Research at the National Center for Atmospheric Research (NCAR) focuses on developing advanced data assimilation techniques to improve solar radiation forecasts, leveraging geophysical data from various sources.
This article, published in the Journal of Geophysical Research: Atmospheres, explores the application of ensemble Kalman filter for geophysical data assimilation in solar radiation forecasting, demonstrating improved accuracy.
The U.S. Department of Energy provides an overview of data assimilation techniques for improving forecasts of solar radiation and other renewable energy sources, highlighting the importance of geophysical data.
Unidata, a consortium of academic institutions, offers a tutorial on geophysical data assimilation, covering the basics and applications in solar radiation forecasting, suitable for researchers and students.
This IEEE publication discusses the integration of machine learning algorithms with geophysical data assimilation for enhancing solar radiation forecasts, presenting a novel approach to predictive modeling.
A video lecture by a renowned expert in the field, hosted on YouTube, discusses the challenges and opportunities in solar radiation forecasting, including the role of geophysical data assimilation.
The Solar Energy Technologies Office, part of the U.S. Department of Energy, provides information on data assimilation techniques for improving solar energy forecasts, including the use of geophysical data.
A conference paper presented at the American Meteorological Society (AMS) meeting discusses the application of ensemble forecasting techniques, incorporating geophysical data assimilation, for predicting solar radiation.