NASA visualizers take data – numbers, codes – and turn them into animations people can see and quickly understand.

Students will analyze a graph showing the amounts of peak energy received at local noon each day over the year changes with different latitudes. 

Students visit a NASA Website called "Eyes on the Earth" to view satellite missions in 3D circling the Earth and learn to navigate to specific satellites to learn about their capability of analyzing our changing planet and air quality.

Students watch a video and answer questions on Dr. Patrick Taylor (Atmospheric Scientist, NASA Langley Research Center) as he discusses the study of clouds and Earth's energy budget by analyzing data from Low Earth Orbit satellites. 

Students watch a NOVA PBS video about the different effects of clouds on climate and Earth's energy budget. Then they answer questions and brainstorm to complete a flow chart of events that might occur if the percentage of absorbing clouds increases.

This lesson is designed to help students analyze the interaction between different cloud heights and Earth's incoming and outgoing energy. 

The extreme temperatures during July 2022 prompt students to investigate a model that displays historical heat wave frequency data to discover the importance of defining terms when interpreting data.

Students use albedo values of common surfaces along with photographic images of Earth taken from the International Space Station to make an argument about specific anthropogenic activities that impact Earth’s albedo.

Students consider the impact of changing conditions on the remote island of Little Diomede, Alaska after they investigate the relationship between seasonal trends in sea ice extent with shortwave and longwave radiation flux described in Earth’s energy budget.

Students analyze two North Pole orthographic data visualizations produced from soil moisture data. After describing trends in the seasonal thaw of land surfaces, students demonstrate their understanding of Earth’s energy budget by explaining relationships and make predictions about the dataset.