UNISALLSKY_OH_TEMP_MAP

This software reads in images from UNIS' Allsky Airglow camera and produces a map of mesosphere 87km temperature above Svalbard.

Author

Rowan Dayton-Oxland University of Southampton Github

Installation

Download or pull from Github

Requirements

Python >= 3.10
.ipynb capabilites e.g. Jupyter Notebook, Visual Studio code
OS independent
numpy, pandas, matplotlib, pathlib, struct, glob, datetime

Contributing

Pull requests are welcome, for major changes please open an issue first to discuss what you would like to change.

Algorithm

Read in images (.img format) from UNIS Allsky airglow camera
Calibrate images by using the corner (dark) values
Calculate the rotational temperature from the P1(2) P1(4) line ratios (Channels 5 and 6 of the camera)
Create temperature keograms

Temperatures are calculated according to the equation

$$ T_{rot} = \frac{\frac{hc}{k} (F_b - F_a)}{ln(\frac{I_{a} A_{b} (2J_{b} + 1)}{I_{b} A_{a} (2J_{a} + 1)})} $$

Where $F,A,J_{a, b}$ are known quantum coefficients, and $I_{a,b}$ are the intensities of the lines in each pixel.

Data for the two emission lines

Symbol	F(J') (cm)	Wavelength (A)	J'	Einstein A	Channel
P1(2)	-45.170339	8399	1.5	0.434	5
P1(4)	113.752553	8465	3.5	0.579	6

Examples

Run the script including all the functions. Current iteration in .ipynb format sonopen the file and run in Jupyter Notebook or another appropriate editor.

Notebook contains examples of generating a single temperature map and a keogram.

import glob

# Get the files from the CH5 and CH6 folders (typical format for UNIS Allsky Airglow Camera data)
Channel5files = glob('filepath/CH5/*.img')
Channel6files = glob('filepath/CH6/*.img')

# Use get_image_pairs to match up the images in time
image_pairs = get_image_pairs(Channel5files, Channel6files)

# To get the time from a specific image file as datetime obj
fname = Channel5files[0]  # For example
time = time_fname[fname]

# To read an image file
header, image = read_img_file(fname)

# To get the dark value from the corner of the image
darkvalue = corner_dark(image)

# To get the calibrated image file (using the corner dark)
calibrated_img = calibrated(image)

# To get a temperature map from a ch5 image and a ch6 image (calibrate first)
  # Example for first pair of images
temp_map = temperature_map(image_pairs[0][0], image_pairs[0][1]) 

# To get the cleaned up version an image or map
cleaned = cleanup_map(image)

# To retrieve a list of all the temperature maps for a folder or some of a folder
tempmaps = get_tempmaps(Channel5files, Channel6files, len(Channel5files))
# or
tempmaps = get_tempmaps(Channel5files, Channel6files, 10)
# To get the first 10 temperature maps

# To produce a keogram from a folder or some of a folder (as above)
timestamps, keogram = get_keogram(Channel5files, Channel6files, len(Channel5files))

Limitations

The calibration of the Airglow camera (true darks, flat fields) is not done so temperature estimates are not accurate - when compared with the 'true' value from the SilverBullet, NIPR and Aura cameras, this code gave 100K higher temperatures. However, it gives a picture of temperature variations which can be useful to detect e.g. pressure waves which will be visible in the keograms.