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more cleanup of documentation
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gmao-rreichle committed Feb 10, 2022
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40 changes: 21 additions & 19 deletions GEOSagcm_GridComp/GEOSphysics_GridComp/GEOSsurface_GridComp/Utils/Raster/create_README.csh
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Expand Up @@ -58,7 +58,7 @@ set toc_rout="`printf '\\n7. GLOBAL RUNOFF ROUTING MODEL DATA ..................
The Mosaic types used for catchment surface elements are determined by computing\\n \
the dominant Mosaic type of all 10-arcsec pixels within the catchment in question.\\n \
\\n \
(b) Deriving Catchment-carbon (Catchment-CN) classes:\\n \
(b) Deriving Catchment-Carbon/Nitrogen (Catchment-CN) classes:\\n \
\\n \
The Common Land Model version 4 (CLM4: Oleson et al., 2010) utilizes 17 vegetation\\n \
classes and the version 4.5 (CLM4.5: Oleson et al., 2013) uses 25 vegetation classes (Table 2).\\n \
Expand Down Expand Up @@ -165,7 +165,7 @@ endif
# Set AGCM/SMAP specifics
#########################
set WGRID=AGCM
set int_str1="`printf 'by overlaying the atmospheric grid on ${NPfafs} number of hydraulic catchments \\n in ${MYMASK} mask file.'`"
set int_str1="`printf 'by overlaying the atmospheric grid on ${NPfafs} hydraulic catchments \\n in the ${MYMASK} mask file.'`"
set sec2_til="`printf ' area, longitude, latitude, ig, jg, cell_frac'`"
set pfafin_des="`printf 'catchment index (1-$NPfafs) after sorting Pfafstetter codes in ascending order'`"
set pfaf_des="`printf 'Pfafstetter code of the hydrologic catchment'`"
Expand Down Expand Up @@ -239,7 +239,7 @@ if($mylai == GLASSA) then
8-day climatology of LAI was computed from these data by temporally averaging over the 37-year period \\n \
(by 8-day periods) on the 3-arcmin grid. Those climatological data were aggregated over \\n \
the pixels of each land element to derive a 8-day LAI\\n \
In order to fill gaps that may exist due to inconsistencies between LAI and GEOS5 masks as well as data gaps themselves\\n \
In order to fill gaps that may exist due to inconsistencies between the LAI and GEOS masks as well as data gaps themselves\\n \
we constructed, at every time slice, a 1°×1° global gridded LAI dataset \\n \
by spatially aggregating the finer resolution LAI climatological data. Missing \\n \
LAI values in the finer resolution datasets were filled with the value for the \\n \
Expand All @@ -251,7 +251,7 @@ if($mylai == GLASSM) then
8-day climatology of LAI was computed from these data by temporally averaging over the 18-year period \\n \
(by 8-day periods) on the 3-arcmin grid. Those climatological data were aggregated over \\n \
the pixels of each land element to derive a 8-day LAI\\n \
In order to fill gaps that may exist due to inconsistencies between LAI and GEOS5 masks as well as data gaps themselves\\n \
In order to fill gaps that may exist due to inconsistencies between the LAI and GEOS masks as well as data gaps themselves\\n \
we constructed, at every time slice, a 1°×1° global gridded LAI dataset \\n \
by spatially aggregating the finer resolution LAI climatological data. Missing \\n \
LAI values in the finer resolution datasets were filled with the value for the \\n \
Expand All @@ -276,7 +276,7 @@ if($mylai == MODIS | $mylai == MODGEO) then
Preprocessing of the two datasets showed that each had potential flaws, \\n \
with GEOLAND2 showing questionable seasonal cycles in Siberia, and MODIS \\n \
showing questionable values over the rain forests. We thus decided to \\n \
produce a merged LAI data product for GEOS5 to avoid these potential \\n \
produce a merged LAI data product for GEOS to avoid these potential \\n \
deficiencies. \\n \
\\n \
The first step in generating the merged product was computing a 10-day \\n \
Expand Down Expand Up @@ -357,7 +357,7 @@ cat << _EOI_ > clsm/intro
Land Boundary Conditions for the
Goddard Earth Observing System Model Version 5 (GEOS-5)
Goddard Earth Observing System (GEOS) Model
${mygrid} Grid
Expand Down Expand Up @@ -423,7 +423,7 @@ cat << _EOI_ > clsm/intro
3.1 Data generation and processing chain
3.2 Data files and images
3.2.1 Mosaic vegetation types and fractions
3.2.2 vegdyn input data (mosaic primary type, canopy height, and roughness) for GEOS5
3.2.2 vegdyn input data (mosaic primary type, canopy height, and roughness) for GEOS
3.2.3 CLM/CLM4.5 and CLM/CLM4.5-carbon vegetation types and fractions
3.2.4 CLM Nitrogen Deposition, annual mean T2m, soil back ground albedo
3.2.5 CLM4.5 ABM, PEATF, GDP, HDM, and soil field capacity
Expand Down Expand Up @@ -461,7 +461,7 @@ cat << _EOI_ > clsm/intro
8.2.1 Country Code, US State Code, Country Name, State Name
8.3 References
APPENDIX I - mkCatchParam tag, input options, and log ........................... A-I
APPENDIX I - mkCatchParam input options and log ................................. A-I
=====================================================================================
=================================== SECTION 1 ======================================
Expand All @@ -470,8 +470,8 @@ APPENDIX I - mkCatchParam tag, input options, and log ..........................
