Researchers in the Philippines have just published a paper announcing a significant breakthrough in weather forecasting. A new algorithm can improve the accuracy of sunny day weather forecasts by up to 94 percent.
Weather forecasting in recent years has grown increasingly reliant on sophisticated computer modeling tools to predict what we can expect when we step out the door on any given day. But having a good idea of the solar radiation has potentially significant benefits, well beyond knowing how to dress for the weather.
The researchers, whose paper was published in the journal Solar Energy, figured out how to improve what are known as Weather Research and Forecasting models, the standard models many meteorologists use, with the help of a Kalman Filter algorithm. The KF algorithm enabled researchers to limit errors in modeling solar radiation in terms of the discrepancy between what was forecast and what was observed, to as low as 6 percent in certain sky conditions.
Even better, the KF algorithm performed at a high level with as few as three training days for the software. Interestingly, the model did better under cloudy conditions than when there were bluebird skies. It can take up to 42 days' worth of sunny sky data to bring the system up to full accuracy, and only a couple of weeks worth of data if the skies are cloudy.
Either way, it appears to be a highly efficient, inexpensive option for radically improving forecast models.
The study shows the exciting potential of being able to use the KF algorithm and the standard WRF modeling to better predict the strength of solar radiation, which can be helpful for optimizing the efficiency of energy production from solar panels.
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In theory, any industry that relies on solar radiation — agriculture, for example — would greatly benefit from more accuracy in predicting the strength and duration of solar energy.
The algorithm also has the potential to lessen the cost of accurate weather modeling. The WRF system is an ensemble that relies on multiple complicated platforms to make predictions, whereas the KF algorithm simplifies the entire computation model. Using fewer models and less computing power means it costs less to make more accurate predictions.
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Definitely 💰 So far, the study has only been applied in the Philippines, but the potential is worldwide.
"Results from the study, the first of its kind to assess performance of WRF-Solar and KF over the Philippines," the study says, "will serve as a basis for a computationally efficient alternative to more expensive higher resolution and multiple ensemble member solar forecasts."
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