tracking plastics in the Mediterranean: 2D Lagrangian model, Mar. Pollut. Bull. 129, 151 (2018).
- S. Liubartseva, M. De Dominicis, P. Oddo, G. Coppini, N. Pinardi, and N. Greggio, Oil spill hazard from dispersal of oil along shipping lanes in the Southern Adriatic and Northern Ionian Seas, Mar. Pollut. Bull. 90, 259 (2015).
- A. A. Sepp Neves, N. Pinardi, and F. Martins, IT-OSRA: Applying ensemble simulations to estimate the oil spill risk associated to operational and accidental oil spills, Ocean Dyn. 66, 939 (2016).
- P. Y. Le Traon, A. Reppucci, E. Alvarez Fanjul, L. Aouf, A. Behrens, M. Belmonte, A. Bentamy, L. Bertino, V. E. Brando, M. B. Kreiner et al., From observation to information and users: The Copernicus Marine Service perspective, Front. Mar. Sci. 6, 234 (2019).
- C. H. Barker, V. H. Kourafalou, C. Beegle-Krause, M. Boufadel, M. A. Bourassa, S. G. Buschang, Y. Androulidakis, E. P. Chassignet, K.-F. Dagestad, D. G. Danmeier et al., Progress in operational modeling in support of oil spill response, J. Mar. Sci. Eng. 8, 668 (2020).
- M. D. Grossi, M. Kubat, and T. M. Özgökmen, Predicting particle trajectories in oceanic flows using artificial neural networks, Ocean Model. 156, 101707 (2020).
- Goal 11: Sustainable cities and communities, https://www.undp.org/content/undp/en/home/sustainable-development-goals/goal-11-sustainable-cities-and-communities.html.
- Facts + Statistics: U. S. catastrophes, https://www.iii.org/fact-statistic/facts-statistics-us-catastrophes.
- The future of cooling, https://www.iea.org/reports/the-future-of-cooling.
- A. Hajat, C. Hsia, and M. S. O'Neill, Socioeconomic disparities and air pollution exposure: A global review, Curr. Environ. Health Rep, 2, 440 (2015).
- A. Mochida and I. Y. Lun, Prediction of wind environment and thermal comfort at pedestrian level in urban area, J. Wind Eng. Ind. Aerodyn. 96, 1498 (2008), 4th International Symposium on Computational Wind Engineering.
- H. Montazeri and B. Blocken, CFD simulation of wind-induced pressure coefficients on buildings with and without balconies: Validation and sensitivity analysis, Build. Env. 60, 137 (2013).
- M. Llaguno-Munitxa, E. Bou-Zeid, and M. Hultmark, The influence of building geometry on street canyon air flow: Validation of large eddy simulations against wind tunnel experiments, J. Wind Eng. Ind. Aerodyn. 165, 115 (2017).
- C. Baker, Wind engineering—Past, present and future, J. Wind Eng. Ind. Aerodyn. 95, 843 (2007).
- P. Klein, B. Leitl, and M. Schatzmann, Driving physical mechanisms of flow and dispersion in urban canopies, Int. J. Climatol. 27, 1887 (2007).
- M. Schatzmann and B. Leitl, Issues with validation of urban flow and dispersion CFD models, J. Wind Eng. Ind. Aerodyn. 99, 169 (2011).
- M. A. Mooneghi, P. Irwin, and A. G. Chowdhury, Partial turbulence simulation method for predicting peak wind loads on small structures and building appurtenances, J. Wind Eng. Ind. Aerodyn. 157, 47 (2016).
- F. Harms, B. Leitl, M. Schatzmann, and G. Patnaik, Validating LES-based flow and dispersion models, J. Wind Eng. Ind. Aerodyn. 99, 289 (2011), 5th International Symposium on Computational Wind Engineering.
- A. Mochida, S. Iizuka, Y. Tominaga, and I. Yu-Fat Lun, Up-scaling CWE. models to include mesoscale meteorological influences, J. Wind Eng. Ind. Aerodyn. 99, 187 (2011).