1. INTRODUCTION
This directory contains land boundary data files that are needed by the land models in
the GEOS-5 climate modeling system. The catchment-tiles (computational units at the
land surface) have been derived for the GEOS5 ${mygrid} grid
the GEOS climate modeling system. The catchment-tiles (computational units at the
land surface) have been derived for the GEOS ${mygrid} grid
`echo "$int_str1"`
The ${mygrid} grid comprises ${NGLOBAL} tiles globally, of which ${NTILES}
are catchment-tiles.
Expand Down Expand Up @@ -650,7 +650,7 @@ cat << _EOS1_ > clsm/soil
(17) clay_rz [w%] percentage clay in the root-zone layer (0-100cm)
(18) WPWET_top [-] wilting point/porosity for the surface layer (0-30cm)
(19) POROS_top [m3/m3] soil moisture content at saturation in the surface layer (0-30cm)
(20) PMAP the fraction of cell covered bt PEATLAND
(20) PMAP [-] fraction of cell (tile?) covered by peatland
========================================================================
Expand Down Expand Up @@ -912,8 +912,8 @@ cat << _EOS1_ > clsm/soil
13.333 3.333 83.333 1.123 2.3751 -1.1086 0.4422 0.0555 0.57369E-06
6.667 6.667 86.667 1.123 2.6893 -1.0407 0.4470 0.0699 0.51317E-06
3.333 3.333 93.333 1.123 2.2797 -1.3816 0.4436 0.0563 0.32091E-06
PEAT (N/A) >=8.72 3.4130 -1.7600 0.8000 0.2162 0.78600E-06
PEAT,Bechtold et al. PEATMAP 3.5000 -0.0300 0.9300 0.3672 0.28000E-06
Peat: HWSD (N/A) (N/A) (N/A) >=8.72 3.4130 -1.7600 0.8000 0.2162 0.78600E-06
or Peat: PEATMAP (N/A) (N/A) (N/A) 3.5000 -0.0300 0.9300 0.3672 0.28000E-06 (Bechtold et al)
------------------------------------------------------------------------
Table 1: Soil Hydraulic Properties for 253 soil classes (adapted from De
Expand All @@ -938,7 +938,6 @@ cat << _EOS1_ > clsm/soil
T. M. Munir, M.B. Nilsson, J. S. Price, M. Röhl, A. Schneider, and B. Tiemeyer, 2019.
PEAT-CLSM: A specific treatment of peatland hydrology in the NASA Catchment Land
Surface Model. J. Adv. Model. Earth Sys., 11, 2130-2162. doi: 10.1029/2018MS001574.
_EOS1_
else
cat << _EOS2_ > clsm/soil
Expand Down Expand Up @@ -1033,8 +1032,8 @@ cat << _EOV1_ > clsm/veg1
with Mosaic types: 1 - Broadleaf Evergreen; 2 - Broadleaf Deciduous; 3 - Needleleaf;
4 - Grassland; 5 - Broadleaf Shrubs; 6 - Dwarf Trees
3.2.2 vegdyn input data (mosaic primary type, canopy height, and roughness) for GEOS5
. file name: vegdyn.data or ../vegdyn_*.dat
3.2.2 vegdyn input data (mosaic primary type, canopy height, and roughness) for GEOS
file name: vegdyn.data or ../vegdyn_*.dat
file format: fortran binaries, little_endian
read ([UNIT]) (primary_veg_type(n),n=1,${NTILES})
read ([UNIT]) (canopy_height (n),n=1,${NTILES})
Expand Down Expand Up @@ -1280,7 +1279,7 @@ cat << _EOF0_ > clsm/README1
Meanwhile, the SiB-based albedo scheme was run at a daily time step over a 1-year
period using the vegetation types, greenness fractions, and leaf area indices
established for GEOS-5 for a given distribution of land elements, as described
established for GEOS for a given distribution of land elements, as described
in sections 3 and 4 above. Averaging the visible diffuse and near-infrared
diffuse albedos generated by the scheme over 8-day periods produced, in effect,
an 8-day ‘climatology’ of this particular scheme’s diffuse albedos. The ratio
Expand All @@ -1294,7 +1293,10 @@ cat << _EOF0_ > clsm/README1
5.2 Data files and movies
5.2.1 MODIS Albedo Climatology [Diffused, Visible (0.3_0.7) and Near-Infrared (0.7_5.0);
Note: GEOS5/CLSM does not read these data
Note: These data are not read into GEOS. They are needed to derive the albedo
scaling parameters (Section 5.2.2), which are used in conjunction with
sun-angle dependent lookup values to achieve albedo values that match
the MODIS climatology while preserving the diurnal cycle.
[Movie 3 : "plots/VISDF.mp4" (Diffused visible) and Movie 4 "plots/NIRDF.mp4"
(Diffused infrared)]
file names : ${AlbFileNames}
Expand Down Expand Up @@ -1451,7 +1453,7 @@ cat << _EOF1_ > clsm/README2
codes together allows the construction of a catchment network within the basin.
Verdin (2013) provided global raster arrays of global Level 12 Pfafstetter codes
at 1-arcmin resolution along with information on mean elevation. These data sets
were used to build the global river channel network slated for use with GEOS-5.
were used to build the global river channel network slated for use with GEOS.
The steps used to generate the river network are as follows. Each catchment
(referred to in this discussion as CatchX) has up to two upstream catchments
Expand Down

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