1 22 nd North America/Europe Data Exchange Meeting Reading December 9-11, 2009 Status report Bruno Lacroix (DPrévi/COMPAS) With contributions from CNRM/GMAP Outlines Operational suite(s) –current configurations (Computers, Models) –Use of data Issues under development French data E-suite Future plans

2 Computing platform NEC Configuration NEC SX9 13 nodes of 16 processors Gflops/CPU, 1 To mem / node 2 machines: Operations 6 nodes since 22 nd September 2009 Research 7 nodes Next (and last) step 2010 Q1 20 nodes (2*10) : 32,7 Tflops + SX8 32 nodes / 8 processors 9.1 Tflops max Until February 2012, 2013 or 2014

3 Data management system: Soprano Architecture

4 Global model up to 102H at 00UTC (cut off 2H20), 72H at 06 (3H), 84H at 12UTC(1H50), 60H at 18UTC (3H) ARPEGE global spectral model T L 538 C2.4 L60  60 levels, from 17m to 5Pa, horizontal resolution from 15km (over France) to 87km  Linear grid with T360 C2.4 orography (1080x540 pts)  12 processors for ARPEGE forecast (10’ for 24H forecast) 4DVAR assimilation :  2 loops of minimization T107 C1 L60 (25 it.), T224 (30 it.)  16 processors (1 SX9 node) for assimilation (40’ between cut-off and P0) data used:  SYNOP, SHIP, BUOY, AIREP, AMDAR, ACARS, TEMP, PILOT  CMW winds GOES 11, 12 + Meteosat 7, 9 + MTSAT-1R, Modis  SEVIRI radiances (Meteosat 9)  AMI (ERS2), Seawind (Quickscat) and ASCAT (Metop) winds  HIRS, AMSU-A, AMSU-B/MHS NOAA15, 16, 17, 18, Metop & AQUA  SSM/I (DMSP F13), AIRS AQUA, GPS ZTD, GPS RO, IASI (Metop)  SST 1/12 degree from NCEP/NESDIS + SSM/I sea ice mask Models configuration

5 ARPEGE horizontal resolution (km) ‏ 70km

6 Assimilation and forecast cycles

ARPEGE-Métropole, very short cut-off ( 1H05 at 00UTC ) 54H run based on 3DVAR FGAT and P6 from previous short cut-off forecast P24H forecast avalaible at 0145 UTC 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 3DVAR ARPEGE Very Short cut-off 00UTC Forecast 54 H

8 Models configuration (follow up) Regional model up to D2 06UTC (at 00, 06, 12 and 12UTC)  ALADIN spectral limited area model  9.5 km resolution on 2740kmx2740km domain, 60 levels (289x289 pts)  3DVAR data assimilation: same data as ARPEGE plus SEVIRI radiances  Idem as dynamical adaptation of IFS  Many coupling files Tropical model 72H range at 00 and 12 UTC  ARPEGE uniform model (T L 539 C1 L60) ~37km  No own data assimilation (interpolation of stretched model analysis)  To be stopped in 2010  Short Range Ensemble Prediction System 102H range  11 runs ARPEGE T L 358 C2.4 L55 (23 to 133km)  Based on singular vector perturbation

9 ALADIN : 24 operationnal domains

10 Coupling files for assimilation ALADIN

11 AROME-France operational since Dec AROME 600x512pts, Dx=2.5km, 41L, Dt=1mn And ALADIN-France 300x300 domain four 30-h forecasts per day over France 3-hourly 3DVar assimilation cycle including radar doppler radial winds, Meteosat radiances, synop T, Hu, wind NH model with 5-species "ICE3" microphysics, 1D TKE scheme, "EDKF" shallow convection, ECMWF radiation "SURFEX" surface model with tiles: soil/vegetation, sea, lake, town

12 AROME operational configuration  the ALADIN-FRANCE operational suite provides : – Lateral boundary conditions – Surface initial conditions : CANARI analysis (OI) at 00, 06, 12 and 18 UTC (the previous AROME forecast is used otherwise). ALADIN cycle AROME cycle time

13 Op. d’obs ARPEGE Hu 2m, T 2m V 10m SEVIRI HR ALADIN (+ SEVIRI HR, Hu 2m,T 2m,V 10m ) AROME (+ radar) GPSRO GPS sol IASI, AIRS SEVIRI CSR Données assimilées dans les modèles

