Abstract. Models are an important tool to predict Earth system dynamics. An accurate
prediction of future states of ecosystems depends on not only model
structures but also parameterizations. Model parameters can be constrained
by data assimilation. However, applications of data assimilation to ecology
are restricted by highly technical requirements such as model-dependent
coding. To alleviate this technical burden, we developed a model-independent
data assimilation (MIDA) module. MIDA works in three steps including data
preparation, execution of data assimilation, and visualization. The first
step prepares prior ranges of parameter values, a defined number of
iterations, and directory paths to access files of observations and models.
The execution step calibrates parameter values to best fit the observations
and estimates the parameter posterior distributions. The final step
automatically visualizes the calibration performance and posterior
distributions. MIDA is model independent, and modelers can use MIDA for an
accurate and efficient data assimilation in a simple and interactive way
without modification of their original models. We applied MIDA to four types of ecological models: the data assimilation linked ecosystem carbon (DALEC)
model, a surrogate-based energy exascale earth system model: the land
component (ELM), nine phenological models and a stand-alone biome
ecological strategy simulator (BiomeE). The applications indicate that MIDA
can effectively solve data assimilation problems for different ecological
models. Additionally, the easy implementation and model-independent feature
of MIDA breaks the technical barrier of applications of data–model fusion in ecology. MIDA facilitates the assimilation of various observations into
models for uncertainty reduction in ecological modeling and forecasting.
A model-independent data assimilation (MIDA) module and its applications in ecology
