Towards Utilizing All-Sky Microwave Radiance Data in GEOS-5 Atmospheric Data Assimilation System

Development of Observing System Simulation Experiments for all-sky radiance data assimilation at NASA GMAO

Min-Jeong KimNASA GMAO/GESTAR

Collaborators: Ricardo Todling, Will McCarty, Ron Gelaro, Ron Errico, Nikki Prive, Jong Kim, Dan Holdaway, Jianjun Jin, and Wei Gu

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GEOS-5 Moisture Analysis: Too wet? Too dry? Where?

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Including Lower Peaking AMSU-A Channels Helped?Not Assimilating Channels 1, 2,

& 3Assimilating Channels 1, 2, & 3Only CLEAR

SKY DATA used

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Including Lower Peaking AMSU-A Channels Helped?Assimilating Channels 1, 2, & 3Not Assimilating Channels 1, 2,

& 3Only CLEAR SKY DATA

used

Observation errors assigned for Clear Sky Condition

Why microwave surface channels don’t make any significant influence..?

N15 AMSU-AChannel

Error (K)

1 3.0

2 2.0

3 2.0

4 0.6

5 0.3

6 0.23

7 0.25

8 0.275

9 0.34

10 0.4

11 0.6

12 1.0

13 1.5

14 2.0

15 3.0

(1) Assigned observation errors are too large? Are we too cautious?

(2) Bias correction process absorb the information?

(3) … ??

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• AMSU-A surface channels in clear sky condition with all other observations currently being assimilated in GMAO were used.

• Estimated observation were calculated using the method in Desroziers et al. 2005.

• The observation errors assigned in GSI satellite obs error table seem to be largely inflated especially for AMSU-A channels 1, 2, and 3.

Comparisons of “used” and “estimated” observation errors

Observation error currently being used in GSIObservation error estimated

AMSU

-A

chan

nels

Observation error (K)

Observation error (K)

AMSU

-A

chan

nels

AMSU

-A

chan

nels

N18 AMSU-A, Clear sky

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• 3DVAR GSI (operational. Which will be updated to 3D-Var Hybrid this summer.)

• Horizontal resolution: 0.5 degree

• Control: Observation error being used operationally in NCEP for AMSU-A channels1, 2, and 3

• Experiment: Reduced observation error for AMSU-A Channels 1, 2, and 3

Experiment Setup

Observation error currently being used in GSI (Control)Observation error estimatedReduced observation error (Experiment)

Observation error (K)

AMSU

-A

chan

nels

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Preliminary Results• AMSU-A lower atmosphere

peaking channels are trying to push water vapor fields toward right direction. It has tendency to reduce moisture fields middle troposphere and increase moisture in lower troposphere near the tropics.

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Developing All-Sky MW Radiance Data Assimilation Components

• Observation operator : Including 3D cloud liquid and ice water • Quality control: keeping cloud affected radiance data while

screening out observation with scattering signals from precipitation• Bias correction: Remove cloud liquid water path from bias

correction predictors• Moisture control variables: (1) q, ql, and qi (2) q, cw(ql+qi)• Background error: NMC method (soon will be updated with 3D-

var hybrid)• Observation error: Started with very simple. Clear sky condition

(same as operational), Cloudy sky: Constants estimated from O-F standard deviation

Developing All-Sky MW Radiance Data Assimilation Components Cloud Control variables & Background Errors

Developing All-Sky MW Radiance Data Assimilation Components Cloud Control variables & Background Errors

Developing All-Sky MW Radiance Data Assimilation Components Cloud Control variables & Background Errors

GEOS-5 Background clouds (Vertically integrated cloud water)

Observed clouds (retrieved cloud liquid water path)

Cloud Analysis Increments (Preliminary results from current development for all-sky MW radiance data assimilation)

Cloud control variable: cw Cloud control variable: ql & qi

Cloud Analysis Increments (Preliminary results from current developments made to GSI)

Single point AMSU-A Observations

Cloud Analysis Increments (Preliminary results from current developments made to GSI)

@850hPa

@850hPa

Δql (when ql/qi control variable)

Δqi ( when ql/qi control variable)

Δql (when cw control variable)

Δqi (when cw control variable)