14 Number of observations (counts of bits of info.)  The number of observations depends on the assimilation time  SYNOP, RADAR Doppler winds, Aircraft measurements and SEVIRI radiances are of great interest to supply information to the data assimilation system. SYNOP / BUOY[ ] / a few units RADAR Doppler winds[0-1000] Ground GPS[ ] Radiosondes (TEMP, PILOT)[ ] Various Aircraft messages[ ] Cloud motion winds[0-20] Scatterometer winds[0-80] ATOVS pixels[ ] SEVIRI[ ] total[ ]

Radar data Assimilation AROME Radar data Assimilation AROME

16 24 radars, 17 Doppler bande-C giving between 2 and 11 PPIs / 15’ BUFR (Z,Vr,statut) archived into BDM (a file /elevation, 1km res.) Data center Opera in January 2011 with UK Met Office (about 70 radars over 29 countries) km 10 km Observations used as profiles Radar products from AROME

17 Explicit observation perturbations, and implicit (but effective) background perturbations. Ensemble assimilation (operational with 6 members…) : simulation of the error evolution Flow-dependent B  b = M  a (+  m ) aa 3DVAR FGAT T359C1L60

18 SIGMAB’s « CLIMATOLOGY » SIGMAB’s « OF THE DAY » 8 dec 2006 r0

19 PEARP2  PEARP2 is based on ARPEGE model  Two runs : at 06TU range 72h / 18TU range 108h  35 members : 1 control member and 34 pertubated membres  Initial state Perturbation : –Singulars vectors over 4 zones > > >4 zones –Use f 6 analyses from AEARP (Assimilation Ensemble ARPege, L. Berre & G. Deroziers) –Amplitude limited by variance-covariance matrix coming from assimilation cycle  Mdel Errors : multi-physics (physic ARPEGE operationnal scheme+ 7 schem validated by GMAP/PROC)multi-physics  Resolution PEARP2 T358L65 C2.4 / augmentation en 2010 T538L65 C2.4 or C3.6 (~15km or 10km over France) OTI (h)résolution EURAT12Tl95 HNC and HS24Tl44 TROP12Tl44

20 PEARP T358L55 C2.4 (~23km over France)

21 Targeted area for singular vectors

22 Assimilation/Forecast Suites  Operational suites Atmospheric models: –Limited-Area ALADIN La Réunion 3100x4600km with 3DVAR assimilation, several research, commercial and transportable dynamical adaptation versions  Chemical Transport Model MOCAGE, Forecasts of air quality up to 96H –3 domains:Global/Europe/France, Horz. resolutions: 4°, 0.5°, 0.1° –Observations currently only used for validation  Ocean Wave Models, Forecasts up to 102H –Global (2), Europe, France, Horz. resolutions: 1°, 1°, 0.25°, 0.1° –assimilate Jason-1 and Envisat altimeter wave height data

23 Operational Suite on SX9 (96 procs) Hour Nb proc

24 Changes in NWP system : IASI, SSM/I F14, statistics from ensemble assimilation cycle (6 members 3DVAR with T359C1L60 forecast) 04/02/2009 Arpège/Aladin: new physical parameterizations, in operation using a Prognostic Turbulent Kinetic Energy (TKE) scheme March 2009 move to SOPRANO data managment environment April 2009 : new ALADIN-France configuration, coupled with IFS at 00 and 12UTC, without data assimilation (dynamical adaptation). 22/09/2009: move to SX9 supercomputer

25 Evolution RMSE Z500 Europe Regular improvment over 23 years

26 Telecom and data received (files) link to Toulouse relevant to US/Europe data exchange: Daily volume of satellite data files received from Exeter : HIRS NOAA16, 17, 19 AMSU-A NOAA15, 16, 18, 19, AQUA AMSU-B/MHS from NOAA15, 16, 18, 19: 700 Mbytes SSM/I and IS from DMSP F13->F15, F16, F17: 280 Mbytes Seawind from Quikscat240 Mbytes? AIRS from Aqua500 Mbytes

Recent advances in the use of observations in the French NWP models September 2006: 20 stratospheric AIRS channels, SSM/I F13 and F15, Ground-based GPS data over Europe September 2007: GPS radio-occultation, ATOVS on MetOp (AMSU-A, MHS), ERS scatterometer, February 2008: Variational Bias Correction for radiances, ASCAT assimilation June 2008: HIRS on Metop, SSM/I F14, Emissivity parametrisation over land for micro- wave, CSR Meteosat, IASI