- T. Yamada and K. Koike, Downscaling mesoscale meteorological models for computational wind engineering applications, J. Wind Eng. Ind. Aerodyn. 99, 199 (2011).
- J. Bao, F. Katopodes Chow, and K. A. Lundquist, Large-eddy simulation over complex terrain using an improved immersed boundary method in the Weather Research and Forecasting Model, Mon. Weather Rev. 146, 2781 (2018).
- A. A. Wyszogrodzki, S. Miao, and F. Chen, Evaluation of the coupling between mesoscale- WRF and LES-EULAG models for simulating fine-scale dispersion, Atmos. Res. 118, 324 (2012).
- C. Talbot, E. Bou-Zeid, and J. Smith, Nested mesoscale large-eddy simulations with WRF: Performance in real test cases, J. Hydrometeorol. 13, 1421 (2012).
- C. García-Sánchez, J. van Beeck, and C. Gorlé, Predictive large eddy simulations for urban flows: Challenges and opportunities, Build. Env. 139, 146 (2018).
- B. Blocken, 50 years of Computational Wind Engineering: Past, present and future, J. Wind Eng. Ind. Aerodyn. 129, 69 (2014).
- M. Neophytou, A. Gowardhan, and M. Brown, An inter-comparison of three urban wind models using Oklahoma City Joint Urban 2003 wind field measurements, J. Wind Eng. Ind. Aerodyn. 99, 357 (2011).
- B. Peherstorfer, K. Willcox, and M. Gunzburger, Survey of multifidelity methods in uncertainty propagation, interference and optimization, SIAM Rev. 60, 550 (2018).
- K. Duraisamy, G. Iaccarino, and H. Xiao, Turbulence modeling in the age of data, Annu. Rev. Fluid Mech. 51, 357 (2019).
- www.magic-air.uk.
- Ambient (outdoor) air quality and health, https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health.
- K. J. Allwine and J. E. Flaherty, Joint Urban 2003: Study Overview and Instrument Locations, PNNL-15967 (Pacific Northwest National Laboratory, Richland, WA, 2006).
- C. García-Sánchez, G. Van Tendeloo, and C. Gorlé, Quantifying inflow uncertainties in RANS simulations of urban pollutant dispersion, Atmos. Environ. 161, 263 (2017).
- C. García-Sánchez and C. Gorlé, Uncertainty quantification for microscale CFD simulations based on input from mesoscale codes, J. Wind Eng. Ind. Aerodyn. 176, 87 (2018).
- C. Gorlé, C. García-Sánchez, and G. Iaccarino, Quantifying inflow and RANS turbulence model form uncertainties for wind engineering flows, J. Wind Eng. Ind. Aerodyn. 144, 202 (2015).
- United Nations Office for Disaster Risk Reduction, Annual Report, United Nations Office for Disaster Risk Reduction (UNISDR), Geneva (2018), https://www.undrr.org/publication/united-nations-office-disaster-risk-reduction-2018-annual-report.
- P. Lynch, The origins of computer weather prediction and climate modelling, J. Comput. Phys. 227, 3431 (2008).
- R. J. Haarsma, M. J. Roberts, P. L. Vidale, C. A. Senior, A. Bellucci, Q. Bao, P. Chang, S. Corti, N. S. Fučkar, V. Guemas et al., High Resolution Model Intercomparison Project (HighResMIP. v1.0) for CMIP6, Geosci. Model Dev. 9, 4185 (2016).
- Research activities in Earth system modelling, Working Group on Numerical Experimentation, Report No. 50, WCRP Report No.12/2020, WMO, Geneva, 2018.
- F. J. Doblas-Reyes, I. Andreu-Burillo, Y. Chikamoto, J. Garcia-Serrano, V. Guemas, M. Kimoto, T. Mochizuki, L. R. L. Rodrigues, and G. J. van Oldenborgh, Initialized near-term regional climate change prediction, Nature Commun. 4, 1715 (2013).
