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Interpolating hourly temperatures for computing agroclimatic metrics.

Eike Luedeling1

  • 1Department of Horticultural Sciences, University of Bonn, Auf dem Hügel 6, 53121, Bonn, Germany. luedeling@uni-bonn.de.

International Journal of Biometeorology
|July 18, 2018
PubMed
Summary
This summary is machine-generated.

Accurate hourly temperature data is crucial for calculating agroclimatic metrics. The new SolveHours procedure effectively fills gaps in temperature records, outperforming other methods for orchard climate analysis.

Keywords:
Agroclimatic metricsHourly temperature dataInterpolationSolveHourschillR

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Area of Science:

  • Agricultural Meteorology
  • Environmental Data Science
  • Climatology

Background:

  • Continuous hourly temperature data are essential for calculating critical agroclimatic metrics like chill and heat accumulation in orchards.
  • Gaps in hourly temperature records are common, forcing reliance on less precise daily data or incomplete hourly datasets.
  • Existing methods for interpolating hourly temperature records have limitations.

Purpose of the Study:

  • To develop and evaluate a novel procedure, SolveHours, for filling gaps in hourly temperature records.
  • To improve the accuracy of agroclimatic metric calculations by generating gapless hourly temperature data.
  • To provide a robust tool for researchers and farm managers dealing with incomplete temperature datasets.

Main Methods:

  • The SolveHours procedure integrates measured hourly temperatures, idealized daily temperature curves, and proxy data to interpolate missing values.
  • It determines daily temperature extremes by solving linear equations and fills initial gaps using bias-corrected proxy data or linear interpolation.
  • An idealized temperature curve is generated, and deviations from recorded data are used to create a final, gapless hourly temperature record.

Main Results:

  • SolveHours achieved high performance, with Ratio of Performance to Interquartile Distance (RPIQ) values of 6.7 and 8.2, and low root mean square errors (1.3-1.6 °C).
  • The procedure significantly outperformed alternative gap-filling algorithms in reproducing accumulated Chill Portions and Growing Degree Hours.
  • The method's effectiveness was validated using a dataset with randomly introduced gaps.

Conclusions:

  • SolveHours provides a highly accurate and reliable method for reconstructing gapless hourly temperature records.
  • This advancement enables more precise calculation of essential agroclimatic metrics for orchard management and research.
  • The SolveHours procedure is available as part of the R programming package 'chillR'.