Project_contrail

Code for analysis of PC constrained high cloud attributes in limited years I am trying my best to find any possible signal of contrail reduction because of the COVID-19 pandemic in 2020 To do so we want to build up strong constrain on meterology field and IWP and AOD in the same time and limit the Cld signal in limited level

Update: I failed this project, no contrail signal can be extracted using this approach, if you want to carry on my tryout, please contact me to get full code and full support, what a pity (┬┬﹏┬┬)

Code for Analysis of PC Constrained High Cloud Attributes in Limited Years

The following bullet points provide an overview of the code for analyzing PC constrained high cloud attributes in limited years to investigate the possible signal of contrail reduction due to the COVID-19 pandemic in 2020:

• The code will analyze high cloud attributes, such as ice water path (IWP), aerosol optical depth (AOD), and cloud signal, to determine if there was a reduction in contrails during the COVID-19 pandemic.
• To build strong constraints on the meteorology field, the code will use a PC constrained approach, which involves identifying and selecting key patterns in the data that explain the majority of the variability.
• The code will focus on a limited number of years to ensure that the analysis is relevant to the COVID-19 pandemic. The specific years to be analyzed will depend on the availability of data and the scope of the analysis.
• To limit the cloud signal, the code will apply a filtering process to remove low-level clouds and other sources of noise in the data. This will help to ensure that any observed changes in high cloud attributes are due to changes in contrails, rather than other factors.
• The code will be written in a programming language such as Python or R, and will utilize relevant libraries for data analysis and visualization, such as Pandas, NumPy, and Matplotlib.
• The code will first import the necessary data, including meteorological data, satellite data for IWP and AOD, and cloud mask data.
• The code will then apply a PCA algorithm to the data to identify key patterns and constrain the meteorology field.
• Next, the code will apply a filtering process to the cloud mask data to remove low-level clouds and other sources of noise.
• The code will then calculate IWP and AOD values for the remaining high clouds, and compare these values to pre-pandemic years to determine if there was a reduction in contrails during the COVID-19 pandemic.
• Finally, the code will generate visualizations, such as scatter plots and heat maps, to help visualize the data and identify any trends or patterns that may be indicative of changes in contrails.

Technical notes:

• The code will need to account for missing or incomplete data, and will need to handle these cases appropriately (e.g., by filling in missing values or excluding incomplete data from the analysis).
• The code will need to use appropriate statistical tests and methods to determine if any observed changes in high cloud attributes are statistically significant.
• The code may need to apply additional filtering or preprocessing steps to the data to ensure that the analysis is robust and accurate.
• The code will need to be well-documented and modular, with clear comments and variable names to facilitate understanding and maintenance.