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Accurate precipitation nowcasting using meteorological radar data is vital for severe weather alerts. Using 60 minutes of past data is optimal; more input data degrades short-term forecast accuracy.

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Area of Science:

  • Meteorology
  • Artificial Intelligence
  • Earth Science

Background:

  • Severe meteorological events, particularly intense rainfall, are increasing globally, posing significant risks to safety, well-being, and economic sectors like agriculture and mining.
  • Effective emergency alerts are critical for mitigating losses and preventing fatalities during extreme weather events.
  • High temporal resolution precipitation forecasting tools, such as meteorological radars and satellites, are essential for timely warnings.

Purpose of the Study:

  • To evaluate a multi-horizon precipitation nowcasting approach using a U-Net machine learning architecture.
  • To investigate the optimal amount of past meteorological radar data (input horizons) required for accurate short-term precipitation forecasts.
  • To determine the impact of increasing input data duration on nowcasting performance.

Main Methods:

  • Utilized a U-Net deep learning architecture for spatial-temporal precipitation nowcasting.
  • Employed meteorological radar data from the Eastern Amazon region.
  • Evaluated the performance of nowcasting models with varying durations of past precipitation data as input (e.g., 60 minutes vs. 120 minutes).
  • Assessed forecast accuracy using metrics such as Root Mean Square Error (RMSE) and Critical Success Index (CSI).

Main Results:

  • Increasing input data beyond 60 minutes negatively impacts short-term precipitation nowcasting performance.
  • Using 120 minutes of input data instead of 60 minutes led to a significant performance decrease: 17.60% increase in RMSE and 7.18% decrease in CSI for short-term forecasts.
  • The study identified 60 minutes of past radar data as the optimal input duration for accurate short-term precipitation nowcasting.

Conclusions:

  • The optimal duration of input data is crucial for accurate spatial-temporal precipitation nowcasting.
  • Excessive input data can degrade the performance of U-Net based nowcasting models for short-term forecasts.
  • These findings provide essential guidance for developing more effective precipitation nowcasting systems, enhancing decision-making during severe weather events.