Modellistica numerica per la circolazione atmosferica e la dispersione di inquinanti S. Trini Castelli & D. Anfossi (ISAC – CNR) & E. Ferrero (DISTA –

Presentazione sul tema: "Modellistica numerica per la circolazione atmosferica e la dispersione di inquinanti S. Trini Castelli & D. Anfossi (ISAC – CNR) & E. Ferrero (DISTA –"— Transcript della presentazione:

1 Modellistica numerica per la circolazione atmosferica e la dispersione di inquinanti S. Trini Castelli & D. Anfossi (ISAC – CNR) & E. Ferrero (DISTA – UNIPMN) Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche

2 Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche METEOROLOGICAL CIRCULATION MODELS Study of local, regional or global meteorological phenomena Meteorological input for air pollution DISPERSION MODELS physical models (wind tunnel, water flumes) mathematical models analytical models (exact analytical solution in simplified conditions) numerical models (approximate numerical solutions using numerical integration techniques) diagnostic models (no time-tendency terms ) prognostic models (full time-dependent equations) M o d e l l i n g …………….

3 Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche Purposes and applications meteorological model: description and forecast of atmospheric processes and circulation on different scales (synoptic, mesoscale, local) dispersion model: analysis and forecast of continuous (Industrial plants or areas) and accidental releases (e.g. Chernobyl (long range), Seveso (short range)) environmental impact evaluation real time monitoring air concentration and ground deposition estimation measurement nets planning strategies processing for emissions downing

4 LONG RANGE Synoptic and Planetary spatial scale Time scale from weeks to months-years ECMWF ANALYSES LONG RANGE DISPERSION MODELS Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche driving

5 MILORD Chernobyl Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche Method for the Investigation of Long Range Dispersion Lagrangian Particle Stochastic model (D. Anfossi, D. Sacchetti, S. Trini Castelli, 1995)

6 MILORD Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche Method for the Investigation of Long Range Dispersion Lagrangian Particle Stochastic model (D. Anfossi, D. Sacchetti, S. Trini Castelli, 1995)

7 Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche REGIONAL/LOCAL DISPERSION MODELS REGIONAL AND MESOSCALE Spatial scale from few tens to few hundreds km Time scale from few hours to few weeks REGIONAL METEOROLOGICAL MODELS driving

8 METEOROLOGICAL MODEL DISPERSION MODEL etc …. Mean Flow Turbulence Transport Diffusion Closure Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche

9 Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche Turbulence characteristics required by air pollution models (Eddy diffusivities, wind velocity variances, Lagrangian time scales) are usually NOT provided directly by meteorological models BUT must be derived from their output using wind and temperature fields and additional fields such as turbulent kinetic energy, turbulent length scale, mixing height, atmospheric surface layer parameters. DISPERSION = TRANSPORT + DIFFUSION (Mean wind) (Turbulence) INTERFACING PARAMETERIZATION SCHEME !!! INTERFACING METEOROLOGICAL and DISPERSION MODELS

10 Boundary layer parameterisation MIRS interfacing code: R M S modelling system Atmospheric circulation model: RAMS Lagrangian particle dispersion model: SPRAY Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche (Regional Atmospheric Modeling System Pielke et al., 1992) (Method for Interfacing RAMS and SPRAY Trini Castelli and Anfossi, 1997, Trini Castelli, 2000) (Brusasca et al., 1989, Anfossi et al., 1998, Tinarelli et al, 2000, Ferrero et al. 2001)

11 RMS modelling system RAMS MIRS SPRAY Fields of - WIND, TEMPERATURE, T.K.E., K (3 D) TOPOGRAPHY, SURFACE FLUXES (2 D) Fields of - WIND, K, SKEWNESS/KURTOSIS, & T L (3 D) TOPOGRAPHY, PBL height (2 D) Fields of - PARTICLE POSITIONS G. L. CONCENTRATION Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche

12 Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche MIRS Surface layer parameters from RAMS fluxes to… from Louis (1979) parameterisation PBL height Gryning and Batchvarova (1990) simplified - Batchvarova and Gryning (1991) complete model Gradient Richardson number profile Diffusion coefficient profileTurbulent kinetic energy profileExternal datasets Convective velocity scale Variances and decorrelation time scales Coupling with Mellor-Yamada scheme Coupling with E-l or E- schemes Hanna (1982) and Degrazia et al. (2000) parameterizations Third and fourth moment of the vertical velocity Chiba (1978), Anfossi(1997)

13 Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche Lagrangian particle models are three-dimensional models for the simulation of airborne pollutant dispersion, able to account for flow and turbulence space-time variations Emissions in the atmosphere are simulated using a certain number of fictitious particles named computer particle. Each particle represents a specified pollutant mass. It is assumed that particles passively follow the turbulent motion of air masses in which they are, thus it is possible to reconstruct the emitted mass concentration from their space distribution at a particular time In these models the temporal evolution of the velocity particles released in the atmosphere, that is in turbulent conditions, is prescribed by the Langevin equation, where velocity fluctuations are considered a Markov stochastical process with x = particle position; u = particle velocity fluctuation; = mean wind velocity; dW = stochastic fluctuation SPRAY deterministic term stochastic term incremental Wiener process

