Probabilistic solar irradiance forecasting using numerical weather prediction ensembles over Australia

2020 ◽  
Author(s):  
Jing Huang ◽  
Lawrence Rikus ◽  
Yi Qin
Keyword(s):  
Numerical Weather Prediction ◽  
Solar Irradiance ◽  
Weather Prediction ◽  
Numerical Weather

Impact of aerosols on the forecast accuracy of solar irradiance calculated by a numerical weather prediction model

2014 ◽  
Vol 223 (12) ◽  
pp. 2621-2630 ◽  
Author(s):  
Ken-ichi Shimose ◽  
Hideaki Ohtake ◽  
Joao Gari da Silva Fonseca ◽  
Takumi Takashima ◽  
Takashi Oozeki ◽  
...  
Keyword(s):  
Prediction Model ◽  
Numerical Weather Prediction ◽  
Solar Irradiance ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Numerical Weather Prediction Model ◽  
Numerical Weather

Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe

Solar Energy ◽  
2013 ◽  
Vol 94 ◽  
pp. 305-326 ◽  
Author(s):  
Richard Perez ◽  
Elke Lorenz ◽  
Sophie Pelland ◽  
Mark Beauharnois ◽  
Glenn Van Knowe ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Solar Irradiance ◽  
Weather Prediction ◽  
Numerical Weather ◽  
The Us

scholarly journals Solar Irradiance Forecasts by Mesoscale Numerical Weather Prediction Models with Different Horizontal Resolutions

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1374
Author(s):  
Ohtake ◽  
Uno ◽  
Oozeki ◽  
Hayashi ◽  
Ito ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Solar Irradiance ◽  
Prediction Models ◽  
Weather Prediction ◽  
Longwave Radiation ◽  
Horizontal Resolution ◽  
Numerical Weather ◽  
Downward Longwave Radiation ◽  
Radiation Processes ◽  
Numerical Weather Prediction Models

This study examines the performance of radiation processes (shortwave and longwave radiations) using numerical weather prediction models (NWPs). NWP were calculated using four different horizontal resolutions (5, 2 and 1 km, and 500 m). Validation results on solar irradiance simulations with a horizontal resolution of 500 m indicated positive biases for direct normal irradiance dominate for the period from 09 JST (Japan Standard Time) to 15 JST. On the other hand, after 15 JST, negative biases were found. For diffused irradiance, weak negative biases were found. Validation results on upward longwave radiation found systematic negative biases of surface temperature (corresponding to approximately −2 K for summer and approximately −1 K for winter). Downward longwave radiation tended to be weak negative biases during both summer and winter. Frequency of solar irradiance suggested that the frequency of rapid variations of solar irradiance (ramp rates) from the NWP were less than those observed. Generally, GHI distributions between the four different horizontal resolutions resembled each other, although horizontal resolutions also became finer.

Twenty-Four Hour Solar Irradiance Forecast Based on Neural Networks and Numerical Weather Prediction

2015 ◽  
Vol 137 (3) ◽  
Author(s):  
C. Cornaro ◽  
F. Bucci ◽  
M. Pierro ◽  
F. Del Frate ◽  
S. Peronaci ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Solar Irradiance ◽  
Weather Prediction ◽  
Numerical Weather ◽  
The One ◽  
Model Output Statistics ◽  
Photovoltaic Plant ◽  
University Of Rome ◽  
The University ◽  
Persistence Model

In this paper, several models to forecast the hourly solar irradiance with a day in advance using artificial neural network techniques have been developed and analyzed. The forecast irradiance is the one incident on the plane of the modules array of a photovoltaic plant. Pure statistical (ST) models that use only local measured data and model output statistics (MOS) approaches to refine numerical weather prediction data are tested for the University of Rome “Tor Vergata” site. The performance of ST and MOS, together with the persistence model (PM), is compared. The ST models improve the performance of the PM of around 20%. The combination of ST and NWP in the MOS approach gives the best performance, improving the forecast of approximately 39% with respect to the PM.

Development and Validation of an Operational, Cloud-Assimilating Numerical Weather Prediction Model for Solar Irradiance Forecasting

2012 ◽  
Author(s):  
Patrick J. Mathiesen ◽  
Craig Collier ◽  
Jan P. Kleissl
Keyword(s):  
Numerical Weather Prediction ◽  
Solar Irradiance ◽  
San Diego ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wrf Model ◽  
Numerical Weather Prediction Model ◽  
Numerical Weather ◽  
The North ◽  
Marine Layer

For solar irradiance forecasting, the operational numerical weather prediction (NWP) models (e.g. the North American Model (NAM)) have excellent coverage and are easily accessible. However, their accuracy in predicting cloud cover and irradiance is largely limited by coarse resolutions (> 10 km) and generalized cloud-physics parameterizations. Furthermore, with hourly or longer temporal output, the operational NWP models are incapable of forecasting intra-hour irradiance variability. As irradiance ramp rates often exceed 80% of clear sky irradiance in just a few minutes, this deficiency greatly limits the applicability of the operational NWP models for solar forecasting. To address these shortcomings, a high-resolution, cloud-assimilating model was developed at the University of California, San Diego (UCSD) and Garrad-Hassan, America, Inc (GLGH). Based off of the Weather and Research Forecasting (WRF) model, an operational 1.3 km-gridded solar forecast is implemented for San Diego, CA that is optimized to simulate local meteorology (specifically, summertime marine layer fog and stratus conditions) and sufficiently resolved to predict intra-hour variability. To produce accurate cloud-field initializations, a direct cloud assimilation system (WRF-CLDDA) was also developed. Using satellite imagery and ground weather station reports, WRF-CLDDA statistically populates the initial conditions by directly modifying cloud hydrometeors (cloud water and water vapor content). When validated against the dense UCSD pyranometer network, WRF-CLDDA produced more accurate irradiance forecasts than the NAM and more frequently predicted marine layer fog and stratus cloud conditions.

The Accuracy of Solar Irradiance Calculations Used in Mesoscale Numerical Weather Prediction

2005 ◽  
Vol 133 (4) ◽  
pp. 783-792 ◽  
Author(s):  
Robert J. Zamora ◽  
Ellsworth G. Dutton ◽  
Michael Trainer ◽  
Stuart A. McKeen ◽  
James M. Wilczak ◽  
...  
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Numerical Weather Prediction ◽  
Solar Irradiance ◽  
Weather Prediction ◽  
The United States ◽  
Model Errors ◽  
Numerical Weather ◽  
Mesoscale Numerical Weather Prediction

In this paper, solar irradiance forecasts made by mesoscale numerical weather prediction models are compared with observations taken during three air-quality experiments in various parts of the United States. The authors evaluated the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and the National Centers for Environmental Prediction (NCEP) Eta Model. The observations were taken during the 2000 Texas Air Quality Experiment (TexAQS), the 2000 Central California Ozone Study (CCOS), and the New England Air Quality Study (NEAQS) 2002. The accuracy of the model forecast irradiances show a strong dependence on the aerosol optical depth. Model errors on the order of 100 W m−2 are possible when the aerosol optical depth exceeds 0.1. For smaller aerosol optical depths, the climatological attenuation used in the models yields solar irradiance estimates that are in good agreement with the observations.

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