30 min-Ahead Gridded Solar Irradiance Forecasting Using Satellite Data

2018 ◽  
pp. 186-198
Author(s):  
Todd Taomae ◽  
Lipyeow Lim ◽  
Duane Stevens ◽  
Dora Nakafuji
Keyword(s):  
Satellite Data ◽  
Solar Irradiance

Solar irradiance estimation from geostationary satellite data: II. Physical models

Solar Energy ◽  
1993 ◽  
Vol 51 (6) ◽  
pp. 457-465 ◽  
Author(s):  
M. Noia ◽  
C.F. Ratto ◽  
R. Festa
Keyword(s):  
Satellite Data ◽  
Solar Irradiance ◽  
Geostationary Satellite ◽  
Physical Models

scholarly journals The response of mesospheric H<sub>2</sub>O and CO to solar irradiance variability in the models and observations

2020 ◽  
Author(s):  
Arseniy Karagodin-Doyennel ◽  
Eugene Rozanov ◽  
Ales Kuchar ◽  
William Ball ◽  
Pavle Arsenovic ◽  
...  
Keyword(s):  
Satellite Data ◽  
Solar Irradiance ◽  
Climate Model ◽  
Middle Atmosphere ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Reference Simulation ◽  
Solar Signal ◽  
Hydrogen Radicals ◽  
Robust Response

Abstract. Water vapor (H2O) is the source of reactive hydrogen radicals in the middle atmosphere, whereas carbon monoxide (CO), being formed by CO2 photolysis, is suitable as a dynamical tracer. In the mesosphere, both H2O and CO are sensitive to solar irradiance variability because of their destruction/production by solar radiation. This enables to analyze the solar signal in both, models and observed data. Here, we evaluate the mesospheric H2O and CO response to solar irradiance variability using the Chemistry-Climate Model Initiative (CCMI-1) simulations and satellite data. We analyzed the results of four CCMI models (CMAM, EMAC-L90MA, SOCOLv3, CESM1-WACCM 3.5) operated in CCMI reference simulation REF-C1SD in specified dynamics mode, covering the period from 1984 to 2017. Multiple linear regression analysis shows a pronounced and statistically robust response of H2O and CO to solar irradiance variability, and to the annual and semiannual cycles. For periods with available satellite data, we compared the simulated solar signal against satellite observations, namely during 1992–2017 for H2O and 2005–2017 for CO. The model results generally agree with observations and reproduce an expected negative and positive correlation for H2O and CO, respectively, with solar irradiance. However, the magnitude of the response and patterns of the solar signal varies among the considered models, indicating differences in the applied chemical reaction and dynamical schemes including the representation of photolyses. We suggest that there is no dominating thermospheric influence of solar irradiance in CO, as reported in previous studies because the response to solar variability is comparable with observations in both, low-top and high-top models. We stress the importance of this work for improving our understanding of the current ability and limitations of state-of-the-art models to simulate a solar signal in the chemistry and dynamic of the middle atmosphere.

Iterative filtering of ground data for qualifying statistical models for solar irradiance estimation from satellite data

Solar Energy ◽  
2006 ◽  
Vol 80 (3) ◽  
pp. 240-247 ◽  
Author(s):  
Jesus Polo ◽  
Luis F. Zarzalejo ◽  
Lourdes Ramirez ◽  
Bella Espinar
Keyword(s):  
Satellite Data ◽  
Statistical Models ◽  
Solar Irradiance ◽  
Iterative Filtering

scholarly journals Short-term forecasting of solar irradiance without local telemetry: A generalized model using satellite data

Solar Energy ◽  
2018 ◽  
Vol 173 ◽  
pp. 566-577 ◽  
Author(s):  
Jesus Lago ◽  
Karel De Brabandere ◽  
Fjo De Ridder ◽  
Bart De Schutter
Keyword(s):  
Satellite Data ◽  
Solar Irradiance ◽  
Short Term ◽  
Generalized Model ◽  
Short Term Forecasting

The Evaluation of Solar Energy Availability at Ground Level Using Satellite Resources

2013 ◽  
Vol 378 ◽  
pp. 40-45
Author(s):  
Mihai Cristi Ceacaru ◽  
Alexandru Dumitrescu ◽  
Viorel Badescu
Keyword(s):  
Solar Energy ◽  
Satellite Data ◽  
Solar Irradiance ◽  
Cloud Cover ◽  
Satellite Images ◽  
Ground Level ◽  
Time Sequence ◽  
Energy Availability ◽  
Reference Map

A well known statistical method for the determination of solar energy available at ground level (HELIOSAT) is presented. The method makes use of satellite data. The data are provided by the geostationary satellite Meteosat. In the first step, a reference map of cloudinees index from satellite data is deduced from the time-sequence of satellite images. Next, is calculated the global solar irradiance from satellite and the global solar at ground, considering cloudinees index from satellite and respectively cloud cover measured at ground .The paper describes some preliminary activities related to the implementation of this method for the latitudes of Romania.

Solar irradiance estimation from geostationary satellite data: I. Statistical models

Solar Energy ◽  
1993 ◽  
Vol 51 (6) ◽  
pp. 449-456 ◽  
Author(s):  
M. Noia ◽  
C.F. Ratto ◽  
R. Festa
Keyword(s):  
Satellite Data ◽  
Statistical Models ◽  
Solar Irradiance ◽  
Geostationary Satellite

scholarly journals A Lookup-Table-Based Approach to Estimating Surface Solar Irradiance from Geostationary and Polar-Orbiting Satellite Data

2018 ◽  
Vol 10 (3) ◽  
pp. 411 ◽  
Author(s):  
Hailong Zhang ◽  
Chong Huang ◽  
Shanshan Yu ◽  
Li Li ◽  
Xiaozhou Xin ◽  
...  
Keyword(s):  
Satellite Data ◽  
Solar Irradiance ◽  
Lookup Table ◽  
Polar Orbiting Satellite
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