Abstract
Precipitation nowcasting can predict and alert for any possibility of abrupt weather changes which may cause both human and material risks. Most of the conventional nowcasting methods extrapolate weather radar echoes, but precipitation nowcasting is still a challenge, mainly due to rapid changes in meteorological systems and time required for numerical simulations. Recently video prediction deep learning (VPDL) algorithms have been applied in precipitation nowcasting. In this study, we use the VPDL PredRNN++ and sequences of radar reflectivity images to predict the future sequence of reflectivity images for up to 1-h lead time for São Paulo, Brazil. We also verify the feasibility for the continuous use of the VPDL model, providing the meteorologist with trends and forecasts in precipitation edges regardless of the weather event occurring. The results obtained confirm the potential of the VPDL model as an additional tool to assist nowcasting. Even though meteorological systems that trigger natural disasters vary by location, a general solution can contribute as a tool to assist decision-makers and consequently issue efficient alerts.
Highlights
The occurrence of severe weather events, such as storms, tornadoes, lightning, strong winds, can cause damage to human lives and social, economic and environmental assets of communities and countries [1,2,3]
We evaluate the usability of a video prediction deep learning (VPDL) model to support nowcasting
We use the Predictive Recurrent Neural Network (PredRNN)++ model as a VPDL model to predict reflectivity images and precipitation edges from weather radar images for up to 1-h lead time and compare the results with an extrapolation-based model used for precipitation nowcasting, Ensemble NowCASTing (ENCAST)
Summary
The occurrence of severe weather events, such as storms, tornadoes, lightning, strong winds, can cause damage to human lives and social, economic and environmental assets of communities and countries [1,2,3]. According to Golnaraghi et al [1], between 1970 and 2012, floods, mass movement, and storms accounted for 85% of disasters linked to weather, climate and water extremes, responsible for more than 1 million deaths and caused an economic loss of about USD 2 billion across the globe. In addition to people in situations vulnerable to severe weather phenomena, floods affect decision-makers in disaster management, energy, agriculture and aviation agencies, which are responsible for safety and administration. Precipitation nowcasting tools have been used to forecast severe weather events. This comprises a detailed description of the current weather to make predictions for a small range (meters to few kilometers) and for a short period of time (0 to 6 h) [6].
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