- K. E. Taylor, R. J. Stouffer, and G. A. Meehl, An overview of CMIP5 and the experiment design, Bull. Am. Meteorol. Soc. 93, 485 (2012).
- T. N. Palmer, F. J. Doblas-Reyes, R. Hagedorn, and A. Weisheimer, Probabilistic prediction of climate using multi-model ensembles: From basics to applications, Philos. Trans. R. Soc. London B 360, 1991 (2005).
- J. Côté, C. Jablonowski, P. Bauer, and N. Wedi, Numerical methods of the atmosphere and ocean, in Seamless Prediction of the Earth System: From Minutes to Months, edited by G. Brunet, S. Jones, and P. M. Ruti, World Meteorological Organization (WMO) No. 1156 (WMO, Geneva, 2015).
- A. Brown, M. Miller, A. Beljaars, F. Bouyssel, C. Holloway, and J. Petch, Challenges for sub-gridscale parametrizations in atmospheric models, in Seamless Prediction of the Earth System: From Minutes to Months, edited by G. Brunet, S. Jones, and P. M. Ruti, World Meteorological Organization (WMO) No. 1156 (WMO, Geneva, 2015).
- S. Bony, B. Stevens, D. M. W. Frierson, C. Jakob, M. Kageyama, R. Pincus, T. G. Shepherd, S. C. Sherwood, A. P. Siebesma, A. H. Sobel et al., Clouds, circulation and climate sensitivity, Nat. Geosci. 8, 261 (2015).
- P. Bauer, A. Thorpe, and G. Brunet, The quiet revolution of numerical weather prediction, Nature (London) 525, 47 (2015).
- B. Stevens and S. Bony, What are climate models missing? Science 340, 1053 (2013).
- O. Bellprat and F. Doblas-Reyes, Attribution of extreme weather and climate events overestimated by unreliable climate simulations, Geophys. Res. Lett. 43, 2158 (2016).
- M. J. Roberts, P. L. Vidale, C. Senior, H. T. Hewitt, C. Bates, S. Berthou, P. Chang, H. M. Christensen, S. Danilov, M. Demory et al., 2018: The benefits of global high resolution for climate simulation: Process understanding and the enabling of stakeholder decisions at the regional scale, Bull. Am. Meteorol. Soc. 99, 2341 (2018).
- C. Prodhomme, L. Batté, F. Massonnet, P. Davini, O. Bellprat, V. Guemas, and F. J. Doblas-Reyes, Benefits of increasing the model resolution for the seasonal forecast quality in EC-Earth, J. Clim. 29, 9141 (2016).
- G. Zappa, L. C. Shaffrey, and K. I. Hodges, The ability of CMIP5 models to simulate North Atlantic extratropical cyclones, J. Clim. 26, 5379 (2013).
- T. Jung, M. J. Miller, T. N. Palmer, P. Towers, N. Wedi, D. Achuthavarier, J. M. Adams, E. L. Altshuler, B. A. Cash, J. L. Kinter et al., High-resolution global climate simulations with the ECMWF Model in Project Athena: Experimental design, model climate, and seasonal forecast skill, J. Clim. 25, 3155 (2012).
- M. J. Roberts, P. L. Vidale, M. S. Mizielinski, M. Demory, R. Schiemann, J. Strachan, K. Hodges, R. Bell, and J. Camp, Tropical cyclones in the UPSCALE ensemble of high-resolution global climate models, J. Clim. 28, 574 (2015).
- S. Bony and J. L. Dufresne, Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models, Geophys. Res. Lett. 32, L20806 (2005).
- M. Wehner, L. Oliker, J. Shalf, D. Donofrio, L. Drummond, R. Heikes, S. Kamil, C. Kono, N. Miller, H. Miura et al., Hardware/software co-design of global cloud system resolving models, J. Adv. Model. Earth Sys. 3, M1000:22 (2011).
