IOP Conference Series: Earth and Environmental Science Evaluation of data assimilation techniques for a mesoscale meteorological model and their effects on air quality model results To cite this article: A Amicarelli et al 2008 IOP Conf. Ser.: Earth Environ. Sci. 1 012033 View the article online for updates and enhancements. Related content - The development of a transponder based technique for the acoustic calibration of SODARs B Piper and S von Hünerbein - Data assimilation: Particle filter and artificial neural networks Helaine Cristina Morais Furtado, Haroldo Fraga de Campos Velho and Elbert Einstein Nehrer Macau - Reconstructing neural dynamics using data assimilation with multiple models Franz Hamilton, John Cressman, Nathalia Peixoto et al. This content was downloaded from IP address 46.3.195.237 on 10/02/20 at 22:24
Evaluation of data assimilation techniques for a mesoscale meteorological model and their effects on air quality model results 1 Amicarelli, Andrea, 1 *Gariazzo, Claudio, 2 Finardi, Sandro, 1 Pelliccioni, Armando, 2 Silibello, Camillo 1 ISPESL Dipartimento Insediamenti Produttivi e Interazione con l Ambiente, Via Fontana Candida, 1 00040 Monteporzio Catone (RM) Italy 2 ARIANET, via Gilino 9, 20128 Milano, Italy * claudio.gariazzo@ispesl.it Abstract. Data assimilation techniques are methods to limit the growth of errors in a dynamical model by owing observations distributed in space and time to force (nudge) model solutions. They have become common for meteorological model applications in recent years, especiy to enhance weather forecast and to support air-quality studies. In order to investigate the influence of different data assimilation techniques on the meteorological fields produced by RAMS model, and to evaluate their effects on the ozone and PM 10 concentrations predicted by FARM model, several numeric experiments were conducted over the urban area of Rome, Italy, during a summer episode. 1. Introduction Atmospheric dispersion models (ADMs) are widely used for air quality assessment. It is well known that the performance of these models strongly depends on the driving meteorological data. The latter are usuy provided by prognostic meteorological models. In theory, these models are capable of producing realistic results. However, models only approximate the description of physical processes through numerical solution of coupled partial differential equations. The modeling capability is therefore limited by the computational resolution, and subgrid phenomena have to be described by proper parameterizations. They consequently cannot replicate observations exactly. Agreement between model solutions and observations can be increased by data assimilation techniques that force model solutions to be more consistent with observations. However, those techniques have to be carefully applied, particularly in developing the data insertion strategy that controls when and where the observations are assimilated or how strongly they affect the solutions. A study devoted to how different data assimilation techniques can influence the meteorological fields and what are the correspondent effects on air quality predictions, has been performed over the urban area of Rome, Italy. 2. Description of the modelling system 2.1. Meteorological modelling The meteorological fields used for this study have been obtained by means of the RAMS ver. 6 [1] prognostic model. To better reproduce the air flow within Rome urban area, the wind fields in the urban domain were calculated by means of the MINERVE [2] diagnostic mass consistent model using observed meteorological data as well as wind vertical profiles produced by RAMS at the boundaries of the urban domain. The meteorological modeling system is completed by SURFPRO [3], a diagnostic module to produce PBL scaling parameters, dry deposition velocities and turbulent diffusivities fields, on the basis of the meteorological fields and landuse maps. c 2008 IOP Publishing Ltd 1
2.2. Chemical Transport Model The dispersion and the chemical evolution of the pollutants are based on the FARM model [4]. FARM is a three-dimensional Eulerian model dealing with transport, dispersion and multiphase chemistry of pollutants in the atmosphere. Photochemical reactions are described by means of SAPRC-90 chemical scheme [5]. The Models-3/CMAQ Aero-3 module [6], based on the modal approach, is employed for particulate matter. In this approach particle size distributions are represented as the superposition of three lognormal sub-distributions or modes. 