1. Report on the 9th HyMeX workshop: science team on data assimilation and ensemble prediction systems 5-year Science Review N. Fourrié

2. SOP2: Intense air-sea exchanges (severe winds, dense water formation) 1 Feb- 15 March 2013 SOP1: Heavy precipitation and flash-flooding 5 Sept-6 Nov 2012 Better quantification of the hydrological cycle and related processes in the Mediterranean, with emphasis on high-impact weather events, inter-annual to decadal variability of the Mediterranean coupled system, and associated trends in the context of global change.. More than 200 instruments deployed About 300 scientists on the field HYdrological cycle in the Mediterranean EXperiment  9th HyMeX workshop in Mykonos island, 20-25 September 2015 : 5-year review and prospectives for the next 5 years.

3. Results Mesoscale data assimilation within cloudy and precipitating systems  Assimilation of field campaign research observations (New observation types : lightning data, assimilation of Hu and T profiles from ground based MW radiometers, airborne and groudbased water vapour lidar) -> AROME-WMED SOP real-time and reanalyses, impact studies (Fourrié et al, 2015; Caumont et al, 2015)  Operational Radar data assimilation : Assimilation of Spanish radars in AROME, weather radar refractivity, polarimetric weather radar observations (Augros et al, 2015)  Satellite data assimilation : assimilation of cloudy radiance and impact on the analysis of cloud parameters (Martinet et al, 2013, 2014ab)Moisture monitoring

4. Results Predictability of HPEs  Background error covariances for HyMeX events (Menetrier et al, 2014, 2015b), benefit of a rapid update high-resolution data cycle assimilation.  Hydrometeorological ensemble forecasting : evaluation of convection-permitting ensemble forecasts (Nuissier et al, 2015; Bouttier et al, 2015)  Data impact studies : DTS radiosoundings (Campins and Navascués, 2015)

5. Prospective New challenges: prediction at very short range and nowcasting in terms of DA and EPS for HPEs and flashfloods. Ambitious topics: development of coupled data assimilation system for atmosphere/ocean, … with coupling between atmospheric, hydrological and ocean models in order to build an integrated forecast systems. Work on observation operator (ground based microwave sounders, radar refarctivity, fual polriztion radar data, airborne cloud radar data and lightning data sensitive to microphysics Development of ensemble variational data assimilation. Methods to generate background perturbation, localization procedures and test of hybrid formulations. Easier inclusion of hydrometeors in the control variable.Allowing progress in the assimilation of obs. performed within precipitating and non- precipitating clouds. Verification topics should be considered and new method such as field alignment methods, structure detection, texture of precipitating fields should be developed for the forecast assessment.

7. Real time version of AROME-WMED Run from September 2012 to March 2013. 3D-Var analysis + 48h forecastat 00UTC. Data in the HyMeX data base, used for the coupling with oceanic and hydrological models (Fourrié et al 2015, GMD). Example of IOP8 Heavy precipitation in Southern Spain with strong damages Andalucia, Murcia and Valencia areas. Simulation of the 3 maxima (even if not well located) at the 48- hour forecast range. 48h-forecast 24h-forecast 24h-precipitation observations

8. Assimilation of new observations: airborne Lidar data LEANDRE3 Lidar observations were assimilated as humidity profiles in AROME-WMED. Assimilation of Lidar data improve the location of the system and its chronology by delaying the formation of bow shape Reflectivity Sim. Reflect.at 800 hPa I.C. AROME Sim reflectivity I.C. AROME-WMED Sim reflectivity I.C. AROME-WMED with Lidar assim

9. Assimilation of humidity and temperature profiles from ground-based microwave radiometers (1/2) MWR data, period, and model 13 MWR stations from MWRnet members 1 humidity profiler (red) 4 temperature profilers (blue) 8 temperature and humidity profilers (purple) 15 October 2011 to 25 November 2011 (41 days) AROME-WMED with 3DVar every 3 h Observation-minus-background statistics Biases (black) can be large Standard deviations (grey) of the same order of that obtained with radiosondes Cimini et al. (Proc. 9th ISTP, 2012), Cimini et al. (MicroRad 2014), Caumont et al. 2015 (subm. QJRMS)

