Modelling African dust. Accuracy & quantitative approach: BSC

Transcripción

1 Modelling African dust. Accuracy & quantitative approach: BSC J.M. Baldasano 1-2, O. Jorba 1 and M.T. Pay 1 1 Earth Sciences Department, BSC 2 ETSEIB-UPC Quantitative methods for evaluating daily contributions of African dust to ambient PMx levels over Europe during dust outbreaks Barcelona, Spain, 27th-28th April 2015

2 tools to detect and quantify African dust on a regular basis in AQ monitoring networks? 1. Air Quality Forecast system CALIOPE over IP and Europe 2. Mineral Dust Modelling BSC-DREAM8b and NMMB/BSC-Chemical Transport Model 3. Observations Near Real Time 2

3 CALIOPE: Air Quality Forecasting System METEOROLOGICAL FORECAST EMISSION FORECAST HERMES-DIS 12 km x 12 km IC/BC nesting EMEP inventory Meteo. fields HERMES-BOUP 4 km x 4 km NCEP GLOBAL SIMULATIONS WRF-ARW 12 km x 12 km WRF-ARW 4 km x 4 km AIR QUALITY FORECAST Meteo. fields Emis. fields DESERT DUST FORECAST IC/BC nesting NCAR MOZART4 CMAQ 12 km x 12 km CMAQ 4 km x 4 km BSC-DREAM8b 50 km x 50 km AIR QUALITY PRODUCTS

4 Mineral Dust Modelling BSC-DREAM8b and NMMB/BSC-Chemical Transport Model 4

5 From BSC-DREAM8b to NMMB/BSC-Dust model 5

6 NMMB/BSC-Chemical Transport Model NMMB/BSC-Chemical Transport Model The main system is build on the meteorological driver NMMB Multiscale: global to regional scales allowed (nesting capabilities) Nonhydrostatic dynamical core: single digit kilometre resolution allowed Fully on-line coupling: weather-chemistry feedback processes allowed Enhancement with a data assimilation system Janjic and Gall (NCAR/TN 2012) Janjic and Vasic (EGU2012) Janjic et al. (MWR 2011) (...) 6

7 Observations Near Real Time Confidence on the CALIOPE system 1. Several published studies: Domain 2. Near-Real Time (NRT) evaluation: Reference Europe Pay et al (2010, 2012a) Basart et al (2012) Baldasano et al (2011) Spain Pay et al (2011, 2012b) Borrego et al (2011) Sicardi et al (2012) Barcelona & Madrid Gonçalves et al (2009) Cataluña (NE Spain) Jiménez et al (2008) Aguilera et al (2013) 7

8 Evaluation method Modelled concentrations are compared against observations from the European Air quality database (AIRBASE, providing Near Real Time (NRT) measurements on an hourly basis over the European domain. The evaluation is based on the annual analysis of classical statistics such as correlation coefficient (r), Mean Bias (MB), and Root Mean Square Error (RMSE) performed on an hourly basis. Rural stations (R), suburban stations (S). XVPCA Note that the evaluation is done in NRT, and observations are not validated

9 AQ Observations Data Volume Model results are evaluated at Near Real Time (NRT) with Air Quality observations from several AQ networks. Pollutants: O 3, NO 2, NO, SO 2, PM 10, PM 25, TOLUENE, BENZENE, XYLENE. 10 main AQ data providers: from EIONET (EEA) to regional government networks. Domain # Stations EU 569 IP 402 CAN 44 AND 90 CAT 81 MAD 47 Provider Times per days Number of files EIONET (EEA) 4 10 Ayuntamiento Madrid 1 1 Comunidad Madrid 1 1 Junta Andalucía 24 1 Govern d Andorra 24 1 Govern de les Illes Balears Generalitat de Catalunya Gobierno Extremadura Xunta de Galicia

