Satellite Land Data Products and Their Assimilation into NCEP Models
Hybrid Variational-Ensemble Data Assimilation at NCEP Daryl Kleist 1 Workshop on Probabilistic...
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Transcript of Hybrid Variational-Ensemble Data Assimilation at NCEP Daryl Kleist 1 Workshop on Probabilistic...
Hybrid Variational-Ensemble Data Assimilation at NCEP
Daryl Kleist
1Workshop on Probabilistic Approaches to Data Assimilation for Earth Systems
Banff, Alberta, Canada – February 2013
with acknowledgements to Kayo Ide, Dave Parrish, Jeff Whitaker, John Derber, Russ Treadon, Wan-Shu Wu, Jacob Carley, and Mingjing Tong
NOAA/NWS/NCEP/EMC
Outline
• Introduction– (Brief) background on hybrid data assimilation
• Hybrid Var/Ens at NCEP
• OSSE-based hybrid experiments
• Future Work and Summary
2
f & e: weighting coefficients for fixed and ensemble covariance respectively
xt’: (total increment) sum of increment from fixed/static B (xf’) and ensemble B
k: extended control variable; :ensemble perturbations
- analogous to the weights in the LETKF formulation
L: correlation matrix [effectively the localization of ensemble perturbations]3
Hybrid Variational-Ensemble(ignoring preconditioning for simplicity)
• Incorporate ensemble perturbations directly into variational cost function through extended control variable– Lorenc (2003), Buehner (2005), Wang et. al. (2007), etc.
ekx
yxHRyxH
LxBxx
t1T
t
1
1T
ef1
fT
fff
2
1
2
1
2
1 N
n
nnββ,J ααα
N
n
nn
1eft xxx α
Outline
• Introduction– (Brief) background on hybrid data assimilation
• Hybrid Var/Ens at NCEP
• OSSE-based hybrid experiments
• Future Work and Summary
5
6
Air Quality
WRF NMM/ARWWorkstation WRF
WRF: ARW, NMMNMM-BGFS, Canadian Global Model
Satellites99.9%
Regional NAMNMM-B
North American Ensemble Forecast System
Hurricane GFDLHWRF
GlobalForecastSystem
Dispersion
ARL/HYSPLIT
Forecast
Severe Weather
*Rapid Updatefor Aviation
ClimateCFS
1.7B Obs/Day
Short-RangeEnsemble Forecast
NOAA’s NWS Model Production Suite
MOM3
NOAH Land Surface Model
CoupledOceansHYCOM
WaveWatch III
NAM/CMAQ
Regional DataAssimilation
Global DataAssimilation
GDAS Hybrid: 22 May 2012
7
• Package included other changes• NPP ATMS (MW): 7 months after launch
• This is by far the fastest we have ever begun assimilating data from a new satellite sensor after launch
• GPS RO Bending Angle replaced Refractivity
• Summary of pre-implementation retrospective testing• Improved Tropical winds• Improved mid-latitude forecasts• Fewer Dropouts• Improved fits to observations of forecasts• Some improvement in NA precip. in winter• Increased bias in NA precip. – decreased rain/no rain skill in summer
(Improved by land surface bug fix)• Overall significant improvement of GFS forecasts
NAM vs NAM parallels upper air stats vs raobs
10
Height RMS error
Day 1 = BlackDay 2 = RedDay 3 = Blue
Vector Wind RMS error
Ops NAM = Solid ; NAMB (with Physics changes) = Dashed ; NAMX (with physics changes and using global EnKF in GSI) = Dash-Dot
Ops NAM
NAMB (improvedphysics)
NAMX (improvedphysics + EnKF)
Thanks to Eric Rogers and Wan-Shu Wu
RAP GSI-hybrid vs. RAP GSI-3dvar
upper-air verification
3dvar
hybrid
Temp Rel Hum
Wind
28 Nov – 3 Dec 2012
+ 6 h forecast RMS Error
3dvar
hybrid
RAP hybrid DA using global ensemble
3dvarhybrid
Assimilation of NOAA-P3 Tail Doppler Radar (TDR) Data using GSI hybrid method for HWRF
• HWRF Model: 3 domains with 0.18-0.06-0.02 degree (27-9-3 km) horizontal resolutions, 43 vertical levels with model top at 50 hPa, with ocean coupling• TC environment cold start from GDAS forecast, TC vortex cycled from HWRF forecast• GSI hybrid analysis using GFS EnKF ensemble 80% of background error covariance from ensemble B. Horizontal localization 150 km, vertical localization 10 model levels for weak storm and 20 model levels for strong storm• Conventional data plus TDR data• Modified gross error check, re-tuned observation error and rejected data dump with very small data coverage for TDR data• 19 TDR missions for Hurricane Isaac, Leslie and Sandy