10. Assimilation of humidity and temperature profiles from ground-based microwave radiometers (2/2) Data assimilation experiments CTRL: assimilation of operational data only DA_T: as CTRL + MWR-derived temperature DA_Q: as CTRL + MWR-derived humidity DA_TQ: as CTRL + MWR-derived temperature and humidity Evaluation Skill scores over whole period show neutral impact Sometimes improvements in QPF Conclusion MWR data can be safely assimilated Sparse network?  improve quality and/or need for direct assimilation of brightness temperature Cimini et al. (Proc. 9th ISTP, 2012), Cimini et al. (MicroRad 2014), Caumont et al. 2015 (subm. QJRMS) 24-h accumulated precipitation from 5 Nov 2011, 06 UTC to 6 Nov 2011, 06 UTC  QPF closer to observations

11. rain gauges NO DA Impact of mesoscale data assimilation ALL Conventional and satellite data used in ALARO DA IOP2 and IOP16 – both over 200mm/24h in Croatia With data assimilation (w. targeted radiosoundings) accumulation maxima are better simulated. For both IOP16 and IOP2 precipitation structures are better forecasted. IOP 2 A. Stanesic, K.Horvath, S. Ivatek-Sahdan, T. Kovacic, B. Ivančan-Picek

12. Mesoscale data assimilation within cloudy and precipitating systems Radar assimilation Assimilation of spanish radars in AROME Data impact weather radar refractivity polarimetric weather radar observations, Satellite assimilation assimilation of cloudy radiance and impact on the analysis of cloud parameters.

13. Prev. OPER : 2.5 km L60OBS OPER : 1.3 km L90 OPER + AEMET RADAR AROME-WMED 2.5 km L60 18h forecasts valid at 06 UTC  Assimilation of spanish radars in AROME 30 th of Sept. 2014 case : different AROME versions fail in forecasting the end of a strong convective event in a SW flow over the Languedoc adding 2 elevations of Z+DOW from 12 spanish radars improve notably the results T. Montmerle (MF) – C. Geijo (AEMET) (P. Brousseau, N. Fourrié (MF)) Toward the use of Eumetnet’s OPERA radar data in NWP systems

14. Effects of Multiple Doppler Radar data assimilation using WRF 3DVAR Preliminary results : positive impact comes from the experiments where all the 3 radars were assimilated both on the lowest and the highest resolution domains. CTL 24h accumulated rainfall ending at 00UTC on 15 September 2012 over Central Italy both observed and estimated by WRF experiments CON_assim3km CON_assim12km CON_assim2 CONMM_assim3km CONMM_assim12km CONMM_assim2 CONMMPOLSPC_assim3km CONMMPOLSPC_assim12km CONMMPOLSPC_assim2 I. Maiello, R. Ferretti, L. Baldini, N. Roberto, E. Picciotti, S. Gentile, P.P. Alberoni, F. S. Marzano

15. Polarimetric radar data assimilation in Arome Radar Zhh (dBZ) elevation 1.2° C. Augros 1D+3Dvar assimilation method (Caumont et al. 2010, Wattrelot et al. 2014) - First step : retrieval of vertical profiles of pseudo-observations (PO) of humidity from Zhh, Zdr and Kdp vertical profiles - Second step : assimilation of relative humidity in 3Dvar Model IWV (kg/m 2 ) PO-Model IWV (kg/m 2 ) PO IWV (kg/m 2 ) IWV = integrated water vapor (kg/m 2 ) Nîmes radar 24/09/2012 at 03 UTC - AWMED 2.5 km

16. Merci de votre attention background Analysis Improvement of the cloud fraction over the whole troposphere Ice RMSE liquid water RMSE cloud fraction RMSE Martinet, P et al (2013), Q.J.R.M.S., Martinet, et al. (2014), Q.J.R. M.S., Martinet et al, (2014). ASL Cloudy IASI radiance assimilation Cloudy IASI observations allow to extract information on cloud fraction and cloud parameter profiles in a 1D context. With cloud fraction in the control variable, it is possible to create cloud layers where there are absent in the background Persistence of cloud information during the first ranges of the forecast in a simplified framework.

