Abstract
Short-term quantitative precipitation forecast is a challenging topic in meteorology, as the number of severe meteorological phenomena is increasing in most regions of the world. Weather radar data is of utmost importance to meteorologists for issuing short-term weather forecast and warnings of severe weather phenomena. We are proposing AutoNowP, a binary classification model intended for precipitation nowcasting based on weather radar reflectivity prediction. Specifically, AutoNowP uses two convolutional autoencoders, being trained on radar data collected on both stratiform and convective weather conditions for learning to predict whether the radar reflectivity values will be above or below a certain threshold. AutoNowP is intended to be a proof of concept that autoencoders are useful in distinguishing between convective and stratiform precipitation. Real radar data provided by the Romanian National Meteorological Administration and the Norwegian Meteorological Institute is used for evaluating the effectiveness of AutoNowP. Results showed that AutoNowP surpassed other binary classifiers used in the supervised learning literature in terms of probability of detection and negative predictive value, highlighting its predictive performance.
Highlights
Forecast of severe weather phenomena, including the quantitative precipitation forecast (QPF), represents a challenging topic in meteorology
AutoNowP is based on training two Convolutional autoencoders (ConvAEs) trained on radar data collected on both stratiform and Mathematics 2021, 9, 1653 convective weather conditions
With the goal of answering research question RQ1, this section introduces our binary classification model proposal, AutoNowP, that consists of two ConvAEs, trained on radar data collected from rainfall conditions with different classes of severity, for recognizing severe phenomena
Summary
Forecast of severe weather phenomena, including the quantitative precipitation forecast (QPF), represents a challenging topic in meteorology. Precipitation nowcasting represents a challenging and actual research topic, referring to producing predictions of rainfall intensities over a certain region in the near future, and playing an important role in daily life [1]. Flood threat is increasing because of climate change impact of heavy precipitation, as for instance the total urban area being exposed to flood has dramatically increased in Europe over the past century. Research on the exposure of flood hazard, using climate models simulations, showed that the climate change presents the potential to actively change the human, assets, and urban areas exposure to flood hazard, but considerable uncertainty in the magnitude of the climate change impact in different regions around the globe exists [3]
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