TDR data for Isaac 2012082712
Outline
• Introduction– (Brief) background on hybrid data assimilation
• Hybrid Var/Ens at NCEP
• OSSE-based hybrid experiments
• Future Work and Summary
13
14
Observing System SimulationExperiments (OSSE)
• Typically used to evaluate impact of future observing systems– Doppler-winds from spaced-based lidar, for example
• Useful for evaluating present/proposed data assimilation techniques since ‘truth’ is known– Series of experiments are carried out to test hybrid variants
• Joint OSSE– International, collaborative effort between ECMWF, NASA/GMAO,
NOAA (NCEP/EMC, NESDIS, JCSDA), NOAA/ESRL, others– ECMWF-generated nature run (c31r1)
• T511L91, 13 month free run, prescribed SST, snow, ice
Availability of SimulatedObservations [00z 24 July]
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AMSUA/MSU AIRS/HIRS
AMSUB GOES SOUNDER
SURFACE/SHIP/BUOY
AIRCRAFT
SONDES
AMVS
SSMI SFC WIND SPDPIBAL/VADWND/PROFLR
3D Experimental Design
16
• Model• NCEP Global Forecast System (GFS) model (T382L64; post May 2011 version – v9.0.1)
• Test Period• 01 July 2005-31 August 2005 (3 weeks ignored for spin-up)
• Observations• Calibrated synthetic observations from 2005 observing system (courtesy Ron Errico/Nikki
Privi)
• 3DVAR• Control experiment with standard 3DVAR configuration (time mean increment compared to
real system and found to be quite similar)
• 3DHYB• Ensemble (T190L64)
• 80 ensemble members, EnSRF update, GSI for observation operators• Additive and multiplicative inflation
• Dual-resolution, 2-way coupled• High resolution control/deterministic component• Ensemble is recentered every cycle about hybrid analysis
– Discard ensemble mean analysis
• Parameter settings• f
-1=0.25, e-1=0.75 [25% static B, 75% ensemble]
• Level-dependent localization
Time Series of Analysis andBackground Errors
17
500 hPaU
850 hPaT
Solid (dashed) show background (analysis) errors
3DHYB background errors generally smaller than 3DVAR analysis errors (significantly so for zonal wind)
Strong diurnal signal for temperature errors due to availability of rawinsondes
3DHYB_(Retuned Spread)
20
U
T
Q
New (RS) hybrid experiment almost uniformly better than 3DVAR
3DHYB_RS-3DVAR
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4D-Ensemble-Var[4DENSV]
As in Buehner (2010), the H-4DVAR_AD cost function can be modified to solve for the ensemble control variable (without static contribution)
Where the 4D increment is prescribed exclusively through linear combinations of the 4D ensemble perturbations
Here, the control variables (ensemble weights) are assumed to be valid throughout the assimilation window (analogous to the 4D-LETKF without temporal localization). Note that the need for the computationally expensive linear and adjoint models in the minimization is conveniently avoided.
K
kkkkkkkk
N
n
nnJ1
1T
1
1T
2
1
2
1yxHRyxHL ααα
N
n
n
kn
k1
exx α
22
Hybrid 4D-Ensemble-Var[H-4DENSV]
The 4DENSV cost function can be easily expanded to include a static contribution
Where the 4D increment is prescribed exclusively through linear combinations of the 4D ensemble perturbations plus static contribution
Here, the static contribution is considered time-invariant (i.e. from 3DVAR-FGAT). Weighting parameters exist just as in the other hybrid variants.
K
kkkkkkkk
N
n
nn,J
1
1T
1
1T
ef1
fT
fff
2
1
2
1
2
1
yxHRyxH
LxBxx ααα
N
n
n
kn
k1
ef xxx α
Single Observation (-3h) Examplefor 4D Variants
23
4DVAR
H-4DVAR_ADf
-1=0.25H-4DENSVf
-1=0.25
4DENSV
Time Evolution of Increment
24
t=-3h
t=0h
t=+3h
H-4DVAR_AD H-4DENSV
Solution at beginning of window same to within round-off (because observation is taken at that time, and same weighting parameters used)
Evolution of increment qualitatively similar between dynamic and ensemble specification
** Current linear and adjoint models in GSI are computationally unfeasible for use in 4DVAR other than simple single observation testing at low resolution
25
4D OSSE Experiments
• To investigate the use of 4D ensemble perturbations, two new OSSE based experiments are carried out.
