[1]
If you use:
%> *ncl_filedump *
<https://www.ncl.ucar.edu/Document/Tools/ncl_filedump.shtml>
MCD19A2.A2019071.h10v11.006.2019073033901.*hdf|* less
Variable: f
Type: file
filename: *MCD19A2.A2019071.*h10v11.006.2019073033901
path: MCD19A2.A2019071.h10v11.006.2019073033901.hdf
file global attributes:
*HDFEOSVersion : HDFEOS_V2.19 StructMetadata_0 : GROUP=SwathStructure*
*END_GROUP=SwathStructure*
[SNIP]
This indicates the file use the* HDF-EOS *'convention'
HDF-EOS supports three geospatial data types: grid, point, and swath,
These data types are *not* accessible via standard HDF library calls.
NCL has a very nice simple feature that will return additional
[value-added] information.
For example: There are no latitude/longitude variables on the original .
*hdf* file.
NCL recognizes addtional 'virtual' file extensions: .*he2* [*.he5*] or
*hdfeos*.
%> *ncl_filedump *
<https://www.ncl.ucar.edu/Document/Tools/ncl_filedump.shtml>
MCD19A2.A2019071.h10v11.006.2019073033901.*hdf.he2|* less
The file will be 'cleaned-up' . Further, it will create classic
latitude/longitude arrays. EG
double *GridLat_grid1km ( YDim_grid1km, XDim_grid1km )*
projection : Sinusoidal
corners : ( -20.00416666486723, -20.00416666486723,
-29.99583333063962, -29.99583333063962 )
long_name : latitude
units : degrees_north
double *GridLon_grid1km ( YDim_grid1km, XDim_grid1km )*
projection : Sinusoidal
corners : ( -85.13204127176456, -74.49885022173193,
-80.83045538778872, -92.36735390691214 )
long_name : longitude
units : degrees_east
These can be read and used by an NCL script.
----
The attached *MCD19A2.JasonHerd.ncl *should do what you want. *It uses the
virtual .he2 file extension.* It is a slight modification of:
*https://www.ncl.ucar.edu/Applications/binning.shtml*
<https://www.ncl.ucar.edu/Applications/binning.shtml>
Example 1
There is much about Example 1 that is a bit out of date ...but it works!
Note that this is 'read-and-plot' script. No filtering by data quality
flags has been performed. That is your job.
*I do not have a complete set of MCD19A2 files for March 12, 2019. *However,
the attached plot gives the flavor of what you will get.
===
The golden rule of data processing is look at tour data. The are multiple
orbits per file.
Good luck
On Fri, Jun 14, 2019 at 12:48 PM Herb, Jason <jherb at albany.edu> wrote:
> Hello,
>
> I am working with the MCD19A2 aerosol data. To get an idea as to the
> overall coverage extent and quantity of measurements for each 1x1km grid
> box, I am attempting to make all measured data to be " 1 " and then add
> with each orbit pass in the file (Ex. the location being studied has a max
> of 6 passes for a day.) Assuming the full number of passes over the studied
> area and say fully clear skies to give no missing data when I map the
> replaced 1 into each grid point and sum the data points the map should show
> a uniform map of 6.
> That being said how can I replace any of the AOD data that falls in to the
> range of 0.00-1.00 to become 1 and sum up for from the array data?
>
> Thanks,
>
> Jason
>
> _______________________________________________
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