MM5-CMAQ air quality modelling process analysis: Madrid case

Transcription

1 MM5-CMAQ air quality modelling process analysis: Madrid case R. San Jose, J.L. P6rez, C. Pleguezuelos, F. Camacho(l) & R.M. Gonzalez(2) l)environmental Software and Modelling Group, Computer Science School, Technical University of Madrid, Campus demontegancedo, Boadilla del Monte Madrid. 2)Departmentof Meteorology and Geophysics, Complutense University of Madrid, Ciudad Univemitaria, Madrid Abstract The usual application of three dimensional air quality models is to predict the spatial and temporal distributions of ambient air pollutants and other species. Third generation complex Eulerian models generate output concentrations by solving systems of partial differential equations. These equations define the timerate of change in species concentrations due to a series of physical and chemical processes (deposition, emission, chemical reactions, horizontal advection, etc.). MM5-CMAQ is configured in such a way that we can have access to quantitative information on the effects of the chemical reactions and other atmospheric processes that are being simulated. In this contribution we have configured an application of the MM5-CMAQ Air Quality Modelling System over the Madrid (Spain) regional domain and study the different physical and atmospheric processes. We have developed an expert analysis software package which is intended to extent and improve in future works - to analyze in detail in a straightforward manner, all the information provided by the MM5-CMAQ modeling system, The huge amount of the output process analysis files (800 Mb NetCDF binary format) requires using these robust visualization packages. The results show that all the chemical and physical processes are filly consistent and the application of such a analysis will be extremely useful for decision taken processes in environmental offices at cities and regional environmental departments. This tool is essential to analyze in detail the process near specific areas of interest (industrial plants, special traffic concentrations, etc.).

2 172 Air Pollution X 1 Introduction The MM5-CMAQ modelling system is a comprehensive tool to simulate the atmospheric flow and its constituents including the transport and chemical transformations, The third generation of air quality modelling systems is an advanced tool, which takes into account large model domains (geographical projection automatic change), aerosol chemisby and aqueous chemistry. They represent the state of the art on modelling systems. CAMX (Environ Co.) and EURAD models (European Ford Research Group and University of Cologne) are also examples of 3rd generation of AQMS. In this contribution we show the implementation of the MM5 Mesoscale Meteorological Model (PSU/NCAR) Stensrud, D. J. et al. 4, Warner T.T. et al.3, Chen F. and J, Dudhia2, Colella P. and P.R. Woodward7, Ge et al.5 and the Community Multiscale Air Quality Model (CMAQ) (Byun, D. W., et al.g from EPA (USA) (third generation of air quality modelling systems) as a global-through-urban scale nested approach. The MM5 is built over a mother domain with 36 x 36 grid cells (81 km spatial resolution) and 23 vertical levels, This makes a domain of 2916 x 2916 km. The nesting MM5 level 1 model domain is built over a 69 x 66 grid cells (27 km spatial resolution) and 23 vertical levels, which makes a model domain of 1863 s 1782 km centered over the Iberian Peninsula. CMAQ model domains are 30 x 30 grid cells for mother domain and 63 x 60 over the nesting level 1 model domain, CMAQ mother domain lower left corner is located at( m, m) at the reference locations (-3.5 W, 40N) and the first and second standard parallels (30N, 60N), The CMAQ nesting level 1 lower left corner is located at ( , ) with the same reference locations. The 9 km MM5 spatial resolution model domain has 54 x 54 grid cells, the 3 km MM5 spatial resolution model domain has 33 x 39 grid cells and finally the 1 km MM5 spatial resolution model domain has 30 x 30 grid cells. The corresponding CMAQ model domains are: 48 x 48 km, reference ( , ) in Lambert Conforrnal projection with 9 km spatial resolution; 27 x 33 grid cells, reference (-54000, -9000) with 3 km spatial resolution and finally, 24 x 24 grid cells, reference (-27000, 33000) with 1 km spatial resolution, 2 Experiment The system uses the EMIMO and EMIMA models (San Jos& et al. l), both are bottom-up and top-down emission inventory approach by using DCW (Digital Chart of the World), GEIA, EMEP and EDGAR global emission inventories. Both model are running automatically and creating the corresponding emission data files with 1 hour temporal resolution for each CMAQ model domain. Figure 1 shows the landuse surface pattern for 1 km resolution as obtained from USGS NOAAIAVHRR land use classification, Figure 2 shows a scheme of MM5- CMAQ modeling system, Figure 2 shows a scheme of the computer architecture. The system is configured to generate proper surface and time series

