Visualizing Station-Level USA Temperature Data in Python

Introduce the USA temperature data and tell how to visualize these data with GeoPandas

Spatiotemporal data are increasingly accessible due to the remarkable development of sensing technologies and advanced information systems. These data provide unprecedented opportunities for discovering spatiotemporal structures and patterns underlying the data. Today, we will introduce a North America temperature dataset, publicly available at https://daac.ornl.gov/DAYMET. To visualize these data, we can consider to use the GeoPandas package in Python. Let’s get started!

USA Temperature Data

Daymet project provides long-term and continuous estimates of daily weather and climatology variables for North America, including daily minimum and maximum temperature, precipitation, vapor pressure, shortwave radiation, and snow water equivalent. These data bring great potential for biogeochemical terrestrial modeling and have myriad applications in many Earth science, natural resource, biodiversity, and agricultural research areas.

In what follows, we consider the station-level data, instead of gridded estimates. There are 5,380 stations over the United States Mainland. In this story, we use the daily maximum temperature data in the United States Mainland from 2010 to 2021 (i.e., 12 years or 4,380 days in total) for evaluation. The data can be represented as a matrix of size 5380-by-4380. In…