Preprints
https://doi.org/10.5194/gmd-2021-33
https://doi.org/10.5194/gmd-2021-33

Submitted as: development and technical paper 01 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 Luo1,2,9 Xin Huang et al.
  • 1Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA
  • 2School of informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
  • 3Computational Sciences and Engineering Division, Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
  • 4Environmental Sciences Division, Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
  • 5International Institute for Earth System Science, Nanjing University, Nanjing, China
  • 6Center for Climate Systems Research, Columbia University, New York, USA
  • 7NASA Goddard Institute for Space Studies, New York, USA
  • 8Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
  • 9Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA

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.

Xin Huang et al.

Status: open (until 27 May 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Xin Huang et al.

Xin Huang et al.

Viewed

Total article views: 233 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
186 42 5 233 1 6
  • HTML: 186
  • PDF: 42
  • XML: 5
  • Total: 233
  • BibTeX: 1
  • EndNote: 6
Views and downloads (calculated since 01 Apr 2021)
Cumulative views and downloads (calculated since 01 Apr 2021)

Viewed (geographical distribution)

Total article views: 213 (including HTML, PDF, and XML) Thereof 213 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 19 Apr 2021
Download
Short summary
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.