Code created for my MPhys Project at the University of Edinburgh. I am using optimisation methods to 'tune' uncertain aerosol parameters in a climate model.
This repository contains:
This Python code was used to analyse satellite data. It was run in a directory containing subsets of NASA's Aqua MODIS Level-3 Monthly Cloud Properties (available at https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/5111/CLDPROP_M3_MODIS_Aqua/), and CERES Energy Balanced and Filled version 4.1 (available at https://ceres-tool.larc.nasa.gov/ord-tool/jsp/EBAFTOA41Selection.jsp) datasets. It calculates large-scale regional averages of various observations: cloud effective radius, top-of-atmosphere (TOA) shortwave (SW) radiation flux, TOA longwave (LW) flux, TOA clear-sky SW and LW fluxes, and global mean TOA net flux over a period of five years.
This Python code was used to process output from the HadAM3 climate model in order to calculate averages of simulated versions of the observations listed above. Its structure borrowed heavily from a similar code written by Professor Simon Tett, the supervisor for this project.
This Jupyter Notebook was used to analyse the output of the DFO-LS study using the StudyConfig.py module written by Professor Simon Tett, the supervisor for this project. I also made use of the pandas, Matplotlib, Numpy and seaborn libraries. This Notebook is just that - it does not contain much commentary or any particular structure as it was used exclusively by myself to analyse data and produce graphs as and when required. It is not intended to be a stand-alone work.