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.