Estimating daily solar radiation in the Argentine Pampas

Guillermo P. Podestá, Liliana Núñez, Carlos A. Villanueva, María A. Skansi

Research output: Contribution to journalArticle

76 Scopus citations

Abstract

Solar radiation is an important input to crop growth models used for risk management and assessment purposes. Methods are explored to estimate daily solar radiation in the Argentine Pampas, one of the most important agricultural areas in the world. Two scenarios are considered: (i) sunshine duration data are available for a given location, or (ii) only daily temperature (minimum and maximum) and precipitation records exist. If sunshine duration data are available, an association between this quantity and atmospheric transmissivity yields daily radiation estimates with a root mean square error (RMSE) of 1.5MJm-2 per day. Without sunshine duration records, daily temperature and precipitation can be used to estimate atmospheric transmittance and then compute daily radiation values. A model linking predictors that are proxies of cloudiness and atmospheric humidity to atmospheric transmittance was fitted using Generalized Additive Models (GAMs), a modern statistical technique that does not assume any a priori functional forms for the association between predictors and predictand. The errors in radiation estimates using temperature and precipitation are larger (RMSE of 3.2MJm-2 per day) than those derived from sunshine duration, but they are comparable to results for other locations and methods. Most importantly, daily radiation estimates have small bias and the errors show no systematic patterns with season or other variables.

Original languageEnglish (US)
Pages (from-to)41-53
Number of pages13
JournalAgricultural and Forest Meteorology
Volume123
Issue number1-2
DOIs
StatePublished - May 20 2004

Keywords

  • Angström-Prescott equation
  • Diurnal temperature range
  • Generalized Additive Models
  • Solar radiation
  • Transmissivity

ASJC Scopus subject areas

  • Forestry
  • Global and Planetary Change
  • Agronomy and Crop Science
  • Atmospheric Science

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