Météo-France status report Hervé Roquet (DP/CMS) & Bruno Lacroix (DPrévi/COMPAS) Météo-France status report Hervé Roquet (DP/CMS) & Bruno Lacroix (DPrévi/COMPAS) Montréal 26/5/ th North America / Europe Data Exchange Meeting Models  Last modifications  Impact of Cut-off time on forecast  Use of data  Meteosat data (geowinds)  AMSU-A data  HIRS radiances added to AMSU-A  AMSU-B  AIRS  Plans Models  Last modifications  Impact of Cut-off time on forecast  Use of data  Meteosat data (geowinds)  AMSU-A data  HIRS radiances added to AMSU-A  AMSU-B  AIRS  Plans

FUJITSU VPP5000: Second half-year 2003 a new configuration with 124 processors (instead of 31) = ex ECMWF computer. 2 separate machines: –60 processors for operations –64 processors for research Supercomputer configuration

June 2003 increase of average model resolution T L 199 to T L 358 reduction of stretching factor 3.5 to 2.4 reduction of minimisation resolution T L 161 to T L 149 December 2003 Use of HIRS raw radiances in 4DVAR Tuning of AMSU-A + weekly run up to 120H based on 00UTC long cut-off (MFSTEP) January 2004 New solver for 4DVAR minimization reduction of iteration numbers (45->40, 20->15) March 2004 New very short cut-off (1H) May 2004 New radiation scheme (FMR15) Last modifications : ARPEGE model

Global model up to 102H at 00UTC, (72H at 06 and 12UTC, 60H at 18UTC) ARPEGE global spectral model T L 358 C2.4 L41  41 levels, up to 1hPa, resolution 23km to 133km  Linear grid with T240 C2.4 orography  3processors for ARPEGE forecast (20’ for 24H forecast) 4DVAR assimilation :  2 loops of minimization T107 C1 L41 (40 it.), T149 (15 it.) Last loop with simplified physics  16 processors for assimilation (30’ between cut-off and P0) data used:  SYNOP, SHIP, BUOY, AIREP, AMDAR, ACARS, TEMP, PILOT  SATOB GOES 9, 10, 12 + BUFR Meteosat 5, 7  HIRS NOAA16 & 17, AMSU-A NOAA15 & 16  SST 0.5 degree from NCEP/NESDIS + SSM/I sea ice mask Models configuration

Previous configuration T298 C3.5 from 19 to 233km Since June 2003, T358 C2.4 from 23 to 133 km

ARPEGE-Métropole, very short cut-off ( 1H at 00UTC ) 5 runs a day and 9 analyses on 6 hour windows [H-3h ; H+3h[ 4 short cut-off analyses to run short range forecasts 1h50 at 00UTC (P102H) and 12UTC (P72H) 3hat 06UTC (P72H) and 18UTC (P60H) 4 long cut-off analyses to produce first guess (7-8h) 1 very short cut-off 1H at 00UTC (P36H) Guess Analysis Short cut-off 18UTC Forecast 60H Analysis long cut-off 18UTC Guess Analysis Short cut-off 00UTC Forecast 102H Analysis long cut-off 00UTC Guess Analysis Very Short cut-off 00UTC Forecast 36H

Impact of 1H cut-off on the forecasts = = experiment : 46 forecast runs at 00h up to 36 H with a 1-hour cut-off and short cut-off first guess compared to 1H50 operationnal run geopotential, verified against radiosondes data  No significant degradation over Europe

Soundings available for 1H50 and 8H cut-off period

Number of available soundings since 1993

Impact of 8H cut-off on the forecasts = = experiment : 23 forecast runs at 00h (once a week from 1/12/2004) with a 8-hour cut-off compared to 1H50 operationnal run (same assimilation) geopotential, verified against radiosondes data  Significant improvement over Europe

The improvement can be seen almost every day. It is mainly given by the late ATOVS data  Importance of the EARS data

Regional model up to J+2 06UTC (at 00, 06, 12 and 12UTC)  ALADIN spectral limited area model  9.5 km resolution on 2740kmx2740km domain.  No data assimilation: it is a dynamical adaptation model.  Many coupling files Tropical model 72H range at 00 and 12 UTC  ARPEGE uniform model (T L 358 C1 L41) ~55km  4D-VAR analysis configuration T L 107 L41 (40/15 it.)  Use of simple bogussing and more SATOB data Seasonal Forecast 4 months range  ARPEGE T63 C1 L31 configuration ARPEGE-Climat  9 runs a month from ECMWF analysis + SST forcing  Experimental Short Range Ensemble Prediction System 60H range  10 runs ARPEGE T L 358 C2.4 L41 (control = oper)  Based on singular vector perturbation Models configuration (follow up)

Area for verification Short range ensemble Forecast (ARPEGE) Targetting over Atlantic Ocean and Western Europe Optimization time window : 0-12h Total Energy norm (initial and final) 16 first SVs No physics SV computation with a T63 regular truncation

