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.
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Students differentiate between data sets of monthly shortwave radiation and monthly cloud coverage to discover a relationship between radiation and clouds by answering analysis questions.
This lesson is designed to help students analyze the interaction between different cloud heights and Earth's incoming and outgoing energy.
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.
This lesson, "Awenasa Goes to Camp!," is a data analysis activity that presents maps of NASA Earth satellite data for a variety of locations across the United States for four unidentified months throughout the year. Each location represents a real science camp th
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.
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 construct explanations about Earth’s energy budget by connecting a model with observations from side-by-side animations of the monthly mapped data showing incoming and outgoing shortwave radiation from Earth’s surface.
Students will analyze a pie chart (circle graph) showing the distribution of different parts of the Earth system's absorption and reflection of energy.
This lesson walks students through the use of Landsat false-color imagery and identification of different land cover features using these as models.