When Europeans experience a heavy temperature drop in winter, chances are that a Sudden Stratospheric Warming (SSW) has happened. This phenomenon is characterized by a strong temperature increase in the stratosphere, possibly up to 50 ̊C within a few days. Being able to predict these events can help meteorologists improve weather forecasting.
Download the poster if you want to get a quick overview about the project. If you would like more information, you can download the report or explore our code. For any feedback, don't hesitate to contact me. Thank you!
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Set the environment variables. Default values for spaceml servers are given here:
scripts/load_environment_variables.sh -
Copy the simulated and real data to the corresponding folders
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Run the preprocessing (see below)
How to run:
- Check and adjust environment variables. Default values can be loaded via
source scripts/load_environment_variables.sh - (Optional, depends on your configuration) Load your python virtual environment:
source YOUR_ENV/bin/activate - Run the preprocessing scripts for both the simulated and real data:
python code/run_preprocessing.pypython code/run_preprocessing_real.py - Run the labeling script:
python code/run_label_generation.py
In order to run the code you have to set up your PYTHONPATH to the code folder
export PYTHONPATH="${PYTHONPATH}:/where/the/code/folder/is/"
Go to the code folder and execute this script:
python classification/run_randomforest_classification.py
The results will be written to the results file defined by the environment variable DSLAB_RESULT_FILE
Go to the code folder and execute this script:
python classification/cnn.py
The results will be written to the results file defined by the environment variable DSLAB_RESULT_FILE
If the resulting Pytorch model weights is to be persisted, use --savemodel flag and set CNN_WEIGHTS environment variable to the directory where the model weights is going to be persisted.
By going to the code folder and executing this script:
python classification/xgboost_simple.py you can see the following output:
usage: xgboost_simple.py [-h] [-d {CP07,U65,ZPOL_temp,U&T}]
[-sp SIMULATED_PATH] [-rp REAL_PATH] [-dt {sim,real}]
[-m {TT,CV}] [-p]
A simple classification scheme using feature engineering and the
XGBoostClassifier
optional arguments:
-h, --help show this help message and exit
-d {CP07,U65,ZPOL_temp,U&T}, --definition {CP07,U65,ZPOL_temp,U&T}
Choose the definition that you want to run
classification
-sp SIMULATED_PATH, --simulated_path SIMULATED_PATH
Choose the input relative path where the simulated
data are
-rp REAL_PATH, --real_path REAL_PATH
Choose the input relative path where the real data are
-dt {sim,real}, --data_type {sim,real}
Choose if the evaluation is going to happen on real
orsimulated data
-m {TT,CV}, --mode {TT,CV}
Choose the evaluation mode
-p, --produce_importance
Choose if you'll produce the feature importances
where you can decide on various parameters. The results will be written to the results file defined by the environment variable DSLAB_RESULT_FILE
By going to the code folder and executing this script:
python prediction/xgboost_prediction.py you can see the following output:
usage: xgboost_prediction.py [-h] [-d {CP07,U65,U&T}] [-sp SIMULATED_PATH]
[-rp REAL_PATH] [-dt {sim,real}] [-m {TT,CV}]
[-cp CUTOFF_POINT] [-fi FEATURES_INTERVAL]
[-sd PREDICTION_START_DAY]
[-pi PREDICTION_INTERVAL] [-p]
A prediction scheme using feature engineering and the XGBoostClassifier
optional arguments:
-h, --help show this help message and exit
-d {CP07,U65,U&T}, --definition {CP07,U65,U&T}
Choose the definition that you want to run
classification
-sp SIMULATED_PATH, --simulated_path SIMULATED_PATH
Choose the input relative path where the simulated
data are
-rp REAL_PATH, --real_path REAL_PATH
Choose the input relative path where the real data are
-dt {sim,real}, --data_type {sim,real}
Choose if the evaluation is going to happen on real
orsimulated data
-m {TT,CV}, --mode {TT,CV}
Choose the evaluation mode
-cp CUTOFF_POINT, --cutoff_point CUTOFF_POINT
Choose the cutoff point of the time series
-fi FEATURES_INTERVAL, --features_interval FEATURES_INTERVAL
Choose the interval where you will calculate features
-sd PREDICTION_START_DAY, --prediction_start_day PREDICTION_START_DAY
Choose the day you will start making predictions for
-pi PREDICTION_INTERVAL, --prediction_interval PREDICTION_INTERVAL
Choose the interval you are going to make predictions
for
-p, --produce_importance
Choose if you'll produce the feature importances
where you can decide on various parameters. The results will be written to the results file defined by the environment variable DSLAB_RESULT_FILE
By going to the code folder and executing this script:
python prediction/xgboost_prediction_autoencoders.py you can see the following output:
usage: xgboost_prediction_autoencoders.py [-h] [-d {CP07,U65,U&T}]
[-sp SIMULATED_PATH] [-rp REAL_PATH]
[-dt {sim,real}] [-m {TT,CV}]
[-cp CUTOFF_POINT]
[-fi FEATURES_INTERVAL]
[-sd PREDICTION_START_DAY]
[-pi PREDICTION_INTERVAL] [-n] [-s]
A prediction scheme using feature engineering and the XGBoostClassifier
optional arguments:
-h, --help show this help message and exit
-d {CP07,U65,U&T}, --definition {CP07,U65,U&T}
Choose the definition that you want to run
classification
-sp SIMULATED_PATH, --simulated_path SIMULATED_PATH
Choose the input relative path where the simulated
data are
-rp REAL_PATH, --real_path REAL_PATH
Choose the input relative path where the real data are
-dt {sim,real}, --data_type {sim,real}
Choose if the evaluation is going to happen on real
orsimulated data
-m {TT,CV}, --mode {TT,CV}
Choose the evaluation mode
-cp CUTOFF_POINT, --cutoff_point CUTOFF_POINT
Choose the cutoff point of the time series
-fi FEATURES_INTERVAL, --features_interval FEATURES_INTERVAL
Choose the interval where you will calculate features
-sd PREDICTION_START_DAY, --prediction_start_day PREDICTION_START_DAY
Choose the day you will start making predictions for
-pi PREDICTION_INTERVAL, --prediction_interval PREDICTION_INTERVAL
Choose the interval you are going to make predictions
for
-n, --denoising Choose if you are going to train the denoising version
-s, --scale Choose if you are going to scale the features
where you can decide on various parameters. The results will be written to the results file defined by the environment variable DSLAB_RESULT_FILE