Coding and Hands-On Activities
Explore these hands-on activities to gain valuable coding experience and follow step-by-step guides to enhance your data analysis skills using real-world oceanography data.
Comparing Two Sea Surface Temperature (SST) Products
Building on Shane Elipot (University of Miami)'s lecture, "Deriving Uncertainties for the Global Drifter Program Hourly Product: Case Studies," this activity allows you to compare the Global Drifter Program (GDP) hourly SST product and the Multi-scale Ultra-high Resolution (MUR) SST analysis. You will be encouraged to explore both datasets, interpret uncertainty estimates, and assess whether the datasets show significant differences.
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Analysis of Ocean Heat Content Monthly Maps Created from Sparse Observations
This activity expands on concepts introduced in Donata Giglio (University of Colorado)'s lecture, "MapEval4OceanHeat: An Objective Assessment of Mapping Methods Used to Estimate Ocean Heat Content Change." You will identify differences in statistical methods based on the output ocean heat content field. Additionally, this activity allows you to visualize different fields such as running averages of regional ocean heat content time series, extreme events, lag-1 autocorrelation, and maps of spatial derivatives, among others.
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Building a Kalman Filter
This final activity from the OceanUQ Summer School is an exploratory exercise that allows you to implement a Kalman filter. The goal is to adjust data, uncertainties, assumptions (both known and unknown) to observe their impact on state reconstructions. Foundational concepts in data assimilation and machine learning are covered in Sarah Williamson (University of Texas at Austin)'s lecture "A Brief Introduction to Data Assimilation" and Aneesh Subramanian (University of Colorado)'s lecture "ML for DA and Model Error Representation."
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