Status Report PP KENDA

[email protected] of PP KENDACOSMO General Meeting, Sibiu, 2 – 5 Sept. 2013 1

Status Report PP KENDA

Christoph SchraffDeutscher Wetterdienst, Offenbach, Germany

Contributions / input by:

Hendrik Reich, Andreas Rhodin, Annika Schomburg, Ulrich Blahak, Yuefei Zeng, Roland Potthast

Yuefei Zeng, Klaus Stephan, Africa Perianez, Michael Bender (DWD)Chiara Marsigli, Tiziana Paccagnella (ARPA-SIM)Lucio Torrisi (CNMCA)Daniel Leuenberger, Luca Weber (MeteoSwiss)Mikhail Tsyrulnikov, Igor Mamay (HMC)Amalia Iriza (NMA)

• general overview

• assimilation of SEVIRI-derived cloud top height in LETKF


LETKF: implementation

experiment chain in NUMEX set up

• GME LETKF experiment for June 2011

• lateral BC by direct interpolation from 60 km to 2.8 km

• KENDA:– 1-hourly cycling, radiosonde, aircraft, wind profiler, synop– 40 ensemble members, basic set-up (only adaptive cov. infl.)– assimilation only, optimally takes 1 real day for 1 day of assimilation,

but in fact: ~ 1 – 4 real months for 1 week of assimilation !

(without forecasts !!) only 3 experiments so far


LETKF: implementation

• other work: • adaptive estimation of observation errors in observation space• multi-step analysis implemented• compare KENDA ensemble forecasts with COSMO-DE-EPS (8 days)

• at MCH / ARPA-SIM : studies on influence of different lateral BC (resolution gap)

• at MCH: compare determinstic LETKF analysis with nudging ana. (7 days)

• Hendrik: new flexible stand-alone scripts to run LETKF experiments without using NUMEX / archive very limited disk space

1 real day for 1 day of LETKF assimilation• to do: implement evaluation / verification tools in script suite• may become very suitable tool for users outside DWD (academia)


Impact of lateral BC resolution

Marsigli, Paccagnella, Montani: Test impact of lateral BC resolution for convection-permitting EPS

• 8 cases from Hymex SOP: 6 Sept. – 5 Nov. 2012

• COSMO-H2-EPS set-up (‘H2-CLEPS’):• 2.8 km, 50 levels• 10 members• IC + BC COSMO-LEPS,

res. 7 km• run at 12 UTC• 36h forecast range

• exp. ‘H2-IFS’: direct nesting• IC and BC from IFS-EPS• res: 0.25 °


mean difference between pairs of ensemble members < spread < rmse (i.e. e.g. betw. Member 1 of H2-CLEPS and Member 1 of H2-IFS)

• 1 exceptional case

• spin-up noise dieswithin 30 min

• impact on precipscores negligible

500 hPa geopotential

Impact of lateral BC resolution


LETKF: implementation of verification

• production of ‘full’ NetCDF feedback files

done: make clean interfaces to observation operators in COSMO …… integrate them into 3DVAR package

to be done (this autumn !) :extend flow control (read correct Grib files etc.)

• ensemble-related diagnostic + verification tool, using feedback files :(Iriza, NMA)

– ensemble scores implemented, further testing required


testing KENDA at MCH, ARPA-SIM

• OSSE

–temperature inversion in Po Valley: assimilate synthetic TEMP

–(semi-)idealised: horizontally homogeneous case; daily cycle of Alpine pumping

develop OSSE capability

COSMO reading feedback files implemented

• no PhD project on idealised OSSE to investigate convective-scale issues


accounting for model error

• stochastic perturbation of physics tendencies (SPPT)(Torrisi)

–perturbation of moist turbulence tendencies implemented

–implemented in (private) V4_26

–tests (LETKF for COSMO-ME): drying effect on precip

–further tests in MCH / ARPA-SIM

• Pattern Generator (for random fields with prescribed correlation scales)(Tsyrulnikov et al.)