3 Air Pollution X 173 concentrations over the nesting level 1 domain and provide this information in the Intemet. (see Figure 3 and 4), JSGS Land Use/Lsnd Cover System Legend ( Modiiied Level 2) I s 16 WaterBork Shnlbld 17 Hehamovs Wetland M&d Shrub]. mdfhsdard 18 Woo&d Wetland SW&m. 19 Bmn or Sparsely Vegetated Deciduou Bm.. >edhaf 20 Hwbmous Dewhmw NeedlebtfFo lt$t 21 Wooded Turdra Evmpm BmatUeef 22 Mimi Tti Ewrpm Nwile!adFo, M 23 Ban sjoundtud.ra M&&Forw 24 Smw or U Figure 1.- USGS 1 km landuse information by using NOAA/AVHRR 1992 landuse data. ENDSSIONS, METEOROLOGICAL MODELLING AND CMAQ SYSTEMS GsE9 CMAQ Chemical Transport Model (ctm). Gowning equations * Transpom algorithms Gas-Phase Chamktry *Plume-in-@d-treatment. Aerosol ChBmistry and Dymmics k -&mlysis Cloud Chemistry and Dymmics - A ggregatior Figure 2.- MM5 CMAQ architecture

4 174 Air Pollution X %-J +& h Model domain, Mother Domain 36x36x23 81 km grid cell $ $!* Figure 3.- Mother and nesting level 1 model domains in the MM5 configuration in this experiment. - I MM5-CMAC) Process Analysis 1 Figure 4.- Nesting level 3 model domain where the process analysis in MM5- CMAQ is applied in this particular experiment. We have selected for this simulation the piece parabolic method (PPM) Colella and Woodward ( 1984)[7], The updated version of the CBM-IV mechanism (36 species, 94 reactions including 11 photolytic reactions) with the isoprene chemistry mechanism and the Arrhenius type rate constant expressions. We have

5 Air Pollution X 175 also used aerosol formation processes and aqueous chemistry. Finally we have used the Modified Euler Backward Iterative (MEBI) Solver, which is mechanism dependent, but it is at least as precise as the QSSA but the computing time required is much lower. In our particular application, we will use the nesting level 3 to apply the process analysis and extract the results. 3 Process Analysis All Eulerian model use the operator splitting technique. As a result, it is relatively easy to obtain quantitative information about the contribution of individual processes to total concentrations, In operator splitting, the solutions of the partial differential equations are obtain by separating the continuity equation for each species into several simpler PDEs or ODES that give the impact of only one or two processes, This simpler PDEs or ODES are then solved separately to obtain the final concentrations. The process analysis allows to obtain quantitative information on the effects of individual processes, and these can be examined and depicted in a number of ways. We can obtain time series plots showing both predicted concentrations and integrated process rates, These type of plots shows daily variations at a cell (or group of cells if data are aggregated) of a predicted species concentration and the change in concentration caused by each process. Plots of this type illustrate the variations in process contributions during the simulation period. Since the integrated process rate analysis can be calculated for every combination of process and species, the amount of output data that can be generated is quite substantial, The processes that are insolated in this way in MM5-CMAQ modeling system and in this particular application are: XYADV (XADV + YADV), advection in XY direction; ZADV, vertical advection; ADJC, mass adjustment for advection; HDIF, horizontal diffusion; VDIF, vertical diffusion; EMIS, emissions; DDEP, dry deposition; CHEM, chemistry; CLDS, cloud processes and aqueous chemistry, These processes can be analyzed over each grid cell and in a three-dimensional domain, The amount of possibilities to analyze the data sets by using the process analysis is large, We can visualize the values (usually with the same units than those used for simulated and observed concentrations (ppb)) obtained for each process over the spatial domain every hour, so that we will have a surface pattern for the 120 hour typical simulation (in our experiment), If we average these 120 surface patterns (corresponding to the 120 hour simulation) for each integrated process rate we obtain a surface pattern for this processes averaged for the simulation period Figure 5 and 6 show 120 hour surface patter averages for chemical and vertical diffusion processes, The center part of the nesting level 3 model domain corresponds to the Madrid city emissions (mainly NOX and VOC s from traffic sources), The level shown

6 176 Air Pollution X in both figures is the first level (surface level is located on the layer over the ground up to 33 m in height). Ozone production is negative in all surface level since the ozone is not emitted at all but the NOX and VOC s primary emissions consume the ozone so that the production is negative. Production is mainly accruing at higher levels. The influence of vertical diffusion processes in the final simulated ozone concentrations is shown in Figure 6. Higher values are observed in the Madrid city areas (center part of the model domain) which is interpreted as that the ozone concentrations are caused partially by the influx (vertically diffused) of ozone from higher layers. This is the opposite effect due to chemical transformations. T:-05tc DATA Sm cm.q_chem_03.d.t awe< bcaoc * 4w0c 2WO0 0 -W ocu -2L400 ; 2c& x (PPB) [~!20] lqd5-cm.4q C%-XN_OS 04-08/02/2002 D Figure hour average surface patter for the chemical process on 03 formation for February, 4-8,2002 period, Simultaneously, we can observe at Figure 7 and 8, the ozone concentrations at station 24 of the Madrid Municipality monitoring network observed and simulated by CMAQ modelling system together with al, the processes studied. Advection, mass adjustment and chemistry use to have a major impact on the ozonce concentrations than deposition, horizontal and vertical diffusion. This analysis can be done for every cell in the model domain.