AnalyseControle ARPEGECEPTropiquesAVARC Vignettes

Brier Skill Score on wind speed (ref. : ECMWF EPS) Tested over a sample of 85 cases (Western Europe)

Comparison T199 C3.5 L 31 / T 358 C2.4 L41 Rank diagram – T199 – Z500 – 24hRank diagram – T358 – Z500– 24h Rank diagram – T358 - Z500 – 48h Rank diagram – T199 - Z500 – 48h

Telecom and data received (files) links relevant to US/Europe data exchange: Daily volume of satellite data files received from Exeter : HIRS, AMSU-A, AMSU-B from NOAA15, 16 and 17: total 350Mb SSM/I from DMSP F13 and F15: total 130 Mb Seawind (240 Mbytes) from Quikscat AIRS+AMSU-A (250 Mbytes) from Aqua Lannion to suitland (CIR 128 kbit/s, MIR 256 kbit/s): –IODC data from Meteosat 5 all channels, full resolution every 30 minutes, and AVHRR data received at Lannion Suitland to Lannion (CIR 128 kbit/s, MIR 256 kbit/s): –GOES-W data for FSDS of Eumetsat

Assimilation of Meteosat data (Chistophe Payan) Use of BUFR winds produced by EUMETSAT with a quality index and disseminated every 90 minutes compared to Use of previously operational SATOB winds produced every 6 hours

 Experiments with the uniform ARPEGE configuration, over the period 23 Dec Jan 2003  SATOB experiment, the operational use  Blacklist north of the 30° North on land  BUFR experiment with conditional use  QI>0.85 (0.90 for HWW in tropics area)  Satob Blacklist plus : - on land for p > 700hPa - in extra-tropics for 350 < p < 800 hPa (data too biased) Assimilation of Meteosat data

Meteosat 5&7 observation fit to first guess and analysis area=50N/50S/113E/50W Used U component Used V component BUFR versus SATOB more data used rms and bias reduction

BUFR winds : present time, near/next future Meteosat BUFR winds assimilated since december 2003 Use of BUFR winds of the other satellites on going Structure of the observation errors as a function of the quality indicator and spatial correlation Bias correction

Status of ATOVS data at Météo-France Operational use of AMSUA raw radiances Raw radiances instead of preprocessed radiances: 22 October 2002 (+ European & American profilers) NOAA17 on top of NOAA15 & NOAA16 : 17 December 2002 Research on locally received data (EARS) Operational suite with HIRS data (+ revision of rain detection for AMSUA ) : December 2003 Pre-operational suite with AMSUB data : Summer 2004

Assimilation of AMSUA raw radiances (Elisabeth GERARD and Florence Rabier) –T s in the control variable –T extrapolation above the model top (1 hPa) up to 0.1 hPa by regression –250 km thinning 0.7 K or CLWP(ch1; ch2) > 0.1 mm

Assimilation of AMSUA raw radiances Time series of rms errors and biases 24 hour forecast 200 hPa geopotential scores over 1 month 22 Aug - 22 Sep 2002 Preprocessed radiances Raw radiances scores computed wrt own analysis Northern Hemisphere Southern Hemisphere rms difference

Experiments with HIRS radiances (Elisabeth Gérard and Delphine Lacroix) –On top of AMSUA data over 3 weeks (23 Dec 2002 – 12 Jan 2003) –250 km thinning (as for AMSUA) water vapour channels

Forecast scores (rms & bias) over Europe with HIRS & without HIRS 24 hour geopotential temperature wind rel. humidity forecast range  48 hour72 hour

TCWV time series Global Globe S. Hem Tropics N. Hem LandSea% WINTER SUMMER Global Globe S. Hem Tropics N. Hem LandSea%

Scores (winter) Time series of rms errors and biases 48 hour forecast hPa geopotential 2 weeks - 26 Dec 2002 – 10 Jan 2003 without HIRS with HIRS scores computed wrt own analysis Northern Hemisphere Southern Hemisphere Tropics

Test suite with HIRS and Meteosat BUFR 44 runs in Autumn 2003

Preparation for assimilation of OBS AMSU-B EARS QuikSCAT AIRS MODIS winds

Assimilation of AMSUB data  9 < scan position < 82  Ts > 278 K and |ob-fg| ch 2 < 5 K  Land orog<1000m/1500m for channels 4/5  Sea54321Conditions for use  In a similar way as for AMSUA & HIRS data:  Scan and air-mass bias correction  250 km horizontal thinning

Effect of assimilating AMSUB data on TCWV (Total Column Water Vapour) field Winter period Spring period

Winter scores wrt radiosondes (27 Nov – 7 Dec 2003) RMS, std dev. and bias errors (without minus with AMSUB data) for geopotential [m] as a function of forecast and vertical ranges Red: degradation from AMSUB Green: improvement from AMSUB Tropics Northern Hemisphere Southern Hemisphere RMS Std dev. Bias = =