3. Methodology 3.1. Models setup and input data To consistently take into account the effect of local as well as distant sources and to describe processes dominated by scales larger than the city scale (e.g. sub-synoptic flow features, photochemical smog), a nested approach considering three domains was adopted. The background domain covers a large portion of the Italian peninsula (62x62 cells at 16 km of grid spacing), the intermediate domain considers a significant portion of Central Italy (66x58 cells at 4 km of grid spacing) and the target domain includes urban area of Rome (61x61 cells at 1 km of grid spacing). A summer episode (20-24 June 2005) was selected for this sensitivity study. It was characterized by high ozone concentrations associated with typical summer Mediterranean high pressure conditions (prevailing local sea-land breeze circulation). To better characterize the meteorological conditions over the Rome area, the data routinely collected by the local monitoring network and by Air Force Meteorological Service/World Meteorological Organization (SMAM/WMO) stations were integrated with surface and upper-air measurements carried out at different locations during experimental field campaigns conducted during June 2005. These measurements include: wind and turbulence parameters collected by means of two sonic anemometers placed on the roof of the Istituto Superiore Prevenzione e Sicurezza sul Lavoro (ISPESL) downtown Rome and in the Villa Pamphili park (west of the city); urban wind and temperature, determined at the Ufficio Centrale di Ecologia Agraria (UCEA) Institute tower (downtown Rome) and at the Istituto Nazionale di Astrofisica (INAF) Astronomical Observatory (top of Monte Mario hill, east of Rome); wind vertical profiles carried out at Villa Pamphili park, by means of a SODAR/RASS instrument, and at the Tor Vergata science park (rural area, south of Rome), by means of the Institute of Atmospheric Sciences and Climate of the National Research Council (CNR- ISAC) Institute SODAR system. The SODAR/RASS system located at Villa Pamphili park was used for comparison with model results. It is a commercial Metek SODAR/RASS system. Narrow acoustic beams are obtained by means of a phased array of 16 loudspeakers. It provided 10 average values between 40m and 400m with a vertical spatial resolution of 20m. Radial wind components are derived from the mean spectra with an accuracy of about 0.1m/s. The 10 minutes averaged data were used in further analyses only if the acceptance test was satisfied. The latter is based on different parameters such as signal to noise ratio and statistical significance of measured signal and ambient noise. Signal to noise ratios were used for quality assurance, selecting only those data with positive values. During the studied period its data availability were 73% at 0 m height. As hourly wind data, required by the RAMS model, were not available for the SODAR systems, the last 10 minutes averaged data, available at every hour, were used instead. SODAR data were integrated with information provided by SMAM/WMO radiosoundings collected every 6 hours at the Pratica di Mare military airport (40 km south of the city). The European Centre for Medium-Range Weather Forecasts (ECMWF) analysis at 0.5 and 6 hours resolution were used for the initial and boundary conditions on the nested computational domains. The emission inventory is based on the Italian national inventory with special attention to the urban traffic emissions of Rome. They were directly estimated from vehicles flows on more than 9000 links making up the 700 Km of the primary road network of Rome. 2
Further details on the emission inventory, models set up and input data can be found e.g. [7]. 3.2. Data Assimilation techniques Three different assimilation methodologies were tested. The first was a nudging approach based on the ISentropic ANalysis (ISAN) preprocessing module available within the RAMS model. The first step of ISAN analysis procedure is to access ECMWF pressure level gridded fields and interpolate them onto the RAMS polar-stereographic grids, creating a polar-stereographic/pressure coordinate dataset over the area of interest. Meteorological fields are then interpolated verticy to both isentropic vertical coordinate and terrain-following, σ coordinate. Once the large scale data has been processed, any available surface and upper air observation may be accessed. The Barnes objective analysis scheme [8] is then applied to the wind, pressure, and relative humidity on the isentropes and the wind, temperature, and relative humidity on the σ levels. User-specified parameters control the smoothing characteristics of the objective analysis and the relative weighted background fields and observations. Time interpolation of the analysed fields are then assimilated to the prognostic fields