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

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

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

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

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

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

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

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

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

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

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

Obs - Background Analysis

3D-VAR Difference from BackgroundForecast

UpdatedForecast

T = 0 T + 3T - 3Time

Obs - Background Analysis

FOTODifference from BackgroundForecast

UpdatedForecast

T = 0 T + 3T - 3Time

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

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

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

,,

New flow-dependent adjusted background error standard deviation

“As is” 500 hPa streamfunction (1e6) background errorstandard deviation

Valid: 2007110600

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

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

Thanks!

Questions?

Extra slides

Bias, FOTO, flow dependent B-1, etc …

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

Guess fields500 hPa

VT: 2007110500

3D-VAR without FOTOLatitude-height cross section along 180E

– Shaded: U-wind increment (m/s)– Thick contour: Temperature increment (K)

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

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