Introducing Gradient Boosting as a universal gap filling tool for meteorological time series

Abstract

In this article, Gradient Boosting (gb) is introduced as an easily adaptable machine learning method to fill gaps caused by missing or erroneous data in meteorological time series. The gb routine is applied on a large data set of hourly time series of the measurands: air temperature, wind speed and relative humidity for station-based observations in Germany covering the period from 1951 to 2015. Our analysis shows that Gradient Boosting produces small errors by estimating missing values with median RMSE for temperature of 0.73 °C, wind speed of 0.82 m/s, relative humidity of 4.3 %, respectively. The comparison between the results achieved by Gradient Boosting with results from other gap filling techniques like neural networks or multiple linear regression shows considerably better statistics. The comparison clearly shows that the Gradient Boosting approach outperforms the other techniques particularly in calculation time, performance and the handling of missing predictor values.