Development of CFES–LETKF Ensemble Data Assimilation System
2nd Data Assimilation WorkshopJAMSTEC Yokohama Institute for Earth Sciences, January 13, 2012
Nobu Komori,1 T. Enomoto,1,2 T. Miyoshi,3 B. Taguchi 1
and Team OREDA1 Earth Simulator Center (ESC), JAMSTEC, Yokohama, Japan2 Disaster Prevention Research Institute, Kyoto University, Uji, Kyoto, Japan
3 Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, U.S.A.
E-mail: [email protected]
Outline of Talk
• AFES-LETKF ensemble DA system
• Atmospheric reanalysis: ALERA & ALERA2
• CFES-LETKF ensemble DA system
• Preliminary results of CLERA-A
• Summary
LETKF: local transform ensemble Kalman filter
Kalman filter for a scalar
: analysis error (co)variance
: forecast error (co)variance
: observation error (co)variance
Kalman gainq: system noiseM: model
xfn = Mxa
n−1
pfn = M2pa
n−1 + qn−1
xan = xf
n + wn
�xo
n − xfn
�
pan = (1− wn) pf
n
wn =pf
n
pfn + rn
pa ≡ E��
xa − xt�2
�
pf ≡ E��
xf − xt�2
�
r ≡ E��
xo − xt�2
�
Kalman filter for a scalar
wn =pf
n
pfn + rn
=(rn)−1
�pf
n
�−1+ (rn)−1
xan =
�pf
n
�−1
�pf
n
�−1+ (rn)−1
xfn +
(rn)−1
�pf
n
�−1+ (rn)−1
xon
(pan)−1 =
�pf
n
�−1+ (rn)−1
Kalman filter (KF)[Kalman 1960]
ensemble KF (EnKF)[Evensen 1994]
ensemble transform KF (ETKF)[Bishop et al. 2001]
local ensemble KF (LEKF)[Ott et al. 2004]
local ensemble transform KF (LETKF)[Hunt et al. 2007]
Ensemble update
δXai = δX
fiT
Approximation by ensemble
Ensemble Kalman filter
Pf ≈ 1m − 1
δXδX
ETKF (Bishop et al. 2001)
ALERA
ALERA
AFES-LETKF experimental reanalysis
• first application of LETKF to full AGCM
• provides analysis ensemble spread as error estimates
• a product of collaboration among JMA, JAMSTEC and CIS
ALERA: specifications
• Observations used in NWP at JMA
• AFES T159L48M40
• from 18UTC 1 May 2005 to 12UTC 11 Jan 2007
• u, v, T, T–Td, z, slp
• available from the Earth Simulator Centerhttp://www.jamstec.go.jp/esc/afes/alera/
