Developments in data assimilation

1. Developments in data assimilation Links between Ensemble Data Assimilation and deterministic analysis Collaboration on field campaigns

2. NCEP, ESRL, University of Oklahoma and NASA Positive results of EnKF for intializing ensembles for tropical cyclone forecasting (Hamill et al, 2010) Development of the EnKF and a Hybrid Variational-Ensemble Data Assimilation System (HVEDAS) CMA: GRAPES Data Assimilation System • 3D-Var and 4D-Var systems in operations or pre-operational stages. • EnKF: comparison study with 3D-Var. • Goal: develop the hybrid variational data assimilation system. • COSMO: KENDA (Km-scale ENsemble-based Data Assimilation) • Implementation following Hunt et al., 2007

3. Current status at JMA (Ensemble Kalman filter and related techniques) • JMA develops the ensemble Kalman filter (LETKF) for both deterministic analysis and ensemble prediction system (EPS). • Direct comparison between LETKF and 4DVAR as deterministic analysis has been performed. • Also the performance of LETKF as EPS is compared with that of the operational system (with SV initial perturbation). • Further developments and research may allow us to implementLETKF as a part of EPS initial analysis in the future.

4. Impact of additional observations and upgrading RTTOV and GSM (LETKF vs 4DVAR) ACC RMSE ACC RMSE Z500 verification against initial NH LETKF : TL159L60 with 50 members(Localization scale 400km)4DVAR : TL319L60 (inner T106L60) System upgrade TR Both LETKF and 4DVAR use the same observations including satellite radiances. SH August 2009 August 2004 (Miyoshi et al. 2010) Using more observations*1, upgrading RTTOV and GSM(reduced gaussian grid), both systems get better in TR and SH. The improvement rates are larger for 4DVAR in TR and NH, and almost same or slightly larger for LETKF in SH*2. *1 Additional observations :: AMSUA・MHS(NOAA18, NOAA19, METOP2), SSMIS IR sounder, CSR from 5 satellites, ASCAT, GPS-RO(CHAMP, METOP), Temperature from aircraft*2 Note that the both experiments are the same season but different year

5. Canada • 4D-Var and EnKF: • both operational at CMC since 2005 • both use GEM forecast model • both assimilate similar set of observations using mostly the same observation operators and observation error covariances • Dependence between systems • EnKF uses 4D-Var bias correction of satellite observations and quality control for all observations

6. Results – 500hPa GZ anomaly correlation • Deterministic forecasts initialized with 4D-Var with operational B and EnKF ensemble mean analyses have comparable quality: 4D-Var better in extra-tropics at short-range, EnKF better in the medium range and tropics • Largest impact (~9h gain at day 5) in southern extra-tropics for 4D-Var with flow-dependent EnKF B vs. 4D-Var with operational B and also better in tropics – smaller improvement during July 2008 Northern extra-tropics Southern extra-tropics 4D-Var Bnmc 4D-Var Benkf EnKF (ens mean) 4D-Var Bnmc 4D-Var Benkf EnKF (ens mean)

7. ECMWF: EnDA operational, used for EPSProposed Operational Implementation for 4D-Var Analysis Forecast Analysis Forecast SST+εiSST EDA Cycle xb+εib i=1,2,…,10 xb xa xf+εif xb xa+εia y+εio EDA scaled Var Variance post-process Variance Recalibration Variance Filtering EDA scaled variances εifraw variances 4D-Var Cycle 7 Slide 7

8. Forecast improvements from EDA based variances in 4D-Var Blue=☺ ☺ ☺ 8

9. At Météo-France, EnDA operational since 2008 (with 6 members…) :simulation of the error evolution eb = M ea (+ em ) Flow-dependent B ea Explicit observation perturbations, and implicit (but effective) background perturbations.

