Dataset for: "A Dual-Frequency Radar Retrieval of Snowfall Properties Using a Neural Network"
Cite this dataset:
Chase, Randy (2020): Dataset for: "A Dual-Frequency Radar Retrieval of Snowfall Properties Using a Neural Network". University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0791318_V2
Use this persistent URL to link to this dataset:
Dataset Description

This is the dataset that accompanies the paper titled "A Dual-Frequency Radar Retrieval of Snowfall Properties Using a Neural Network", submitted for peer review in August 2020. Please see the github for the most up-to-date data after the revision process: https://github.com/dopplerchase/Chase_et_al_2021_NN

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.

1) 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. The column names are D: Maximum dimension in meters, M: particle mass in grams kg, sigma_ku: backscatter cross-section at ku in m^2, sigma_ka: backscatter cross-section at ka in m^2, sigma_w: backscatter cross-section at w in m^2. The first column is just an index column.

2) 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 combining the PSDs and DDA/GMM particles randomly to build the training and test dataset.

3) 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.

4)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.

5) 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.

6) New in this version - 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

7) New in this version - 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 transferring the exact datafiles used.

The GPM-DPR data are avail. here: http://dx.doi.org/10.5067/GPM/DPR/GPM/2A/05
Subject Physical Sciences
License CC0
Funder U.S. National Aeronautics and Space Administration (NASA) -Grant:80NSSC17K0439
Corresponding Creator Randy Chase
Downloaded 206 times
Version DOI Comment Publication Date
2 10.13012/B2IDB-0791318_V2 Two additional files are added to the .zip folder and described in the data description as numbers 6 and 7. 2020-11-18
1 10.13012/B2IDB-0791318_V1 2020-08-10
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