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Im, Jungho
Intelligent Remote sensing and geospatial Information Science (IRIS) Lab
Research Interests
  • Remote sensing, Artificial Intelligence, Geospatial modeling, Disaster monitoring and management, Climate change

Precipitation nowcasting using ground radar data and simpler yet better video prediction deep learning

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Precipitation nowcasting using ground radar data and simpler yet better video prediction deep learning
Han, DaehyeonChoo, MinkiIm, JunghoShin, YejiLee, JuhyunJung, Sihun
Issue Date
GISCIENCE & REMOTE SENSING, v.60, no.1, pp.2203363
Skillful quantitative precipitation nowcasting (QPN) is important for predicting precipitation in the upcoming few hours and thus avoiding significant socioeconomic damage. Recent QPN studies have actively adopted deep learning (DL) to generate precipitation maps using sequences of ground radar data. Although high skill scores in forecasting precipitation areas of weak intensity (similar to 1 mm/h) have been achieved, the horizontal movement of precipitation areas could not be accurately simulated, exhibiting poor forecasting skills for stronger intensities. For lead times up to 120 min, this study suggests using an improved radar-based QPN model that utilizes a state-of-the-art DL model termed simpler yet better video prediction (SimVP). An independent evaluation using ground radar data in South Korea from June to September 2022 demonstrated that the proposed model outperformed the existing DL models in terms of critical score index (CSI) with a lead time of 120 min (0.46, 0.23, and 0.09 for 1, 5, and 10 mm/h thresholds, respectively). Three case analyses were conducted to reflect various precipitation conditions: heavy rainfall, typhoons, and fast-moving narrow convection events. The proposed SimVP-based QPN model yielded robust performance for all cases, producing a comparable or highest CSI at the lead time of 120 min with a 1 mm/h threshold (0.49, 0.69, and 0.29 for heavy rainfall, typhoon, and narrow convection, respectively). Qualitative evaluation of the model indicated better results in terms of displacement movement and reduced underestimation than other models under the high variability of precipitation patterns of the three cases. A comparison of model complexity among DL-QPN models was conducted, taking into consideration operational applications across various study areas and environments. The proposed approach is expected to provide a new baseline for DL-based QPN, and the improved prediction using the proposed model can lead to reduced socioeconomic damage incurred as a result of short-term intense precipitation.
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