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GSI History and Upgrades in Atmospheric Data Assimilation

This document discusses the history and upgrades of the GSI system, a global analysis system used for atmospheric data assimilation. It covers the development of the system, operational upgrades, globally assimilated data types, radiance assimilation, Tb quality control issues, bias correction, and new GSI options.

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GSI History and Upgrades in Atmospheric Data Assimilation

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  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?

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

  22. 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

  23. Guess fields 500 hPa VT: 2007110500

  24. 3D-VAR without FOTO Latitude-height cross section along 180E Shaded: U-wind increment (m/s) Thick contour: Temperature increment (K)

  25. 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

  26. 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”

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