Data for manuscript "Emulator-based calibration of a dynamic grassland model using recurrent neural networks and Hamiltonian Monte Carlo" by Aakula et al.
Description
Data and python code for the manuscript "Emulator-based calibration of a dynamic grassland model using recurrent neural networks and Hamiltonian Monte Carlo", for performing emulator hyperparameter optimization and training. Python file optimize_LSTM_emulator.py can be used either for training an LSTM emulator with predefined hyperparameters or to optimize hyperparameters from a given hyperparameter space. The training data for each fold is included in the files of shape training_data_fold_{}.parquet. The data is obtained from model simulations, including model inputs (meteorological forcings obtained from ERA5 data), model parameters (sampled from distributions defined in the manuscript) and model (BASGRA) outputs. Text file examples.txt gives instructions and examples on running the script.
Files
examples.txt
Files
(383.3 MB)
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Additional details
Identifiers
- URL
- https://etsin.fairdata.fi/dataset/f2b98b47-19db-4158-8093-49584d433fb2
- B2SHARE Legacy Record ID
- bac65edfcadb459b89248090d2412240
Funding
- Research Council of Finland
- Strategic Research Council at the Research Council of Finland
- Ministry of Agriculture and Forestry of Finland
- Business Finland
- EU Horizon Europe
- European Union – NextGenerationEU
FMI metadata
- Process step
- The meteorological data, obtained from ERA5, and BASGRA model outputs were averaged to weekly values.
- Model
- BASGRA (Basic Grassland Model): https://doi.org/10.1016/j.ecolmodel.2019.108925
- Parameter
- Topic category
- environment
Temporal Coverage
Ranges:
End date: 2023-04-30