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A Correlated K-Distribution (CKD) tool generates CKD gas-optics models in a number of steps, some which may require human intervention. One of the most interesting parts of the CKDMIP project will be to understand how differences in how each step is performed feed through to differences in the accuracy of fluxes and heating rates. The page is an attempt to gather the necessary information about the CKD tools participating in CKDMIP, specifically:
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Computing incoming solar radiation for each shortwave g point: g-point weights are calculated using a solar spectral weighting (the exact spectrum used depends on configuration – the GA7 configuration uses NRLSSI data meaned over the period 2000-2011). The solar flux per band and the g-point weights can be varied at runtime according to a varying solar spectrum.
CMA
Reference(s): Zhang et al., 2003: An optimal approach to overlapping bands with correlated k distribution method and its application to radiative calculations, J. Geophys. Res., 108(D20), 4641, doi:10.1029/2002JD003358; Zhang et al., 2005: A Comparison Between the Two Line-by-Line Integration Algorithms[J]. Chinese Journal of Atmospheric Sciences, 2005, 29(4): 581-593. doi:10.3878/j.issn.1006-9895.2005.04.09; Zhang et al., 2006: The effects of the choice of the k-interval number on radiative calculations, doi:10.1016/j.jqsrt.2005.05.090.
Implementation details: Coded in Fortran.
Selecting band boundaries: We have different narrow band schemes for various research purpose to balance the accuracy and speed of radiative transfer computations. In general, the number of major gases and the variation in the Planck function in each band are taken into consideration.
Line-by-line model: All the absorption coefficients are calculated by LBLRTM v12.8; They are put into LBLZS2000 (Zhang et al., 2005) radiative transfer model to calculate fluxes and cooling rates in our calculation.
Reordering spectrum: We have a unique mapping from wavenumber to g space. Reordering for the major gas in each band is done at a reference pressure and temperature level, while the absorption coefficients of other levels and other gases are following the reference reordering. See Zhang et al. (2003) for details.
Choosing number of g points: We have an automated procedure to choose g points for every band. See Zhang et al. (2006).
Partitioning g space for one gas: The partitioning of g space is optimized on the basis of Gaussian Quadrature in order to satisfy the accuracy conditions. We make following changes on each Gaussian Quadrature point:
PN(IG)=2*WGT(IG)*XG(IG)
PNUJ(IG)=XG(IG)**2
where XG(IG) and WGT(IG) are the initial abscissa and Gaussian weight respectively, PNUJ(IG) and PN(IG) are the abscissa and Gaussian weight we used.
Partitioning g space for multiple gases: The partitioning of g space is the same for every gas.
Computing absorption of one gas: The absorption coefficients computed from LBLRTM v12.8 are first reordered, and then the effective absorption coefficients are obtained by the above Gaussian Quadrature, i.e., they are averaged with about 100 absorption coefficients at each g point. These effective absorption coefficients are given as a look-up table in 22 pressure and 3 temperature, independent of concentration.
Computing combined absorption of multiple gases: We have three methods for overlapping band based on completely correlated, completely uncorrelated, and partly correlated, which depend on each band. See Zhang et al. (2003).
Computing Planck function for each longwave g point: The Planck function is first computed for the band mean and then partitioned amongst the g points.
Computing incoming solar radiation for each shortwave g point: The band-mean of incoming solar radiation is computed for every shortwave band, and the value for each g point is obtained by Gaussian weights. The solar spectrum is from ‘mean-ssi_nrl2.nc’ in the CKDMIP software.