Sammanfattning

Most environmental and agronomic models use climatic inputs such as temperature, solar radiation and rainfall. In Canada, few automatic weather stations can monitor incoming solar radiation. Hence, surface incoming solar radiation must be estimated from other meteorological data. The aim of the present research was to conduct a comparative study of three empirical models for the estimation of surface incoming solar radiation on a horizontal surface. The first two methodologies use the traditional and long-utilized linear approach based on latitude and daily temperature range A more recent methodology uses neural networks (NN) to build a similar regression based on latitude and temperature range, but providing more flexibility in the formulation as well as a non-linear activation function. Global daily solar radiation data from 11 stations located in northeastern America was used to optimize and test the different models. Even though coefficients of the classical models were locally optimized using a standard unconstrained non-linear scheme, NNs markedly improved the estimation of incoming surface solar radiation. (C) 2008 Elsevier B.V. All rights reserved.

Nyckelord

global solar radiation; neural networks; empirical models; crop growth models; mesonet

Publicerad i

Agricultural and Forest Meteorology
2008, Volym: 148, nummer: 8-9, sidor: 1332-1340
Utgivare: ELSEVIER SCIENCE BV

UKÄ forskningsämne

Jordbruksvetenskap


Publikationens identifierare

DOI: https://doi.org/10.1016/j.agrformet.2008.03.012

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/25848