TTL Subvisible Cirrus Layers
Data Files
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Text files that can be obtained from Nasa’s Cloud Physics Lidar webpage under “TC4”
project label (http://cpl.gsfc.nasa.gov/). The text files are labeled well to explain what is
contained in them.
Excel files were produced from the Text files in order to load into Matlab
Source Code Files
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Includes a folder for the date of each science flight (July 7, 9, and 14 were test flights)
Includes a Raw_layers.m file that was used to creat the Raw_data_workspace.mat
variable
Each folder contains
- Raw_ Data_ Workspace .mat file. This includes a Matlab matrix for each of the flight
dates created from the excel files mentioned above (they look very similar to the
excel files)
- Layer_plots.m file. This script will create a long row vector for each of cloud top,
cloud base, and thickness for sensed layers. It then plots the data. The layer plot
files also discriminate what data to use. The .mat files may have information on
several layers of clouds but only the highest two layers are used. Also, in the
interest of obtaining info on the desired cloud type, there is a minimum height
required for the base of the cloud layer in order to be included in the cloud vector.
This minimum height is listed near the top of the script and can be changed based
on the appearance of the clouds in the plot (Note: For unknown reasons, when
changing the minimum height in layer_plots script, Matlab sometimes won’t
acknowledge that anything was changed and will continue to run the script with the
previous values until Matlab is restarted. This bug has occurred on several different
machines). The layer plots file must be run before running the stats script. It
creates the variables used in the stats script. This was intentional as the user should
examine the layer plots and decide what to set the height discrimination at before
running the stats script.
- Stats_script.m file. This script takes the row vectors for top ht, base ht, and
thickness and processes them. The processing starts by creating a data matrix from
the vectors. The script will break the vector up at a fixed increment (it performs this
for increments of length 200, 100, 50, and 25) and takes those new fixed increment
vectors and makes a matrix from them. For example, for the length 100 increment,
a matrix will be created of dimensions n x 100. If the length 100 vector that is
created has no values in it (i.e. if there was a blank spot in the clouds along the
plane track for that period) the script skips it and moves on until it finds a data
point. In addition, if a vector has blank spots in it (ex: 2, 4, 8, 0, 0, 0, 0, 3, 9, 0, 4) it
will shuffle all values to the left (new vector: 2, 4, 8, 3, 9, 4, 0, 0, 0, 0, 0). Once this is
complete, the script computes statistics for each row of the matrix (row must have
at least 20 data points in it) and loads them into a stats matrix of 12 columns (the
columns are labeled in the excel summary files). There will generally be a 8 stats
matrices for each flight (two layers each with 25, 50, 100, and 200 column matrices).
For the August 18 July 22 flight, there was very little data above 8km, so there is no
stats script. For July 29, the second layer had very few points so there will only be 4
matrices for this flight.
Excel Summaries
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These are summaries of the stats matrices created for each flight. They are labeled
as to the information contained and there is a an excel file for each science flight
with the exception of August 18 and July 22 (not enough data in region of interest).
Justin Walker
360 710 8696
jew6@u.washington.edu
corporaldiaz@gmail.com