HyMeX

http://www.hymex.org/Email: hymex@cnrm.meteo.fr

Véronique Ducrocq, Philippe DrobinskiChairs of HyMeX Executive Committee

CNRM-GAME, Toulouse, and IPSL

and coauthors

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 201025 fatalities

Damages: ~ € 600 millionsHyMeX 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,…)

Mediterranean extreme events

Intense air-sea exchanges - GoalsMediterranean 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

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-floodingSOP2: Intense air-sea exchanges (severe winds, dense water formation)

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

issuesNuissier et al. - CNRM

TTM1-a High-resolution ensemble hydrometeorological modelling for quantification of uncertainties (implementation plan available at http://www.hymex.org)

Name Institution, Country

email Specific task

Davolio Silvio ISAC CNR, Italy s.davolio@isac.cnr.it  

Homar Sanatner Victor UIB, Spain victor.homar@uib.es Coordination

Montani Andrea ARPA-SIMC, Italy amontani@arpa.emr.it Coordination / provision of COSMO-LEPS fields

Nuissier OlivierCNRM-GAME

(Météo-France & CNRS)

olivier.nuissier@meteo.frFrench contact/

provision of AROME-EPS fields

Béatrice VincendonCNRM-GAME

(Météo-France & CNRS)

Beatrice.vincendon@meteo.fr

Provision of ISBA-TOPMODEL EPS fields

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.

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  

 

List of models/system 

Organisation Model Mesh 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

7 511X415 40 12 +132h Interpolation from ECMWF EPS

ECMWF EPS

3h 16

CNRM-GAMEAROME-

EPS2.5 365X377 60 00,12 + ~ 30h 3D-Var PEARP 1h/3h 8 (16)

CNRM-GAME ISBA-TOPMO

DEL EPS

/ / / 00,12 + ~ 30h / Forcing : Either

AROME-EPSOr

perturbed deterministi

c AROME rainfall fields

/  Either

 8(16)

Or 

up to 50