Precipitation Nowcasting Exploring the Impact of Echo Top Heights in Generative Models
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2023-01-01
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en
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Abstract
Accurate short-term forecasting of rainfall, also known as precipitation nowcasting,
is critical for a wide variety of sectors. From agriculture to early flood warning
systems, reliable precipitation forecasts are essential for informed decision-making.
Numerical Weather Prediction systems forecasting rainfall are not updated frequently
enough and lack the spatial high-resolution required for early warning on
short time scales. Deep learning nowcasting approaches can fill this gap and create
fine-resolution forecasts. Generative Adversarial Networks (GAN) have lately
shown promising results to improve forecasting of the challenging growth and dissipation
processes of rainfall. However, forecasts of intense precipitation remain a
challenge. This study investigates the prospect of improving deep learning nowcasting
by the inclusion of Echo Top Height data. For this, the state-of-the-art Deep
Generative Model of Radar was modified to include Echo Top Height data alongside
precipitation input from the Netherlands. Both the original and the modified
model were tested for accuracy on a range of continuous and categorical metrics as
well as the Fraction Skill Score. It could be shown that the inclusion of ETH data
improves the forecasting of low precipitation events on all metrics. The forecasting
of high precipitation events was improved for large-scale applications, however, it
did not improve small-scale evaluation as ETH inclusion tended to mislocate high
precipitation events.
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Faculteit der Sociale Wetenschappen