the atmospheric data assimilation component
DESCRIPTION
The Atmospheric Data Assimilation Component. NCEP CFSRR 1 st Science Advisory Board Meeting 7-8 Nov 2007. GSI History. The GSI system was initially developed as the next generation global analysis system Wan-Shu Wu, R. James Purser, David Parrish - PowerPoint PPT PresentationTRANSCRIPT
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The Atmospheric Data Assimilation Component
Contributions fromLidia Cucurull Jim PurserJohn Derber Miodrag RancicYong Han Xiujuan SuDaryl Kleist Russ TreadonMark Liu Paul van DelstHaixia Liu Wan-Shu WuDave Parrish Shuntai Zhou
NCEP CFSRR 1st Science Advisory Board Meeting 7-8 Nov 2007
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GSI History
• The GSI system was initially developed as the next generation global analysis system– Wan-Shu Wu, R. James Purser, David Parrish
• Three-Dimensional Variational Analysis with spatially Inhomogeneous Covariances. (MWR, 2002)
• Originated from SSI analysis system– Replace spectral definition of background errors
with grid point representation• Allows for anisotropic, non-homogenous structures• Allows for situation dependent variation in errors
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Operational GSI applications
System Implementation date
Mode
Physical SST retrieval
9/27/2005 CRTM + analytical solution
NAM (regional) 6/20/2006 3D-VAR
RTMA 8/22/2006 2D-VAR
Global 5/1/2007 3D-VAR
HWRF 6/19/2007 3D-VAR
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Global GSI upgrades• 5/1/2007 - initial implementation• 5/29/2007
– data upgrade• Replace GOES 5x5 with 1x1 sensor based radiances• Assimilate METOP-A HIRS, AMSU-A, MHS radiances
• 11/27/2007– Data upgrade
• Replace Version 6 SBUV/2 ozone data with Version 8 data– Reduce high ozone bias in SH polar regions
• Assimilate high resolution JMA atmospheric motion winds– Slight reduction in 200 hPa vector wind rms forecast error
– Code upgrade• Addition of many new options to be turned on Spring 2008
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Globally assimilated data types• “Conventional” data
– Sondes, ship reports, surface stations, aircraft data, profilers, etc
• Satellite data– Winds
• SSM/I and QuikSCAT near surface winds• Atmospheric wind vectors
– Geostationary and POES (MODIS), IR and water vapor
– Brightness temperatures (Tb)• Operational: ATOVS, AQUA, GOES sounder, …• Experimental: AMSRE, SSM/IS, IASI, …• New for CFSRR SSU
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Globally assimilated data types
• Satellite data (continued)– Ozone
• Operational: SBUV/2 profile and total ozone• Experimental: OMI and MLS capabilities
– COSMIC GPS radio occulation• Refractivity (operational) or bending angle
– Precipitation rates• SSM/I and TMI products
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Radiance (Tb) Assimilation
• GSI uses Community Radiative Transfer Model (CRTM) as its fast radiative transfer model– CRTM developed/maintained by JCSDA– Features:
• Reflected and emitted radiation from surface (emissivity, temperature, polarization, etc.)
• Atmospheric transmittances dependent on moisture, temperature, ozone, clouds, aerosols, CO2, methane, ...
