1. Implementation of KENDA at ARPA-SIMC and first experiences with an OSSE Chiara Marsigli Tiziana Paccagnella ARPA Emilia-Romagna, SIMC

2. Outline Implementation of KENDA at ECMWF (sms system) Set-up of the OSSE suite First results Discussion items

3. KENDA suite at ECMWF MARS retrieval of conventional observation creation of the directories and set-up of the runs (ARPA) bufr2netcdf get Boundary Conditions from ECMWF ENS (and Ics for the cold start) int2lm get analysis from previous KENDA cycle COSMO run KENDA analysis step processing of output output archiving currently 20 members 3-hourly cycles

4. OSSE set-up NATURE RUN optimal configuration, generates the pseudo-observations (fof files) FOF 0003060912 15182124 CONTROL RUN “dried” run, qv reduced by half in the Initial Condition 10 IC original qv 10 IC halved qv KENDA CYCLE

5. KENDA OSSE suite at ECMWF get fof from nature run (psuedo-observations) modification of the IC (like in the control run) only for members 1-10 Execution of: Nature run Control run KENDA OSSE is driven by the specification in the.def file (file for the definition of the sms suite)

6. Set-up of the experiments KENDA cycle: – 3-hourly cycles, 24 hours – 20 members – BCs from ECMWF ENS (also ICs for cold start) – no model perturbations – observations: TEMP SYNOP AIREP

7. qv lev 40-41 +3h NATURE RUN CONTROL RUN

8. qv lev 40-41 +6h NATURE RUN CONTROL RUN

9. qv lev 40-41 +12h NATURE RUN CONTROL RUN

10. KENDA cycle - qv lev 40-41 +3h BACKGROUND (lff)ANALYSIS (laf) MEAN SPREAD

11. KENDA cycle - qv lev 40-41 +6h MEAN SPREAD BACKGROUND (lff)ANALYSIS (laf)

12. qv lev 40-41 +6h NATURE RUN CONTROL RUN KENDA ANALYSIS

13. KENDA cycle - qv lev 40-41 +9h BACKGROUND (lff)ANALYSIS (laf) MEAN SPREAD

14. KENDA cycle - qv lev 40-41 +12h BACKGROUND (lff)ANALYSIS (laf) MEAN SPREAD

15. qv lev 40-41 +12h NATURE RUN CONTROL RUN KENDA ANALYSIS

16. Issues under investigation “sufficient” spread is needed in the OSSE ensemble after the KENDA step: – the ensemble mean is correctly driven towards the pseudo- observations – the ensemble spread is greatly reduced pseudo-observation rejected if the spread is too small techincal issues to be solved related to quality control checks (pseudo-observations rejected if they are too different from the background, which may be needed for an OSSE) further testing: localisation, multi-step analysis suggestions for this OSSE are welcome!

17. Thank you!

18. Heght of the levels LevelHeight (m)Pressure (hPa) 50101000 40800900 303000670 207000400 1013500150 12150035

19. Test cases (Hymex SOP) I.T. ensemble runArea of the main meteorological phenomenonKENDA cycle 2012092512Alps2012092400 2012092512 2012092612Liguria Tuscany2012092500 2012092612 2012101100Corse Central Italy2012101000 2012101100 2012101112Corse Central Italy2012101000 2012101112 2012102512Northern Italy (IOP16)2012102400 2012102512