28 Evolution in obs number H. Bénichou Since July 2008, more than 2 million data per day

29 Radiances: ATOVS received with long cut-off –NOAA15 (AMSU-A) –NOAA16 (AMSU-A, AMSU-B) –NOAA17 (HIRS, AMSU-B) –NOAA18 (AMSU-A, MHS) –Aqua (AMSU-A) –Metop (HIRS, AMSU-A, MHS) H. Bénichou

30 Radiances: ATOVS used with long cut-off –NOAA15 (AMSU-A) –NOAA16 (AMSU-A, AMSU-B) –NOAA17 (HIRS, AMSU-B) –NOAA18 (AMSU-A, MHS) –Aqua (AMSU-A) –Metop (HIRS, AMSU-A, MHS) H. Bénichou

31 –SSMI (DMSP-F13): 7 channels – AIRS (Aqua) : 54 channels over 324 – IASI (Metop): 51 channels over 314 Radiances:SSMI, AIRS and IASI

32 Winds: CMW (all in BUFR), MODIS, Seawind, AMI, ASCAT

33 Assimilation of MSG SEVIRI Clear Sky Radiances 10.8  m channel Associated percentage of cloud free  CSR product from Meteosat-8/-9 (MSG/MSG-2)  Hourly product  Assimilation of –2 WV channels in 4DVar  250 km thinning CSR

34 Ground-based GPS: Station selection

35 Radio Occultation GPS Before screening After screening 10% data used

36 Evolution in managed/used data ratio 75% satellite data / 25% in situ data used

37 Land surface emissivity at microwave frequencies  Developments to assimilate surface sensitive satellite channels over land (Karbou et al., 2009)  Use of a dynamically retrieved emissivity to better assimilate AMSUA/B sounding channels over land in operations since July 2008

38 Assimilation of AMSUB over land surface upper atmosphere observation structure function Assimilation of AMSUB surface sensitive channels over land channels 2 (150 GHz) and 5 (183+/-7 GHz) where orog > 1000 m Emissivity dynamically derived from 89 GHz channel assigned to those channels AMSUB channels already assimilated over land channel 3 (183+/-1 GHz, where orog > 1500 m) channel 4 (183+/-3 GHz, where orog > 1000 m)

39 Impact on total column water vapour (TCWV) Average over the period 1 Aug-14 Sep’06 EXP = CTR + additional AMSUB channels over land EXP-CTR CTR TCWV diurnal cycle at TOMB

40 Assimilation of SSM/I over land surface upper atmosphere observation structure function  Assimilation of SSM/I channels 3 to 7 over land –22V / 37V / 37H / 85V / 85H  Emissivity –dynamically retrieved from 19V/19H channels –assigned to channels of same polarization with a frequency parameterization  Quality control –no coastal point, no land point with | lat | > 60°  Variational bias correction (VarBC) –“  Ts” instead of “Ts” as one of the predictors –Emissivity dynamically retrieved from 19V channel Only used over sea for the moment

Water vapour (TCWV & specific humidity profile) Average over the period 15 Jul-13 Sep’06 Control TCWV increments Mean= kg.m -2 (0.1%) Experiment TCWV increments Mean= kg.m -2 (0.2%) EXP-CTR TCWV analysis difference Mean= kg.m -2 (0.6%) EXP-CTR q analysis difference iso = 0.05 g.kg hPa 20°N more humidity in EXP

42 Impact of advanced infrared sounder radiances in the french global NWP ARPEGE model 1. Overview  Current operational configuration 2. Use of IASI data  Channels selection + Impact on forecasts  Increase of IASI density  Extension to Water Vapour channels 3. Cloud-affected Radiances  Method  Impacts from AIRS (analysis + forecasts

43 1. Current operational configuration IASI operationally assimilated in : - ''long wave'' temperature channels are assimilated, - clear condition (1 flag/channel, McNally & Watts, 2003): AIRS operationally assimilated in : - ''long wave'' temperature channels are assimilated, - Clear and cloudy conditions - Over open sea Sept, 06 → Jul, 081 Jul. 08 → 4 Feb. 09 Since 4 Feb. 09 Clear 19 channels (stratos)54 channels (+35 tropos)54 channels Cloudy ØØ54 channels Sept, 06 → Jul, 081 Jul. 08 → 4 Feb. 09 Since 4 Feb. 09 Open seaØ50 channels64 channels LandØØ50 channels Sea iceØØ32 channels

