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Standardisation of temperature observed by automatic weather stations
A Joyce1, J Adamson, B Huntley
1Environmental Research Centre, Department of Biological Sciences, University of Durham, Durham, UK. a.n.joyce@durham.ac.uk
Environmental Monitoring and Assessment
|June 20, 2001
Summary
Researchers calibrated automatic weather stations (AWS) to improve temperature data accuracy in northern England. This method reduces systematic errors, enhancing spatial temperature difference identification.
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.
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