High-resolution modelling in mountainous areas:

MAP results

Evelyne RichardLaboratoire d’AérologieCNRS / Univ. Paul SabatierToulouse, France

What are the skills of high-resolution models to forecast orographically influenced precipitation?

Does explicit (versus parameterized) convection lead to a gain in predictability?

IOP 2a – 17 September 1999

A short, intense, isolated, convective event

70 mm within 12 hours

Sensitivity experiments performed with Meso-NH

MAP IOP 2a: IR Meteosat

MAP target area

18:00 UT 19:00 UT 20:00 UT

MAP – IOP 2AComposite radar reflectivity @ z = 2km

25

0 k

m

21:00 UT 22:00 UT 23:00 UT

Toce Ticino watershed

Observation

Simulation (Δx =2km)

20:00 UT

23:00 UT

Reflectivity @ 2000m

ECMWF: OP. ANA 1999ECMWF: OP. ANA 1999

RADAR

12 hour accumulated precipitation

SIMULATION

17:00 UT 18:00 UT

19:00 UT 20:00 UT

Composite radar reflectivity @ z = 2km

Z > 60 dBz

25

0 k

m

Radar Retrieval (S-Pol)

Simulation (Meso-NH)

(x) hail + graupel

(o) hail

graupel

hail

18:00 UT

19:00 UT

20:00 UT

rain rain

12

km

100 km

Great !

My model is doing a good job

ECMWF: OP. ANA ECMWF: OP. ANA 19991999

ARPEGE: OP. ANA. 1999ARPEGE: OP. ANA. 1999 ECMWF: OP. ANA. ECMWF: OP. ANA. 20022002

ECMWF: REANALYSISECMWF: REANALYSIS

MAP - IOP2A:

Intense Convection

Strong sensitivity to initial state

Low predictability

RADAR OBSERVATIONS

ANA. OP. 1999ANA. OP. 1999 REANALYSISREANALYSIS

850hPa water vapor mixing ratio : 17 September 1999 12UTC

IOP 2a

REANA (NO MAP REANA (NO MAP DATA)DATA)

ECMWF ANALYSIS MAP ECMWF REANALYSIS

Low-level convergence between the Ligurian and Adriatic flows

Increase in the model resolution -> higher mountains -> the Ligurian flow is blocked

Streamlines at 1000 m, 17/09/99 12 UTC

MAP IOP 2aLascaux et al., 2004

Prectitability ?Still a long way to

go !

IOP2b 20/21 September 1999:

Orographic enhancement of a frontal system

200 mm within 30h

Model intercomparison : MC2, MM5, MOLOCH,Meso-NH

How do the models compare with each other ?

19 Sept. 1999 12:00 20 Sept. 1999 12:00

METEOSAT infrared

Sensitivity to the analysis

ECMWF Op. Analysis MAP Reanalysis

Max: 512 mm Max: 482 mm Mean: 78 mm Mean: 87 mm

The different models:

• MESO-NH 10 KM + 2.5 KM

• MOLOCH 10 KM + 2 KM

• MM5-RE 27 KM + 9 KM + 3 KM

• MM5-E1 18 KM + 6 KM + 2 KM

• MC2 40 KM + 10 KM +2 KM

Initial and boundary conditions from ECMWF operational analyses

From 19 Sep. 12 UTC to 20 Sep. 18 UTC

(30 hours)

MAP - IOP2B - 19-20 Sep. 1999

Intercomparison exercise

4 non-hydrostatic models with horizontal resolution of 2 to 3 km

Initialization based upon ECMWF operational analysis

Accumulated precipitation from the 19th 15 UTC to the 20th 18UTC

Toce-Ticino watershed

Rain gauges

Time evolution of the mean hourly precipitation rate

Radar

Time evolution of the correlation coeffecient (wrt rain gauges)

Heidke skill scores as a function of precip. class

1h precip. 27h precip.

Comparison with rain gauge measurements (121 points)

Correlation 1h

Correlation 6h

Correlation 27h

Mean Bias

MESONH 0.33 0.49 0.62 + 28 % MOLOCH 0.31 0.47 0.62 - 22 % MM5-RE 0.27 0.43 0.55 - 16 % MM5-E1 0.37 0.53 0.63 + 28 %

MC2 0.30 0.47 0.63 - 32 % RADAR 0.48 0.55 0.60 - 54 %

Toce watershed

1532 km2

Hydrological response

Grossi et al., 2004

Grossi et al., 2004

How does the flow over complex terrain modify the growth mechanisms of precipitation particles?

Three Doppler radars

Monte Lema – Ronsard – S Pol

• Dual Doppler analysis

• 3D wind fields rerievals

• Microphysical retrievals

Monte Lema

S Pol

Ronsard

Medina and Houze, 2003Medina and Houze, 2003

U/Nh < 1

U/Nh > 1

dry snow

wet snow

light rain

graupelriming

heavy rain

coalescence

Blocked and stable case

Unblocked and unstable case

To what extend the models able to reproduce this contrasted behaviour in the microphysics ?

SnowGraupel

Hail

Cloud Rain

Ice

IOP2A

IOP2a ( Strong convection)- Deep system-Large amount of hail and graupel

Mean vertical distribution of the hydrometeors

IOP8 ( Stratiform event)- Shallow system- Large amount of snow

IOP8

Snow

Dominant microphysical processes:

DEPOSITION on ice(and sublimation)

Growth of graupelby RIMING

AUTOCONVERSIONof pristine ice

MELTING-CONVERSIONof the snow (into graupel)

ACCRETION of cloud dropletsby raindrops

Depletion of graupel by

WET GROWTH of hail

IOP2a IOP8

Conclusion:

IOP 2A (Predictability)

The use of non-hydrostatic high-resolution models will improve the precipitation forecast but only to some extend.

Further improvement is tied to the improvement of the model initial state

Adding mesoscale data in a global assimilation system is insufficient

•Mesoscale data assimilation system

•Limited area ensemble forecast

(MAP D-PHASE)

IOP 2b (Model Intercomparison)

– Very good consistency of the accumulated precipitation pattern

– Model results over/under estimate the total precipitation by a factor ranging from +30% to -30%

– The accuracy of the model precipitation is rather weak for the hourly rainfall but fairly reasonable for the precipitation accumulated over the 30h time period of the event

– However model results are not yet accurate enough to be used for hydrological forecast on small watersheds

Explicit microphysical schemes do provide fairly realistic results …

The contrasted microphysical behaviour between different IOPs is reasonably reproduced

• Convective – flow over ----> Strong riming and coalescence

• Stratiform – blocked flow ----> Melting of snow

http://www.aero.obs-mip.fr/map/MAP_wgnum

(1) LA CNRS/UPS, Toulouse, France

(2) CNRM, Météo-France, Toulouse, France(3) RPN, Montréal, Canada

(4) ISAC, CNR, Bologna, Italy(5) University of L ’Aquila, Italy(6) University of Milano, Italy

(7) University of Munich, Germany(8) University of Brescia, Italy

(9) University of Waterloo, Canada

N. Asencio (2), R. Benoit (3), A. Buzzi (4), R. Ferretti (5), F. Lascaux (1), P. Malguzzi (4), S. Serafin (6), G. Zängl (7), J-F. Georgis (1), R. Ranzi (8), G. Grossi (8), N. Kouwen (9)