44 IASI assimilation: general features  Level 1C radiances are received via EumetCast in Toulouse (whole BUFR including 8461 channels)  A subset of 314 channels is retained in the Operational Observational DataBase (commonly chosen with other NWP centres)‏  Radiances are bias corrected using VarBC

45 2.a. Use of IASI data Channels selection Sea 64 channels Land 50 channelssea-ice32 channels Weighting functions

46 Geopotential: RMSE(noIASI wrt ECMWF) – RMSE(OPER wrt ECMWF)‏ Positive impact in mid-latitude and polar region in the troposphere 2.a. Use of IASI data Impact of IASI on forecast  96h forecast range NHSH 72h forecast range NH SH

47  In operational configuration: –Pre-selection: Only data from detector #1 1 fov AMSUA over 2 1 scanline over 2 –Selection during screening:1 profile per 250km box  In order to increase density –Pre-selection: Only data from detector #1 More complex pattern  –Selection during screening: 1 profile per 125km box –Between 3.5 and 4 more profiles are assimilated 2.a. Increase IASI density

48 2.a. Increase IASI density Typical data coverage over a 6-hour assimilation window (# of used channels / profile) example for 4 th March 2009, 00UTC analysis time 1 profile / 125km box   1 profile / 250km box

49 2.b. Impact of IASI density increase  250 km  125 km  Positive impact mainly for southern hemisphere 72h forecast range NHSH 96h forecast range NHSH Geopotential: RMSE(noIASI wrt ECMWF) – RMSE(OPER wrt ECMWF)‏

50 Add 9 WV channels (1320, and between and cm-1) Everywhere (sea, land, sea ice). sigma_o(WV) = 4 K (sigma_o(LW) = 0.5 – 1 K) 2.c. Extension to WV channels: (Settings + impact on the analysis) Slight improvement of the innovation (obs- first guess) for other satellite humidity observations (MHS, HIRS 11 & 12)

51 2.c. Extension to WV channels: impact on forecasts Positive impact on forecast wrt ECMWF analysis for large domains Statistically significant for geopotential in the upper-troposhere for hour for NH Geopotential at 96h forecast range IASIWV REF rmseBias

52  Relative humidity (12h forecast) wrt ECMWF analysis  Statistically significant in the whole troposphere until 24h forecast range for NH IASIWV REF 2.c. Extension to WV channels: impact on forecasts rmseBias

53 3.a. Cloud-affected radiances: Method (Pangaud et al, 2009, MWR) CO2-Slicing Cloud parameters retrieval (CTP et Ne) Use of CTP and Ne into RTTOV Simulation of cloudy radiance Cloud-Detect Flag cloudy channels Assimilation of cloudy channels 600hPa<CTP<950hPa AIRS: sigma_o(cloudy) = sigma_o(clear) = 1

54 3.b. Cloud-affected radiances: Impact on AIRS analysis EXP: assim clear + cloudy observations REF: assim clear observations only Cloudy obs assimilated Clear obs assimilated More observations are assimilated, particularly for tropospheric channels (potentially more contaminated by clouds). Geographical coverage of assimilated observations for the channel 239 (478 hPa:mid-troposphere). 01/09/06 à 00UTC Bath september 2009, EUMETSAT Meteorological Satellite Conference

55 3.b. Cloud-affected radiances: Impact on forecasts from AIRS blue:positive = reduction of RMSE red :negative = increase of RMSE Statistics accumulated from 01/09/06 to 04/10/06 RMSE difference with respect to radiosonde data Altitude (hPa) Forecast range (h) GEOPOTENTIALTEMPERATURE Forecast range (h) Significant up to 72h forecast range

56 Data monitoring User/password available upon request

57 News on upper-air observations

58 TEMP/TEMPSHIP  Nancy (12UTC) : stop end 2010  Lyon : 06UTC only  Rapa : 18UTC only  Takaroa: Stopped in August 2009  Tubuai and Amsterdam: impact study  Nimes :Robotsonde (MODEM) in 2010 –Autosonde (Vaissala) at Bordeaux  One more ASAP end 2009  A fourth one in early 2010

59 Windprofilers network La Ferté Vidame available on GTS Marignane, Clermont Ferrand and Lannemezan Available on bilateral basis End of Nice profiler

