1. HyMeX http://www.hymex.org/ Email: hymex@cnrm.meteo.frhymex@cnrm.meteo.fr Véronique Ducrocq, Philippe Drobinski Chairs of HyMeX Executive Committee CNRM-GAME, Toulouse, and IPSL and coauthors

2. Mediterranean extreme events Heavy precipitation - Goals Satellite cloud top temperature 15 June 2010 – 8 UTC Mediterranean Sea is a key region for heavy precipitation formation, but almost void of observations Var (SE France), 15 June 2010 25 fatalities Damages: ~ 600 millions HyMeX will improve kowledge on local processes (cloud-aerosol interaction, moist flow interacting with complex terrain) HyMeX will improve documentation of the upstream condtions over the Sea ~ half of the humidity feeding the precipitating systems is extracted from the Mediterranean Sea Most of the initial development of the coastal precipitating systems occured offshore HyMex Goal: To advance the predictability of heavy precipitation (location, timing and amount of heavy precipitation) – in order to improve warning of these events – by quantifying and reducing uncertainties in the high-resolution numerical weather prediction systems (data assimilation, cloud processes representation,…)

3. Mediterranean extreme events Intense air-sea exchanges - Goals Mediterranean is a large complex terrain region particularly prone to wind gusts (regional winds, Mediterranean depression) Gulf of Genoa depression Impact on the marine ecosystems (nutriment vertical mixing) Impact on the Mediterranean water budget Impact on the Mediterranean Sea circulation Impact on the Atlantic water HyMex Goal: To advance the understanding of the high interanual variability of dense water formation through improvement of regional air-sea coupled models and observations to address the question of the evolution of DWF with the climate change

4. EOP: Enhanced existing observatories and operational observing systems in the target areas of high-impact events: budgets and process studies (+ dedicated short field campaigns) LOP : Current operational observing system and observatories over the whole Mediterranean basin: budgets (data access) SOP: Special observing periods of high-impact events in selected regions of the EOP target areas (aircraft, R/V, balloons,…): process studies A « Nested » approach to tackle the whole range of processes and interactions and to estimate budgets Observation Strategy SOP1: Heavy precipitation and flash-flooding SOP2: Intense air-sea exchanges (severe winds, dense water formation)

5. Fine-scale predictability (limited-area model) Uncertainties on meso-scale initial conditions Development of Ensemble forcasting systems at high-resolution (AROME) for HyMeX SOPs Uncertainties on synoptic-scale initial conditions and lateral boundary conditions Model errors convective-scale ensemble atmospheric forecasts Hydrometeorological ensemble forecasting (ISBA-TOPMODEL) Convective-scale predictability of Mediterranean Heavy Precipitation Events within HyMex: scientific issues Nuissier et al. - CNRM

6. TTM1-a High-resolution ensemble hydrometeorological modelling for quantification of uncertainties (implementation plan available at http://www.hymex.org) NameInstitution, Country emailSpecific task Davolio SilvioISAC CNR, Italys.davolio@isac.cnr.it Homar Sanatner VictorUIB, Spainvictor.homar@uib.esCoordination Montani AndreaARPA-SIMC, Italyamontani@arpa.emr.itCoordination / provision of COSMO-LEPS fields Nuissier Olivier CNRM-GAME (M é t é o-France & CNRS) olivier.nuissier@meteo.fr French contact/ provision of AROME- EPS fields B é atrice Vincendon CNRM-GAME (M é t é o-France & CNRS) Beatrice.vincendon@meteo. fr Provision of ISBA- TOPMODEL EPS fields

7. 1) LAM ensemble prediction systems (EPS) with parameterized convection and typical horizontal resolution of about 10 km, employed for short and medium range forecasts (up to 5 days). 1a) Analysis of the interactions between large-scale perturbations provided by the driving global systems and local perturbations specifically generated for the LAM EPS. 1b) Quantify the additional benefits of multimodel EPS (a synergy with TIGGE-LAM activity is recommended). 1c) Development of methodologies for generating perturbation to the initial conditions. 1d) Development of methods to account for uncertainties in soil/surface description. 2) Convection permitting ensemble prediction systems (CPEPS) with explicit convection and horizontal resolution of a few kilometres, employed for short range predictions (up to 48 hours). 2a) Study of the predictability at convective scale and development of procedures for generating ensemble perturbations at high resolution. 2b) Design and implementation of CPEPS. 2c) Evaluation of the performance (if any) and quantification of additional benefits of CPEPS vs LAM EPS. 2d) Assessment of the relative impact of uncertainties in larger-scale forcing, initial condition, model physics and lack of intrinsic predictability on forecast quality, in particular for high impact weather and heavy precipitation events. 2e) Quantify the additional benefits of multimodel CPEPS.

8. 3) Hydrological ensemble predictions. 3a) Implementation of meteorological ensemble pre-processing procedures to remove biases in the meteorological inputs and to downscale the meteorological information at the space and time scales relevant for hydrological applications. 3b) Implementation of hydrological ensemble driven by meteorological input provided by different atmospheric ensemble systems. 3c) Implementation of multi-model hydrological ensemble systems. 4) Calibration methods. 4a) Development of calibration techniques to remove biases and systematic errors, thus improving the reliability of ensemble systems. 4b) Development of hydrological post-processing and product generation procedures to remove complex biases from raw hydrological ensemble forecasts. 5) Verification methods. Assessment of forecast accuracy and reliability for raw and calibrated ensemble system (both meteorological and hydrological). Explore the value of fuzzy verification methods, object-oriented methods

9. List of models/system OrganisationModelMesh size (km) # of grid points # of levels Initial times and forecast ranges (h) Type of data assimilation Model providing LBC data LBC update interval (h) # of members ARPA-SIMC (I) for COSMO COSMO- LEPS 7511X4154012 +132hInterpolation from ECMWF EPS ECMWF EPS 3h16 CNRM-GAME AROME- EPS 2.5365X3776000,12 + ~ 30h3D-VarPEARP1h/3h8 (16) CNRM-GAMEISBA- TOPMO DEL EPS /// 00,12 + ~ 30h /Forcing : Either AROME- EPS Or perturbed deterministi c AROME rainfall fields / Either 8(16) Or up to 50