THESIS
2021
1 online resource (48 pages) : color illustrations
Abstract
With the objective of making a high-resolution and precise forecast of regional precipitation, precipitation nowcasting has become an important technique to give the early public warning
on potential landslides and floods caused by heavy precipitation. Many deep learning models
have recently been shown to outperform traditional Numerical Weather Prediction (NWP)
models, extrapolation-based methods for precipitation nowcasting. However, current deep
learning-based precipitation nowcasting is seriously limited by the following two perspectives.
Firstly, the predicted precipitation images from current deep learning models will become
blurry with increasing lead-time. Secondly, most of the current deep learning models can only
provide precipitation nowcasting within 2 hours. To overcome the...[
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With the objective of making a high-resolution and precise forecast of regional precipitation, precipitation nowcasting has become an important technique to give the early public warning
on potential landslides and floods caused by heavy precipitation. Many deep learning models
have recently been shown to outperform traditional Numerical Weather Prediction (NWP)
models, extrapolation-based methods for precipitation nowcasting. However, current deep
learning-based precipitation nowcasting is seriously limited by the following two perspectives.
Firstly, the predicted precipitation images from current deep learning models will become
blurry with increasing lead-time. Secondly, most of the current deep learning models can only
provide precipitation nowcasting within 2 hours. To overcome these problems, we proposed a
novel task-segmented architecture called TS-RainGAN built with recurrent generative
adversarial networks for precipitation nowcasting. In our experiments of 6-hour precipitation
nowcasting using radar images at 1.3 km resolution, TS-RainGAN can produce photo-realistic
images. At the same time, other deep learning models such as ConvLSTM, MIM, and
PredRNN++ will result in considerable blurry images. In addition, TS-RainGAN can attain the
highest scores evaluated by common skill metrics (CSI, FAR, and POD) and maintains a good
sharpness mark throughout the 6-hour nowcast. To our knowledge, TS-RainGAN is the first
deep learning model that can handle 6-hour precipitation nowcasting using radar data and
produces radar-based rainfall nowcast without obvious blurriness.
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