scholarly journals Global radiation, photosynthetically active radiation, and the diffuse component dataset of China, 1981–2010

2018 ◽  
Vol 10 (3) ◽  
pp. 1217-1226 ◽  
Author(s):  
Xiaoli Ren ◽  
Honglin He ◽  
Li Zhang ◽  
Guirui Yu
Keyword(s):  
Solar Radiation ◽  
Photosynthetically Active Radiation ◽  
Global Radiation ◽  
Terrestrial Ecosystem ◽  
Diffuse Radiation ◽  
Research Network ◽  
Diffuse Component ◽  
Ecosystem Productivity ◽  
Ecosystem Models ◽  
Active Radiation

Abstract. Solar radiation, especially photosynthetically active radiation (PAR), is the main energy source of plant photosynthesis, and the diffuse component can enhance canopy light use efficiency, thus increasing ecosystem productivity. In order to predict the terrestrial ecosystem productivity precisely, we not only need global radiation and PAR as driving variables, but also need to treat diffuse radiation and diffuse PAR explicitly in ecosystem models. Therefore, we generated a series of radiation datasets, including global radiation, diffuse radiation, PAR, and diffuse PAR of China from 1981 to 2010, based on the observations of the China Meteorology Administration (CMA) and the Chinese Ecosystem Research Network (CERN). The dataset should be useful for the analysis of the spatiotemporal variations of solar radiation in China and the impact of diffuse radiation on terrestrial ecosystem productivity based on ecosystem models. The dataset is freely available from Zenodo on the following website: https://zenodo.org/record/1198894#.Wx6–C_MwWo (https://doi.org/10.11922/sciencedb.555, Ren et al., 2018).

scholarly journals Global radiation, photosynthetically active radiation, and the diffuse components dataset of China, 1981–2010

2018 ◽  
Author(s):  
Xiaoli Ren ◽  
Honglin He ◽  
Li Zhang ◽  
Guirui Yu
Keyword(s):  
Solar Radiation ◽  
Photosynthetically Active Radiation ◽  
Global Radiation ◽  
Terrestrial Ecosystem ◽  
Diffuse Radiation ◽  
Research Network ◽  
Ecosystem Productivity ◽  
Ecosystem Models ◽  
Active Radiation ◽  
The Impact

Abstract. Solar radiation, especially photosynthetically active radiation (PAR), is the main energy source of plant photosynthesis; and the diffuse component can enhance canopy light use efficiency, thus increasing ecosystem productivity. In order to predict the terrestrial ecosystem productivity precisely, we not only need global radiation and PAR as driving variables, but also need to treat diffuse radiation and diffuse PAR explicitly in ecosystem models. Therefore, we generated a series of radiation datasets, including global radiation, diffuse radiation, PAR, and diffuse PAR of China from 1981 to 2010, based on the observations of China Meteorology Administration (CMA) and Chinese Ecosystem Research Network (CERN). The dataset should be useful for the analysis of the spatio-temporal variations of solar radiation in China and the impact of diffuse radiation on terrestrial ecosystem productivity based on ecosystem models. The dataset is freely available from the Zenodo at the website of https://zenodo.org/record/1198894 (DOI: 10.11922/sciencedb.555).

scholarly journals Estimation of Incident Photosynthetically Active Radiation from GOES Visible Imagery

2008 ◽  
Vol 47 (3) ◽  
pp. 853-868 ◽  
Author(s):  
Tao Zheng ◽  
Shunlin Liang ◽  
Kaicun Wang
Keyword(s):  
Photosynthetically Active Radiation ◽  
Terrestrial Ecosystem ◽  
New Method ◽  
High Temporal Resolution ◽  
Atmospheric Conditions ◽  
Ecosystem Models ◽  
Surface Conditions ◽  
Active Radiation ◽  
Ground Measurement ◽  
Visible Imagery

Abstract Incident photosynthetically active radiation (PAR) is an important parameter for terrestrial ecosystem models. Because of its high temporal resolution, the Geostationary Operational Environmental Satellite (GOES) observations are very suited to catch the diurnal variation of PAR. In this paper, a new method is developed to derive PAR using GOES data. What makes this new method distinct from the existing method is that it does not need external knowledge of atmospheric conditions. The new method retrieves both atmospheric and surface conditions using only at-sensor radiance through interpolation of time series of observations. Validations against ground measurement are carried out at four “FLUXNET” sites. The values of RMSE of estimated and ground-measured instantaneous PAR at the four sites are 130.71, 131.44, 141.16, and 190.22 μmol m−2 s−1, respectively. At the four validation sites, the RMSE as the percentage of estimated mean PAR value are 9.52%, 13.01%, 13.92%, and 24.09%, respectively; the biases are −101.54, 16.56, 11.09, and 53.64 μmol m−2 s−1, respectively. The independence of external atmospheric information enables this method to be applicable to many situations in which external atmospheric information is not available. In addition, topographic impacts on surface PAR are examined at the 1-km resolution at which PAR is retrieved using the GOES visible band data.

scholarly journals The response of the Amazon ecosystem to the photosynthetically active radiation fields: integrating impacts of biomass burning aerosol and clouds in the NASA GEOS Earth system model