• Cosmic background radiation (important for microwave)• View geometry (local zenith angle, view angle (polarization))• Instrument characteristics (spectral response functions, etc.)• Scattering from clouds, precipitation and aerosols
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Tb Quality Control Issues• Instrument problems
– Example: Increasing noise in AQUA ASMU-A channel 4• Inability to properly simulate observations
– Example: GSI/CRTM set up to simulate clear sky Tb
• IR and Microwave radiances– IR radiances cannot see through clouds – cloud heights difficult
to determine– Microwave impacted by thicker clouds and precipitation
• Less impacted by thin clouds (bias corrected) – Surface emissivity and temperature not well known for
land/snow/ice• Complicates cloud and precipitation detection
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Bias Correction
• Currently bias correct– Radiosonde data (radiation correction)– Brightness temperatures
• Biases can be much larger than signal crucial to bias correct the data
• NCEP uses a 2 step process for Tb
– Scan angle correction – based on position– Air Mass correction – based on predictors
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New GSI options (tested/ready)
• CFSRR will exercise several new GSI options pertaining to – Time component
• FOTO (First-Order Time-extrapolation to Observations)
– QC• Variational QC and tighter gross checks• Tighter QC for COSMIC GPSRO data
– Background error• Flow dependent variation in background error variances• Change land and snow/ice skin temperature background
error variances
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FOTO First-Order Time-extrapolation to Observations
• Many observation types are available throughout 6 hour assimilation window– 3D-VAR does not account for time aspect– FOTO is a step in this direction
• Generalize operators in minimization to use time tendencies of state variables– Improves fit to observations– Some slowing of convergence
• compensated by adding additional iterations
Miodrag Rancic, John Derber, Dave Parrish, Daryl Kleist
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Obs - Background Analysis
3D-VAR Difference from BackgroundForecast
UpdatedForecast
T = 0 T + 3T - 3Time
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Obs - Background Analysis
FOTODifference from BackgroundForecast
UpdatedForecast
T = 0 T + 3T - 3Time
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Variational QC• Most conventional data quality control is
currently performed outside GSI– Optimal interpolation quality control (OIQC)
• Based on OI analysis along with very complicated decision making structure
• Variational QC (VarQC) pulls decision making process into GSI– NCEP development based on Andersson and
Järvinen (QJRMS,1999)– Iteratively adjust influence of observations on analysis
as part of the variational solution consistency
Xiujuan Su
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Variational QC implementation
• Only applied to conventional data• Slowly turned on in first outer loop to
prevent shocks to the system• Some slowing of convergence
– compensated by adding additional iterations• In principle, VarQC allows removal of
OIQC step • This, however, has not been done (yet).• When VarQC on, GSI ignores OIQC flags
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Situation dependent B-1
• One motivation for GSI was to permit flow dependent variability in background error
• Background error variances modified based on 9-3 hr forecast differences in Tv, and Ps
– Variance increased in regions of rapid change– Variance decreased in “calm” regions – Global mean variance ~ preserved
Daryl Kleist, John Derber
,,
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New flow-dependent adjusted background error standard deviation
“As is” 500 hPa streamfunction (1e6) background errorstandard deviation
Valid: 2007110600
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Land & Snow/Ice variance change
• Operational global GSI has a uniform standard deviation of 1K for the skin temperature
• Modify GSI code to allow different values over ocean, land, and snow/ice– Increase from 1 to 3K over land and snow/ice
• Results in – More satellite data being assimilated– More realistic skin temperature analysis (not used)– Slight improvement in forecast skill
Daryl Kleist
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CFSRR GSI
• Based on 11/27/2007 GSI with addition of– SSU processing (requires updated CRTM)– Possible adjustment to Tb QC for early satellites– …
• Includes GSI options targeted for Spring 2008 global implementation– FOTO– VarQC– Situation dependent rescaling of background error– Tskin variance tweaks
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Thanks!
Questions?
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Extra slides
Bias, FOTO, flow dependent B-1, etc …
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Bias Correction (general)
• Simulated - observed differences can show significant biases
• Bias can come from– Biased observations– Deficiencies in the forward models– Biases in the background
• Would like to remove bias except when it is due to the background
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Guess fields500 hPa
VT: 2007110500
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3D-VAR without FOTOLatitude-height cross section along 180E
– Shaded: U-wind increment (m/s)– Thick contour: Temperature increment (K)
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3D-VAR with FOTOLatitude-height cross section along 180E
– Shaded: U-wind increment (m/s)– Thick contour: Temperature increment (K)
Note asymmetry and smaller magnitude increments at off times
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Surface pressure backgrounderror standard deviation fields
a) with flow dependent re-scalingb) without re-scaling
Valid: 2007110600
HPC Surface Analysis
b)
a)rescaled
“as is”
L