The Atmospheric Data Assimilation Component

1. The Atmospheric Data Assimilation Component NCEP CFSRR 1st Science Advisory Board Meeting 7-8 Nov 2007

2. 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

3. Operational GSI applications

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. FOTOFirst-OrderTime-extrapolationtoObservations • 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

12. Difference from Background Forecast Updated Forecast T - 3 T = 0 T + 3 Time 3D-VAR Analysis Obs - Background

13. Difference from Background Forecast Updated Forecast T - 3 T = 0 T + 3 Time FOTO Analysis Obs - Background

14. 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

15. 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

16. 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

17. “As is” 500 hPa streamfunction (1e6) background error standard deviation Valid: 2007110600 New flow-dependent adjusted background error standard deviation

18. 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

19. 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

20. Thanks! Questions?

22. Extra slides Bias, FOTO, flow dependent B-1, etc …

23. 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

24. Guess fields 500 hPa VT: 2007110500

25. 3D-VAR without FOTO Latitude-height cross section along 180E Shaded: U-wind increment (m/s) Thick contour: Temperature increment (K)

26. 3D-VAR with FOTO Latitude-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

27. HPC Surface Analysis a) L rescaled b) • Surface pressure background • error standard deviation • fields • with flow dependent re-scaling • without re-scaling • Valid: 2007110600 “as is”