2021 ◽  
Vol 21 (18) ◽  
pp. 14177-14197
Author(s):  
Huisheng Bian ◽  
Eunjee Lee ◽  
Randal D. Koster ◽  
Donifan Barahona ◽  
Mian Chin ◽  
...  
Keyword(s):  
Primary Production ◽  
Biomass Burning ◽  
Photosynthetically Active Radiation ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Diffuse Radiation ◽  
Ecosystem Productivity ◽  
Active Radiation ◽  
Fertilization Effect ◽  
The Impact

Abstract. The Amazon experiences fires every year, and the resulting biomass burning aerosols, together with cloud particles, influence the penetration of sunlight through the atmosphere, increasing the ratio of diffuse to direct photosynthetically active radiation (PAR) reaching the vegetation canopy and thereby potentially increasing ecosystem productivity. In this study, we use the NASA Goddard Earth Observing System (GEOS) model with coupled aerosol, cloud, radiation, and ecosystem modules to investigate the impact of Amazon biomass burning aerosols on ecosystem productivity, as well as the role of the Amazon's clouds in tempering this impact. The study focuses on a 7-year period (2010–2016) during which the Amazon experienced a variety of dynamic environments (e.g., La Niña, normal years, and El Niño). The direct radiative impact of biomass burning aerosols on ecosystem productivity – called here the aerosol diffuse radiation fertilization effect – is found to increase Amazonian gross primary production (GPP) by 2.6 % via a 3.8 % increase in diffuse PAR (DFPAR) despite a 5.4 % decrease in direct PAR (DRPAR) on multiyear average during burning seasons. On a monthly basis, this increase in GPP can be as large as 9.9 % (occurring in August 2010). Consequently, the net primary production (NPP) in the Amazon is increased by 1.5 %, or ∼92 Tg C yr−1 – equivalent to ∼37 % of the average carbon lost due to Amazon fires over the 7 years considered. Clouds, however, strongly regulate the effectiveness of the aerosol diffuse radiation fertilization effect. The efficiency of this fertilization effect is the highest in cloud-free conditions and linearly decreases with increasing cloud amount until the cloud fraction reaches ∼0.8, at which point the aerosol-influenced light changes from being a stimulator to an inhibitor of plant growth. Nevertheless, interannual changes in the overall strength of the aerosol diffuse radiation fertilization effect are primarily controlled by the large interannual changes in biomass burning aerosols rather than by changes in cloudiness during the studied period.

scholarly journals MODELOS DE ESTIMATIVA DA RADIAÇÃO FOTOSSINTETICAMENTE ATIVA GLOBAL E DIFUSA EM FUNÇÃO DA RADIAÇÃO DE ONDAS CURTAS E DO ÍNDICE DE CLARIDADE (Kt)

Irriga ◽  
2002 ◽  
Vol 7 (2) ◽  
pp. 123-129
Author(s):  
Eduardo Nardini Gomes ◽  
João Francisco Escobedo
Keyword(s):  
Solar Radiation ◽  
Photosynthetically Active Radiation ◽  
Model Development ◽  
Diffuse Radiation ◽  
Global Solar Radiation ◽  
Clearness Index ◽  
Active Radiation ◽  
Diffuse Solar Radiation ◽  
Proposed Model ◽  
Index 2

MODELOS DE ESTIMATIVA DA RADIAÇÃO FOTOSSINTETICAMENTE ATIVA GLOBAL E DIFUSA EM FUNÇÃO DA RADIAÇÃO DE ONDAS CURTAS E DO ÍNDICE DE CLARIDADE (Kt)   Eduardo Nardini GomesJoão Francisco EscobedoDepartamento de Recursos Naturais, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista, CP 237, CEP 18603-970, Botucatu – SP, Fone: (0xx14) 6802-7162   1 RESUMO  O presente trabalho apresenta equações de estimativa da radiação fotossinteticamente ativa global () e difusa () em função das respectivas radiações global () e difusa () do espectro solar total, bem como a estimativa da fração PAR difusa da PAR global () em função do índice de transmissividade atmosférica ().A base de dados foi adquirida no período de 01/06/1999 a 31/09/2000 na Estação de Radiometria Solar da FCA-UNESP, Botucatu. Foram utilizados dados adicionais, diferentes dos utilizados na geração dos modelos, de forma a possibilitar uma validação adequada dos modelos propostos.   UNITERMOS: radiação fotossinteticamente ativa global e difusa, transmissividade atmosférica, modelos de estimativa da radiação solar.   GOMES, E.N., ESCOBEDO, J.F  MODELS FOR GLOBAL AND DIFFUSE PHOTOSYNTHETICALLY ACTIVE RADIATION IN RELATION TO GLOBAL, DIFFUSE RADIATION  AND CLEARNESS INDEX.   2 SUMMARY  This work describes typical correlations between global solar radiation () and its global PAR component (), diffuse solar radiation () and its diffuse PAR component (), clearness index () and the diffuse PAR fraction of global PAR (). Database was recorded from June 1st 1999 to September 31st 2000 at the  Solar Radiometric Station, Botucatu, SP. Additional data which are not part of the model development were used to validate each  proposed model.  KEYWORDS: global and diffuse photosynthetically active radiation, clearness index, estimating models.

