Figure 26 shows the global total bias (air-mass part plus scan angle part) for theactive channels of the AMSUA-N15 and HIRS3-N17. In this figure, CNTL is theoperational GDAS total bias for these channels as a reference. Most of the channels (o-g)equilibrate within about one month, except for HIRS3_N17 channel 12, which took about2.5 months. <strong>The</strong>refore, 3 months was chosen as the general training length for all thehistorical satellites.<strong>The</strong> next prerequisite to running the spin-up experiments was to assemble a timeseries of active channels and sensors for all the historical satellites, the SATINFO files,marked with periods of known outages and poor quality data. <strong>The</strong> starting point was a setof tables and scripts received, via JCSDA collaboration, from the MERRA reanalysis, seeBosilovich, 2008. <strong>The</strong> tables and scripts were updated based upon the ERA-40 qualitycontrol list and the <strong>NCEP</strong> historical satellite document maintained by co-author DennisKeyser, at:http://www.emc.ncep.noaa.gov/mmb/data_processing/Satellite_Historical_Documentation.htm.Once a working set of SATINFO files were created (and the historical set of SSUinstruments were included in the CRTM as noted in 3.1.2), a set of bias correction spinup experiments listed in Table 3 was carried out by running the CFSR over the indicatedperiods when the satellite data first became available (See Figure 21). In 2 cases, <strong>NOAA</strong>-6 and 11 operated during two periods, so the bias correction spin up had to be done twice.Once each spin up experiment was complete, the post-processing step was carried out onthe (o-g) diagnostic files to create starting SATANG files. It was then paired with theBIASCR file from the last cycle of the training period for use when the final CFSRassimilation began assimilating that particular instrument and sensor. Two features of thisbias correction scheme should be noted. One is the scan angle dependent bias is thedominant part of the total bias, and the other is the predictor coefficients of the air-massbias usually responds to the atmospheric state very quickly, usually within one or twodays.- 66 -
REFERENCESAccadia, C., S. Mariani, M. Casaioli, and A. Lavagnini, 2003: Sensitivity of Precipitation<strong>Forecast</strong> Skill Scores to Bilinear Interpolation and a Simple Nearest-NeighborAverage Method on High-Resolution Verification Grids, Wea. <strong>Forecast</strong>ing, 18, 918-932.Akmaev, R.A., and H.-M.H. Juang, 2008: Using enthalpy as a prognostic variable inatmospheric modeling with variable composition. Quart. J. Roy. Meteor. Soc., 134,2193-2197.Alpert, J. C., M. Kanamitsu, P. M. Caplan, J.G. Sela, G. H. White, and E. Kalnay, 1988:Mountain induced gravity wave drag parameterization in the NMC medium-rangeforecast model. Proc. 8 thConf. on NWP, Baltimore, MD.Alpert, J. C., S.-Y. Hong, and Y.-J. Kim, 1996: Sensitivity of cyclogenesis to lowertropospheric enhancement of gravity wave drag using the environmental modelingcenter medium range model. Proc. 11 th Conf. on NWP, Norfolk, VA.Alpert, J.C., 2004: Subgrid-scale Mountain blocking at <strong>NCEP</strong>. Proc. 20 th Conf. onWeather and <strong>Forecast</strong>ing, Seattle, WA.Andersson, E., and H. Järvinen, 1999: Variational quality control. Quart. J. R. Meteor.Soc., 125, 697-722.Anderson, J., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano, 2009: <strong>The</strong>Data Assimilation Research Testbed: A Community Facility. Bull. Amer. Meteor.Soc., 90, 1283–1296.- 67 -
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ABSTRACTThe NCEP Climate Forecast S
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1. IntroductionThe first reanalysis
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In this paper we only discuss globa
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the reanalysis was halted to addres
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online archive of data for reanalys
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density MESONET data is included in
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The 1B datasets were calibrated usi
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- Page 20 and 21: The third GSI feature enabled in th
- 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
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- 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 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
- Page 75 and 76: 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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- Page 85 and 86: Figure 21: Same as Figure 2, but fo
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- Page 89 and 90: Figure 1: Diagram illustrating CFSR
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- Page 97 and 98: Figure 9: Same as in Figure 2, but
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- Page 105 and 106: Figure 17: Same as in Figure 16, bu
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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
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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
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Satellite Starting date Ending Date