Title: Allgemein
1Status 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
2LETKF implementation
- experiment chain
- in NUMEX set up
- GME LETKF exp. (Nens 40)
- June 2011 for lateral BC
- somewhat too little spread
- particularly over Europe,
- but LBC spread at least as
- large as from COSMO-SREPS (Nens 12)
ana. spread
ana. rmse
- for COSMO-LETKF .
- lateral BC by direct interpolation
- from 60 km to 2.8 km
- (moderate noise at model top
- surface, acceptable)
- MCH / ARPA-SIM
- resolution gap IFS-EPS
- (32 km to 2.8 km) tested ok
3LETKF implementation
- experiment chain in NUMEX set up
- lateral BC by direct interpolation from 60 km to
2.8 km - KENDA
- 1-hourly cycling, radiosonde, aircraft, wind
profiler, synop 40 ens. members - 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
- 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)
- offline adaptive estimation of obs errors in
observation space - multi-step analysis approach (different
localization radii for different sets of obs)
4LETKF ensemble forecasts (20 ens. members)
4 12 June 2011
KENDA / COSMO-DE-EPS
precipitation
max. 10-m wind gusts
2-m temperature
RMSE
spread
? results of LETKF (without explicit surface
/ soil perturbations)
larger rmse, larger spread
larger rmse, initially larger spread
larger rmse, equal spread
5LETKF at MCH, compare det. LETKF analysis with
nudging
- COSMO-2, Nens 40, LETKF as at DWD
- lateral BC from IFS (det./EPS) soil moisture
from COSMO-2
- LETKF technically works
- comparison with surface observations suggest
- generally LETKF better than NO_OBS, but worse
than nudging - assimilation of surface pressure appears to work
particularly well - LETKF_DET analysis very close to LETKF ensemble
mean - SPPT has only small but positive impact
6LETKF implementation of verification
- production of full NetCDF feedback files
- done COSMO observation operators (conventional
obs) - integrated in 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
7accounting for model error
- stochastic perturbation of physics tendencies
(SPPT) - (Torrisi)
- implemented in (private) V4_26
- tests at CNMCA / MCH / ARPA-SIM (WG7)
- Pattern Generator (for random fields with
prescribed correlation scales) - (Tsyrulnikov et al.)
- based on a stochastic partial differential
equation approach - basic version developed, being revised to make it
efficient - being embedded in COSMO code
- 2-D version planned for next year
8high-resolution obs
- radar
- obs operators finished, assimilation works
technically - radial winds vr in LETKF Yuefei Zeng (DWD,
until summer 2014) - ? 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
9high-resolution obs
- (SEVIRI-based, radiosonde-corrected) cloud top
height see next slides - (Schomburg)
- 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.
- new task microwave radiometer Raman lidar
T- , q- profiles - (Haefele, MCH)
10use of (SEVIRI-based) cloud top height (CTH)
observations in LETKF method
if cloud observed with cloud top height CTHobs
, what is the appropriate type of obs increment ?
Z km
- avoid too strong penalizing of members with high
humidity - but 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 for - a best fitting model level k, i.e. with
minimum distance d
model profile
Cloud top
CTHobs
(if above a layer with cloud fraction gt 70 ,
then choose top of that layer)
- use f (RHobs1) f (RHk)
- and CTHobs hk
- as 2 separate obs increments in LETKF
RH
11use of (SEVIRI-based) cloud top height (CTH)
observations in LETKF method
Z km
type of obs increment , if no cloud observed ?
