towards utilizing all-sky microwave radiance data in geos-5 atmospheric data assimilation system...
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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?
3
Including Lower Peaking AMSU-A Channels Helped?Not Assimilating Channels 1, 2,
& 3Assimilating Channels 1, 2, & 3
Only CLEAR SKY DATA
used
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Including Lower Peaking AMSU-A Channels Helped?Assimilating Channels 1, 2, & 3Not Assimilating Channels 1, 2,
& 3
Only 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
AM
SU
-A
channels
Observation error (K)
Observation error (K)
AM
SU
-A
channels
AM
SU
-A
channels
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 estimated
Reduced observation error (Experiment)
Observation error (K)
AM
SU
-A
channels
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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
Pre
ssu
re
(hP
a)
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
Pre
ssu
re
(hP
a)
kg/kg
Pre
ssu
re
(hP
a)
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 loopsP
ressu
re
(hP
a)
kg/kg
Cloud liquid water (ql) increment
What would happen if we had additional outer Loops ?Single point AMSU-A observations test
Pre
ssu
re
(hP
a)
Pre
ssu
re
(hP
a)
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 predictorConst., tlap, tlap2, scan angle,
cloud liquid water pathConst., 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
AM
SU
-A
channels
AM
SU
-A
channels
AM
SU
-A
channels
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
LatitudeLatitude Latitude
Latitude Latitude Latitude
P (
hP
a)
P (
hP
a)
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