• The original (not reduced) set of inflation parameters are used.
• Exact configuration as was used in the 3D OSSE experiments, but with 4D features
– 4DENSV• No static B contribution (f
-1=0.0)
• Analogous to a dual-resolution 4D-EnKF (but solved variationally)
• To be compared with 3DENSV
– H-4DENSV • 4DENSV + addition of time invariant static contribution (f
-1=0.25)
• This is the non-adjoint formulation
• To be compared with 4DENSV
27
OSSE-based comparison of 3DHYB andH-4DENSV (with dynamic constraints)
U
T
Q
4DHYB-3DHYB
Something in the 4D experiments is resulting in more moisture in the analysis, triggering more convective precipitation
28
Summary of 4D Experiments
• 4DENSV seems to be a cost effective alternative to 4DVAR
• Inclusion of time-invariant static B to 4DENSV solution is beneficial for dual-resolution paradigm
• Extension to 4D seems to have larger impact in extratropics (whereas the original introduction of the ensemble covariances had largest impact in the tropics)
• Increased convection in 4D extensions remains a mystery (it is not the weak constraint on unphysical moisture as I originally hypothesized)
• Original tuning parameters for inflation were utilized. Follow-on experiments with tuned parameters (reduced inflation) and/or adapative inflation should yield even more impressive results.
Outline
• Introduction– (Brief) background on hybrid data assimilation
• Hybrid Var/Ens at NCEP
• OSSE-based hybrid experiments
• Future Work and Summary
33
Summary
34
• NCEP successfully implemented hybrid variational-ensemble algorithm into GDAS
• NCEP aggressively pursuing application of hybrid to other systems– Mesoscale (NAM), HWRF, Rapid Refresh (and HRRR follow on), storm scale ensemble
– Future Reanalysis• Have already run preliminary tests for 1981-1983 periods, attempting to capture QBO
transitions (a difficult problem for reduced observing system periods)
• Extensions to the GDAS hybrid are ongoing, including 4DEnsVar
GDAS Hybrid
35
• EMC targeting T1148 SL GFS implementation within next year– How to configure hybrid? What can be afforded computationally on new
machine?
• Merging DA and EPS efforts– Currently have separate EnKF (DA) and BV-ETR (EPS) cycles/perturbations
• Improving the ensemble for the hybrid– Stochastic physics (Jeff Whitaker)
– TC Initialization (Yoichiro Ota)
• Hybrid developments– Improved localization, flow-dependent (and/or scale-dependent) weighting
– Testing and preparing 4d-ensemble-var for implementation
– Helping efforts toward cloudy/precip. radiances
TC relocation for EnKF(work done by Yoichiro Ota)
1. Update TC center position (latitude and longitude) by the EnKF
2. Use updated positions as inputs to the TC relocation
3. Apply this procedure before the EnKF analysis and GDAS analysis
Apply TC relocation used in deterministic analysis to each ensemble member, but allowing TC structure perturbations and some TC position spread.
Blue: first guess positionRed: Updated positionGreen: TC vital position
The idea is to separate linear problem (TC location space) and nonlinear problem (actual relocation of fields).
Example: spaghetti diagram
Before relocation After relocation
TC relocation of this method can reduce the uncertainty on the TC position, maintaining the TC structure perturbations and some of the position uncertainty.
Courtesy Yoichiro Ota
Comparison with GEFS TC relocation
EnKF analysis with TC relocation EnKF 6 hour forecast perturbation + GEFS TC relocation
GEFS operational TC relocation scheme destroyed almost all initial position uncertainty and create very small spread around TC.Courtesy Yoichiro Ota
6th WMO Symposium on DA
39
• NCEP will be hosting the next WMO DA Symposium from 7-11 October 2013 at NCWCP
• Call for papers will be out soon
Spectral Cost Function
41
• Assume increment cost function is in spectral space
s1se
s αβν L
osTssTs
fss
2
1
2
1J,J ναν zBxz
yxHRyxH
LxBxx
st
1Tst
s1Tsse
sf
1Tsf
sf
ssf
2
12
1
2
1ααββα,J
sf
1sf
s xBz β
• Perform variable transforms
New Control Variables andSpectral-dependent Weights
42
• New spectral control variables then become
s1se
s νβα L
s1sf
sf Bzx
β
• GSI control variables are in physical space, however, so we introduce spectral transform operator, S
BzSSx1s
f1
f β
νβα LSS1s
e1