Implementation of KENDA at ARPA-SIMC and first experiences

with an OSSEChiara 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 directoriesand set-up of the runs

(ARPA) bufr2netcdf

get Boundary Conditions from ECMWF ENS (and Ics for the cold start)

int2lmget 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 RUNoptimal configuration, generates the pseudo-observations (fof files)

FOF FOF FOF FOF FOF FOF FOF

00 03 06 09 12 15 18 21 24

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 ECMWFget 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 OSSEis 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 40-41 +3h

NATURE RUN

CONTROL RUN

qv lev 40-41 +6h

NATURE RUN

CONTROL RUN

qv lev 40-41 +12h

NATURE RUN

CONTROL RUN

KENDA cycle - qv lev 40-41 +3hBACKGROUND (lff) ANALYSIS (laf)

MEAN

SPREAD

KENDA cycle - qv lev 40-41 +6h

MEAN

SPREAD

BACKGROUND (lff) ANALYSIS (laf)

qv lev 40-41 +6hNATURE RUN

CONTROL RUN

KENDA ANALYSIS

KENDA cycle - qv lev 40-41 +9hBACKGROUND (lff) ANALYSIS (laf)

MEAN

SPREAD

KENDA cycle - qv lev 40-41 +12hBACKGROUND (lff) ANALYSIS (laf)

MEAN

SPREAD

qv lev 40-41 +12hNATURE 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

Level Height (m) Pressure (hPa)

50 10 1000

40 800 900

30 3000 670

20 7000 400

10 13500 150

1 21500 35

Test cases (Hymex SOP)

I.T. ensemble run Area of the main meteorological phenomenon KENDA cycle

2012092512 Alps 20120924002012092512

2012092612 Liguria Tuscany 20120925002012092612

2012101100 Corse Central Italy 20121010002012101100

2012101112 Corse Central Italy 20121010002012101112

2012102512 Northern Italy (IOP16) 20121024002012102512