Progress in Cloudy Microwave Satellite Data Assimilation at NCEP Andrew Collard 1, Emily Liu 4,...
-
Upload
claude-ramsey -
Category
Documents
-
view
217 -
download
0
Transcript of Progress in Cloudy Microwave Satellite Data Assimilation at NCEP Andrew Collard 1, Emily Liu 4,...
Progress in Cloudy Microwave Satellite Data Assimilation at NCEP
Andrew Collard1, Emily Liu4, Yanqiu Zhu1, John Derber2, Daryl Kleist3, Rahul Mahajan1
1IMSG@NOAA/NWS/NCEP2NOAA/NWS/NCEP3Univ. Of Maryland
4SRG@NOAA/NWS/NCEP
24 February 2015 1NOAA Satellite Science Week
Outline
• 3DEnsVar and 4DEnsVar• Cloud Information in Ensembles• Assimilating Cloudy Microwave Radiances• Conclusions
24 February 2015 NOAA Satellite Science Week 2
Outline
• 3DEnsVar and 4DEnsVar• Cloud Information in Ensembles• Assimilating Cloudy Microwave Radiances• Conclusions
24 February 2015 NOAA Satellite Science Week 3
f & e: weighting coefficients for fixed and ensemble covariance respectively
xt’: (total increment) sum of increment from fixed/static B (xf’) and ensemble B
ak: extended control variable; :ensemble perturbations
- analogous to the weights in the LETKF formulationL: correlation matrix [effectively the localization of ensemble perturbations]T: operator mapping from ensemble grid to analysis grid
GSI Hybrid [3D] EnVar(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 xαTxx
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. Again, no TLM or ADJ (so this is NOT 4DVar)!
K
kkkkkkkk
N
n
nn,J
1
1T
1
1T
ef1
fT
fff
2
1
2
1
2
1
dxHRdxH
αLαxBxαx
N
n
n
kn
k1
ef xαTxx
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. Again, no TLM or ADJ (so this is NOT 4DVar)!
K
kkkkkkkk
N
n
nn,J
1
1T
1
1T
ef1
fT
fff
2
1
2
1
2
1
dxHRdxH
αLαxBxαx
N
n
n
kn
k1
ef xαTxx
3DVar vs 3DHybrid vs 4DHybrid
Northern Hemisphere Southern Hemisphere
4DHYB-3DHYB
3DVAR-3DHYB
Move from 3D Hybrid (current operations) to Hybrid 4D-EnVar yields improvement that is about 75% in amplitude in comparison from going to 3D Hybrid from 3DVAR.
4DHYB ----3DHYB ----3DVAR ----
4DHYB ----3DHYB ----3DVAR ----
Outline
• 3DEnsVar and 4DEnsVar• Cloud Information in Ensembles• Assimilating Cloudy Microwave Radiances• Conclusions
24 February 2015 NOAA Satellite Science Week 8
10
1124 February 2015 NOAA Satellite Science Week
Outline
• 3DEnsVar and 4DEnsVar• Cloud Information in Ensembles• Assimilating Cloudy Microwave Radiances• Conclusions
24 February 2015 NOAA Satellite Science Week 12
Properties of AMSU-A Radiances
Ch. 1
24 February 2015 NOAA Satellite Science Week 13
• AMSU-A sensors see deep into the clouds, giving iinformation on temperature, moisture and cloud structure. Much less sensitive to ice clouds
• Large temperature sensitivity where the cloud peaks
Properties of AMSU-A Radiances
Ch. 1
We now ensure non-zero cloudJacobians even where cloud is absent from background
24 February 2015 NOAA Satellite Science Week 14
• AMSU-A sensors see deep into the clouds, giving iinformation on temperature, moisture and cloud structure. Much less sensitive to ice clouds
• Large temperature sensitivity where the cloud peaks
Properties of AMSU-A Radiances
Ch. 1
This looks odd:Ask me!
We now ensure non-zero cloudJacobians even where cloud is absent from background
Broad Jacobians mean we need good background error information to put increments in the right place in the vertical
24 February 2015 NOAA Satellite Science Week 15
• AMSU-A sensors see deep into the clouds, giving iinformation on temperature, moisture and cloud structure. Much less sensitive to ice clouds
• Large temperature sensitivity where the cloud peaks
Observation Error for AMSU-A under All-sky Conditions
Observation error is assigned as a function of the symmetric cloud amount
Gross check ±3 of the normalized FG departure (accept Gaussian part of the samples)
Before QC
After QC
Error Model
Obs. error used in the analysis
16
Non-precipitating Samples
Normalized by std. dev. of the OMF in each symmetric CLWP bin
Gaussian
Un-normalized
Normalized
Clear-sky vs. All-sky
Thick clouds that are excluded from clear-sky assimilation are now assimilated under all-sky condition Rainy spots are excluded from both conditions
Clear-sky OMF All-sky OMF
24 February 2015 NOAA Satellite Science Week 17
FirstGuess
Analysis
First Guess
Analysis
Outline
• 3DEnsVar and 4DEnsVar• Cloud Information in Ensembles• Assimilating Cloudy Microwave Radiances
– Analysis Increments• Conclusions
24 February 2015 NOAA Satellite Science Week 19
Clear sky increments:Cloud increments come from correlations in the ensembles
All sky increments:Additional cloud increments from cloudy microwave observations.
Outline
• 3DEnsVar and 4DEnsVar• Cloud Information in Ensembles• Assimilating Cloudy Microwave Radiances
– Retention through the forecast• Conclusions
24 February 2015 NOAA Satellite Science Week 22
First Guess
F00
F01
27
F02
F03
F04
F05
F06
F07
F08
F09
Analysis - Guess vs. F00 - Guess
3DEnsVar prexp02eCloud Water Mixing Ratio
2013110300
36
Analysis - Guess
F00 - Guess
Impact – 500hPa Height
Clear SkyCloudy Radiance
Clear SkyCloudy Radiance
N. Hemis S. Hemis
-ve means positive impact
24 February 2015 NOAA Satellite Science Week 39
Conclusions• Cloud background error information from the
3DEnsVar and 4DEnsVar hybrid systems provides detailed flow-dependent covariances needed for microwave cloudy assimilation.
• “Spin-down” still occurs in the first model cycle after assimilation.
• This can be minimized through appropriate bias correction and quality control
• Assimilation of all-sky microwave radiances is providing small positive impact.
24 February 2015 NOAA Satellite Science Week 40