# Predicting GPS-based PWV Measurements Using Exponential Smoothing

## TL;DR

This paper presents an exponential smoothing approach to accurately predict missing GPS-derived PWV measurements, effectively capturing seasonal patterns and achieving low error rates for short-term forecasts.

## Contribution

The study introduces a novel application of exponential smoothing for filling missing PWV data, improving accuracy in atmospheric remote sensing applications.

## Key findings

- Root mean square error of 0.1 mm at 15-minute lead time
- Effective capture of seasonal variability in PWV data
- Successful prediction with 30 hours of past data

## Abstract

Global Positioning System (GPS) derived precipitable water vapor (PWV) is extensively being used in atmospheric remote sensing for applications like rainfall prediction. Many applications require PWV values with good resolution and without any missing values. In this paper, we implement an exponential smoothing method to accurately predict the missing PWV values. The method shows good performance in terms of capturing the seasonal variability of PWV values. We report a root mean square error of 0.1~mm for a lead time of 15 minutes, using past data of 30 hours measured at 5-minute intervals.