Welcome to the respository that is associated with the manuscript titled: A Dual-Frequency Radar Retrieval of Snowfall Properties Using a Neural Network
(Under Review August 2020)
Authors: Randy J. Chase, Stephen W. Nesbitt and Greg M. McFarquhar Corresponding author: Randy J. Chase (randyjc2@illinois.edu)
Here we have the data used in the manuscript. Please email me if you have specific questions about units etc.
- DDA/GMM database of scattering properties: base_df_DDA.csv
This is the combined dataset from the following papers: Leinonen & Moisseev, 2015; Leinonen & Szyrmer, 2015; Lu et al., 2016; Kuo et al., 2016; Eriksson et al., 2018
- Synthetic Data used to train and test the neural network: Unrimed_simulation_wholespecturm_train_V2.nc, Unrimed_simulation_wholespecturm_test_V2.nc
This was the result of combineing the PSDs and DDA/GMM particles randomly to build the training and test dataset.
- Notebook for training the network using the synthetic database and Google Colab (tensorflow): Train_Neural_Network_Chase2020.ipynb
This is the notebook used to train the neural network.
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Trained tensorflow neural network: NN_6by8.h5 This is the hdf5 tensorflow model that resulted from the training. You will need this to run the retrieval.
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Scalers needed to apply the neural network: scaler_X_V2.pkl, scaler_y_V2.pkl These are the sklearn scalers used in training the neural network. You will need these to scale your data if you wish to run the retrieval.
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Example notebook of how to run the trained neural network on Ku- Ka- band observations. We showed this with the 3rd case in the paper: Run_Chase2021_NN.ipynb
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APR data used to show how to run the neural network retrieval: Chase_2021_NN_APR03Dec2015.nc
The data for the analysis on the observations are not provided here because of the size of the radar data. Please see the GHRC website (https://ghrc.nsstc.nasa.gov/home/) if you wish to download the radar and in-situ data or contact me. We can coordinate transfering the exact datafiles used.
The GPM-DPR data are avail. here: http://dx.doi.org/10.5067/GPM/DPR/GPM/2A/05