high-resolution obs

• radar

–obs operators finished

–radial winds vr in LETKF: Yuefei Zeng (DWD, until summer 2014)

monitoring of 3d volumn data (statistics of observation increments) :large scatter, data quality problems

need to test thinning / superobbing strategies 3-hour assimilation with 1-hrly cycle done (different localization radii)

–vr + reflectivity Z in LETKF: Theresa Bick (HErZ-I Bonn, until end 2014 at least)

• GPS slant path delay

–obs operator (incl. TL / adjoint) implemented in 3DVar, approximations tested

–implementation in COSMO should start soon


high-resolution obs

• (SEVIRI-based, radiosonde-corrected) cloud top height : see next slides(Schomburg)

• new task: Raman lidar and microwave radiometer T- , q- profiles(Haefele, MCH)

• direct assimilation of SEVIRI radiances (window channels for cloud info)(Perianez)

–technically implemented (obs operator (RTTOV), reading / writing)

–work on monitoring / assimilation start in Nov.


• avoid too strong penalizing of members with high humiditybut no cloud

• avoid strong penalizing of members which are dry at CTHobs but have a cloud or even only high humidity close to CTHobs

search in a vertical range hmax around CTHobs fora ‘best fitting’ model level k, i.e. with minimum ‘distance’ d:

Z [km]

Cloud top

RH [%]

CTHobs

2

max

2 )(1))()((min obskobskkCTHh

hRHfRHfd

function of relative humidity

height ofmodel level

k

= 1

• use f (RHobs=1) – f (RHk)and CTHobs – hk as 2 separate obs increments in LETKF

if cloud observed with cloud top height CTHobs ,what is the appropriate type of obs increment ?

(if above a layer with cloud fraction > 70 %, then choose top of that layer)

use of (SEVIRI-based) cloud top height (CTH) ‘observations’ in LETKF: method


3

6

9

12

Z [km]

„no cloud“

„no cloud“

„no cloud“

CLC

• assimilate cloud fraction CLC = 0 separatelyfor high, medium, low clouds

• model equivalent: maximum CLC within vertical range

type of obs increment , if no cloud observed ?

3

6

9

12

Z [km]

- no data -

„no cloud“

Cloud top

model profile

use of (SEVIRI-based) cloud top height (CTH) ‘observations’ in LETKF: method


relative humiditycloud covercloud water

cloud iceobserved cloud top

3 lines on one colour indicate ensemble mean and mean +/- spread

• 1 analysis step , 17 Nov. 2011, 6 UTC (wintertime low stratus)

vertical profiles

• example: missed cloud event

CTH single-observation experiments


observed cloud top

observation location


specific water content [g/kg]

relative humidity [%]

cross section of analysis increments for ensemble mean




temperature profile (mean +/- spread)


first guess analysis

1000 m

2000 m

3000 m

observed cloud top

LETKF introduces inversion due to RH(CTH) T cross correlations in first guess ensemble perturbations

270 K 280 K 290 K 270 K 280 K 290 K


relative humiditycloud covercloud water

cloud iceobserved cloud top

3 lines on one colour indicate ensemble mean and mean +/- spread

vertical profiles

• example: false alarm cloud assimilated quantity: cloud fraction (= 0)



high cloud cover [octas]

FG

ANA

LETKF decreases erroneous cloud cover despitevery non-Gaussian distributions

mid-level cloud cover [octas]

FG

ANA


FG

ANA

low cloud cover [octas]

cover


observation increments - histogram over ensemble members



FG

ANA

low cloud cover [octas] mid-level cloud cover [octas] high cloud cover [octas]

cover


observation increments - histogram over ensemble members

FG

ANA

FG

ANA

LETKF decreases erroneous cloud cover despitevery non-Gaussian distributions


0:00 UTC 6:00 UTC 12:00 UTC 17:00 UTC

cycled assimilation of dense CTH obs

observed cloud top height (CTH)