- T. C. Schulthess, P. Bauer, N. Wedi, O. Fuhrer, T. Hoefler, and C. Schär, Reflecting on the goal and baseline for exascale computing: A roadmap based on weather and climate simulations, Comput. Sci. Eng. 21, 30 (2019).
- D. P. Dee, M. Balmaseda, G. Balsamo, R. Engelen, A. J. Simmons, and J. Thépaut, Toward a consistent reanalysis of the climate system, Bull. Am. Meteorol. Soc. 95, 1235 (2014).
- T. J. Phillips, G. L. Potter, D. L. Williamson, R. T. Cederwall, J. S. Boyle, M. Fiorino, J. J. Hnilo, J. G. Olson, S. Xie, and J. J. Yio, Evaluating parameterizations in general circulation models: Climate simulation meets weather prediction, Bull. Am. Meteorol. Soc. 85, 1903 (2004).
- M. J. Rodwell and T. N. Palmer, Using numerical weather prediction to assess climate models, Q. J. R. Meteorol. Soc. 133, 129 (2007).
- J. Ruiz and M. Pulido, Parameter estimation using ensemble-based data assimilation in the presence of model error, Mon. Weather Rev. 143, 1568 (2015).
- M. T. Heath, A tale of two laws, Int. J. High Perf. Comp. Appl. 29, 320 (2015).
- B. N. Lawrence, M. Rezny, R. Budich, P. Bauer, J. Behrens, M. Carter, W. Deconinck, R. Ford, C. Maynard, S. Mullerworth et al., Crossing the Chasm: How to develop weather and climate models for next generation computers? Geosci. Model Dev. 11, 1799 (2018).
- P. Kogge and J. Shalf, Exascale computing trends: Adjusting to the “new normal” for computer architecture, Comput. Sci. Eng. 15, 16 (2013).
- N. P. Wedi, Increasing horizontal resolution in numerical weather prediction and climate simulations: Illusion or panacea? Philos. Trans. R. Soc. London A 372, 20130289 (2014).
- G. Zaengl, D. Reinert, P. Rípodas, and M. Baldauf, The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core, Q. J. R. Meteorol. Soc. 141, 563 (2015).
- C. Kuehnlein, P. K. Smolarkiewicz, and A. Dörnbrack, Modelling atmospheric flows with adaptive moving meshes, J. Comput. Phys. 231, 2741 (2012).
- W. Deconinck, P. Bauer, M. Diamantakis, M. Hamrud, C. Kühnlein, P. Maciel, G. Mengaldo, T. Quintino, B. Raoult, P. K. Smolarkiewicz, and N. P. Wedi, Atlas: A. library for numerical weather prediction and climate modelling, Comput. Phys. Commun. 220, 188 (2017).
- T. C. Schulthess, Programming revisited, Nat. Phys. 11, 369 (2015).
- P. D. Nooteboom, Q. Y. Feng, C. López, E. Hernández-García, and H. A. Dijkstra, Using network theory and machine learning to predict El Niño, Earth Syst. Dyn. 9, 969 (2018).
- S. S. Baboo and I. K. Shereef, An efficient weather forecasting system using artificial neural network, Int. J. Environ, Sci. Devel. 1, 321 (2010).
- V. M. Krasnopolsky and M. S. Fox-Rabinovitz, Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction, Neural Netw. 19, 122 (2006).
- T. Schneider, S. Lan, A. Stuart, and J. Teixeira, Earth System Modeling 2.0: A. blueprint for models that learn from observations and targeted high-resolution simulations, Geophys. Res. Lett. 44, 12396 (2017).
- J. T. Overpeck, G. A. Meehl, S. Bony, and D. R. Easterling, Climate data challenges in the 21st century, Science 331, 700 (2011).
- T. Peacock and M. H. Alford, Is deep-sea mining worth it? Sci. Am. 318, 72 (2018).