14 Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche DKarlsruheflat neut/unst USAIdaho fallsflatlow wind USAEPA-RUSVAL hill (wind tunnel)neutral USAEPA-RUSVAL valley (wind tunnel)neutral USAIndianapolisurbanall stabilities CHTRANSALPalpine regionunstable NLillestromflat – snow coveredstable DKCopenhagenflat coastunstable DTRACTcomplexall stabilities IVado Ligurecomplex coastall stabilities FMarseillecomplex coast all stabilities ITurinurban/complexall stabilities BRCubatão very complex coastall stabilities JTsukuba and Ohicomplex coastall stabilities IBrenner Highwayalpine regionall stabilities I-FTorino-Lione Highwayalpine region all stabilities Examples of R M S applications

15 The modelling system RMS: RAMS-MIRS-SPRAY TRACT Brazil Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche R M S In collaboration with Dr. A. Kerr (USP) In collaboration with Dr. J Carvalho (ULBRA)

16 R M S Running on the highway! Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche Courtesy of The modelling system RMS: RAMS-MIRS-SPRAY In collaboration with Drs. G Brusasca, G. Tinarelli, S. Finardi

17 Observed data speed (ms -1 ) Observed data u (ms -1 ) E-l simulation speed (ms -1 ) E-l simulation u (ms -1 ) MY82 simulation speed (ms -1 ) MY82 simulation u (ms -1 ) EPA – RUSVAL wind tunnel experiment RAMS sensitivity to turbulence closure R M S

18 MY82 closure + (1) + (3) (2) (3) E-l closure + (2) + (3) (1) Scatter plots of the RMS simulated concentrations against measurements EPA-RUSVAL: closure scheme effect on dispersion R M S

19 Cumulative frequency distribution (c.f.d.) of normalized mean concentrations χ. Observed data: solid line; RMS with E-l closure: dotted line; RMS with MY82 closure: dashed line C is the concentration corrected subtracting the background, Q is the tracer flow rate h c is a convenient length scale of the experiment EPA-RUSVAL: concentration distribution R M S

20 Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche OHI (Japan) nuclear plant site. Testing the effect of alternative turbulence closures (in collaboration with MHI Fluid Dynamics Lab., Dr. Ohba, Dr. Hara)

21 Testing the effect of alternative turbulence closures also on TRACT (in collaboration also with CESI, Dr. Alessandrini) TRACT is back! El-ISO El-SMA MY MY-Hanna Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche R M S

22 Regional down to local scale R M S Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche

23 Low wind case, September 1999 Wind velocity at 10 m Wind velocity at 150 m Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche R M S

24 start: 09.02.2000 11 GMT (12 LST) end: 10.02.2000 15 GMT (16 LST) R M S Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche Foehn case, February 2000

25 30.06.2000 12:00 SPEED (m/s)TEMPERATURE (K) Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche

26 30.06.2000 18:00 SPEED (m/s)TEMPERATURE (K) Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche

27 Comparison with observations: time evolution of wind speed and temperature at the surface Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche

28 31/5/2001 00:00 - 1/6/2001 00:00 (Sicily coast) 3-D particles and g.l. concentrations – hourly imagines Courtesy of Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche

29 Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche NUMBERS AND NUMERICS! RAMS parallel versions 5.0, 6.0 : parallel efficiency 68% 90 % ( Tremback C., personal communication ) n. of processors computer hardware model configuration SPRAY versions 3.! : parallelization in process at AriaNet ( Brusasca G., Tinarelli G., Finardi S., Morselli M.G. )

30 Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche NUMERICS AND COMPUTERS! Past to present at ISAC-TO AlphaServer DS20E Tru64 Unix microprocessor 21264 - 833MHz CPU (2!) Parallel present at DFG-UNITO ( Prof. G. Boffetta ) 3 Server TYAN GX28 2GB RAM CPU AMD Opteron 244 (2 x 3 = 6) Networking Gb Ethernet Parallel next future at ISAC-TO + DFG-UNITO 5 Server TYAN GX28 2GB RAM CPU AMD Opteron 244 (2 x 5 = 10) Networking Myrinet 2000 Fiber

31 Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche

32 Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche In RAMS (A 1,A 2,B 1,B 2,C)=(0.92, 16.6, 0.74, 10.1, 0.08) From MIRS to SPRAY From Chiba (1978) Level 2.5: B.L approximation, horizontal homogeneity Mellor-Yamada 1982

33 Istituto di Scienze dellAtmosfera e del Clima - Torino Consiglio Nazionale delle Ricerche E- l isotropic In RAMS (K-theory) From MIRS to SPRAY From Chiba (1978)