At the core of scientific visualization is the representation of data graphically - through images, animations, and videos - to improve understanding and develop insight. Data visualizers develop data-driven images, maps, and visualizations from information collected by Earth-observing satellites, airborne missions, and ground measurements. Visualizations allow us to explore data, phenomena and behavior; they are particularly effective for showing large scales of time and space, and "invisible" processes (e.g. flows of energy and matter) as integral parts of the models.

This video provides tips for teachers on helping students make sense of data to help them understand and work with data. It is based on the work of Kristin Hunter-Thomson of Dataspire.org and uses data from the My NASA Data Earth System Data Explorer.

Follow along as NASA visualizer Kel Elkins walks you through three visualizations (Dust Crossing, Typhoon Hagupit, and Aquarius Sea Surface Salinity) and answers questions about his work, education, and career.

Information from satellites if often used to display information about objects. This information can include how things appear, as well as their contents. Explore how pixel data sequences can be used to create an image and interpret it.

Scientific data are often represented by assigning ranges of numbers to specific colors. The colors are then used to make false color images which allow us to see patterns more easily. Students will make a false-color image using a set of numbers.

Students analyze the stability and change of sea level after watching a visualization of sea level height around the world. 

This mini lesson helps students visualize how the Hydrosphere and Cryosphere interact to produce changes in land and sea ice. 

Geospatial Information Scientists and Technologists research geospatial data or develop geospatial technologies. Geospatial data is data that has a geographic component associated with it, such as coordinates or an address, and geospatial technologies are the technologies used to collect and analyze geospatial data. 

Students explore the spatial patterns observed in meteorological data and learn how this information is used to predict weather and understand climate behavior.

A model analyst develops models to help visualize, observe, and predict complicated data. Model analysis is the process of taking large amounts of data and separate it into a structure that makes it intelligible to the binary process of computers. An analyst also manages the flow of information between different user groups through the use of relational databases.