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Related Experiment Video

Updated: May 19, 2026

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Detecting and correcting sensor drifts in long-term weather data.

Georg von Arx¹, Matthias Dobbertin, Martine Rebetez

¹Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland. georg.vonarx@wsl.ch

Environmental Monitoring and Assessment

|August 24, 2012

Summary

A new procedure effectively removes sensor drift in long-term meteorological data, crucial for accurate climate analysis. This method enhances the reliability of high-frequency relative humidity (RH) data, improving scientific conclusions.

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Area of Science:

Meteorology
Environmental Science
Data Science

Background:

Quality control of extensive meteorological datasets is challenging.
Sensor drifts, stalled values, and scale shifts can compromise data integrity.
No standard procedure exists for high-frequency meteorological data quality control.

Purpose of the Study:

To present a novel procedure for removing sensor drift in high-frequency meteorological data.
To demonstrate the procedure's effectiveness using 15-year hourly relative humidity (RH) data.
To establish a reliable method for enhancing the quality of long-term monitoring data.

Main Methods:

Implemented a procedure involving basic quality control, relative homogeneity testing, and drift removal.
Analyzed 15-year hourly relative humidity (RH) data from 28 stations (202 sensor periods).
Applied detrending techniques to correct observed sensor drifts.

Main Results:

Significant sensor drifts detected in 40.6% of sensor operation periods.
Drifts varied complexly with absolute RH values and were strongest near 100% RH.
Detrending adjusted RH values by an average of 1.96%, with larger adjustments in some cases.

Conclusions:

The developed detrending procedure effectively removes sensor drifts in high-frequency data.
The method enhances the accuracy of meteorological data, preventing flawed scientific conclusions.
The procedure is applicable to other meteorological parameters and time series with reference data.