Paper
9 October 2007 A method to compute solar radiation at surface in any time interval based on NCEP re-analysis
Author Affiliations +
Abstract
Diurnal variation of solar radiation at surface is of importance to data assimilation, weather and climate model assessment. However, the shortage of solar radiation data has limited full use of other meteorological data. Solar radiation at surface can not be simply calculated by interpolation in any time interval because it is heavily influenced by solar hour angle, cloud, water vapor and aerosols etc., which brings great troubles to model applications. This paper presents a method to compute mean solar radiation at surface in any time interval and develops a data set of hourly mean solar radiation that can be used to assess models by use of NCEP 6-hourly mean of downward solar radiation flux at surface. Also, while comparing to measured hourly mean of solar radiation, results show that the calculated hourly mean solar radiation agrees closely with observation in numerical value and variation trend, which illuminates that the method is efficient. The calculated hourly-mean solar radiation reflects the diurnal variation all over the world and it can be used as land model forcing, It is helpful to simulation, validation and assessment of the weather and climate model and can make up the shortage of measured solar radiation data.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lanjun Zou, Wei Gao, Tongwen Wu, and Xiaofeng Xu "A method to compute solar radiation at surface in any time interval based on NCEP re-analysis", Proc. SPIE 6679, Remote Sensing and Modeling of Ecosystems for Sustainability IV, 66790R (9 October 2007); https://doi.org/10.1117/12.730626
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Solar radiation

Atmospheric modeling

Data modeling

Solar radiation models

Climatology

Clouds

Environmental sensing

Back to Top