Wazzup Pilipinas!?
These pyranometers installed at the Manila Observatory on the Ateneo de Manila University campus in Quezon City, Metro Manila, are used to measure actual solar radiation for comparison with forecast projections. Using a mathematical algorithm, Ateneo researchers were able in some cases to minimize the discrepancy between forecasts and actual observations down to just 6%. CREDIT: Lyndon Mark P. Olaguera
Directly benefiting the Philippines’ solar power, agriculture, and other industries, an international team of researchers led by the Ateneo de Manila University and the Manila Observatory has pioneered a way to improve sunny weather forecasts by as much as 94%.
Weather forecasters and scientists around the world rely on computer-generated simulation tools to predict the weather days in advance, with the Weather Research and Forecasting (WRF) Model being one of the most well-known and widely used. In particular, forecasts of how much sunlight an area receives on a given set of days have all manner of uses—from helping ordinary people decide how to dress up and go about their day, to enabling entire industries to adjust their operations in response to the effects of solar radiation.
The Ateneo-led researchers improved WRF-Solar forecasts by applying a mathematical algorithm called a Kalman Filter (KF). Using data from various Metro Manila weather stations, they found that under some conditions they could minimize the discrepancy between forecasts and actual observations to as little as 6%.
In more technical terms, using KF on WRF-Solar forecasts of global horizontal irradiance for Metro Manila reduced mean bias error (MBE) by up to 94% and root mean square error (RMSE) by 12%, on as short as three days worth of training data. The optimal number of training days varied by season, with 42 days for the dry season (January to March) and 14 for the wet season (June to August). The KF algorithm also excelled at correcting cloudy-period forecasts, albeit with slight inaccuracies for clear skies due to overcompensation for cloudy periods.
These results suggest that KF is a promising alternative to more computationally expensive forecasting methods for solar energy applications. This pioneering research highlights the potential of combining WRF-Solar and KF to enhance solar energy forecasting, vital for renewable energy planning in the Philippines. The findings also emphasize the need for further model optimization across diverse Philippine landscapes to ensure reliable solar energy predictions tailored to the country's unique climatic conditions.
“Results from the study, the first of its kind to assess performance of WRF-Solar and KF over the Philippines, will serve as a basis for a computationally efficient alternative to more intensive higher resolution and multiple ensemble member solar forecasts. Future work intends to focus on applying this method over different topographies in the Philippines, given the availability of irradiance data,” the researchers said.
Directly benefiting the Philippines’ solar power, agriculture, and other industries, an international team of researchers led by the Ateneo de Manila University and the Manila Observatory has pioneered a way to improve sunny weather forecasts by as much as 94%.
Weather forecasters and scientists around the world rely on computer-generated simulation tools to predict the weather days in advance, with the Weather Research and Forecasting (WRF) Model being one of the most well-known and widely used. In particular, forecasts of how much sunlight an area receives on a given set of days have all manner of uses—from helping ordinary people decide how to dress up and go about their day, to enabling entire industries to adjust their operations in response to the effects of solar radiation.
The Ateneo-led researchers improved WRF-Solar forecasts by applying a mathematical algorithm called a Kalman Filter (KF). Using data from various Metro Manila weather stations, they found that under some conditions they could minimize the discrepancy between forecasts and actual observations to as little as 6%.
In more technical terms, using KF on WRF-Solar forecasts of global horizontal irradiance for Metro Manila reduced mean bias error (MBE) by up to 94% and root mean square error (RMSE) by 12%, on as short as three days worth of training data. The optimal number of training days varied by season, with 42 days for the dry season (January to March) and 14 for the wet season (June to August). The KF algorithm also excelled at correcting cloudy-period forecasts, albeit with slight inaccuracies for clear skies due to overcompensation for cloudy periods.
These results suggest that KF is a promising alternative to more computationally expensive forecasting methods for solar energy applications. This pioneering research highlights the potential of combining WRF-Solar and KF to enhance solar energy forecasting, vital for renewable energy planning in the Philippines. The findings also emphasize the need for further model optimization across diverse Philippine landscapes to ensure reliable solar energy predictions tailored to the country's unique climatic conditions.
“Results from the study, the first of its kind to assess performance of WRF-Solar and KF over the Philippines, will serve as a basis for a computationally efficient alternative to more intensive higher resolution and multiple ensemble member solar forecasts. Future work intends to focus on applying this method over different topographies in the Philippines, given the availability of irradiance data,” the researchers said.
Catching sun in more ways than one, fisherfolk as well as farmers and other outdoor workers would benefit greatly from improved weather forecasts of sunny-weather days. This information is also invaluable to the solar power industry, which relies on forecasts to help manage its energy output and distribution. CREDIT: Quang Nguyen Vinh / Pexels.com
Shane Marie Visaga, Patric John Pascua, Leia Pauline Tonga, Lyndon Mark Olaguera, Faye Abigail Cruz, James Bernard Simpas, Sherdon Niño Uy, and Jose Ramon Villarin from the Ateneo de Manila University’s School of Science and Engineering Department of Physics and the Manila Observatory; Rafael Alvarenga from the University of French Guiana’s UMR Espace-Dev; Anthony Bucholtz from the US Naval Postgraduate School/CIRPAS Airborne Research Facility; Angela Monina Magnaye from the University of Tsukuba in Japan; and Elizabeth Reid from the US Naval Research Laboratory’s Marine Meteorology Division published their paper, “Application of Kalman filter for post-processing WRF-Solar forecasts over Metro Manila, Philippines,” last November 15 in the journal, Solar Energy.
Shane Marie Visaga, Patric John Pascua, Leia Pauline Tonga, Lyndon Mark Olaguera, Faye Abigail Cruz, James Bernard Simpas, Sherdon Niño Uy, and Jose Ramon Villarin from the Ateneo de Manila University’s School of Science and Engineering Department of Physics and the Manila Observatory; Rafael Alvarenga from the University of French Guiana’s UMR Espace-Dev; Anthony Bucholtz from the US Naval Postgraduate School/CIRPAS Airborne Research Facility; Angela Monina Magnaye from the University of Tsukuba in Japan; and Elizabeth Reid from the US Naval Research Laboratory’s Marine Meteorology Division published their paper, “Application of Kalman filter for post-processing WRF-Solar forecasts over Metro Manila, Philippines,” last November 15 in the journal, Solar Energy.
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