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11 Observations Near Real Time PM10 automatic measurements: adjustment factor Provincia Municipio Estación Media captador (gravimétrico) Media analizador (beta) Ecuación regresión Valor r 2 y nº muestras Modificaciones en los datos forzados al pasar por el origen Granada Granada GRANADA NORTE y=0.995x (41) y=0.9669x 1.03 Almería Almería MEDITERRÁNEO y=1.3223x (25) y=1.4919x 0.67 Almería Carboneras PLAZA DEL CASTILLO y=0.8825x (38) y=0.9865x 1.01 Córdoba Córdoba LEPANTO y=0.9073x (37) y=0.7983x 1.25 Cádiz San Fernando SAN FERNANDO Y=0.599x (41) y=0.9003x 1.11 Cádiz La Línea LA LINEA y=1.0055x (41) y=1.177x 0.85 Cádiz Los Barrios LOS BARRIOS y=1.2388x (23) y=1.3062x 0.77 Málaga Málaga CARRANQUE y=1.1304x (34) y=1.1924x 0.84 Jaén Jaén RONDA DEL VALLE y=0.8161x (37) y=0.9695x 1.03 Jaén Torredonjimeno TORREDONJIMENO y=0.7304x (40) y=0.7434x 1.35 Jaén Bailén BAILÉN y=0.8667x (40) y=0.9344x 1.07 Huelva Huelva CAMPUS EL CARMEN y=0.6536x (39) y=0.7339x 1.22 Huelva Matalascañas MATALASCAÑAS Y=0.7629x (56) y=0.8265x 1.05 Huelva Moguer MOGUER y=0.8871x (18) y=0.8202x 1.36 Sevilla Alcalá de Guadaira ALCALÁ DE GUADAIRA y=0.8896x (27) y=0.9567x 1.21 Factor 11

12 Dust episode: over IP 12

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18 PM10 µg/m /11/ /11/ /11/2014 Dif Tipo Emi. CCAA Alt El Ejido URB F Andalucia 97 Granada Norte URB T Andalucia 689 Motril Granada URB F Andalucia 50 Mazagón SUB I Andalucia 31 Avda. Marconi Cadiz URB T Andalucia 4 Río San Pedro Cadiz URB F Andalucia 1 Cobre Las Cruces RUR I Andalucia 1 Matalascañas RUR F Andalucia 22 Santa Clara SUB F Andalucia 29 Sierra Norte RUR F Andalucia 573 Cuatro Caminos Madrid URB T Madrid 699 Majadahonda Madrid SUB F Madrid 730 Algete Madrid SUB F Madrid 720 Guadalajara URB F Castilla Mancha 700 Leon URB T Castilla León 838 Palencia URB T Castilla León 953 Salamanca SUB F Castilla León 743 Valladolid URB T Castilla León 700 Iturrama Pamplona URB F Navarra 449 Lleida URB T Cataluña 172 Cubelles (Poliesportiu) SUB I Cataluña 18 Pista de Silla Valencia URB T C.Valenciana 11 Almassora C.P. Ochando C.Valencia URB I C.Valenciana 27 Buñol C. Valenciana SUB I C.Valenciana 400 Lorca SUB I Murcia 340 Corlab-1 A Coruña URB T Galicia 35 Lugo URB F Galicia 450 Plaza de Toros Oviedo URB T Asturias 276 Hermanos Felgueroso Gijón URB T Asturias 10 Tetuán Santander URB F Cantabria 30 Centro Santander URB T Cantabria 9

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21 Conclusions tools to detect and quantify African dust on a regular basis in AQ monitoring networks? YES But, we need several complex tools that need to be adjusted and improved, and define a specific application protocol 21

22 WMO Western Mediterranean 2-3/04/2014

23 Thank you for your attention Webs: Air Quality Forecasts Europe / Spain: BSC-DREAM8b mineral dust model forecasts North Africa/Europe/East-Asia: NMMB/BSC-Dust mineral dust model forecasts Global and Sahara desert area: Acknowledgements: BSC is recognized as a Severo-Ochoa centre of excellence (SEV ) of the Spanish Government.; CGL R from MINECO. Simulations have been performed in the Marenostrum supercomputer. 28