nwp and amma case studies

NWP and AMMA case studies

J.-P. Lafore, F. Beucher, F. Pouponneau, F. Rabier, C. Faccani, N. Fourrié,

F. Karbou, P. Moll, M. Nuret, J-L Redelsperger, J. Stein

CNRM-GAME, Météo-France and CNRS, Toulouse, France

With thanks to:

A. Agusti-Panareda, P. Bauer (ECMWF, Reading), O. Bock, P. Drobinski

(IPSL/LMD and LATMOS, France), G. Berry, C. Thorncroft (SUNNY, US), W. Thiaw (NCEP, US), J. Heming (UKMET), R.D. Torn (NCAR, U. New York), J-B

Ngamini (ASECNA, Senegal), Z. Mumba (ACMAD, Niger)…

WWRP-THORPEX African Regional Committee meeting8-10 May 2012, Geneva, Switzerland

évaluation AERES 15-17 janvier 2008

2

1. Some NWP Scores Extra-Tropics (1/3)

1980 1990 2000 2010

Always in progress!Reduction of dispersion

Europe Atlantic domain

Geopotential Z - RMS (m) @ 500 hPa

Range


3

1. Scores for Tropics: wind field V (2/3)

2000 20051995 2010

72h

• Wind intensity is a more pertinent variable in the Tropics• Its RMS @72h is large ~5 m/s 850 hPa) and increases with altitude (~8 m/s 250 hPa) • Dispersion between models is ~1 to 2 m/s (850 to 250 hPa)• Progresses are slow!

Tropiques/RS (55) RMS of V (m/s) @ 250-850 hPa @ 72h range (1995-2010) range 1 to 10 days

V850

V250

• Wind RMS error against TEMP observations increases fast with the forecast range at the same rate for all models

V250

V85072h


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EUMETSAT (28-30 June 2011)WMO RAI Dissemination Expert Group 2nd Meeting

1. African Eaterly Waves scores for 4 NWPs in 2007 (3/3)Correlation coefficients as fct of the range of 700 hPa curvature vorticity

Private communication by Gareth Berry et al. 2008

• Weak forecast skill for AEWs ( 2 days)• Large dispersion between models for the Wave-Convection link and the variability

at 3 longitudes


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2. AMMA opportunity (1/7)

AMMA-1 International project 2002-2012 (Redelsperger et al. 2006) http://amma-international.org/

AMMA legacy:– Better understanding of the West African Monsoon

– Observations of the WAM: improvement of the operational observation network (soundings…), GPS, driftsondes, surface conditions, satellite, research observations (lidar, radar, aircraft…)

opportunity to evaluate NWP models

and the impact of observations

30th Conference on Hurricanes and Tropical Meteorology (15-20 April 2012)

http://amma-international.org/




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2. Impact on quantitative prediction of precipitation over Africa (2/7)

Higher scores for AMMABC

Lowest scores for NO AMMA

CNTR: data from GTS

AMMA: from the AMMA database

AMMABC: AMMA + bias correction

PreAMMA: with a 2005 network

NOAMMA: No Radiosonde data

Faccani et al, 2009

• Positive impact of the assimilation of AMMA dataset• Very poor performances of NOAMMA• Best performance of AMMABC


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2. Downstream impact (3/7)

Impact on geopotential at 500hPa, averaged over 45 days 72hr forecasts: AMMABC vs PREAMMA

Faccani et al, 2009


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2. AEW case study in 2006: Pre-Helene TS Torn (2010) (7/7)

• Ensemble-Based (96 members) Sensitivity Analysis (EnKF)• WRF model (36-12-4 km)• Sensitivity to: initial state, convection schemes, resolution

Rain, Curvature Vort. (mean, var)

1. Weak skill (<2 days)2. Initial state

• Wave: at early stage• mid-layer e: later

3. Better for CRM

1.

2. PropagationGrowing ratePDF PDF

3.