10. Errors of the day for 3-hr forecasts provided by the Ensemble Data Assimilation Ens Assim. 3D-Var Fgat Klaus storm. The error maximum is better forecast by the 4D-Var version of the ensemble assimilation. Ens Assim. 4D-Var 24/01/2009 at 00h/03h

11. Impact of the « errors of the day » on Jokwe TC prediction Partie III : Utiliser les erreurs « du jour » L’impact des variances d’erreur sur la prévision cyclonique Etude d’impact dans le cas du cyclone tropical Jokwe With constant errors With errors of the day Forecast range (hours)

12. AMMA: The African Monsoon Multidisciplinary Analysis Better understand the mechanisms of the African monsoon and prevent dramatic situations (Redelsperger et al, 2006) Enhanced observations over West Africa in 2006 In particular, major effort to enhance the radiosonde network (Parker et al, 2008)

13. Similar results obtained at Meteo-France and ECMWF Monthly averaged RR better with bias correction Impact on monthly mean precipitation over Africa AMMABC: AMMA + bias correction NOAMMA: No Radiosonde data CONTROL: No bias correction

14. Similar structures in the IFS obtained with the assimilation of MERIS (Bauer, 2009) and of AMSU-B (Karbou, 2010) Assimilation of satellite data over land TCWV(MERIS-CTL) Developments at ECMWF Inter-comparisons : MERIS versus AMSU-B TCWV(AMSUB-CTL) Developments at Météo-France

15. The Concordiasi Project • A better use of satellite data, including IASI on board MetOp for analyses, forecasts and reanalyses over polar regions • Improving our understanding of the interactions between ozone depletion, stratospheric clouds and dynamics Part of THORPEX-IPY Cluster

17. Concordiasi: the international team • Participating Institutes: • CNES, CNRS (LMD, LGGE, LA), Météo-France • NSF, Purdue University, NCAR, University of Colorado, University of Wyoming • Alfred Wegener Institute, UK Met Office • Polar institutes: IPEV, PNRA, USAP, BAS • ECMWF, BSRN • Collaborating institutes: • NWP centres (Australia…), NASA/GMAO, UCLA, …. • Overview of Concordiasi: “The Concordiasi project in Antarctica” Rabier et al, Bulletin of the American Meteorological Society, January 2010. • Website www.cnrm.meteo.fr/concordiasi/

18. The Concordiasi Project

19. Dropsondes to calibrate the assimilation and for predictability studies Localized singular vectors are computed at ECMWF Computed October the 3rd for dropsondes the 4 th • Most of the sondes are dropped when coinciding MetOp overpasses + A-train • Part of the dropsondes are deployed in sensitive areas • Some in the Weddell Sea • Calibration/validation of IASI assimilation, • including cloudy cases • validation of AVHRR winds and error specification… • Model validation • Comparison of monitoring statistics Data impact studies intercomparison of sensitivity to observations

20. Dropsondes (at the date of 20101019) • On the GTS; Also, high-res data can be provided 11 driftsondes launched. Almost 300 dropsondes

21. Ozone depletion and particlesLagrangian real-time observations Colors: date since the launching of Balloon PSC16 Particle counter Could be used to validate Chemical-transport models

22. HyMeXan experimental program dedicated to the hydrological cycle in Mediterranean • « Nested » approach necessary to tackle the whole range of processes and interactions and estimate budgets Enhanced existing observatories and operational observing systems in the target areas of high-impact events: budgets and process studies (+ dedicated short field campaigns) Enhanced current operational observing system over the whole Mediterranean basin: budgets (data access  ‘data policy’) LOP EOP ? SOP Special observing periods of high-impact events in selected regions of the EOP target areas (aircrafts, ships,…): process studies

23. 2014 2012 2013 2011 First EOP/SOP series Intercompaison of data assimilation, including radar … --- Target Areas of the first EOP/SOP series Hydrometeorological sites Ocean sites Key regions for dense water formation and ocean convection SOP1 in order to document: - Heavy precipitation and Flash-flooding - Ocean state prior the formation of dense water SOP2 in order to document: - Dense Water Formation and Ocean convection - Cyclogenesis and local winds EOP/SOP for the NW Med. TA Phasing with T-NAWDEX campaign field