9
- assimilate cloud fraction CLCobs 0
- separately
- for high, medium, low clouds
- model equivalent
- maximum CLC within vertical range
no high cloud
6
model profile
no medium cloud
3
no low cloud
CLC
12CTH single-observation experiments
- 1 analysis step , 17 Nov. 2011, 6 UTC
(wintertime low stratus)
- example missed cloud event
vertical profiles
relative humidity cloud cover cloud water cloud
ice observed cloud top
3 lines on one colour indicate ensemble mean and
mean /- spread
13CTH single-observation experiments
- example missed cloud event
cross section of analysis increments for ensemble
mean
specific water content g/kg
observation location
relative humidity
observed cloud top
14CTH single-observation experiments
- example missed cloud event
temperature profile (mean /- spread)
3000 m
first guess
analysis
2000 m
observed cloud top
1000 m
270 K
280 K
290 K
270 K
280 K
290 K
- LETKF introduces inversion due to RH(CTH) ? T
cross correlations - in first guess ensemble perturbations
15CTH single-observation experiments
- example false alarm cloud ? assimilated
quantity cloud fraction ( 0)
vertical profiles
relative humidity cloud cover cloud water cloud
ice observed cloud top
3 lines on one colour indicate ensemble mean and
mean /- spread
16CTH single-observation experiments
- example false alarm cloud ? assimilated
quantity cloud fraction ( 0)
observation increments - histogram over
ensemble members
low cloud cover octas
cover
17cycled assimilation of dense CTH obs
1-hourly cycle over 21 hours, 13 Nov., 21 UTC
14 Nov. 2011, 18 UTC (wintertime low stratus)
observed cloud top height (CTH)
000 UTC
600 UTC
1200 UTC
1700 UTC
18cycled 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 octa
- cloud top height m ?
000 UTC
600 UTC
1200 UTC
1700 UTC
19cycled assimilation of dense CTH obs
time series of first guess errors of ensemble
mean / spread of ensemble
averaged over cloudy obs locations
RMSE
- underdispersive,
- but no trend
- for reduction
- of spread
spread
averaged over cloud-free obs locations
20cycled assimilation of dense CTH obs
time series of first guess errors of RH at
observed CTH (det. run), averaged over cloudy
obs locations
no assimilation with cloud assimilation
RMSE
bias
- CTH assimilation reduces RH (1-hour
forecast) errors
21cycled assimilation of dense CTH obs
time series of first guess errors of RH at
observed CTH (det. run), averaged over cloudy
obs locations
no assimilation with cloud assimilation
assimilation of conventional obs only
assimilation of conventional cloud obs
localization scale adaptive / 20 km
RMSE
bias
- CTH assimilation reduces RH (1-hour
forecast) errors
22cycled assimilation of dense CTH 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
23cycled assimilation of dense CTH obs
cloud assimilation
satellite obs
no assimilation
conventional only
conventional cloud
CTH obs
No assim
total cloud cover of first guess fields after 20
hours of cycling
24use of (SEVIRI-based) cloud top height (CTH)
observations in LETKF
- 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 pdfs
- no sign of filter collapse (decrease of spread)
- next evaluate forecast impact
25Status of PP KENDA
Thank you for your attention Questions ?
26LETKF implementation
GME COSMO
ensemble members 40 1 (3DVar) 40 1 (det. Run)
horiz. resolution (ens.) 60 km (ni128) 2.8 km
horiz. resolution (det.) 30 km (ni256) 2.8 km
vertical localis. length scale (ln p) 0.3 (fut. 0.075 - 0.5) 0.3 0.075 0.5
horiz. localisation length scale 300 km 100 km
adaptive horiz. localisation - (not used) used in latest exp.
additive covariance inflation 3DVar - B -
adaptive multiplicative cov. infl. yes yes
conventional obs yes yes
satellite radiances AMSU-A -
GPS-RO new exps -
radar data - passive
update frequency 3 h 1 h (? 30 / 15 min)
27LETKF implementation
- multistep analysis (batch assimilation)
implemented ? motivation - local / nonlocal observations (e.g. radiances)
- different observation errors ? better use
different localization scales - in view of adaptive localization different obs
densities (conventional / radar) - (Perianez et al. work on paper with theoretical
concept toy model/ idealised experiments) - next step test with radar / SEVIRI CTH data
- 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)
28cycled assimilation of dense CTH obs
false alarm cloud cover (after 20 hrs cycling)
high clouds
mid-level clouds
low clouds
conventional cloud
conventional obs only
29Low cloud cover (COSMO)
1700 UTC
Cloud assim
No assim
Cloud conv
conv