Improved Accuracy Models For Hourly Diffuse Solar Radiation

2005 ◽  
Vol 128 (1) ◽  
pp. 104-117 ◽  
Author(s):  
T. Muneer ◽  
S. Munawwar
Keyword(s):  
Solar Radiation ◽  
Regression Models ◽  
Global Radiation ◽  
Diffuse Radiation ◽  
Evaluation Procedure ◽  
Accuracy Score ◽  
Error Statistics ◽  
Diffuse Solar Radiation ◽  
Energy Applications ◽  
Extensive Evaluation

Solar energy applications require readily available, site-oriented, and long-term solar data. However, the frequent unavailability of diffuse irradiation, in contrast to its need, has led to the evolution of various regression models to predict it from the more commonly available data. Estimating the diffuse component from global radiation is one such technique. The present work focuses on improvement in the accuracy of the models for predicting horizontal diffuse irradiation using hourly solar radiation database from nine sites across the globe. The influence of sunshine fraction, cloud cover, and air mass on estimation of diffuse radiation is investigated. Inclusion of these along with hourly clearness index, leads to the development of a series of models for each site. Estimated values of hourly diffuse radiation are compared with measured values in terms of error statistics and indicators like, R2, mean bias deviation, root mean square deviation, skewness, and kurtosis. A new method called “the accuracy score system” is devised to assess the effect on accuracy with subsequent addition of each parameter and increase in complexity of equation. After an extensive evaluation procedure, extricate but adequate models are recommended as optimum for each of the nine sites. These models were found to be site dependent but the model types were fairly consistent for neighboring stations or locations with similar climates. Also, this study reveals a significant improvement from the conventional k-kt regression models to the presently proposed models.

scholarly journals Radiación fotosintéticamente activa evaluada en la ciudad de Zacatecas

2020 ◽  
pp. 1-11
Author(s):  
Mónica Montserrat Escobedo-Sánchez ◽  
Ricardo Conejo-Flores ◽  
Sergio Miguel Durón-Torres ◽  
Juan Manuel García-González
Keyword(s):  
Decision Making ◽  
Solar Radiation ◽  
Production Planning ◽  
Photosynthetically Active Radiation ◽  
Detailed Knowledge ◽  
Active Radiation ◽  
Incident Solar Radiation ◽  
Daily Average ◽  
Life On Earth

The present investigation is related to one of the most important processes for the development of life on Earth; photosynthesis, an essential process in the cycle and development of living beings, centered on solar radiation that is useful for plants to carry out this process, Photosynthetically Active Radiation (PAR). The objective of this work is to generate information on the PAR through a database to collaborate in the decision-making of farmers in the area. For this purpose, a quantum sensor installed in building 6 of the UAZ Siglo XXI Campus was used. According to Abal (2013), in agricultural and production planning, it is especially important to have a detailed knowledge of incident solar radiation on the earth's surface (Abal and Durañona, 2013). When collecting, treating and analyzing the data, it was found that the daily average PAR is 819.52 μmol of photons m-2 s-1 (179.47 W m-2), if only the sunny hours are taken into account. It can be concluded that according to the PAR received in the evaluation region and the type of nutrients in the soil, other crop alternatives to those traditionally used can be sought.

scholarly journals First Effort at Constructing a High-Density Photosynthetically Active Radiation Dataset during 1961–2014 in China

2019 ◽  
Vol 32 (10) ◽  
pp. 2761-2780 ◽  
Author(s):  
Wenmin Qin ◽  
Lunche Wang ◽  
Ming Zhang ◽  
Zigeng Niu ◽  
Ming Luo ◽  
...  
Keyword(s):  
Photosynthetically Active Radiation ◽  
Back Propagation ◽  
Control Process ◽  
High Density ◽  
Research Network ◽  
Bias Error ◽  
Ecosystem Research ◽  
Vegetation Growth ◽  
Active Radiation ◽  
Quality Control Process

Abstract Photosynthetically active radiation (PAR) is a key factor for vegetation growth and climate change. Different types of PAR models, including four physically based models and eight artificial intelligence (AI) models, were proposed for predicting daily PAR. Multiyear daily meteorological parameters observed at 29 Chinese Ecosystem Research Network (CERN) stations and 2474 Chinese Meteorological Administration (CMA) stations across China were used for testing, validating, and comparing the above models. The optimized back propagation (BP) neural network based on the mind evolutionary algorithm (MEA-BP) was the model with highest accuracy and strongest robustness. The correlation coefficient R, mean absolute bias error (MAE), and RMSE for MEA-BP were 0.986, 0.302 MJ m−2 day−1 and 0.393 MJ m−2 day−1, respectively. Then, a high-density PAR dataset was constructed for the first time using the MEA-BP model at 2474 CMA stations of China. A quality control process and homogenization test (using RHtestsV4) for the PAR dataset were further conducted. This high-density PAR dataset would benefit many climate and ecological studies.

Sign in / Sign up

Export Citation Format

Share Document