7 Air Pollution X 177 T:-0St61 <9.5 DATA S~ cm.q_vdlf_03.dat 6WO0 WJoo 4WO0 2WO0 o -M om.2c 400 ; Zok x (WE) [Ma] NM5.CMAQ WJIF_03 04-OB/02/200Z Figure hour average surface patter for the vertical diffision process on 03 formation for February, 4-8, 2002 period, 4 Summary We have implemented the MM5-CMAQ modeling system over the Madrid domain by using three different nesting levels and one mother domain in order to capture all long range transport of pollutants and to be fully consistent with the boundary and initial concentration profiles. The MM5 has also been implemented over the same nesting architecture. The process analysis in CMAQiModel-3 has been activated over the whole domain and for each grid cell, The results have been analyzed for ozone concentrations for surface patterns and some station-cells. The tool indicates, in a quite precise manner, the different rates for each of the process analyzed. Further works will be done on improving the software expert tool to analyze the results in a fully automatic way. The results are completely consistent with the chemical and atmospheric processes.

8 178 Air Pollution X MM5-CMAQ 3KM RESOLUTION (27X33) 120 HOURS 04.08, FEBRUARY,2002 CELL(I 8,1 1) 200 I, I I line 1 + line z Y lines v I j~ (1 s & * l\ ~~ 50., 8 0( > [.50 f < ( ii }, -1oo LINE1 NADV; LINE2 ZADV; LINE3 ADJC; LINE4 CHEM; LINE5 OBS; LINE6 SIM, Figure 7.- Simulated ozone concentrations and process analysis for XYADV, ZADV, ADJC and CHEM at Casa de Campo monitoring station(18, 11), * 40 MM5.CMAQ 3KM RESOLUTION (27X33) 120 HOURS 04-08, FEBRUARY,2002 CELL(I 8,1 1) Z n. & LINE 1 VDIF; LINE2 HDIF; LINE3 DDEP; LINE4 OBS, LINE5 SIM; Figure 8.- Simulated ozone concentrations and process analysis for VDIF, HDF, DDEP at Casa de Campo monitoring station(18, 11),

9 Acknowledgements Air Pollution X 179 We would like to thank Professor Dr. Daewon Byun formerly at Atmospheric Modeling Division, National Exposure Research Laboratory, U.S. E.P.A., Research Triangle Park, NC and currently Professor at the University of Houston, Geoscience Department for providing full documentation of CMAQ and help. We also would like to thank to U. S.E.P,A. for the CMAQ code and PSUiNCAR for MM5 V3.0 code. References [1] San Jos& R., M.A, Rodriguez, 1, Salas and R,M, Gonddez (2000). On the use of MRF/AVN global information to improbd the operational air quality model OPANA, Environmental Monitoring and Assessment, 65, Kluwer Eds, [2] Chen, F., and J. Dudhia, 2001: Coupling an advanced land-surface/hydrology model with the Penn State/ NCAR MM5 modeling system, Part I: Model implementation and sensitivity, Mon. Wea, Rev,, 129, , [3] Warner, T. T. and H. M. Hsu, 2000: Nested-model simulation of moist convection: The impact of coarse-grid parameterized convection on fine-grid resolved convection through lateral-boundary-condition effects, Mon. Wea. Rev., 128, , [4] Stensrud, D. J., J. -W. Bao, and T. T. Warner, 2000: Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems. Mon. Wea. Rev., 128, [5] Ge, Xiaozhen, Li, Feng, and Ge, Ming, 1997: Numerical analysis and case experiment for forecasting capability by using high accuracy moisture advectional algorithm, Meteorology and Atmospheric Physics, Vienna, Austria, 63(3-4), [6] Byun, D., J. Young, J, Gipson, J. Godowitch, F. Binkowski, S. Roselle, B. Benjey, J, Pleim, J. Ching, J. Novak, C, Coats, T, Odman, A, Hanna, K, Alapaty, R. Mathur, J. McHenry, U. Sankar, S. Fine, A, Xiu and C. Jang (1998). Description of the Models-3 Community Multiscale Air Quality (CMAQ) model. Proceedings of the American Meteorological Society 78* Annual Meeting, January 11-16, Phoenix, AZ. [7] Colella P. and P.R. Woodward (1984): The piecewise parabolic method (PPM) for gas-dynamical simulations, J, Comp. Phys 54,