Spring scores wrt radiosondes (17 Mar – 3 Apr 2004) RMS, std dev. and bias errors (without minus with AMSUB data) for geopotential [m] as a function of forecast and vertical ranges Red: degradation from AMSUB Green: improvement from AMSUB Tropics Northern Hemisphere Southern Hemisphere RMS Std dev. Bias = =

Assimilation of AMSUB data - Conclusion Increase of humidity over land in Tropics and Southern Hem. and slightly in Northern Hem. Positive impact on the forecast scores (for geopotential, temperature and slightly for wind) Experiments ongoing Spring period to be run again with adjusted bias correction –Bias correction tuning necessary as assimilating AMSUB data modifies the background, i.e. the way AMSUA/AMSUB/HIRS data are being used

Research experiments with locally received AMSUA data. (N. Fourrié) Nesdis/Bracknell data Data available for the operational production, 1h50 cut-off time Lannion data 45W/40E/70N/30N Even more rapidly available, but smaller area & only NOAA16/NOAA17 EARS data Eumetsat ATOVS Retransmission Service Data rapidly available No blind orbit for NOAA17 13 March UTC

« EARS-Lannion » hybrid product since February 2004 : Recalibrated level 1c radiances computed from level 1a ATOVS data received from EARS HRPT stations and locally at Lannion

Impact of EARS and Lannion data in addition to Bracknell data (rms/bias wrt radiosondes) 250 hPa 12 forecast range (hour)  3648 BracknellBracknell+EARS+Lannion First step: assimilation in operational model ARPEGE Next step: assimilation in regional model ALADIN (… AROME) in research mode AMSUA, HIRS, AMSUB (observation density, bias correction, …)

Towards an assimilation of the Quikscat data First test to be set up : - preprocessing (like ECMWF) of the raw data, (inversion at 50km of resolution). - choice of the most likely solution, produced by the inversion, and her opposite - final choice among the deviation against the first guess - quality control against rain, sea-ice and land contamination - thinning at 100km

AIRS : Atmospheric InfRared Sounder Thomas Auligné 324 out of 2378 channels 1 spot out of 18

Flat bias correction for each channel calculated over all active data Basic definition for  o : 0.6 K for upper temperature channels 1 K for lower temperature channels 2 K for water-vapor channels AIRS : assimilation suite Bias correction Observation error estimation Assimilation period of 19 days :  CTRL = latest ARPEGE suite (including HIRS) EXP = CTRL + AIRS (all data in 6h assim window) + more iterations in the 2 nd 4D-Var minimisation

- Gross check : 150 < Tb < 350 & (obs - fg) < 20 - First-guess check : (obs - fg)² <  (  o ² +  b ²) - Channels in O 3 and SW bands, over land, peaking above/near model cloud top (1hPa), at edges of scan are blacklisted 176 channels used AIRS : assimilation suite Channel selection Mitch Goldberg cloud detection scheme: based on thresholds recomputed for ARPEGE model LW window channel: Tb(965.43cm -1 ) > 270 K Model SST versus SW window channel ( cm -1 ) (night only) Model SST versus predicted SST (from channels , , , cm -1 ) VIS/NIR imager: less than 10 % cloud in pixel (day only) Information on a pixel basis Cloud detection

GeopotentialTemperature VERIF = ECMWF analysis = = Forecast range Pressure RMS CTRL - RMS EXP

GeopotentialTemperature Humidity VERIF = TEMP observations

“Conservative” assimilation (only 176 channels, over clear pixels, flat bias correction) is neutral/slightly positive for summer experiment  To be confirmed/improved with more extensive testing Pre-operational by summer 2004 Conclusion for AIRS data

Use of MODIS winds at Météo-France First experiment with a 3-weeks data set of Terra Modis winds Basic - Modis winds used "as is“ Terra Modis only 17 march – 5 april 2004 Horizontal density thinning: average of ~250 km No use of quality index

Example of AMVs distribution for one analysis 16 may 00 UTC

Quality indicator-based selection : only Modis winds with q i larger than 0.80 will be used “ECMWF filtering”: over land winds used above 400 hPa, over sea, IR winds above 700 hPa and WV winds above 550 hPa Horizontal density thinning: average of ~250 km (priority: obs time, qi) Further experiment MODIS winds

Scores of the first experiment (9 days of assimilation)

Towards meso-scale assimilation

AROME Forecast 2.5km / 2008

MSG/Seviri WV 6,2  Tb on 12 Feb 2003, DVar specific humidity increments 3DVAR ALADIN Assimilation of Water Vapor radiances MSG clear sky (resolution 10km)

Plans Summer 2004: AMSU-B, AIRS, EARS,Quikscat Autumn DVAR ALADIN + Meteosat8 WV radiances Spring 2005 GOES BUFR winds, MODIS winds