Int64 also valid type

[milankl]

Using Int64 for the time coordinate seems to work without problems

output_startdate = DateTime(2000,1,1)
time_string = "hours since $(Dates.format(output_startdate, "yyyy-mm-dd HH:MM:0.0"))"
dim_time = NcDim("time",0,unlimited=true)
var_time = NcVar("time",dim_time,t=Int64,atts=Dict("units"=>time_string,"long_name"=>"time"))

and then on every time step with changing time_hrs

NetCDF.putvar(netcdf_file,"time",[round(Int64,time_hrs)],start=[i_out])
NetCDF.sync(netcdf_file)

ncdump then yields

milan@milanmac SpeedyWeather.jl % ncdump -c run0006/run0006.nc
netcdf run0006 {
dimensions:
	lat = 48 ;
	time = UNLIMITED ; // (41 currently)
	lon = 96 ;
	lev = 8 ;
variables:
	int64 time(time) ;
		time:units = "hours since 2000-01-01 00:00:0.0" ;
		time:long_name = "time" ;
...
 time = 0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96, 
    102, 108, 114, 120, 126, 132, 138, 144, 150, 156, 162, 168, 174, 180, 
    186, 192, 198, 204, 210, 216, 222, 228, 234, 240 ;

[visr]

That's right, it's good to add Int64 as well since it's a commonly used type. The list is not exhaustive, see nctype2jltype:

    NC_BYTE => Int8,
    NC_UBYTE => UInt8,
    NC_SHORT => Int16,
    NC_USHORT => UInt16,
    NC_INT => Int32,
    NC_UINT => UInt32,
    NC_INT64 => Int64,
    NC_UINT64 => UInt64,
    NC_FLOAT => Float32,
    NC_DOUBLE => Float64,
    NC_CHAR => ASCIIChar,
    NC_STRING => String,