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Predicting GPS-based PWV Measurements Using Exponential Smoothing

With the spirit of reproducible research, this repository contains all the codes required to produce the results in the manuscript:

S. Manandhar*, S. Dev*, Y. H. Lee and S. Winkler, Predicting GPS-based PWV Measurements Using Exponential Smoothing, IEEE AP-S Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, 2019 (* Authors contributed equally).


Please cite the above paper if you intend to use whole/part of the code. This code is only for academic and research purposes.

Code organization

The codes are written in python. The codes are tested in python3 version.


  • matplotlib: pip3 install matplotlib
  • Tkinter: sudo apt-get install python3-tk
  • statsmodels: pip3 install statsmodels
  • scipy: pip3 install scipy


  1. python3 Run this script to compute the predicted PWV values, based on the historical PWV data. This generated Figure 1 of the paper. You need to change the value of the parameter start_index in the file, to check for different slices of time-series data.
  2. python3 Run this script to obtain the distribution of RMSE values w.r.t. historical data and lead times. This generates Figure 2 of the paper. It saves the figure as ./results/rmse.pdf and the corresponding numpy array as rmse_matrix_for_grid.npy.
  3. python3 Run this script to obtain the benchmarking results of the different methods. This generates the results in Table 1 of the paper. The results are stored in ./results/comparison.txt file.


  • For a particular past time observation, we compare the performance of various methods for various lead times, and store in a text file.
  • Performs the experiments for various lead times and various historical observations. This is repeated for several times, and the average is reported.
  • Performs pwv forecasting for sample datapoints and plots the figure with train and test legends
  • Reads the matlab files, performs pre-processing and returns the data series


Predicting PWV values using exponential smoothing






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