Cloud Analysis Increments

Smaller obs error

Larger obs error

Pres

sure

(h

Pa)

kg/kg

Sensitivity to Observation ErrorSingle point AMSU-A observations test

Cloud liquid water (ql) increment

Sensitivity to Observation ErrorSingle point AMSU-A observations test

Smaller obs error

Larger obs error

Pres

sure

(h

Pa)

kg/kg

Pres

sure

(h

Pa)

K

Larger obs error

Smaller obs error

q increment Tv increment

What would happen if we had additional outer Loops ?Single point AMSU-A observations test

2 outer loops

3 outer loops

4 outer loopsPr

essu

re

(hPa

)

kg/kg

Cloud liquid water (ql) increment

What would happen if we had additional outer Loops ?Single point AMSU-A observations test

Pres

sure

(h

Pa)

Pres

sure

(h

Pa)

Kkg/kg

Tv incrementq increment

No significant difference in using 2, 3, and 4 outer loops

No significant difference in using 2, 3, and 4 outer loops

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Experiment Setup• 3DVAR GSI (operational. Which will be updated to 3D-Var Hybrid this

summer.)• Horizontal resolution: 0.5 degree• Observation operator : CRTM version 2.1.3• Test period: 06/01/2013 – 07/31/2013

CNTL EXP

ObservationsAll observations being assimilated

in the operational system+ AMSU-A Channels 1, 2, and 3

All observations being assimilated in the operational system + AMSU-A

channels 1, 2, and 3 + AMSU-A Cloudy Radiance Data

Observation operator Not simulate cloudy radiance Simulate cloudy radiance

Moisture state variable q q, ql, qi

Moisture control variable q q, ql, qi

Background error Relative humidity Relative humidity, ql, qi

Bias correction predictor Const., tlap, tlap2, scan angle, cloud liquid water path Const., tlap, tlap2, scan angle

Observation error Errors being used operationally

Clear sky data: Errors operationally Cloudy sky data: Observation errors estimated and then inflated with STD

of O-F.

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Comparisons of Analysis Results

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Comparisons of Analysis Results

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Comparisons of “used” and “estimated” observation errors

• AMSU-A radiance data in all- sky condition with all other observations currently being assimilated in GMAO were used.

• Estimated observation errors were calculated using the method in Desroziers et al. 2005.

• The observation errors currently assigned for all-sky AMSU-A data for our experiments seem to be too large especially for AMSU-A channels 1, 2, and 3.

Observation error currently being used in All-sky radiance MW experimentsObservation error estimated

AMSU

-A

chan

nels

AMSU

-A

chan

nels

AMSU

-A

chan

nels

N18 AMSU-A, All-sky

Observation error (K)

Observation error (K)

Histogram for O-F for All-Sky MW Radiance DA

Gaussian Fit

Huber normObservation Error Model for All-Sky MW Radiance DA

Goal: Including moisture physics during the analysis process to improve the balance among moisture variables.

For example,(1) Preventing the analysis system from making clouds in dry atmosphere and making it moisten atmosphere instead..(2) When background is almost saturated, generating clouds instead of making atmosphere supersaturated

Utilizing moisture physics TL/AD in GSI

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Utilizing Linearized GEOS-5 Microphysics Models Developed by Dan Holdaway (NASA GMAO) Utilizing 4DVAR setting in GSI except

• applying for 1 time step• using moisture physics parts only without using dynamics part of

model TL/AD

J(δx) = δxTB-1δx + ∑n(HnMn δxo-dn)T Rn-1 (HnMn δxo-dn)

In “4DVar” setting, Mn (Δt) = TMcT*

If Mn = I : 3D-Var

Mc is cubed grid model, T is grid transformation operator M = TMdynMphysT*

set as Identity matrix (I) in this test

4D-Var:

Utilizing Linearized GEOS-5 Microphysics ModelsResults from Initial tests in 2˚ resolution, 02/16/2014 00Z

analysisAll-Sky AMSU-A Data

Assimilated

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OSSE Developments for All-Sky Microwave

Radiance DA

• To evaluate and tune up present and proposed techniques for all-sky microwave radiance data assimilation by exploiting known truth.

