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The Atmospheric Data Assimilation Component Contributions from Lidia CucurullJim Purser John DerberMiodrag Rancic Yong HanXiujuan Su Daryl KleistRuss Treadon.

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Presentation on theme: "The Atmospheric Data Assimilation Component Contributions from Lidia CucurullJim Purser John DerberMiodrag Rancic Yong HanXiujuan Su Daryl KleistRuss Treadon."— Presentation transcript:

1 The Atmospheric Data Assimilation Component Contributions from Lidia CucurullJim Purser John DerberMiodrag Rancic Yong HanXiujuan Su Daryl KleistRuss Treadon Mark LiuPaul van Delst Haixia LiuWan-Shu Wu Dave ParrishShuntai Zhou NCEP CFSRR 1 st 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 SystemImplementation date Mode Physical SST retrieval 9/27/2005CRTM + analytical solution NAM (regional)6/20/20063D-VAR RTMA8/22/20062D-VAR Global5/1/20073D-VAR HWRF6/19/20073D-VAR

4 Global GSI upgrades 5/1/ 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 (T b ) 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 (T b ) 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 T b 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 T b 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 T b –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 FOTO F irst -O rder T ime-extrapolation to O bservations 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 Obs - Background Analysis 3D-VAR Difference from Background Forecast Updated Forecast T = 0T + 3T - 3 Time

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

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 T v, and P s –Variance increased in regions of rapid change –Variance decreased in “calm” regions –Global mean variance ~ preserved Daryl Kleist, John Derber

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

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 T b QC for early satellites –… Includes GSI options targeted for Spring 2008 global implementation –FOTO –VarQC –Situation dependent rescaling of background error –T skin variance tweaks

20 Thanks! Questions?

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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:

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


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