An ensemble study of HyMeX IOP6 and IOP7a Alan Hally (1,2), Evelyne Richard (1), Véronique Ducrocq...

An ensemble study of HyMeX IOP6 and IOP7a

Alan Hally(1,2), Evelyne Richard(1), Véronique Ducrocq(2)

(1) LA, University of Toulouse, France

(2) CNRM, Météo-France, Toulouse, France

WWOSC2014, 16th-21th August 2014, Montreal - Canada

Motivations• Numerical forecasting of heavy precipitation events

(HPEs) has progressed immensely, but accurate and timely precipitation forecast for flash-flood prediction still remains a challenge

• Initial and boundary conditions have great importance

• Errors in the parameterisation of physical processes can lead to incorrect descriptions of convection

Ensemble study of IOP6 and IOP7a: Sensitivity to physical and IC/BC uncertainties

Methodology (1)

• All simulations undertaken with Meso-NH• Initial (IC) and boundary condition (BC) uncertainties

investigated using 4 different sets of conditions– Météo-France AROME analyses (2.5km)(Label – AR)– HyMeX-dedicated AROME-WMED analyses (2.5km)(WM)– ECMWF analyses (~16km)(WF)– ARPEGE analyses (10.5km over France)(AP)

• Ensembles labelled ICBC(6)(7a) depending on the IOP


Methodology (2)• Perturbations are introduced upon the microphysical time

tendencies as follows

jjmic PROCrt

r

j

)*)(

(


Microphysical time tendency of any water specie

Random multiplication factor

Source/sink of microphysical process perturbed

Ice

Snow

Graupel

Cloud droplets

Raindrops

Source and sink of each warm microphysical process perturbed by same factor

Methodology (3)

• r has a value in the range 0.5 – 1.5 for perturbing microphysics and turbulence tendencies


• Perturbations are introduced upon the turbulent tendency of any state variable X as follows

i

j

turb

x

Xur

t

X

)''()(

Turbulent time tendency of state variable X Random multiplication factor

Turbulent flux of X

Each turbulent flux term perturbed by same perturbing factor

• Physical process ensembles contained 10 perturbed members + CTRL run

• The most accurate member of the ICBC ensemble served as CTRL run for physical process ensembles

• Warm microphysical processes perturbed in ensembles labelled WA(6)(7a)

• Warm and cold microphysical processes simultaneously perturbed in WC(6)(7a) ensembles

• Warm and cold microphysical and turbulence processes simultaneously perturbed in MT(6)(7a) ensembles


Description of Ensembles

Model & Simulation set-up

• Meso-NH– 288 x 288 point grid– 2.5km horizontal resolution– 4s time step– ICE3 microphysics– 1D turbulence (Cuxart et al.)

• IOP6 – Simulations initialised 23rd Sept at

18UTC– 24 h simulation period

• IOP7a– Simulations initialised 26th Sept at

00UTC– 24 h simulation period


Description of Case Studies – IOP6

On the evening of the 23rd of September and into the early hours of the 24th, a trough led to low-level conditions favorable to convection over the SE Massif Central mountains with moderate to weak low-level flow. This instigated an intense and fast-moving convective line, with a convective rainfall peak at 02UTC.

Z + T 500hPa (18UTC) θ and winds at 950hPa (18UTC)


Description of Case Studies – IOP7a

In the early hours of the 26th of September, a cold front approached Western Mediterranean areas with warm and moist low-level southerly flow ahead of it. Over the target area, a convective system appeared in the early morning peaking at 08UTC in the presence of strong low-level flow. The passing of the front brought a second rainfall peak at 17UTC.


Z + T 500hPa (18UTC) θ and winds at 950hPa (18UTC)

Results – IOP6 – ICBC Ensemble


All simulations displace convective line to the north over the Cévennes.

AR the only simulation to simulate rain in the Gard department

AR WM

WF AP

Results – IOP6 – ICBC Ensemble


AR most correctly simulates the intensity of the peak.

AR also gives the highest spatial correlation and correct standard deviation with the OBS

All simulations display a time delay of 3 h with the OBS.

Results – IOP6 – Physical Process Ensembles


MT6

WC6

WA6

WARMCOLDTURBULENCE

WARMCOLD

WARM

Results – IOP6 – Physical Process Ensembles


MT6WARMCOLDTURBULENCE

WC6WARMCOLD

WARMWA6

Results – IOP7a – ICBC Ensemble


Convective Frontal Passage

AR WM

WF AP

All simulations succeed in capturing both convective line and frontal rainfall.

WM simulation pushes convective line to the east of observed area.

WF simulation over-estimates intensity of convective peak.

Results – IOP7a – ICBC Ensemble


Both peaks seen by all simulations.

AR and WF give highest spatial correlations with the OBS.

AR gives most accurate frontal peak.

No time delay with the OBS as in IOP6.

Results – IOP7a – Physical Process Ensembles


MT7a

WC7a

WA7a

WARMCOLDTURBULENCE

WARMCOLD

WARM

Results – IOP7a – Physical Process Ensembles


MT7aWARMCOLDTURBULENCE

WC7aWARMCOLD

WARMWA7a

Sensitivity to the Different Perturbations


WARMCOLDTURBULENCE

INITIAL AND BOUNDARY CONDITIONS

IOP6

IOP6 IOP7a

IOP7a

Conclusions & Perspectives• IOP6 (low model skill and moderate to weak low-

level flow) more sensitive to physical perturbations than IOP7a (high model skill and strong low-level flow)

• Dispersion introduced in surface rainfall of ICBC and physical perturbation ensembles comparable when model skill and low-level flow are weak (IOP6) but not when both are high (IOP7a)

• Results published in NHESS : Hally et al (2014)• Perform a similar analysis for less synoptically forced

cases


Thank You

An ensemble study of HyMeX IOP6 and IOP7a Alan Hally (1,2), Evelyne Richard (1), Véronique Ducrocq...

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