Nowcasting precipitation using GANs and data from KNMI on the radboud server.
This repository contains the code and models to replicate the basic run of the model in the thesis of K. Schreurs.
Real-time precipitation radar data is used from 2008 till 2020. The data past 2018 is publicly available at the KNMI data platform. This dataset was used to train and validate the model
In order to run the code on your machine, you need to specify the following directories from the radboud server (ceph/knmimo):
./preprocessed/rtcor_prep./preprocessed/rtcor_rain_labels./preprocessed/rtcor_heavy_rain_labels./dataset_rtcor
In the config_GAN.py change the path to your data (path_data) and to your project (path_project) to match your system. Furthermore the real-time dataset can have different names (rtcor or RAC). Check your data to see if the prefix of your data matches the one stated in the config file (prefix_rtcor).
In order to execute a basic run, preprocessed data is used. When you want to preprocess or label the data yourself, these scripts can found in the preprocessing folder.
A subselection of the data was used, only samples with sufficient rain were included. Each sample in the dataset was labeled as rainy or not rainy.
To obtain these labels you can run the python script rainyday_labeler.py, with as argument the year you want to label (example: rainyday_labeler.py 2019, this would label all the samples from 2019)
A generator is used to retrieve parts of the data during runtime (Datagenerator class in batchcreator module). The generator loads the input, target pairs by filename. To create these input and target pairs you can run the python script create_traindata_IDs.py and change the time interval and input and output length and the filename to your needs.
To obtain the results of an basic run, execute the basic_run.py file.