WRF Land Surface Schemes and Paris Air Quality D. Khvorostyanov, L. Menut, Ch. Zheng, J.-C. Dupont, M. Haeffelin Laboratoire de Météorologie Dynamique IPSL Ecole Polytechnique, 91128 Palaiseau, France Atelier AMA 2013
Predicting January 2009 PM10 Pollution Event... Four model configurations differing only in the Land Surface Model (LSM) Individual configs could diagnose critical threshold exceedances LSM-related uncertainty taken into account => information threshold prediction failure Is there the best LSM choice?
COSY Experimental Daily Air Quality Forecast CHIMERE (Menut et al., 2013) is an off-line chemistry-transport model, a French CNRS national tool: http://www.lmd.polytechnique.fr/chimere Developed by IPSL/LMD and INERIS Used by more than 160 users for research or forecast Involved in projects such as GEMS, AMMA, GEOMON, CIRCE, MACC, ATOPICA, etc. used with WRF (3.4): weather research and forecast model widely used in the World community MM5 ECMWF IFS LMDz. WRF and CHIMERE are used in daily experimental forecasts for the COSY project: http://www.lmd.polytechnique.fr/cosy COSY project is designed to test various physical options to evaluate their impact on regional air quality
WRF Surface Schemes and Experimental Design Four land surface models (LSM) of WRF Soil heat conduction WRF-TDiff CHIMERE TDiff Soil thermal conductivity and heat capacity WRF-RUC CHIMERE RUC Surface heat budget WRF-PX CHIMERE WRF-Noah CHIMERE Comparison to SIRTA and MeteoFrance meteorological data Soil moisture calculation PX Noah Runoff formulation Bare soil evaporation Evaporation from vegetation Direct evaporation from the canopy Comparison to AIRPARIF surface concentrations Evapotranspiration Soil texture and vegetation type LSMs, prognostic variables, and number of levels or layers Soil T 5 - TDiff: 5-layer Thermal Diffusion scheme (MM5) - RUC: Rapid Update Cycle (Smirnova et al, 2000) 6 ( 10) - Pleim and Xiu (Xiu and Pleim, 2001) 2 - Noah: Based on (Chen and Dudhia, 2001) model 4 Soil Q 0 6 ( 10) 2 4 Vegetation Q 0 1 1 1
Observation Data Sources The SIRTA atmospheric observatory (http://sirta.ipsl.polytechnique.fr) located at Palaiseau in the Paris region provides in situ data for the SIRTA main meteorological variables, such as Palaiseau, France 2m temperature, humidity, sensible and latent heat fluxes, wind speed, etc. (Haeffelin et al, 2005). Time varying vertical profiles of Lidar backscattered power and polarization at multiple wavelengths are used to infer the diurnal cycle of the depth of French the planetary boundary layer (PBL) National and the height, structure and type of Air Quality clouds (water/ice) Network (AIRPARIF) AIRPARIF (www.airparif.asso.fr) is a French network of 68 stations (50 permanent automatic stations and 18 temporary stations in the proximity of the trafic. They are situated within 100 km around Paris and mesure the quality of the air respired by the population of more than 11 million in the whole region.
Summer 10m Sensible Heat Flux: SIRTA
Summer 10m Sensible Heat Flux: SIRTA
Summer 10m Sensible & Latent Heat Fluxes
Summer & Winter SSHF
Multi-station distributions of basic statistics
Summer and winter 2m RH
Summer and winter 10m wind
Summer and winter precipitation (daily max)
Summer and winter O3 and NO2: Air Parif
Winter PM10: Alert Threshold on January 11, 2009
Summer and Winter PM10 and PM2.5
Conclusions Four LSMs were used for a sensitivity study at a regional scale for summer 2009 and winter 2008-2009 over the Paris area The NOAH LSM shows the best performance for summer and winter PM. It also shows best performance for summer O3 together with the PX scheme. NOAH also shows the best overall scores for the sensible and latent heat fluxes measured at the SIRTA. RUC and PX show the lowerest scores for the heat flux daily maxima. This is translated in their lower performance when predicting O3 daily maxima. NOAH is again the best in O3 daily maxima prediction TDIFF shows the highest scores predicting winter 2m temperature. It also shows the best scores for summer precipitation; NOAH also shows high scores.