Hi,
My understanding is that "mkgriddata" and "mksurfdata" tools can only be used to generate the regular grid. In order to run CLM for non-regular grids like T62, I downloaded the generated data (griddata, fracdata, and surfdata) at T62 from the ncar inputdata directory.
However, I found two versions of data sets are not consistent with each other. Here is the detail information:
fracdata_94x192_gx1v4_060711.nc
fracdata_94x192_T62_c081219.nc
griddata_94x192_060711.nc
griddata_94x192_c081219.nc
surfdata_94x192_060711.nc
surfdata_94x192_c081219.nc
I tend to use the version c081219 which seems to be more recent, but found that the fracdata has constant values for landmask and landfrac over the global - the data does not differentiate the land and ocean. If I use the version 060711, I noticed that surfdata_94x192_c081219.nc claims more land points over the tropical compared with surfdata_94x192_060711.nc and some difference exists in the arctic region as well.
Any information about how to chose these data sets will be high appreciated.
Xiang
My understanding is that "mkgriddata" and "mksurfdata" tools can only be used to generate the regular grid. In order to run CLM for non-regular grids like T62, I downloaded the generated data (griddata, fracdata, and surfdata) at T62 from the ncar inputdata directory.
However, I found two versions of data sets are not consistent with each other. Here is the detail information:
fracdata_94x192_gx1v4_060711.nc
fracdata_94x192_T62_c081219.nc
griddata_94x192_060711.nc
griddata_94x192_c081219.nc
surfdata_94x192_060711.nc
surfdata_94x192_c081219.nc
I tend to use the version c081219 which seems to be more recent, but found that the fracdata has constant values for landmask and landfrac over the global - the data does not differentiate the land and ocean. If I use the version 060711, I noticed that surfdata_94x192_c081219.nc claims more land points over the tropical compared with surfdata_94x192_060711.nc and some difference exists in the arctic region as well.
Any information about how to chose these data sets will be high appreciated.
Xiang