It has been four days since I turned in my honors draft and four days since I last looked at my thesis. This is the longest I have gone without it in 2 months. I felt a lot of relief after turning the draft in and I am genuinely excited to get feedback and begin the editing and writing process over again. However, I am also filled with overwhelming fear of rejection and self doubt. I think that is natural?
I know I still have some major things I need to get done before the final. Unfortunately, a lot of that is research related! I still need to manually classify all of the images from the high resolution set and get Jessica and Isabel to help me out. I gave them a deadline of next Friday which means I can’t really start running code to evaluate until then. Fortunately, those codes are already written but it still might take a lot of time to run and I am sure I will have to do a lot of trouble shooting. That leaves me one week to finish the classification work up and turn in the final!!! I am pretty stressed about that time crunch, but the best thing I can do is get to work on other aspects so running code is the only thing I have to do that week.
I know I also have to work on the network analysis too. I am pretty disheartened that my first methodology didn’t work out the way I wanted it too. I need to try something new and have something finalized by this Friday.
Anyway, enough with the feelings…time to start working! I think that writing a new abstract will help me get back into the swing of things!
Abstract
Clouds are an integral component of our atmosphere and are involved in complex interactions that regulate the climate of our Earth. It is necessary to have a comprehensive observational record of both cloud type and cloud cover in order to look at trends, better understand atmospheric processes, and to compare with models. Unfortunately, there are problems with both manual and instrumental observations because clouds are very difficult to objectively classify into distinct categories. This paper examines the science behind cloud classification and the network of tools, algorithms, and instruments that shape our understanding of clouds. Constructing an automated cloud classification algorithm for sky images confronts problems of observational subjectivity and examines the validity of technological solutions.