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An improved model for determining degree-day values from daily temperature data.

C Cesaraccio1, D Spano, P Duce

  • 1Istituto per il Monitoraggio degli Agroecosistemi, CNR, Sassari, Italy. cesaraccio@imaes.ss.cnr.it

International Journal of Biometeorology
|January 5, 2002
PubMed
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This study introduces a new empirical model for estimating hourly mean temperatures using daily weather data. This improved temperature estimation leads to more accurate growing degree-day (GDD) calculations for various applications.

Area of Science:

  • Agricultural Meteorology
  • Environmental Science
  • Data Modeling

Background:

  • Hourly weather data provide the most accurate growing degree-day (GDD) estimations.
  • Daily temperature data are frequently used as a proxy, but often lack precision.
  • Accurate temperature data are crucial for agricultural and ecological modeling.

Purpose of the Study:

  • To develop and validate a novel empirical model for estimating hourly mean temperatures.
  • To improve the accuracy of growing degree-day (GDD) calculations using estimated hourly temperatures.
  • To compare the performance of the new model against existing methods.

Main Methods:

  • Developed an empirical model using sine and square-root functions to describe diurnal temperature variations.

Related Experiment Videos

  • Calibrated the model with several years of hourly data from five California weather stations.
  • Validated the model against independent datasets and compared GDD estimates with other methods.

    • Main Results:

    • The empirical temperature model demonstrated good accuracy, with root-mean-square errors generally below 2.0 degrees C.
    • The model outperformed published methods in estimating hourly temperatures.
    • Degree-day values calculated using the model's hourly temperatures showed superior accuracy across seasons.

    Conclusions:

    • The proposed empirical model offers a reliable and accurate method for estimating hourly mean temperatures from daily data.
    • This model significantly enhances the precision of growing degree-day (GDD) calculations.
    • The findings are valuable for applications requiring accurate temperature data, particularly in agriculture and climate studies.