Standardisation of temperature observed by automatic weather stations.

Area of Science:

• Environmental Science
• Climatology
• Meteorology

Background:

• Accurate surface air temperature data are crucial for environmental monitoring.
• Automatic weather stations (AWS) networks are widely used but can exhibit systematic errors.
• Establishing a common standard for temperature sensor data is essential for reliable analysis.

Purpose of the Study:

• To develop and validate a method for correcting temperature data from an AWS network.
• To improve the accuracy of daily mean, maximum, and minimum surface air temperature measurements.
• To enhance the identification of spatial temperature variations within the Moor House National Nature Reserve.

Main Methods:

• Collected daily temperature data from five AWS deployed alongside an official Environmental Change Network station.
• Calculated correction constants by optimizing the concordance correlation coefficient between AWS and the official station.
• Relocated corrected AWS sensors near in-situ stations for further calibration to a common standard.

Main Results:

• Developed correction constants to adjust AWS data to the official station's standard.
• Achieved a mean error not exceeding +/- 0.2 K for daily mean, maximum, and minimum temperatures.
• Quantified systematic measurement errors, improving spatial temperature difference identification.

Conclusions:

• The applied calibration procedure effectively reduces systematic errors in AWS temperature data.
• This method ensures a higher degree of accuracy and consistency across the temperature sensor network.
• Improved data quality facilitates more reliable ecological and climate change research in the region.