1-hourly cycle over 21 hours, 13 Nov., 21 UTC – 14 Nov. 2011, 18 UTC(wintertime low stratus)


20

0:00 UTC 6:00 UTC 12:00 UTC 17:00 UTC

cycled assimilation of dense CTH obs :LETKF setup

• thinning: use obs at every 5th grid pt.• adaptive covariance inflation, adaptive localisation scale ( ~ 35 km)• Observation error variances : relative humidity = 10 %

cloud cover = 3.2 octacloud top height [m] :



FGANA

RMSE

bias

analysis error smaller than first guess (FG) errors

time series of errors for deterministic run, averaged over cloudy obs locations

‘CTH’ CTHobs heightk ‘RH’ 1 - RHk (k : ‘best fitting model level’)

‘RH at obslev’(i.e. at observed CTH)


22


RMSE

spread

time series of errors of ensemble mean / spread of ensemble

averagedover

cloudy obs

locations

averagedover

cloud-free obs

locations

underdispersive,but no trend for reductionof spread


RMSE

bias

time series of first guess errors (det. run), averaged over cloudy obs locations


no assimilationwith cloud assimilation

CTH assimilation : hardly reduces height error of selected level k

reduces RH (1-hour forecast) errors

‘RH at obslev’



RH at obslev

RMSE

bias

no assimilationwith cloud assimilation

CTH assimilation : reduces RH errors related to low + mid-level cloud

time series of first guess errors, averaged over cloudy obs locations



time series of first guess errors, averaged over cloud-free obs locations(errors are due to false alarm cloud)

mean square error of cloud fraction [octas]


low clouds mid-level clouds high clouds


‘false alarm’ cloud cover

(after 6 hrs cycling)

with cloud assimilation

no assimilation


time series of first guess errors, averaged over cloudy obs locations


assimilation of conventional obs only assimilation of conventional + cloud obs

localization scale: adaptive / 20 km

RMSE

bias

CTH assimilation : reduces RH (1-hour forecast) errorseven slightly reduces height error of selected level k


no assimilation

RH at obslev


first guess fields after 20 hours of cycling (cloudy obs locations)

CTH obs

cloud assimilation

conventional only conventional + cloud

[email protected] of PP KENDACOSMO General Meeting, Sibiu, 2 – 5 Sept. 2013 29PBPV – 03/2013

29

No assim

no assimilation cloud assimilation

conventional only conventional + cloud

total cloud coverof first guessfields after

20 hours of cycling

CTH obs

satellite obs




time series of first guess errors, averaged over cloud-free obs locations(errors are due to false alarm cloud)

mean square error of cloud fraction [octas]

error reduced(almost) everywhere


low clouds mid-level clouds high clouds


‘false alarm’ cloud cover

(after 20 hrs cycling)

conventional+ cloud

conventionalobs only


Summary

•assimilation of CTH by LETKF reduces errors of first guess (1-h forecast)

tends to introduce humidity / cloud where it should (+ temperature inversion)

tends to reduce ‘false-alarm’ clouds

despite non-Gaussian pdf’s

•no sign of filter collapse (decrease of spread)

•next: evaluate forecast impact

use of (SEVIRI-based) cloud top height (CTH) ‘observations’ in LETKF



with cloud

assimilation

no assimilation‘CTH‘ OBS-FG RH RH at obslev


first guess fields after 20 hours of cycling (cloudy obs locations)

CTH obs


Low cloud cover (COSMO)

PBPV – 03/201335

17:00 UTCCloud assim

conv Cloud +conv

No assim


RMSE and bias for different heights separately: Cloud assim. vs. no assim.

RH@oblsev


LETKF: validation of forecasts from KENDA

COSMO-DE EPS

EPS on KENDA

intermediate

combined

Documents

Status Report PP KENDA