Submitted as: development and technical paper01 Apr 2021
Submitted as: development and technical paper | 01 Apr 2021
Review status: this preprint is currently under review for the journal GMD.
A Model-Independent Data Assimilation (MIDA) module and its applications in ecology
Xin Huang1,2,Dan Lu3,Daniel M. Ricciuto4,Paul J. Hanson4,Andrew D. Richardson1,2,Xuehe Lu5,Ensheng Weng6,7,Sheng Nie8,Lifen Jiang1,Enqing Hou1,Igor F. Steinmacher2,and Yiqi Luo1,2,9Xin Huang et al.Xin Huang1,2,Dan Lu3,Daniel M. Ricciuto4,Paul J. Hanson4,Andrew D. Richardson1,2,Xuehe Lu5,Ensheng Weng6,7,Sheng Nie8,Lifen Jiang1,Enqing Hou1,Igor F. Steinmacher2,and Yiqi Luo1,2,9
Received: 05 Feb 2021 – Accepted for review: 31 Mar 2021 – Discussion started: 01 Apr 2021
Abstract. Models are an important tool to predict Earth system dynamics. An accurate prediction of future states 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 black-box applications of data-model fusion in ecology. MIDA facilitates the assimilation of various observations into models for uncertainty reduction in ecological modeling and forecasting.
In the data-rich era, data assimilation is widely used to integrate abundant observations into models to reduce uncertainty in ecological forecasting. However, applications of data assimilation are restricted by highly technical requirements. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module which is friendly to ecologists with limited programming skills. MIDA also supports flexible switch of different models or observations in DA analysis.
In the data-rich era, data assimilation is widely used to integrate abundant observations into...