The files contained in this directory are the results of the 2017/06/27 cloud simulation run from DAG for the purposes of 
creating a neural network training set for RSP observations during ORACLES. The 06/27 run was performed for altitudes
Of 5000 and 7000m, so an additional run on 20170703 was performed at 6000m. 

THESE FILES ARE INTENDED FOR P-3 MID ALTITUDE OBSERVATIONS:

The loop over parameters is as follows:

    sizeb=[0.01, 0.03, 0.05, 0.07, 0.1, 0.15]                        ;6
    n_sizeb=n_elements(sizeb)

    Alt = [5000,6000,7000]                                ;3
    n_alt = n_elements(alt)

    cod = [2.5, 5.0, 10.0, 15.0, 20.0, 30.0]    ;6
    n_cod=n_elements(cod)

    sizea=[5.0, 7.5, 10.0, 12.5, 15.0, 20.0]    ;6
    n_sizea=n_elements(sizea)

    sza=[30.0]                    ;5 to 75 in 5 increments,    sza and azi are now iterated with VEC_INTERP
    n_sza=n_elements(sza)

    azi=[0.0]                    ;0 to 180 in 2 increments,    sza and azi are now iterated with VEC_INTERP
˚    n_azi=n_elements(azi)


-rw-r--r--  1 kknobels  staff  1957489105 Jul  7  2017 NN_clouds_20170627-20170703.nc.gz		-no noise, rel azi 0-90 
-rw-r--r--  1 kknobels  staff  3352953768 Feb 14 12:04 NN_clouds_20170627-20170703_std.nc		-same as above, but ‘standardized’ so that the mean across
													 cloud types (for each geometry and wavelength) is subtracted from the training set
													 values. The result is divided by the computed measurement uncertainty based on that
													 mean value.