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

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SW and LW radiations at one-hour interval. A maximum-random cloud overlappingscheme is used for cloudy sky radiative transfer, and a climatological aerosol schemeprovides the global distribution of aerosol optical depth. Stratospheric volcanic aerosols arealso included. For CO2 amount, historical monthly mean observed data available at WMOGlobal Atmosphere Watch web site http://gaw.kishou.go.jp is used. This data is reprocessedinto 15 degree latitude/longitude horizontal grid which is used in the CFSR. In addition tothe major radiatively absorbing gases (water vapor, ozone, and CO2), many other radiativelyactive green house gases are also included. They are prescribed as global annual meanvalues adapted from National Institute of Standards and Technology (NIST).The RRTMG-LW employs a computationally efficient correlated-k method for radiativetransfer calculations. It contains 16 spectral bands with various numbers of quadrature points(g-points) in each of the bands that sum up to a total of 140 g-points. Active gas absorbersinclude H2O, O3, CO2, CH4, N2O, O2, and four types of halocarbons (CFCs). TheRRTMG-SW contains 14 spectral bands with various numbers of g-points in each of thebands to a total of 112. It uses a fast two-stream radiative transfer scheme, and includesmany absorbing gases (H2O, O3, CO2, CH4, N2O, O2).3.2.2 Enthalpy as prognostic variableThe implementation of specific enthalpy, C p T, (where C p is the specific heat at constantpressure and T is the temperature) as the thermodynamic prognostic variable, instead of thetraditional virtual temperature, is given in Juang (2009). With this approach, both the gasconstant R and specific heat at constant pressure C p of air are defined as weighed mean of Rand C p for individual gases in the air. Thus this formulation takes into account the variationof R and C p in space and time. However, current physical parameterizations do not takeadvantage of variation of C p and R. The thermodynamic equation is more accurate whenformulated in terms of enthalpy and this is very important for atmospheric models that reachup to 600 km in altitude, where density is very small.3.2.3 Modification to shallow convection and vertical diffusion- 26 -
Both current operational CFS and GFS have very poor prediction of marine stratus nearthe west coast of major continents. This poor performance may be attributed to theparameterization of shallow convection and background vertical eddy diffusion (Moorthi etal, 2009). The shallow convection parameterization follows Tiedtke et al. (1983). It isapplied wherever deep convection parameterization is not active. In this scheme, the highestpositively buoyant level below the 0.7 * P s (where P s is surface pressure) level for a testparcel from the top of the surface layer is defined as the shallow convection cloud top. Thecloud base is the lifting condensation level (LCL) for the same test parcel. Enhanced verticaleddy diffusion is applied to temperature and specific humidity within this cloud layer. Thediffusion coefficients are prescribed with a maximum value of 5 m 2 s -1 near the center andapproaching zero near the edges. The GFS also includes a background eddy verticaldiffusion to enhance mixing close to the surface, where eddy diffusion calculated by thePBL parameterization is considered inadequate, particularly in the stable regime . Thediffusion coefficient decreases exponentially with pressure, with the surface value set to 1.0m 2 s. The combination of shallow convection and background diffusion reduce the strengthof low level inversions off the west coast of continents thus reducing the prediction ofmarine stratus. In the version of the model used in CFSR, two simple modifications(Moorthi et. al., 2009) are used to improve the prediction of marine stratus. Themodifications are 1) limit the shallow convection top to be below the low-level inversionwhen condition for cloud top entrainment instability (Deardorf, 1980; Randall 1980) is notsatisfied and 2) set background vertical diffusion to zero above low-level inversions.Combination of these two modifications, although not perfect, does improve the predictionof marine stratus.4. The Ocean4.1 The Sea Surface Temperature AnalysisTwo daily sea surface temperature (SST) analysis products have been developed usingoptimum interpolation (OI). Both products have a spatial grid resolution of 1/4°. Oneproduct uses Advanced Very High Resolution Radiometer (AVHRR) infrared satellite- 27 -
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 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 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
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 93 and 94:
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
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
Page 114 and 115:
Figure 26: The global total bias fo
Page 116 and 117:
Figure 28: The yearly total of trop
Page 118 and 119:
Figure 30: The vertical structure o
Page 120 and 121:
Figure 32: The global number of tem
Page 122 and 123:
Figure 34: The same as Figure 33, b
Page 124 and 125:
Figure 36: Monthly mean Sea ice ext
Page 126 and 127:
Figure 38: 2-meter volumetric soil
Page 128 and 129:
Figure 40: Schematic of the executi
Page 130 and 131:
Figure 42: Yearly averaged Southern
Page 132 and 133:
Figure 44: Monthly mean hourly surf
Page 134 and 135:
Figure 46: Global mean temperature
Page 136 and 137:
Figure 48: Zonal mean total ozone d
Page 138 and 139:
Figure 50: The subsurface temperatu
Page 140 and 141:
Figure 52: Vertical profiles of the
Page 142 and 143:
Figure 54: Zonal surface velocities
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ProgramPREVENTSACQCACAR_CQCCQCCQCVA
Page 146:
Satellite Starting date Ending Date
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