17. Predictability of HPEs  Background error covariances  Hydrometeorological ensemble forecasting  Data impact studies  Assess the benefit of rapid update high-resolution data assimilation cycle. (See P.Brousseau talk)

18. Achieved work within HyMeX for algorithmic developments in data assimilation  Large ensembles at convective scale Experimenting with large ensembles of data assimilations (100 members of AROME cycled 3D-Var at 2.5 km) Documented the background errors for HyMeX events (Menetrier et. al. 2014) Work on surface perturbations including SST

19. Achieved work within HyMeX for algorithmic developments in data assimilation  A filtering theory for background error covariances – Developped a general theory for filtering sampling noise in the covariances (Menetrier et. al. 2015a) – Applied it to AROME for a HyMeX event (Menetrier et. al. 2015b), using large ensembles as a reference. – Experimented with a 3D-Var using ensemble-derived variances, but with mixed results (last workshop) σ b for specific humidity at the surface (kg/kg).

20. Evaluation of two convection-permitting ensemble systems during HyMeX SOP1  Comparable resolution for both ensembles, but better discrimination for AROME-EPS.  HSSs are comparable but AROME-EPS has a better frequency bias. Heidke Skill Score Frequency bias Résolution (Brier) ROC AREA - 2 ensembles AROME-EPS vs. COSMO-H2-EPS -Evaluation over a 53 day period of SOP1 (6 sept. to 5 nov. 2012) - 2 verification domains Forecast and verification domain Nuissier et al, 2015

21. Evaluation of two convection-permitting ensemble systems during HyMeX SOP1 -Predictability of HPE during IOP 16a (26/10/2012) 3-h accumulated surface precipitation valid at 15:00 UTC on 26/10/2012, simulated (coloured areas) and observed (cercles) and 10-m wind (arrows), mslp (shaded areas),  v (dashed lines) and moisture flux at 925 hPa AROME-EPS COSMO-H2 Impact of a surface low pressure and deflected low-level flow around Alps (better represented in AROME-EPS) advecting strong moisture towards Var. Key role of low-level convergence between cold air from Pô valley and a warm and moist low-level flow, nearly missing in COSMO- H2. VAR LIGURIA TUSCANY AROME-EPS Nuissier et al 2015, submitted to QJRMS.

22. Impact of Data Targeting System (DTS) observations Objective: to improve short-range forecast of high impact weather events in the mediterranean area (heavy precipitation,…) From 11 September 2012 to 30 October 2012: 53 cases, 403 additional observations supported by EUCOS launched at 06UTC and 18UTC for selected days (373 additional radiosoundings in the AROME-WMED domain from 2012-09-11 18UTC to 2012-10-30 06UTC) = DTS soundings Blue: operational network / Orange: HyMeX research mobile radiosounding (CNRM) Red: HyMeX research radiosounding (KIT) / xx: additional DTS soundings

23. experiments: the baseline (CTRL), extra radiosoundings (DTS_RS), double density of atovs in sensitive regions (ATOVS2_B) extra radiosoundings and double dens. of atovs in sensitive regions (ATOVS2_RS). Equitable threat score for 12-h accumulated precipitation for all the cases registered during SOP1 Mean relative RMS (in %) for different param. & obs. sys. Exp. Improvements larger than 1.0 % standed out in green boxes and deteriorations lower than -1.0 % in red boxes. Statistically significant results marked with a *. Extra RS improve first guess quality over land and extra sat. data in sensitive area (Campins and Navascués, 2015, QJRMS submitted.) DTS radiosoundings and research radiosondes Impact of Data Targeting System (DTS) observations

24. Test of the EnKF algorithm with WRF coded in DART Carrió and Homar (IUB) AT UIB, by means of sensitivity experiments using a prototypical Ensemble Kalman Filter system, the contribution of standard operational observational means to ameliorate the low-level maritime environment and thus improve the simulation of the initiation and maritime evolution of mesoscale convective systems is investigated. Observation assimilation behind the front produces a forecast with a better agreement with verification observations. IOP13: Incremental energy (kinetic+internal) between EnKF and NOA

25. Study of the « economic » value of Météo-France forecast systems in terms of rain forecasting during HyMeX SOP1 To objectively quantify the « economic value » of AROME-EPS based on probabilities of occurrence The economic value of AROME-EPS higher when HyMeX SOP1 observations included into the verification sample and if the calculation is made over the Mediterranean regions. The highest Economic Value Area (EVA) is obtained over the Italian « LT » domain (Liguria-Toscana + Corsica + Sardegna). It is lower over « CV » domain (Cévennes-Vivarais, South- Eastern France) and « CAT » domain (Catalonia + Balearic Islands, Spain). E. Chabot and F. Bouttier