Using Data to Improve Ecological Forecasts (Forecasts & Data)
This module introduces students to concepts and methods for data assimilation, or the process of updating forecast models to incorporate new data as they become available (Niu et al. 2014). Students fit an autoregressive time series model to predict chlorophyll-a at a NEON lake site of their choice and examine the effect of updating the initial (starting) conditions of the model with chlorophyll-a data at different temporal frequencies (e.g., updating the model once a week vs. once a day) and with low vs. high observation uncertainty. Seehttp://module7.macrosystemseddie.org for a detailed description of all module materials; module code for the R Shiny application and RMarkdown as well as instructor materials are also published with DOIs in Lofton et al. (2024a), Lofton et al. (2024b), and Lofton et al. (2024c), respectively.