at every time step through a nudging technique, realised by a forcing term added to the prognostic equation of pressure, wind, temperature and humidity. The forcing term acts on every grid point of the computational mesh. The RAMS simulation was carried out using 16 surface stations and three SODAR/radiosounding profiles to produce ISAN analysis (RAMSISAN hereafter). The second approach was an Observation Data Assimilation (ODA) technique. The ODA method is a generalized observational nudging scheme. It examines each station, interpolates data in time to each timestep, then performs a kriging interpolation to produce three dimensional value and covariance fields. These will only nudge the model fields in the grid cells close enough to the monitoring stations. Different parameters can be used to control the behaviour of the ODA scheme, such as: frequency at which to update the interpolated observational value and covariance fields with the kriging scheme (600 s in our case); relative weights applied to the ODA nudging weights for each grid (0.8 and 1.0 for intermediate and target domain respectively); radii for the kriging scheme to control the smoothness of the analysis at the surface and upper air. For the latter two different values were tested: 20 and 40 km (RAMSODA20 hereafter) and 6.7 and 13.2 km (RAMSODA7 hereafter) for respectively surface and upper air radii. The distance where the influence drops by a factor of e -2 was set at one order of magnitude lower than the above values. The other ODA parameters were set at the suggested values. The third assimilation procedure was obtained by coupling a diagnostic model (MINERVE) with a prognostic (RAMS) meteorological model to improve the description of urban scale winds in the target domain (MINER-RAMS hereafter). Wind profiles produced by RAMS at the boundaries of the target domain, together with data from 16 surface stations and the three SODAR/radiosounding profiles, were used to reconstruct the 3D wind fields during the selected episode by means of the MINERVE model. For temperature, humidity and pressure fields the values predicted by RAMS were used. In order to consider the possibility to not use observations in the meteorological simulation and to better quantify the effect of the data assimilation techniques, a simulation with no data assimilation (forecasting mode) was also performed (RAMSnoass hereafter). Here only the background ECMWF meteorological fields are provided to RAMS to define initial and boundary conditions. 4. Results and discussions For each of five simulations previously described, meteorological fields have been produced and stored at hourly resolution during the selected episode. The meteorological fields show the typical evolution of land- and sea-breeze circulations frequently occurring in this season. According to the results obtained at the Villa Pamphili urban park station, the sea-breeze starts at about 11am (direction 250 ), whereas the land-breeze between 9pm and 1am (direction 25 ). The highest SODAR sea-breeze wind speed value at 36 m (app. 7 m/s) occurs at 3-5 pm, whilst the land-breeze one at 6 am (app. 3 m/s). A statistical analysis was conducted comparing 3
the estimated wind values with SODAR observations at the Villa Pamphili urban park station. The SODAR range gates centered at 40 and 0 m heights with 20 m of thickness were used as observed values, whereas the RAMS vertical levels centered at 32 and 0 m with respectively 24 and 100 m of thickness were used as simulated values. Results are shown on Table 1 and 2 for wind speed and direction respectively. Table 1. Statistical comparison of modelled and SODAR observed wind speed at the Villa Pamphili urban park station. value (m/s) RMS (m/s) Bias (m/s) RMS Bias (m/s) Gross Err. (m/s) R FAC2 (%) (m) (m) (m) (m) (m) (m) (m) 36 0 36 0 36 0 36 0 36 0 36 0 36 0 SODAR 2.7 4.9 1.5 2.3 / / / / / / / / / / MINER-RAMS 2.4 4.9 1.3 2.2-0.3-0.1 0.4 0,7 0.4 0.3 0.97 0.96 99 97 RAMSnoass 2.9 4.7 2.1 2.9 0.3-0.3 2.1 3.1 1.6 2.4 0.36 0.30 66 66 RAMSISAN 3.2 4.9 2.3 3.1 0.5 0.0 1.7 2.9 1.5 2.5 0.63 0.44 59 60 RAMSODA20 2.5 3.4 1.6 1.9-0.1-1.6 1.2 2.1 0.9 2.1 0.68 0.51 82 68 RAMSODA7 2.9 4.4 1.9 2.7 0.3-0.5 1.7 2.8 1.3 2.1 0.48 0.38 77 70 Table 2. Statistical comparison of modelled and SODAR observed wind direction at the Villa Pamphili urban park station. Gross Error ( ) Gross error Stand. Dev. ( ) (m) (m) 36 0 36 0 MINER-RAMS 6 7 17 RAMSnoass 50 48 51 49 RAMSISAN 45 39 48 41 RAMSODA20 31 31 36 36 RAMSODA7 34 36 39 38 As far as the wind speed is concerned (table1), the best performance is obtained by the diagnostic/prognostic assimilation approach (MINER-RAMS). This is an expected result due to the intrinsic