Crepes_veges data: Solving calibration and reanalysis challenges of ocean BGC dynamics with neural schemes: a 1D NNPZD case-study

Repository for the paper 'Solving calibration and reanalysis challenges of ocean BGC dynamics with neural schemes: a 1D NNPZD case-study.' Description: This study is codenamed 'VEGES - Vertical Experiment Gauging Estimation Strategy'. It is part of the PhD project "CREPES - Carbon REconstructed Per an Emulator through Supervision" (Carbone REconstruit Par Emulateur Supervisé). This dataset contains 7 different files: Calibration_DA.py to calibrate the BGC model using a 4Dvar-based scheme. Calibration_NN.py to calibrate the BGC model using a UNet-based scheme. environment.yaml to install the suitable python environment with correct package version. func_file.py that contains the needed functions. Generator_data.py to generate all the necessary data sets. model_file.py that contains the UNet model. Notebook_analysis_plot.ipynb to plot the paper results. The Data sets used in the article are compressed in "Generated_Datasets_part1.zip" and "Generated_Datasets_part2.zip". For their correct use, you can extract them and place them in a single "Generated_Datasets" folder.The results used in the article are compressed in "Res.zip".The Data are generated using a set of forcing profiles from polgyr, stored in "FORCING_40km.zip". 1. Install the correct packages with their associated version with the environment.yaml file.2. Generate the different data sets: run Dataset_Generator.py3. Use freely the different methods (run Calibration_DA.py, Calibration_NN.py)