Meteorology students hardly experience smooth and expeditious data analysis. When it comes to results, they oftentimes plunge to nebulous insights and vague conclusions. Despite how clearly the method used, meteorology students shouldn’t take the idea of being creative in handling the data for granted. The quality of results totally depends on how creative they scramble the data and extract its insights. Hence the better result quality comes the more beneficial explanation. For the practical instance, when harnessing the time series meteorological dataset on analysis, many students trivialize the existence of another possible domain on the dataset. Many students don’t treat…


Hi folks! This is my very first post on Medium since I’ve been realized that blogspot isn’t so-called popular anymore. In the past few months, I’ve got several messages from my college peers who have read my thesis and felt kind of amazed by my satellite data visualization (unbelievable). So I tried to just give them some raw scripts and succinctly deciphered how’re the scripts work. It was a struggle for me to recall those meteorological terms and such. Right after that, I felt why don’t I try to recollect and enshrine my college meteorological-related works on Medium? …

Tio Faizin

Atmospheric Data Science Enthusiast | Data Analyst in Fintech.

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