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

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Presentation transcript:

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

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

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

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

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)

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

qv lev h NATURE RUN CONTROL RUN

qv lev h NATURE RUN CONTROL RUN

qv lev h NATURE RUN CONTROL RUN

KENDA cycle - qv lev h BACKGROUND (lff)ANALYSIS (laf) MEAN SPREAD

KENDA cycle - qv lev h MEAN SPREAD BACKGROUND (lff)ANALYSIS (laf)

qv lev h NATURE RUN CONTROL RUN KENDA ANALYSIS

KENDA cycle - qv lev h BACKGROUND (lff)ANALYSIS (laf) MEAN SPREAD

KENDA cycle - qv lev h BACKGROUND (lff)ANALYSIS (laf) MEAN SPREAD

qv lev h NATURE RUN CONTROL RUN KENDA ANALYSIS

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!

Thank you!

Heght of the levels LevelHeight (m)Pressure (hPa)

Test cases (Hymex SOP) I.T. ensemble runArea of the main meteorological phenomenonKENDA cycle Alps Liguria Tuscany Corse Central Italy Corse Central Italy Northern Italy (IOP16)