Simulating large emitters using CMAQ and a local
- Slides: 30
Simulating large emitters using CMAQ and a local scale finite element method. Analysis in the surroundings of Barcelona Albert Oliver, Raúl Arasa, Agustí Pérez-Foguet, Mª Ángeles González HARMO 17 Budapest May 2016
Motivation To Improve the prediction at fine scales: • Large emitters • Near source transport and chemistry Current approaches: • Nested grid modelling • Adaptive grid modelling • Hybrid modelling • Plume-in-grid modelling • Statistical models • CFD HARMO 17 · May 2016 · Budapest · 2
Proposed approach In this work we propose to compare two different approaches • WRF-ARW/AEMM/CMAQ • Nested grid modelling • 1 km – 300 m • Adaptive Finite Element method • Plume-in-grid modelling HARMO 17 · May 2016 · Budapest · 3
Proposed approach WRF-ARW/AEMM/CMAQ Meteorology Transport reaction Emissions Initial and Boundary conditions Local FEM Mass consistent model (Wind) Plume rise (Briggs) Transport and reaction HARMO 17 · May 2016 · Budapest · 4
Outline WRF-ARW/AEMM/CMAQ model Local scale Finite Element model Application to Barcelona surroundings Conclusions HARMO 17 · May 2016 · Budapest · 5
WRF-ARW The mesoscale meteorological model used is Weather Research and Forecasting—Advanced Research (WRF-ARW) version 3. 6. 1 Specially suited to the subscale grid modelling Time-splitting methods, and high order (both time and space) HARMO 17 · May 2016 · Budapest · 6
AEMM Air Emission Model of Meteosim (AEMM v 3. 0) developed by Meteosim S. L. Numerical, deterministic, Eulerian, local-scale model It allows to obtain the intensity of emissions in different areas, either anthropogenic (traffic, industry, residential, etc. ) or natural (emissions caused by vegetation or erosion dust) for the area of interest HARMO 17 · May 2016 · Budapest · 7
CMAQ CMAQ v 5. 0. 1 CB-5 chemical mechanism AERO 5 aerosol module EBI solver Discretization 1 km The WRF-ARW/AEMM/CMAQ approach has been used successfully in urban areas (Catalonia, Madrid), industrial areas (Tarragona, Ponferrada), and arid areas (Perú, Chile) HARMO 17 · May 2016 · Budapest · 8
Adaptive Finite Element Method Convection-Diffusion-Reaction equation Wind field Plume rise FEM discretization Adaptivity HARMO 17 · May 2016 · Budapest · 9
Adaptive finite element method Convection – diffusion – reaction equation A. Oliver et al. Adaptive Finite Element Simulation of Stack Pollutant Emissions over Complex Terrains. Energy 2013. HARMO 17 · May 2016 · Budapest · 10
Wind field Interpolate Wind field from WRF-ARW Mass-consistent model HARMO 17 · May 2016 · Budapest · 11
Plume rise Briggs equations • Buoyancy Momentum HARMO 17 · May 2016 · Budapest · 12
FEM discretization • Temporal discretization: Crank-Nicolson • Spatial discretization: Least Squares FEM • System solver: Conjugate gradient preconditioned with an Incomplete Cholesky Factorization HARMO 17 · May 2016 · Budapest · 13
Adaptivity Mesh adaptation • Mesh is only adapted to topography and plume rise • Necessity to adapt to the solution • Error indicator using log (wide range of solutions) Mesh refinement fixing a minimum size L. Monforte and A. Pérez-Foguet. A multimesh adaptive scheme for air quality modeling with the finite element method. Int. J. Numer. Meth. Fluids 2014 HARMO 17 · May 2016 · Budapest · 14
Application Barcelona surroundings HARMO 17 · May 2016 · Budapest · 15
Results Barcelona surroundings CMAQ nested domains HARMO 17 · May 2016 · Budapest · 16
Results Zoom of the nested domain HARMO 17 · May 2016 · Budapest · 17
Results Day: 2/12/2013 • High concentration levels Simulation 48 h (24 h spin up) CMAQ grid resolution 1 km, 32 layers FEM domain 20 x 20 km, resolution from 1 km to ~1 m HARMO 17 · May 2016 · Budapest · 18
FEM Mesh HARMO 17 · May 2016 · Budapest · 19
FEM Mesh HARMO 17 · May 2016 · Budapest · 20
Max. 1 h levels FEM CMAQ HARMO 17 · May 2016 · Budapest · 21
Measurement stations HARMO 17 · May 2016 · Budapest · 22
Near emitter station HARMO 17 · May 2016 · Budapest · 23
Near emitter station Streamlines from the emitter HARMO 17 · May 2016 · Budapest · 24
Near emitter station Streamlines from the emitter Hour description of wind not enough HARMO 17 · May 2016 · Budapest · 25
Near emitter station HARMO 17 · May 2016 · Budapest · 26
Station distant to the emitter HARMO 17 · May 2016 · Budapest · 27
Station distant to the emitter HARMO 17 · May 2016 · Budapest · 28
Conclusions Combination of a nested grid and a local scale finite element model is a promising approach The WRF-ARW/AEMM/CMAQ system using a 1 km grid captures the observed data far from the emitter Near the emitter, the finite element model is closer to the measured data, while far from the emitter the CMAQ-ARW model is better. HARMO 17 · May 2016 · Budapest · 29
Future work Use an smaller resolution for the wind field in the local scale model. Explore how to combine both models, operationally, in a hybrid model. HARMO 17 · May 2016 · Budapest · 30
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