HIGHLIGHTS:

GapMET provides accurate gap-filling methods for meteorological data series.

Simple linear regression gap-filling had more accuracy for Mato Grosso’s automatic weather station network density.

The use of satellite data as reference series reduces chances of fails to gap-fill, but also reduces the filling accuracy.

ABSTRACT

This paper aimed to introduce the GapMET software, developed by the authors, and evaluate the accuracy of its six methods for gap-filling the main meteorological variables monitored by weather station in the state of Mato Grosso, Brazil, using reference time series from neighbour weather station and/or remote sensing products. The methods were tested on seven different databases, with 25 to 80% artificial gaps, and their accuracy was given by the number of gaps left unfilled, the bias, the RMSE, and Pearson’s correlation. The GapMET software showed good results in filling meteorological gaps regardless of the method applied. Methods that use only one neighbour weather station as a reference series showed better results because, in the state, the minimum distance for a weather station to have at least three neighbours as reference was 350 km, reducing the climatic similarity between them and consequently the accuracy when more than one reference series were needed. The use of satellite reference series reduced the probability of unfilled gaps; however, it showed higher bias and RMSE and lower correlations.

Key words:
time series; missing data; automated weather stations; ERA5-Land