• To understand what contribution cloud/precipitation affected microwave radiance data assimilation can add to analysis

Collaborators: Ron Errico and Nikke Prive (NASA GMAO)

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Nature Run:(1) ECMWF Nature Run: Free-running “forecast” from 2006 model

T511L91 reduced linear Gaussian grid (approx 35km). SST and sea ice cover is real analysis for that period. Three-dimensional cloud liquid water and cloud ice water fields are available. However, Three-dimensional precipitation fields are not available.

(2) GEOS-5 Nature Run: High spatial(7km) and temporal (30min) results.

Three dimensional cloud liquid water, cloud ice water, rain, and snow fields are included.

Assimilation system: NCEP/GMAO GSI (3DVAR) and GMAO GEOS-5 model. Resolution 0.5 x 0.625 degree grid, 72 levels. 3DVAR will be updated with 3DVAR-Hybrid in the summer 2014.

Observation data: Conventional, GPSRO, SATWND, IASI, AIRS, AMSU-A, HIRS4, MHS + cloud and precipitation affected MW radiance

OSSE Developments for All-Sky Microwave Radiance DA

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Validation of OSSE SetupSquare root of zonal mean of temporal variance of analysis minus background fields for June 21-June 30, 2005. Clear sky radiance data of AMSU-A surface channels were additionally assimilated.

T, OSSE q, OSSE U , OSSE

T, Real q, Real U, Real

Latitude Latitude Latitude

Latitude Latitude Latitude

P (h

Pa)

P (h

Pa)

K

K

kg/kg

kg/kg

m/s

m/s

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Cloudy simulations were made with the CRTM using ECMWF Nature Run for AMSU-A and MHS.• GEOS-5 new Nature Run which has high spatial and temporal resolution and has 3D cloud

and precipitation fields will used as well in future. • Avoiding the issues with using the same Radiative trasfer model in simulation and

assimilation, using RTTOV in simulations is one of near future plans.

RealJune 19, 2013 00Z

OSSEJune 19, 2005 00Z

Cloud liquid water analysis increments. Projected on AMSU-A NOAA-18 observed locations. “All-sky” AMSU-A data were assimilated.

OSSE for All-Sky Microwave Radiance Data Assimilation

Validation of OSSE Setup for All-Sky MW Radiance Data

Future Work Work towards making lower peaking channels contribute good things on

analysis.• Examine and test with updated clear sky observation error(tnoise_clear)

for MW lower peaking channels• Revisit QC and look into bias correction behaviors• Through OSSE, understanding information and impacts those surface

channels can bring to analysis and find strategies to improve the current methods to assimilate them.

Further examine impacts of using moisture TL/AD models on analysis increments through experiments.

Continue to develop OSSE for all-sky microwave radiance data assimilation to evaluate and tune up present and proposed techniques for all-sky microwave radiance data assimilation by exploiting known truth.• Better understand the impacts of observation error and moisture

background error models on analysis. How important to assume correct observation error model or background error model ?

• Different moisture control variables

Currently testing and running experiments with all-sky AMSU-A and MHS radiance data. We are getting these all-sky microwave radiance data assimilation components ready so that we can assimilate microwave imager data from NASA GPM Microwave Imager(GMI) and AMSR2 in near future.

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Backup slides

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Simulated observations(made with OSSE simulation package developed by Ron Errico et al.)

1. All observations created using bilinear interpolation horizontally, log-linear interpolation vertically, linear interpolation in time.

2. Radiance observations created using CRTM version 2.1.33. No used of NR snow coverage4. Locations for all “conventional” observations given by

corresponding real ones, except no drift for RAOBS5. SATWNDS not associated with trackable features in NR.

Simulated observation errors(made with OSSE simulation package developed by Ron Errico et al.)1. Some representativeness error implicitly present2. Gaussian noise added to all observations3. AIRS errors correlated between channels4. Observation errors for SATWND and non-AIRS radiances

horizontally correlated (using isotropic, Gaussian shapes)5. Conventional soundings and SATWND observational errors

vertically correlated (Gaussian shaped in log-p coordinate)6. Tuning parameters are error standard deviations, fractions of

variances for correlated components, vertical and horizontal scales

OSSE Developments for All-Sky Microwave Radiance DA