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Related Experiment Video

Updated: Apr 8, 2026

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Published on: December 12, 2013

Nowcasting daily minimum air and grass temperature.

¹Agrometeorology Discipline, Soil-Plant-Atmosphere Continuum Research Unit, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa. savage@ukzn.ac.za.

International Journal of Biometeorology

|July 1, 2015

Summary

This summary is machine-generated.

Accurate nowcasts of daily minimum air temperature, available hours in advance, can benefit various sectors. This study developed models predicting minimum air temperatures with small errors, showing operational potential.

Keywords:

Diurnal temperature modelling Early warning of minimum temperature Frost Sub-hourly temperatures Surface temperature

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Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

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Area of Science:

Meteorology
Atmospheric Science
Applied Mathematics

Background:

Accurate daily minimum temperature predictions are crucial for economic sectors and human activities.
Existing methods for predicting minimum temperatures lack site-specific accuracy and timely availability.

Purpose of the Study:

To investigate the accuracy of nowcasting daily minimum air and grass temperatures using sub-hourly temperature data.
To evaluate various temperature models for predicting minimum temperatures 2 or 4 hours before sunrise.

Main Methods:

Tested exponential and square root models to predict nighttime temperature decrease and determine minimum temperature.
Utilized sub-hourly grass, grass-surface, and air temperature data from multiple global sites.
Implemented models in a near real-time web-based system and dataloggers.

Main Results:

Exponential models achieved root mean square errors (RMSEs) <1 °C for 2-hour ahead air temperature nowcasts.
A constant coefficient exponential model also showed RMSEs <1 °C.
Square root models and 4-hour ahead predictions generally resulted in higher RMSEs, especially for grass temperatures.

Conclusions:

The developed exponential model shows operational potential for site-specific air temperature minimum nowcasting.
Increased errors were observed for grass minimum temperature predictions and 4-hour ahead forecasts.
Further refinement is needed for grass temperatures and longer-range predictions.