inst/extdata/methods_FLARE.md

In cboettig/forecast-standards: EFI ecological forecasting output and metadata standards

Methods Description

Forecast timestep 1 day

Forecast time horizon

16 days

Data assimilation

Data Assimilation used: Yes If, DA used - type of method: EnKF If, DA used - Number of parameters calibrated: 3 If, DA used - Sources of training data (DOI, GitHub): https://github.com/CareyLabVT/SCCData/tree/carina-data

Model Description

Type of model (Empirical, process-based, machine learning): Process-based Model name: General Lake Model-AED V3 Location of repository with model code: GLM: https://github.com/AquaticEcoDynamics/GLM AED: https://github.com/AquaticEcoDynamics/libaed2 Model citation: Hipsey et al. 2019 GMD Total number of model process parameters: Hard to count

Model Covariates

Type (i.e., meteorology): meteorology Source (i.e., NOAA GEFS): NOAA GEFS 16-day

Type (i.e., meteorology): Stream Inflow Source (i.e., NOAA GEFS): https://github.com/CareyLabVT/SCCData/tree/diana-data

Uncertainty

Answers: No, Derived from data, Propagates, Assimilates

Initial conditions: Assimilates Parameter: Propagates, Assimilates Parameter Random Effects: No Process (within model): Propagates, Assimilates Multi-model: No Driver: Derived from data Scenario: Yes - oxygen system on and off

Method for propagating uncertainty (Analytic, ensemble numeric): ensemble numeric If Analytic, specific method: NA If ensemble numeric, number of ensembles: 210

cboettig/forecast-standards documentation built on Jan. 1, 2023, 8:21 a.m.