Python implementation of the AOD retrieval algorithms (include ozone affected bands model)

This is a python implementation of the code developed in [1]. The scrips are in "Code" folder. Here, we provide a small description of those codes:

• aeronet_processing_data.py: Takes de data provided by AERONET, extract the main features, and creates a .json file. You can get AERONET data using: https://aeronet.gsfc.nasa.gov/cgi-bin/data_display_aod_v3?site=Santiago_Beauchef&nachal=0&year=2024&month=4&aero_water=0&level=2&if_day=0&if_err=0&place_code=10&year_or_month=0 please download the files into the aeronet folder.
• aod_display: Using the .json files and estimates the AOD as [1] and plot the daily AOD profiles.
• aod_display_v4.py: Using the .json files and estimates the AOD and plot the daily AOD profiles under V4 type data.
• calibration_class.py: Code used to calculate the calibration constants as [1].
• calibration_v4.py: Code used to calculate the calibration constants for the V4 data type.
• measurement_class.py: create an object to process data obtained from V4 Photometer.
• ozone_model.py: functions to process ozone model.
• processing_data.py: scrip to fix data depending on the available information measurements (save the data into a .json file).

The end scrips are aod_display.py or aod_display_v4.py.

[1] Garrido, C.; Toledo, F.; Diaz, M.; Rondanelli, R. Automated Low-Cost LED-Based Sun Photometer for City Scale Distributed Measurements. Remote Sens. 2021, 13, 4585. https://doi.org/10.3390/rs13224585