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3. CRMs: AEWs - Convection (1/2)

AMMA well-documented case 23-29 July 2006 Barthe et al., 2010, Cuesta et al. 2010 – Monsoon surge + AEW + Convection

Observations AROME (CRM @ 5 km) ARPEGE

RR (mm/h) + Vm (m/s)

• NWP at low resolution: Wrong diurnal cycle and AEW-convection coupling• High-resolution (CRM): Better representation of the AEW-convection link


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3. CRMs: precipitation distribution (2/2)

Precipitation (latitude)

ARPEGE


Different distributions of precipitation

• Meridional distribution• Rain regimes:

• ARPEGE: weak events are too frequent intense events are rare• CRM: distribution of events in better agreement with TRMM

• QPF scores • improved for CRM• positive impact of data assimilation (AMSU-B)

Rain regimes contribution to total precipitation


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Coupling with Dust

AROME (5 km) coupled with a dust module on a large domain

Evaluation on the March 2006 dust storm (Kocha et al 2011)

– Positive feedback of dust on the cold surge intensity

Simulation of the whole June 2006 month

– Diurnal cycle, dust lifted by convective wakes

– Negetive feedback on the Heat Low

Forecast in June 2011 during the FENNEC experiment

– Good forecast skill of convective dust storm

00UTC

Wind, Dust extinction @ surfaceMSG: aerosols - clouds


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Conclusion

Poor NWP skill over Tropics and especially over Africa is as compared with extra-tropics

Due to large Roosby Radius (non-balanced flow, except TC),

to the lack of observations and

to the key role played by the physics (dry and moist convection, surface, radiation, turbulence, aerosols…)

Nevertheless large scale thermodynamical and dynamical structures are rather well depicted and are very useful for forecasters.

Major progresses in recent years especially in the assimilation area (microwave data) and the dispersion between models decrease

Nevertheless the forecast skill of the water cycle and of precipitation progresses very slowly

CRMs (few km) improve drastically some aspects of forecasts relative to convection (life cycle, duration, propagation…), the diurnal cycle, dusts…

Need to improve the representation of convection (dry air issue for Africa) and its coupling with AEWs, surface, aerosols…

Intra-seasonal variability potential predictability to be exploited


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Further work

Treatment of the Ougadougou flood case (2009) Comparison of different CRMs:

– COSMO (KIT, Germany)– AROME (Météo-France)– New metrics (MCS tracking…)– Ensemble simulations (COSMO)

Analysis at different scales (link with the ISV) Predictability

– Wave-convection link– Coupling with the surface




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Some References

Agustí-Panareda A, Beljaars A, Cardinali C, Genkova I, Thorncroft C. 2010a. Impact of assimilating AMMA soundings on ECMWF analyses and forecasts. Wea.Forecasting 25: 1142–1160. doi: 10.1175/2010WAF2222370.1.

Andreas H. Fink et al., 2011: “Operational meteorology: observational networks, weather analysis and forecasting”. Atmospheric Science Letters, Volume : 12, Issue : 1, Special Issue : Sp. Iss. SI, Pages : 135-141. Doi : 10.1002/asl.324

Faccani C, Rabier F, Fourri´e N, Agust´ı-Panareda A, Karbou F, Moll P, Lafore JP, Nuret M, Hdidou FZ, Bock O. 2009. The impact of the AMMA radiosonde data on the French global assimilation and forecast system. Weather and Forecasting 24: 1268–1286.

Karbou F, Rabier F, Lafore JP, Redelsperger JL, Bock O. 2010b. Global 4D-Var assimilation and forecast experiments using AMSU observations over land. Part II: impact of assimilating surface sensitive channels on the African Monsoon during AMMA. Weather and Forecasting 25: 20–36.

Redelsperger J-L, Thorncroft CD, Diedhiou A, Lebel T, Parker DJ, Polcher J. 2006. African Monsoon Multidisciplinary Analysis: An international research project and field campaign. Bull. Am. Meteorol. Soc. 87: 1739–1746.

Torn R. D. 2010: Ensemble-Based Sensitivity Analysis Applied to African Easterly Waves.. Weather and Forecasting 25: 20–61-78. doi: 10.1175/2009WAF2222255.1



nwp and amma case studies

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