The NCEP Climate Forecast System Reanalysis - NOAA National ...

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Appendix C: The Data AccessTo address a growing need for remote access to high volume numerical weatherprediction and global climate models and data, NOAA’s National Climatic Data Center(NCDC), along with the National Centers for Environmental Prediction (NCEP) and theGeophysical Fluid Dynamics Laboratory (GFDL), initiated the NOAA OperationalModel Archive and Distribution System (NOMADS, Rutledge, et al., 2006) project. TheNOMADS framework was developed to facilitate climate model and observational datainter-comparison capabilities as discussed in documents such as the IntergovernmentalPanel on Climate Change (IPCC, 2001), and advances a direct recommendation by theNational Academies of Science, Board on Atmospheric Sciences and Climate (BASC) toimprove multi-model ensemble diagnostics capabilities (National Academies of Science,NRC, 2007).Of the 250Tb CFSR total output (estimated at the time of this writing),approximately 10Tb are considered to be the “Tier-1” or “high priority” data. These datawere determined by several requirements workshops conducted by NCEP, and at anNCDC hosted Reanalysis Town Hall Workshop at the January 2008 Annual Meeting ofthe American Meteorological Society. At the AMS meeting NCDC obtained feedback onuser requirements for this massive dataset. An outcome of that Workshop culminated inthe generation of a user web page soliciting additional user requirements. The NOAAReanalysis Community Web Forum can be accessed at:http://nomads.ncdc.noaa.gov/NOAAReanalysis/cfsrr.The primary NOAA access point for CFSR is NCDC NOMADS located at:http://nomads.ncdc.noaa.gov. On NOMADS data access to CFSR is facilitated by fileand variable type organization most requested by users based on previous reanalysisoutputs. The “Tier-1” data include a suite of variables aggregated in a long time-series,and a suite of monthly mean variables. The monthly mean and time series data areavailable as separate files by variable type, month and year for the period of record tofacilitate user access. A separate dataset of monthly mean data have been aggregated foraccess as individual files of specific type so users can make a single request for the entireperiod of record of individual variables (e.g., 31 years of 500hPa heights). These “longtime-series” datasets will greatly reduce processing time on the NCDC NOMADS whilealso providing improved user access response times.To facilitate user access the National Center for Atmospheric Research(NCAR) has agreed to act as a mirror site to the NCDC's NOMADS access point and isscheduled to provide access to CFSR. The NCAR access point is: http://dss.ucar.edu/.Given the massive volume of these data, users are cautioned that extremely highvolume data requests may be throttled at NCDC based on the size of the individualrequest and the number of concurrent users.- 64 -
Appendix D: Details of Satellite bias correction spin up experiments for CFSRThis CFSR covers the period from 1979 to 2008. During these 30 years, a series ofNOAA polar orbiting satellites operated for periods of these years (Figure 21). Theradiance measurements from these satellites were directly assimilated into the analysissystem, which is one of the major improvements of the CFSR over R2. Substantial biasesexist when the radiance measurements are compared to the model-simulated radiances.These biases are complicated and relate to instrument calibration, data processing anddeficiencies in the radiative transfer model. Accurate bias corrections for these satelliteobservations were required for the success of the CFSR. This part of the paper describesthe method to estimate the biases of these historical satellite observations.A variational satellite bias correction scheme was introduced by Derber and Wu(1998) when direct assimilation of radiance began. This scheme has been continuallydeveloped at NCEP since then and is used in the GSI system adapted for the CFSR.Radiance bias is composed of two parts: the satellite scan angle dependent bias(SATANG) and the air-mass dependent part (BIASCR). The SATANG file is updatedfollowing the analysis step as the weighted average of the previous cycle’s SATANG file(containing the bias) and the departure between the new radiance measurements and themodel-simulated radiances, o-g, (where o is the observation and g is the forecast guess).SATANG is allowed to evolve very slowly (the weight given to the new (o-g) is 1/120).The air mass bias is predicted as the linear combination of five predictors and theircorresponding coefficients, the BIASCR file. The five predictors are, respectively, aconstant 0.01, the solar zenith angle, the cloud liquid water, CLW, (only applied tomicrowave instruments), the temperature lapse rate and the square of the lapse rate. Thepredictor coefficients are calculated using the variational method (the predictorcoefficients are additional analysis variables and included in the minimization).The biases from the historical satellite radiances can be estimated using a two-stepprocedure. First, the GDAS is run for a training period, starting from guess values, andthe statistics of the (o-g) are collected throughout this training period. Second, thecollected radiance statistics are then post-processed to calculate a SATANG file. For agiven scan position, channel, sensor, and satellite, the SATANG value is the average ofthe (o-g) weighted by the specified observation error inverse over the whole globe andthe entire training period.Several preliminary experiments have been carried out to determine startingvalues and how long the training period should be. These experiments were run over theperiod of 2006110118 to 2007013018 using zeroed out SATANG and BIASCR files. Tosimulate historical MSU and HIRS2 channels 2-14, only AMSUA-N15 channels 3, 5, 7and 9 and HIRS3-N17 channels 2-14, respectively, were assimilated. (The CRTM SSUcell pressure correction noted in section 3.1.2 was still in development while these testswere run.) The weight given to the new (o-g) is 0.033 which is larger than 0.00833 usedin the analysis, since the purpose was to spin up the bias correction. The preliminaryexperiments show that the predictor coefficients, except for the CLW term, can start fromzero and spin up rapidly, usually within a month (not shown). The coefficients for theCLW evolve very slowly, as shown in Figure 25, thus, the CLW coefficients fromNOAA-15 are used as guess values of the training period for all the historical MSUchannels.- 65 -
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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
Page 14 and 15: The 1B datasets were calibrated usi
Page 16 and 17: MetOp is Europe's first polar-orbit
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
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 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
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
Page 83 and 84: experiments using SSM/I wind speed
Page 85 and 86: Figure 21: Same as Figure 2, but fo
Page 87 and 88: Figure 45: The fit of 6-hour foreca
Page 89 and 90: Figure 1: Diagram illustrating CFSR
Page 91 and 92: Figure 3: Same as in Figure 2, but
Page 103 and 104: Figure 15: Radiance instruments on
Page 105 and 106: Figure 17: Same as in Figure 16, bu
Page 107 and 108: Figure 19: Global average Tb first
Page 109 and 110: Figure 21: Same as in Figure 16, bu
Page 111: Figure 23: Comparisons of the SSU b
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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
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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
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