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Next Generation Multiscale Adaptive
Mesh Atmospheric Modelling, Rapid
Response and Data Assimilation
Fangxin Fang, Jeff Gomes
29, August, 2017
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AMCG website (http://www.imperial.ac.uk/earth-science/research/research-
groups/amcg/)
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Fluidity❖Open Source Model Software for
Multiphysics Problems
❖Unstructured FEM Meshes
❖Anisotropic Adaptive Mesh
technology
❖User-friendly GUI
❖Python interface to calculate
diagnostic fields, to set
prescribed fields and user-
defined boundary conditions
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Atmospheric Modelling
Ocean, flooding multifluid flow
modelling
Data science laboratory
HPC
Uncertainty quantification
➢ Analysis, real-time updating and
representation of uncertainties.
Reduced order modelling
➢ Emergency rapid response
Data assimilation➢ Optimisation of
uncertainties➢ Minimisation of misfit
between observational data and numerical results
Adaptive targeted observations➢ Optimal
experimental design
➢ Optimal sensor locations
Multi-scale predicative modelling with multi-physical Fluidity
Optimal design, risk assessment, decision and policy-making processes risk
Predictive and Uncertainty Model Framework
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Computational Multi-Fluid Flow Dynamic Model❖ Porous media model embedded in Fluidity are based on:
• new family of FE element-pairs (PnDG-Pm and PnDG-PmDG) and
• numerical formulation (overlapping CVFEM) that ensures high-order
accuracy on the solution fields (i.e., pressure, velocity, saturation,
temperature etc)
❖ The model has been used on transport of contaminant in subsurface media
(mining spillage), oil and gas production, nuclear waste repository etc;
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Atmospheric Environmental Model: Critical bridge between
human activities and environmental change (IAP)
Clean Day PM2.5~15 µg m-3
Polluted Day PM2.5~200 µg m-3Severely Polluted Day PM2.5~470 µg m-3
Beijing, Olympic site
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Human Activities(urbanization, motorization and
industrialization, emissions)
CO2, CO, NO2, SO2, O3, aerosols, etc
Atmospheric dynamical transport
Atmospheric chemical transformation
Ocean System
Land , Water, Snows
Terrestrial Ecosystems
Human Health Effects
Agriculture and Ecosystem
Regional air pollution
Global air pollution
Deposition (Acid rain, etc)Natural emissions(carbon,
nitrogen)
Climate Change
Radiative forcing
(scatting ,absorbing, cloud)
Climate issues
Environmental issues
Atmospheric Environmental Model: Critical bridge between
human activities and environmental change (IAP)
Air pollution is largely
influenced by human
activities, and can be
reduced by human.
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Urban Air Pollution Modelling
(MAGIC, supported by EPSRC) @Elephant Castle, London
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Air Pollutant Modelling
(IAP-ICL, supported by NSFC/ EPSRC)
SO2 released
from over 100
power plants
(over Beijing
and 55 cities)
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Chemical modelling (IAP-ICL, Zheng etc.) (NO and NO2 released from over 100 power plants)
Continuous formation/consumption of NOx and Ozone over 5 days.
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3D chemical modelling (IAP-ICL, Zheng etc.) (NO and NO2 released from over 100 power plants)
1 day
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2010-03-19_04 2010-03-20_04
2010-03-20_18 2010-03-21_04
Dust storm
(IAP-ICL,
Zheng etc.)
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CloudAdaptive mesh
3D Atmospheric Modelling: Cyclone 3D Simulation (IAP-ICL)
Rain
Potential Temperature
Velocity Vector
Vapor Water
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Tohoku event from 2011
(Visiting PhD student from Japan)
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Fig. 11. Situation of Study Area in Greve, Municipality of Denmark, see Soledad (2014)
Natural Disaster: Simulating Flooding – Denmark
(R. Hu etc. supported by the EU PEARL project)
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Surface Surface with mesh
Natural Disaster: Simulating Flooding – Denmark
(R. Hu etc. supported by the EU PEARL project)
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Atmospheric Modelling
Ocean, flooding multifluid flow
modelling
Data science laboratory HPC
Uncertainty quantification
➢ Analysis, real-time updating and
representation of uncertainties.
Reduced order modelling
➢ Emergency rapid response
Data assimilation➢ Optimisation of
uncertainties➢ Minimisation of misfit
between observational data and numerical results
Adaptive targeted observations➢ Optimal
experimental design
➢ Optimal sensor locations
Multi-scale predicative modelling with multi-physical Fluidity
Optimal design, risk assessment, decision and policy-making processes risk
Predictive and Uncertainty Model Framework
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Data Assimilation (DA)DA methods:
❖ Optimal interpolation;
❖ Nudging;
❖ 3D-Var;
❖ 4D-Var (Adjoint);
❖ Ensemble KF
Motivation for DA:
❖ To improve the predictability of numerical
models;
❖ Uncertainty sensitivity analysis;
❖ Optimisation of uncertainties in models;
❖ Goal-based error measure and mesh
adaptivity;
❖ Design optimisation;
❖ Adaptive observation (Optimisation of
sensors locations).
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PM2.5 before DA
Differences
PM2.5 after DA
Sources: CNEMC
IAP
Work from Institute of Atmospheric and Physics, China
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DA: Optimal sensor location
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Navier-Stokes Equations:
Full discretised system (high dimensional):
Projecting onto the reduced space using SVD/POD
Discretising equations (1) and (2)
Wring the reduced order model in a general form
The function f is
represented
by deep learning
Reduced Order Modelling
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Reduced Order Modelling (POD) and Deep Learning (Xiao etc)
Top panel: Full modelling
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Rapid modelling: Air flow (Elephant Castle London)
(Xiao etc. supported by MAGIC –EPSRC)
Left: reduced order modelling; right: high fidelity modelling
CPU time: seconds (Reduced order model); 3 hours (10 cores, Full fidelity model)
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Future work
HPCDA
Fluidity
NAQPM WRF
Adaptive-mesh air quality model
Gas chemistry
Adaptive-mesh weather forecast
Liquid chemistry
PM chemistry
Microphysics
Cumulus parameterization
Surface Layer
Planetary Boundary Layer, Radiation
Reginal Model Global Model
Future workFuture work
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Thanks
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Rapid modelling: Flow past two buildingsTop: reduced order modelling; bottom: high fidelity
modelling