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The third GSI feature enabled in the CFSR is non-linear variational quality control(VarQC), (Anderson and Järvinen, 1999), which replaces the OIQCBUFR program(Woollen, 1991) that was used in R1 and R2 (Woollen et al. 1994). In the VarQCprocedure, conventional GSI observation innovations must first pass gross error checks.Then an innovation weight is computed based on its consistency with the solution of thevariational minimization based on all available observations, including radiances, withadditional input coming from the probabilities of error for the various observations. Anyobservation with a weight of .25 or greater is effectively used in the minimization.Typically in a pass/fail QC procedure, an observation with a comparable weight of lessthan .7, or so, would be rejected from the process completely.Another innovative feature of the CFSR GSI is the use of the historical concentrations ofcarbon dioxide (CO 2, http://gaw.kishou.go.jp ) when the historical instruments wereretrofit into the CRTM, which originally only included TOVS, MSU and HIRS2 forNOAA-11 and NOAA-14. Table 2 lists the values of CO 2 concentrations (ppmv) used inthe CRTM to calibrate satellites back to TIROS-N in 1979.3.1.4 Satellite bias correction spin up for CFSRThe direct assimilation of radiances represents one of the major improvements of theCFSR over R2. However, substantial biases exist when observed radiances are comparedto those simulated by the CRTM depiction of the guess. These biases are complicated andrelate to instrument calibration, data processing and deficiencies in the radiative transfermodel. A variational satellite bias correction scheme was introduced by Derber and Wu(1998) to address this issue when direct assimilation of radiances began at NCEP. Thisscheme has been continually developed and is used in the GSI system adapted for theCFSR. Before the radiances of a new instrument can be assimilated, its unique set ofstarting bias corrections must be determined by a separate spin-up assimilation. In thecase of CFSR, each set of historical instruments in Figure 21 required an individual spinup.Since the TOVS instruments had never been assimilated by a GSI based GDAS, apreliminary set of tests were run (not shown) which determined that a 3 month spin-up- 20 -
was required prior to the introduction of those historical instruments in the CFSR. Adetailed explanation of this important step is given in Appendix D.Examples of the bias correction values actually applied to the CFSR over theTOVS period of the CFSR, 1979-1998, may be seen in global averaged, 4 times dailyaveraged time series for MSU channels 1-4 and SSU channels 1-3 in Figure 27. (Thespin-up of the SSU channels was done at the same time). The one measure of thesuccessful spin-up procedures is the lack of discontinuities in the transitions betweensuccessive instruments. The break in the MSU time series are a result of the recalibrationthat was applied beginning in 1986 as noted in section 2.4.2.3.1.5 Transition to Real Time CFSROnce CFSR stream 4 (see section 8 for execution layout) completed February 2009, adecision point was reached. The operational GSI had gone through several upgradesduring the CFSR execution, the latest in February being a major addition to the CRTM tosimulate the hyper-spectral channels of the IASI instrument, onboard the new ESAMETOP satellite. The IASI radiances had become operational in March 2009.In order to continue to meet the goal of providing the best available initial conditions tothe CFS, in the absence of staff and resources to maintain the CFSR GSI into the future,it was decided to make the transition to the CDAS mode of CFSR. The operational GSI,present and future implementations, would replace the CFSR GSI, and the coupledprediction model would be “frozen” to that of the CFS v2. Historical observationaldatasets would be replaced with the operational data dumps. One consequence of theswitch to the operational GSI would be, because the anticipated operationalimplementation of FOTO did not happen, the period of March 2009 forward would berun without FOTO.3.1.6 QBO problem in the GSIThe QBO can only be fully depicted in assimilation systems by sufficient direct windobservations, since the underlying physical mechanism is based on the dissipation ofupwardly propagating gravity waves (Lindzen and Holton, 1968) which filtered out by- 21 -
Page 2 and 3: ABSTRACTThe NCEP Climate Forecast S
Page 4 and 5: 1. IntroductionThe first reanalysis
Page 6 and 7: In this paper we only discuss globa
Page 8 and 9: the reanalysis was halted to addres
Page 10 and 11: online archive of data for reanalys
Page 12 and 13: density MESONET data is included in
Page 14 and 15: The 1B datasets were calibrated usi
Page 16 and 17: MetOp is Europe's first polar-orbit
Page 22 and 23: the hydrostatic assumption. Soon af
Page 24 and 25: esolution with 28 sigma layers in t
Page 26 and 27: SW and LW radiations at one-hour in
Page 28 and 29: SST data. The other uses AVHRR and
Page 30 and 31: y Gent and McWilliams (1990; see al
Page 32 and 33: from the Global Temperature-Salinit
Page 34 and 35: through the end of the data set in
Page 36 and 37: The passive microwave weather filte
Page 39 and 40: from the sea ice/ocean back to the
Page 41 and 42: weight is assigned to the gauge ana
Page 43 and 44: Stream 6: 1 Jan 1994 to 31 Mar 1999
Page 45 and 46: in the mid 1990’s, the period whe
Page 47: e due to a change over the oceans (
Page 52 and 53: In Figure 49, we show the temporal
Page 54 and 55: distributed in time and space. Desp
Page 56 and 57: forecast model at a lower resolutio
Page 58 and 59: Appendix A: AcronymsAER Atmospheric
Page 60 and 61: ONPCMDIPIRATAPROFLRQBOQuikSCATR1R2R
Page 62 and 63: TMP2M 2m air temperature 24 / 473TM
Page 64 and 65: Appendix C: The Data AccessTo addre
Page 66 and 67: Figure 26 shows the global total bi
Page 68 and 69:
Argo Science Team, 2001: The global
Page 70 and 71:
Compo, G.P., J.S. Whitaker, and P.D
Page 72 and 73:
ozone Mapping and Profiler Suite (O
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Global Forecast System. Manuscript
Page 77 and 78:
and climate models. Atmos. Chem. Ph
Page 79 and 80:
performance Earth system modeling w
Page 81 and 82:
with mesoscale numerical weather pr
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experiments using SSM/I wind speed
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Figure 21: Same as Figure 2, but fo
Page 87 and 88:
Figure 45: The fit of 6-hour foreca
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Figure 1: Diagram illustrating CFSR
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Figure 3: Same as in Figure 2, but
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Page 95 and 96:
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
Figure 15: Radiance instruments on
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Figure 17: Same as in Figure 16, bu
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Figure 19: Global average Tb first
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Figure 21: Same as in Figure 16, bu
Page 111:
Figure 23: Comparisons of the SSU b
Page 114 and 115:
Figure 26: The global total bias fo
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Figure 28: The yearly total of trop
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Figure 30: The vertical structure o
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Figure 32: The global number of tem
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Figure 34: The same as Figure 33, b
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Figure 36: Monthly mean Sea ice ext
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Figure 38: 2-meter volumetric soil
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Figure 40: Schematic of the executi
Page 130 and 131:
Figure 42: Yearly averaged Southern
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Figure 44: Monthly mean hourly surf
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Figure 46: Global mean temperature
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Figure 48: Zonal mean total ozone d
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Figure 50: The subsurface temperatu
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Figure 52: Vertical profiles of the
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Figure 54: Zonal surface velocities
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ProgramPREVENTSACQCACAR_CQCCQCCQCVA
Page 146:
Satellite Starting date Ending Date
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