60 GPS surface network IGN : RGP about 170 stations in January 2009

61 E-suite in research environnement Cycle 35T2_op1 (including RTTOV9) New resolution : T798 C2.4 L70 (10km over France) Dt=600s (first version : 720s) 2 loops of minimization in 4DVAR:  T107 C=1 L70 Dt=1800s 25iter  T323 C=1 L70 Dt=1350s 30 iter Use of a stratiform precipitation scheme in second minimization Use of a 6-member assimilation ensemble with 4D-VAR T399 C1 L70, use of background error variances depending on the flux for all parameters (only vorticity in oper version) with a tuned spatial filter Evolution of turbulence scheme ALADIN-France: Change of resolution: 7.5 km, 70 levels Switch off Aladin-France as intermediate coupling model between global and convective scale systems beg AROME L60 direct coupling with ARPEGE

62 E-suite observation part  Reduction to 125 km of box sizes used to select satellite data instead of 250 km in oper version. (P. Moll …)  9 additional chanels Water Vapor IASI (land + sea) and 4 surface IASI channels de surface (sea)  Assimilation of humidity observations in low troposphere with AMSU-B over land  New RTTOVS coefficients for AIRS  Use of clear sky MODIS CMW  improved sea-ice mask

63 (A. Joly)

64 Change of horizontal thinning for radiances in ARPEGE  Operational horizontal thinning presently is 250 km  In E-suite, horizontal thinning is decreased to 125 km => ~ 3.5 times more radiances are assimilated More impact in Southern Hemis. because this area has less conventional data & because we assimilate more data over sea than over land Geopotential height 1 isoline = 1 m Wind 1 isoline = 0.2 m/s Example: increased density only for IASI Scores with respect to ECMWF analyses over a 3-week period RMS(250km) – RMS(125km)

65 125km (instead of 250km) thinning (P. Moll) Multiplication by 4 of data incoming the screening  more expensive ! In screening output, observations really used : Numbre in million TotalSat% sat obs Oper1,30,9573% New3,83,4892%

3H background errors statistics, given by new assimilation ensemble Arpege assim. d’ens. 4D-Var assim. d’ens. 3D-Var Fgat  Klaus storm, maxim error variances better forecasted (position+amplitude) with 4D-Var version 24/01/2009 à 00h/03h

67 Preliminary Results … scores (70 cases) with respect to radiosondes (TP) and IFS analysis (AC): GeopotentialTemperature Vent TP AC

68 New vertical resolution AROME From L41 to L60 (+ 37% CPU) : Increased vertical resolution mainly in the boundary layer: -1st level from 17mto 10m -27 level below 3000m (instead of 15)  Spectral coupling above 100hPa – Vorticité, divergence et temperature – 20 first wave numbers (scale > 100 km) - alt L41 ARO L60 ARO L70 ARP/ALA (m) oper dbl dbl

69 24 radars: 16 in C band (yellow circles) + 8 in S band (green circles). Volumes reflectivity (from 2 to 13 elevations). 22 Doppler radars (red circles), 2 planned (dashed red circles) Radar data assimilation : French network

70 Radar data assimilation : Inversion method of reflectivity profiles Caumont, 2006: use of model profiles in the vicinity of the observation as representative database  Consistency between the retrieved profile and clouds/precipitations that the model is able to create  Possibility of wrong solution if the model is too far from reality… needs check

71 Données utilisées RADAR AROME GuessAROME ANALYSE Z pseudo-an Z obs Important for increments alance in convective situations High departure to first guess allowed Thinning:1 obs. on 15 kms boxes to avoid correlated observations and representaivity erros Sigma Obs increasing linearly up to 160 kms Retrieved profiles when

72 Future plans PEARP with 35 members at 06 and 18 UTC  Spring 2010: Nec phase 2 (2* 10 nodes) ARPEGE 10km L70, AROME 2.5km L60 (direct coupling with ARPEGE), ALADIN 7.5km L70, high density radiances, more IASI and AIRS channels, extended condition of use, NOAA-19, radar reflectivity (AROME only)  Late 2010 ALADIN 3D-VAR Outre-Mer (Polynesia, New Caledonia, Antilles-Guyana) Configuration coupled with IFS using LBC project New data : SSM/IS F16 and F17, AVHRR winds, GRAS on Metop, Iscat

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