nature of diagnostic models, where initial interpolation of measurements is followed by a divergence adjustment to ensure mass-consistency. Among the other assimilation procedures available in RAMS model (ISAN and ODA), the ODA with 20 km of influence radius (RAMSODA20) shows the best performance in terms of mean bias (-0.1 m/s), mean gross error (mean of the absolute values of modeled and observed differences; here 0.9 m/s), correlation coefficient R (0.68) and FAC2 (percentage of data that satisfy 0.5<= P m /P o <=2 where P m and P o are the modeled and observed parameter respectively; here 82%), at least at 36 m height. At 0 m, this assimilation technique underestimates the observed value (3.4 vs. 4.9 m/s), also confirmed by the correspondent mean bias value (-1.6 m/s), but with the best values of mean gross error (2.1 m/s), R (0.51) and FAC2 (the percentage of simulated values 68%). The same assimilation procedure, but with a lower influence radius (RAMSODA7), shows worse performance than that with 20 km (higher mean gross error and lower R values), overestimating the wind speed at 32 m (0.3 m/s) and underestimating it at 0 m 4
height (-0.5 m/s). The results obtained using RAMS in forecasting mode (RAMSnoass), where only ECMWF fields are used, provide the worst performance in terms of mean gross error and R at both heights. Although it shows a mean bias value as those obtained with the ODA technique, the highest values of RMS bias and gross error demonstrate that deviations from the observed values are larger than those obtained with assimilation techniques. The application of the isentropic analysis (RAMSISAN) improves the correlation coefficient R (0.63 and 0.44) respect to the values obtained without data assimilation (RAMSnoass), but overestimations of the observed wind speed are detected at 32 m (3.2 vs. 2.7 m/s) with the largest RMS values (2.3 and 3.1 m/s at 32 and 0 m respectively). According to the statistical comparison results for wind direction, shown in table 2, the best performance is obtained by the diagnostic assimilation approach (MINER-RAMS), as already observed for wind speed. The ODA assimilation procedure seems to guarantee lower mean gross errors (about 31-36 degrees) than those calculated by means of both ISAN and forecasting mode. No significant variation with height has been detected. The SODAR observed and model calculated wind speed up to 400 m were used to produce pdf functions. Results are shown in figure 1: the model results underestimate the frequencies of the highest speeds whilst those of the lowest ones are overpredicted; MINER-RAMS and RAMSODA20 show the smer variances. SODAR MINER-RAMS RAMSnoass mean: 4.5m/s st. dev.: 2.5m/s max: 15.0m/s mean: 3.5m/s st. dev.: 2.1m/s max: 9.8m/s mean: 3.7m/s st. dev.: 2.6m/s max: 12.4m/s RAMSISAN RAMSODA20 RAMSODA7 mean: 4.0m/s st. dev.: 2.8m/s max: 10.5m/s mean: 2.9m/s st. dev.: 1.8m/s max: 7.7m/s mean: 3.5m/s st. dev.: 2.4m/s max: 11.7m/s Figure 1. Frequencies of SODAR observed Vs. modelled wind speed (m/s) using data up to 400 m. As far as the spatial and temporal distribution of wind speed is concerned, the MINER-RAMS results show a more effective reproduction of sea-breeze structure and evolution than that obtained applying the RAMSISAN assimilation procedure, followed by RAMSODA20 technique. Discrepancies in the reproduction of both land and sea breezes were sometimes detected among the different assimilation procedures. In particular, significant differences were found in reproducing the sea breeze development and its inland dynamic on 24 June, as shown in figure 2. As an example, 5
while the MINER-RAMS and RAMSISAN assimilation techniques produce a complete penetration of sea-breeze over the urban target domain at 15:00 lst, the remaining ones show a sea-breeze which only diffuses inland over the first 40 km, with an opposite wind direction over the rest of the territory. Other discrepancies are detected for the land-breeze, particularly in terms of spatial distribution of wind speed. For the remain days (20-23 June) those discrepancies were not detected. MINER-RAMS RAMSnoass RAMSISAN RAMSODA20 RAMSODA7 Figure 2. Surface wind fields on June 24 th at 15:00 obtained with different assimilation procedures. Table 3 shows a statistical comparison of modeled and observed surface temperature at Villa Pamphili urban park station. The forecasting simulation (RAMSnoass) usuy underestimates the observed mean temperature (mean bias -1.5 C) with the largest mean gross error (2.2 C) and the lowest R (0.76). The ODA technique with 7 km of influence radius shows about the same performances, with a slight improvement in the mean gross error and R values. The temperature 6
