These IDL, FORTRAN 90, Python, and Matlab routines are available 
to aid in reading the RSS bytemap daily and time-averaged data files.  
The read routines have been tested and work correctly within 
our PC environment. We do not guarantee that they work perfectly 
in a different environment with different compilers.  If portability 
is a problem, we suggest using the Python code.

We provide data from the TMI instruments on TRMM.  This platform flies in 
a semi-equatorial orbit and measures only between +40/-40 deg latitude.
The values within a few degrees of +/- 40 are of poorer quality than other 
grid cells.  Ocean products in these files include sea surface temperature,
surface wind speed, atmospheric water vapor, cloud liquid water, and rain rate.

To convert between grid location and latitude/longitude values use
	Longitude  is 0.25*xdim- 0.125   where xdim and ydim are grid cell numbers
	Latitude   is 0.25*ydim-40.125

The FORTRAN subroutines are located in read_tmi_subroutines_v4.f
read_tmi_day returns a 1440x320x7x2 real array called tmi_data.
A description of the data within this array is at the top of the 
subroutine.  read_tmi_averaged returns a 1440x320x6 real array
called tmi_data.  The 3-day, weekly and monthly binary files
can all be read with this one routine.  Just supply the correct
filename with path.  These routines have been tested with 
Intel Visual Fortran 90.  Data files must be unzipped prior to 
using these routines. Use a file unzipper of your choice.


The IDL read_tmi_day_v7.pro routine requires a full path filename
and returns five 1440x320x2 real arrays.  The time-averaged
data files are read using read_tmi_averaged_v7.pro.  This routine
returns four 1440x320 real arrays.  A description is provided 
within the routine.  These routines have been tested with IDL 8.1
Data files do not need to be unzipped when using the /compress keyword
in the read call.


Matlab read routines, read_tmi_day_v7.m and read_tmi_averaged_v7.m, 
function similar to the IDL routines listed above and have been tested 
with Matlab 7.1  Data files must be unzipped before using the matlab 
routines.


The Python code consists of tmi_daily_v7.py and tmi_averaged_v7.py
These routines require the use of bytemaps.py.  The example_usage.py 
code provides a main program that you can use to test the data or 
adapt to your needs.  Description of file contents is provided at the 
top of each routine.


C++ read routines consist of tmi_daily.h, tmi_daily.cpp and tmi_example.usage.cpp
as well as averaged files for each.  The tmi_example.usage.cpp provides
a main program that can be used to test the data or use as a starting
point to adapt to your needs.  A description of the file contents is provided
at the top of each routine.  dataset.h and dataset.cpp are required 
for programs to work.

Once you have further developed one of these skeleton programs
to suit your processing needs, you may wish to use the appropriate verify file 
to confirm that the data files are still being read correctly.
Please check that you have the correct verify file before contacting us.

If you have any questions regarding these programs, 
or the RSS binary data files, contact our support desk at
at support@remss.com