Development of radar precip intensity band fraction modelling, trained on extended dataset
closes #45, closes #64, closes #65, closes #54
Function in fraction_model_dev_reduced_bands_azml.ipynb for calculating u and v wind fields - could be incorporated into data prep.
closes #93
Updated train/test sampling to ensure all ensemble members for same time and space are in the same sample
closes #82
Updated prd_pipelines.py and prd_cluster_train_demo.py to generalise to both fractional and mean precip rate cases. Therefore merging fraction_model_pipeline and model_pipeline folders.
closes #103
Exploration of approaches to reduce class imbalance including resampling of different bands and weighting bands when training model. Further assessment required of best approach.
Development of radar precip intensity band fraction modelling, trained on extended dataset closes #45, closes #64, closes #65, closes #54
Function in fraction_model_dev_reduced_bands_azml.ipynb for calculating u and v wind fields - could be incorporated into data prep. closes #93
Updated train/test sampling to ensure all ensemble members for same time and space are in the same sample closes #82
Updated prd_pipelines.py and prd_cluster_train_demo.py to generalise to both fractional and mean precip rate cases. Therefore merging fraction_model_pipeline and model_pipeline folders. closes #103
Exploration of approaches to reduce class imbalance including resampling of different bands and weighting bands when training model. Further assessment required of best approach.