Progress in Cloudy Microwave Satellite Data Assimilation at NCEP Andrew Collard 1, Emily Liu 4, Yanqiu Zhu 1, John Derber 2, Daryl Kleist 3, Rahul Mahajan

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



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



10

1124 February 2015 NOAA Satellite Science Week

Outline



Properties of AMSU-A Radiances

Ch. 1


• 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


Ch. 1

We now ensure non-zero cloudJacobians even where cloud is absent from background





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




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


FirstGuess

Analysis

First Guess

Analysis

Outline

• 3DEnsVar and 4DEnsVar• Cloud Information in Ensembles• Assimilating Cloudy Microwave Radiances

– Analysis Increments• Conclusions


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


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


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.


Documents

Progress in Cloudy Microwave Satellite Data Assimilation at NCEP Andrew Collard 1, Emily Liu 4, Yanqiu Zhu 1, John Derber 2, Daryl Kleist 3, Rahul Mahajan