Data assimilation of trace gases in a regional chemical transport model: the impact on model forecasts E. Emili 1, O. Pannekoucke 1,2, E. Jaumouillé 2, A. Piacentini 1, D. Cariolle 1 & Météo-France MOCAGE team 1 Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique Toulouse, France 1 2

Overview 2 Description of the regional assimilation system Observation and background errors Assimilation of surface O 3, NO 2 and CO Skills of short-term forecasts Assimilation of satellite O 3 and NO 2 Conclusions

MOCAGE + variational data assimilation = VALENTINA 3 o Semi-lagrangian chemical transport model: MOCAGE (Météo France - CNRM) Global (2.0 deg) and regional (0.2 deg) nested domains 47 vertical levels up to 5 hPa RACM+REPROBUS chemistry (91 transported species, >300 reactions) IFS (ECMWF) meteorological forcing TNO+MACCity emissions Surface model increments of O 3 (ppbv) after the assimilation of European in-situ data (AIRBASE): o Variational data assimilation: VALENTINA (CERFACS) 3D-Var (1h assimilation windows) Initial state optimization Assimilation of column/profile/surface data 3D background error matrix (B) o Operations within MACC-II project: Daily forecasts (96h) Daily re-analyses (D-1) Yearly re-analyses ( )

Summer-time O 3 episode: 6-12 July UTC UTC UTC UTC Surface temperature (C°) Surface O 3 (ppbv) Measured O 3 (ppbv) AIRBASE O 3 hourly measurements (≈1000 sites): MOCAGE control simulation: Meteorological forcing (IFS): EU O 3 limit

Observations and background errors 5 O 3 super-observations average Standard deviation of measurements within 50 km 1)Selection of super-sites with n>4 sites within 50 km (twice the model grid resolution) 2)Calculation of spatial standard deviation (  o ) for each super-observation-> temporal average 3)Calculation of model Rmse (control simulation – super-observations) at each super-site 4)Background error  B = (Rmse 2 -  o 2 ) 1/2 -> sites average   B raw approximation of background error: overestimation of true forecast error, missing temporal variability, biases are counted in the error. Background horizontal error correlation fixed to 25/12 km for O3,CO/NO2 Ozone observation error  o in July 2010 (ppbv) Ozone background error  B in July 2010 (ppbv)

Observations and background errors 6 July 2010 January 2012 Average Observation error (%) Average Background error (%) NO 2 observation and background errors are the highest (small scale variability, emissions uncertainty, vertical mixing…) O 3 observation error is about 1/3 of background error CO observation and background errors are comparables O 3 background error doubles in winter (under investigation)

7 Analysis skills Control run without data assimilation Analysis Independent observations Control run without data assimilation Analysis Independent observations

8 Forecasts skills January O3July O3 January NO2July NO2 January COJuly CO Diurnal cycle of model and observations values: Control run without data assimilation 00+24h possible MACC forecast ( initialized with the analysis at 00 UTC ) 1h forecast, e.g. the assimilation background Analysis Independent observations

Forecasts skills 9 MACC analysis for D-1 produced at 09 UTC (observations collection): delay between D forecast and latest analysis > 9h Which gain if this delay could be reduced? Rmse % Bias %

Forecasts skills 10 What is the impact of assimilating one species on the forecast of the others? Validation of O 3 1h forecasts: 7-17 Jul Jan 2012

11 Validation of IASI (MetOP) and surface analyses with ozonesonde data (6-12/7/2010): Ozonesondes MOCAGE control simul. hPa Number of ozonesondes profiles available during the episode (14 totally, 11 in continental Europe) IASI corrects the free troposphere O 3 negative bias Surface obs correct the positive bias at the surface The combination of the two gives the best model profile up to 200 hPa Ozonesondes Surface obs. analysis Ozonesondes AIRBASE+IASI analysis Ozonesondes IASI analysis hPa Negative free troposphere bias Positive surface bias Satellite and surface O 3 analysis

12 NO 2 tropospheric columns from OMI (Aura) and GOME-2 (MetOP) assimilated in MOCAGE: - Surface bias reduction (10%) at rural sites in winter - Not significant impact observed in summer (too short NO 2 lifetime) Control run columnsSatellite columnsAnalysis columns Satellite NO 2 analysis

Conclusions and perspectives 13  European surface re-analyses well constrained by the dense AIRBASE network. O 3 re-analysis scores better than NO 2 and CO ones, what is the impact of observation error in validation exercises?  Forecasts skills depend on the species and the season. Reducing the delay from the last available analysis to 3h might reduce the forecast bias by a factor 3 (O 3 in summer).  A positive impact of correcting one species on the forecast of other species is not demonstrated. Need deeper investigation of the chemical system.  Assimilation of satellite data corrects model tropospheric columns, but positive impact at the surface is not clearly demonstrated. Need satellite products with enhanced boundary layer sensitivity.

14 Thanks for your attention Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique Toulouse, France