predictions are better reproduced by using either the ODA with 20 km of influence radius or the isentropic ISAN assimilation procedures, which show similar performances. MINER-RAMS temperature results were not taken into account, as this assimilation procedure has been used only for the wind field, while temperatures were derived from RAMSISAN results. The meteorological fields obtained using the discussed assimilation techniques were then separately provided, together with emission data and initial/boundary conditions, to the chemical transport model FARM to evaluate their possible effects on ground level predicted pollutants concentrations. Fifteen monitoring stations located in the urban area were used to compare measured with simulated pollutants concentration. Daily PM 10 (particulate matter with characteristic length smer than 10µm) and hourly ozone observed concentrations were available for comparisons. PM 10 results are shown in table 4. All simulations results are affected by underestimation of measured PM 10 concentrations. It was mainly due to an underestimation in the organic components of the aerosol, as reported by Gariazzo et al [7], and in a lesser extent to underestimations of sulphate and sea-crustal components. Here what is more interesting to notice is the relative differences among simulations driven by the different meteorological fields. The comparison based on daily average observations likely hides some effects produced by differences among the meteorological simulations, but relevant differences among the highest daily average values remain evident. Table 3. Statistical comparison of modelled and observed surface temperature at the Villa Pamphili urban park station. value ( C) RMS obs. ( C) Bias ( C) RMS Bias ( C) Gross Error ( C) Observed 24.7 3.8 / / / / RAMSnoass 23.2 2.2-1.5 2.4 2.2 0.76 RAMSISAN 25.0 3.3 0.3 1.0 0.8 0.96 RAMSODA20 24.2 3.2-0.5 1.2 1.0 0.95 RAMSODA7 23.3 2.4-1.5 1.9 1.8 0.88 R Table 4. Comparison of modelled and observed surface daily PM 10 averaged over stations. (µg/m 3 ) Max (µg/m 3 ) Observed 37 43 MINER-RAMS 17 21 RAMSnoass 15 25 RAMSISAN 14 17 RAMSODA20 16 19 RAMSODA7 14 22 Table 5. Statistical comparison of modelled and observed surface ozone averaged over stations. Max value (ppb) Time avg value (ppb) RMS (ppb) Bias (ppb) RMS Bias (ppb) Gross Error (ppb) R FAC2 Time avg Time avg Time avg Time avg Time avg Time avg Time avg Observed 60.5 36.7 60.7 21.0 7.8 / / / / / / / / / / MINER-RAMS 68.6 43.2 61.2 15.1 7.3 6.4 0.5 10.5 8.8 9.7 7.1 0.88 0.31 77 100 RAMSnoass 89.8 45.4 64.8 17.6 9.9 8.6 4.1 12.1 14.2 11.6 10.3 0.82-0.28 75 100 RAMSISAN 69.1 48.0 64.4 15.1 6.4 11.2 3.7 9.7 7.2 12.4 6.4 0.91 0.49 76 100 RAMSODA20 80.6 44.6 64.3 16.9 7.7 7.8 3.6 10.3 10.8 10.8 8.9 0.88 0.02 77 100 RAMSODA7 88.7 45.5 65.1 17.8 9.9 8.7 4.4 12.0 14.0 11.8 10.3 0.82-0.24 76 100 As far as the ozone is concerned, both hourly values of the period and those occurring at the hours when ozone peaks are detected (), were considered for the statistical comparison. Table 5 shows (%) 7
the modelled/measured ozone concentrations results for the different simulations. As already noticed for meteorology, the best results are obtained with diagnostic MINER-RAMS assimilation procedure, followed by RAMSODA20 approach, with a general overestimation. In general results are better for the daytime ozone () than for the whole analyzed period ( data). It is well known the difficulty encountered by photochemical models in correctly simulating night-time ozone concentrations. Different behaviors are observed for the different models when data are analyzed as a whole series ( data) and when the analysis is restricted to the daily ozone peaks (). In particular, among the RAMS assimilation techniques, the RAMSISAN results show the worst performance when data are considered (48 µg/m 3 ), but the best one when analysis is restricted to daytime peaks (64.4 µg/m 3 ). MINER-RAMS RAMSnoass RAMSISAN RAMSODA20 RAMSODA7 Figure 3. PM 10 concentration maps averaged over the simulation period for different assimilation procedures (the lower limits of the colour classes are reported in legend). It is worth noticing that with respect to the performance obtained without using data assimilation (RAMSnoass), the application of these techniques (ISAN or ODA) do not genery produce 8