Hunt et al. 2007; Miyoshi and Yamane 2007; Miyoshi et al. 2007a
ALEDAS: AFES-LETKF Ensemble Data Assimilation System
observation
forecast
t=t0 t=t1
analysisanalysis
LETKF
AFES
LETKF: Hunt et al. 2007; Miyoshi and Yamane 2007; Miyoshi et al. 2007
AFES: Numaguti et al. 1997; Ohfuchi et al. 2004; Enomoto et al. 2008
Miyoshi et al. 2007a SOLA
850 (u,v) and standardized U850 spread
mean
spread
Enomoto et al. 2010 GRL
spread leads spread lagsdays
-0.2
0
0.2
0.4
0.6
-10 -8 -6 -4 -2 0 2 4 6 8 10
Lag correlation betweenmean and spread
0.52
Enomoto et al. 2010 GRL
Effects of buoy observation
Q. Moteki Pers. Comm.
ALL the TRITON buoys (blue ∆) observe surface pressure
ONLY the TAO buoys on the equator (red ∆) observe surface pressure
Influence of sondes in Matsuno-Gill pattern
Moteki et al. 2011 QJRMS
ALERA (w/o MISMO sondes)
with MISMO sondes
ALERA – with MISMO
MISMO Oct–Dec 2006in the Indian Ocean
Influence on typhoon genesis
Impact of Arctic buoys
Inoue et al. 2009 GRL
accuracy in the region w/ Arctic buoys (●) is as high as
in the mid-latitudes
error becomes larger over the entire Arctic
ALERA2
ALERA2 features
• Larger ensemble size (from 40 to 63)
• Improved model physics (cloud and land surface etc)
• Covariance localization by distance
• High-res SST and ice (NOAA daily 1/4° OI SST)
• Publicly obtainable observations (prepbufr)
• Forecast values (precip, radiative and surface fluxes)
Low cloud fraction
Kuwano-Yoshida et al. 2010 QJRMS
Cloud water
Kuwano-Yoshida et al. 2010 QJRMS
FT=120 Z500 RMSD
48
49
50
51
52
53
54
55
56
1.22 2.0 2.1 2.2 2.3 2.4 2.7 2.7m 3.6
48.9
49.5
51.351.251.6
52.1
53.1
53.8
55.4
ALERA
T159L48 T119L48
+MATSIRO
+PDF cloud
against JMA analysis, August 2004m
Error covariance localization by distance
Noise due to local patches
ALERA ALERA2
Miyoshi et al. 2007b SOLA
Surface
Sondes Aircraft
Ships & buoys
QuikScatAMV
GPCP ALERA2
JRA25 ALERA2 spread
2003 2004 2005 2006 2007 2008 2009 2010 2011
stream 2010
ALERA2
PALAU 2008
Mirai Arctic Ocean Cruise
Mirai Arctic Ocean Cruise
VPREX 2010
T-PARC
Winter T-PARC
PALAU 2005
MISMO
CINDY 2011
ALERA
stream 2008
ALERA2 streams
CFES-LETKF
Motivations
• Remove underestimation of ensemble spreadnear the sea surface
• Improve SST–precipitation correlation
➡ Replace AFES with CFES
Saha et al. 2010 BAMS
ObsCFSR
R1R2
SST lead Prec Prec lead SSTLag days
Lag Correlation of Prec. and SST over Western Pacific (winter)
CFES mini
• AFES T119L48, mstrn-X, Emanuel, MATSIRO
• OFES 0.5°x0.5° 54 lev., Sea ice, Noh-Kim mixed layer
• 177 years: interannual to decadal variability
• Richter et al. 2010 GRL,Taguchi et al. 2012 JC, ...
コンター: 平均場, 陰影:標準偏差
CFES 中解像度阪
NCEP/NCAR再解析
冬季ストームトラック活動度
B. Taguchi Pers. Comm.
storm-track activity in wintercontour: mean, color: std. deviation
CFES mini
NCEP/NCAR Reanalysis
Precipitation (DJF)
CMAP
CFES
AFES (T239)
CFES mini
Precipitation (JJA)
CMAP
CFES
AFES (T239)
CFES mini
CLERA-A
• Initial conditions from 41-year OFES spin-up with CORE.v2 Corrected Interannual Forcing
• 39 members + control
• 4 CFES per node
• Atmospheric DA only to study influence of the surface boundary uncertainties
• Starting from 1 August 2008
• Preliminary result on 10 September 2008
MPI implementation
CFES
EnCFES
AFES
...
MPI_COMM_WORLD= MPIA_COMM_WORLD
... ...
MPIA_COMM_WORLD MPIO_COMM_WORLD
MPI_COMM_WORLD = MPI_COMM_COUPLE
EnAFES
...
MPIA_... ...
MPI_COMM_WORLD
MPIA_... MPIA_...
......
......
......MPIO_...
MPI_COMM_COUPLE
MPI_COMM_WORLD
MPI_COMM_COUPLE MPI_COMM_COUPLE
MPIA_... MPIA_... MPIA_...MPIO_... MPIO_...
forecast
6h window
analysis
Forecast–Analysis CycleAFES-LETKF Ensemble DA System
LETKFAFES restartIC
t-3t-2t-1tt+1t+2t+3
merge
split
anal
gues
BC obs
forecast
6h window
analysis
Forecast–Analysis CycleCFES-LETKF Ensemble DA System
LETKFAFES restartIC
t-3t-2t-1tt+1t+2t+3
merge
split
anal
gues
IC OFES restart obs
CLERA-A
ALERA2 Surface temperature
spreadmean
mean spread 2008080500
CLERA-A
ALERA2 Surface temperature
spreadmean
mean spread 2008091000
CLERA-A
ALERA2 Latent heat flux
spreadmean
mean spread 2008091000
CLERA-A − ALERA2
Difference in zonal mean spread
Temperature Specific humidity
Eq NPSP Eq NPSP
1000
850
700
2008091000
Summary
• The CFES-LETKF ensemble DA system has been constructed.
• Ocean ensemble creates perturbed surface BC.
• Needs to handle oceanic biases and drift for a longer run.
Thank You!
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