significant effects on the improvement of both mean value prediction (64.3-65.1 ppb vs. 64.8 ppb for RAMSnoass) and mean bias (3.6-4.4 ppb vs. 4.1 ppb for RAMSnoass) during the daytime () hours. A reduction in both the RMS values is obtained applying the ISAN and ODA at 20 km assimilation techniques (e.g. from 14.2 to 7.2 ppb for RMS of mean bias). Differences among performances have instead been detected at the same hours for both the mean gross error (6.4-10.3 ppb vs. 10.3 ppb for RAMSnoass) and the R values (-0.24-0.49 vs -0.28 for RAMSnoass). These statistical results suggest that the application of assimilation techniques in the RAMS model can account for up to 1% (0.5 ppb) in the daytime mean value and in reducing the mean bias of the same amount. A more significant improvement is obtained for both the mean gross error and the RMS bias during daytime. MINER-RAMS RAMSnoass RAMSISAN RAMSODA20 RAMSODA7 Figure 4. Daily PM 10 concentration maps on June 24 th obtained with different assimilation procedures (the lower limits values in legend). Time series of ozone concentrations are well reproduced by different model simulations, but there is a systematic overestimation during nighttime (RAMSISAN above ) and in the last day. MINER- 9
RAMS, RAMSISAN and RAMSODA20 simulations better reproduce the observed ozone behavior, whilst RAMSODA7 values are very close to RAMSnoass with lower performances. Changes in wind speed and directions influence concentration values and their fields shape. As can be seen in figure 3, PM 10 maps averaged over the full period show the same main features, with a few differences in particular areas. Larger differences are observed for the highest concentration maps. Other differences are detected during those days when the provided meteorological fields are different, such as during June 24 th. Here the differences in the sea breeze reproduction, produce significant discrepancies among the simulations, especiy for PM 10 maps, as shown in figure 4. 5. Conclusions In order to investigate the influence of different data assimilation techniques available in the meteorological model RAMS, a study has been conducted over the urban area of Rome, Italy, during a summer episode. The effects produced by the different meteorological fields on the ozone and PM 10 concentrations predicted by the chemical transport model FARM have been also evaluated. Three different assimilation methodologies were tested. Two of them are available within the RAMS modeling system: the ISentropic ANalysis (ISAN) preprocessing analysis and the Observation Data Assimilation (ODA) techniques. The third method was the coupling of a diagnostic (MINERVE) with a prognostic (RAMS) meteorological models. Results show the diagnostic/prognostic approach provided the best results in reproducing observed wind, PM 10 and ozone concentrations, followed by the ODA techniques used with 20 km of influence radius. The assimilation techniques used in RAMS model was found to influence up to 1% (0.5 ppb) in the daytime ozone mean value and in reducing of the same amount its mean bias. Higher reductions were found for ozone mean gross errors (up to 6% corresponding to 3.9 ppb). Significant differences among pollutants concentration maps were observed, especiy during those days when the different assimilation techniques showed significant differences in the meteorological fields. References [1] Cotton W.R., Pielke R. A., Walko R. L., Liston G. E., Tremback C. J., Jiang H., McAnelly R.L., Harrington J. Y., Nicholls M. E., Carrio G.G., McFadden J. P. 2003 RAMS 2001: Current status and future directions. Meteorol. Atmos. Phys. 82 5-29. [2] Aria Technologies 2001 Minerve Wind Field Models version 7.0 General Design Manual (ARIA Tech. Report) [3] ARIANET 2005 SURFPRO (SURrface-atmosphere interface PROcessor) User's guide (ARIANET Tech. Report). [4] Silibello C., Calori G., Brusasca G., Giudici A., Angelino E., Fossati E., Peroni E., Buganza E., Degiarde E. 2005 Modelling of PM10 concentrations over Milan urban area: validation and sensitivity analysis of different aerosol modules. In: Proceedings of 5th International Conference on Urban Air Quality (Valencia, Spain, 29-31 March 2005). [5] Carter W.P.L 1990. A detailed mechanism for the gas-phase atmospheric reactions of organic compounds. Atmospheric Environment 24A 481-5. [6] Binkowski F. S. 1999 The aerosol portion of Models-3 CMAQ In Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System Eds D.W. Byun, and J.K.S. Ching (EPA-600/R-99/030, National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC, 10-1-10-16) Part II: chapters 9-. [7] Gariazzo C., Silibello C., Finardi S., Radice P., Piersanti A., Calori G., Cecinato A., Perrino C., Nussio F., Cagnoli M., Pelliccioni A., Gobbi G. P., Di Filippo P. 2007 A gas/aerosol air pollutants study over the urban area of Rome using a comprehensive Chemical transport model Atmospheric Environment Vol. 41/34 pp. 7286-7303. [8] Barnes S.L. 1973 Mesoscale Objective Map Analysis Using Weighted Time Series Observations NSSL 10