Accuracy Evaluation of Composite Hybrid Surface Rainfall (HSR) Using KMA Weather Radar Network

Lyu, Geunsu; Jung, Sung-Hwa; Oh, Young-a; Park, Hong-Mok; Lee, GyuWon

doi:10.5467/jkess.2017.38.7.496

Cited by 2 publications

(1 citation statement)

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“…Korean Meteorological Administration (KMA) has provided radar data based on Hybrid Surface Rainfall (HSR) method since 2018. The HSR method is characterized by the synthesis of reflectivity at the hybrid surface that is unaffected by the ground clutter, beam blockage, non-meteorological echoes, and bright band (e.g., Kwon et al, 2015;Lyu et al, 2015;Lyu et al, 2017). The size of data is 2305 × 2880 (or, 1152.5 km × 1440 km) and the spatial and temporal resolutions of the HSR data are 0.5 km and 5 min, respectively.…”

Section: Radar Datasetmentioning

confidence: 99%

Advancing very short-term rainfall prediction with blended U-Net and partial differential approaches

Ha,

Park

2024

Front. Earth Sci.

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Accurate and timely prediction of short-term rainfall is crucial for reducing the damages caused by heavy rainfall events. Therefore, various precipitation nowcasting models have been proposed. However, the performance of these models still remains limited. In particular, the current operational precipitation nowcasting method, which is based on radar echo tracking, such as the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE), has a critical drawback when predicting newly developed or decayed precipitation fields. Recently proposed deep learning models, such as the U-Net convolutional neural network outperform the models based on radar echo tracking. However, these models are unsuitable for operational precipitation nowcasting due to their blurry predictions over longer lead times. To address these blurry effects and enhance the performance of U-Net-based rainfall prediction, we propose a blended model that combines a partial differential equation (PDE) model based on fluid dynamics with the U-Net model. The evaluation of the forecast skill, based on both qualitative and quantitative methods for 0–3-h lead times, demonstrates that the blended model provides less blurry and more accurate rainfall predictions compared with the U-Net and partial differential equation models. This indicates the potential to enhance the field of very short-term rainfall prediction. Additionally, we also evaluated the monthly-averaged forecast skills for different seasons, and confirmed the operational feasibility of the blended model, which contributes to the performance enhancement of operational nowcasting.

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Section: Radar Datasetmentioning

confidence: 99%

Advancing very short-term rainfall prediction with blended U-Net and partial differential approaches

Ha,

Park

2024

Front. Earth Sci.

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Assessing the Impact of Rainfall Inputs on Short-Term Flood Simulation with Cell2Flood: A Case Study of the Waryong Reservoir Basin

Kim,

Nam

et al. 2024

Hydrology

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This study explored the impacts of various rainfall input types on short-term runoff simulations using the Cell2Flood model in the Waryong Reservoir Basin, South Korea. Six types of rainfall data were assessed: on-site gauge measurements, spatially interpolated data from 39 Automated Synoptic Observing System (ASOS) and 117 Automatic Weather System (AWS) stations using inverse distance weighting (IDW), and Hybrid Surface Rainfall (HSR) data from the Korea Meteorological Administration. The choice of rainfall input significantly affected model accuracy across the three rainfall events. The point-gauged ASOS (P-ASOS) data demonstrated the highest reliability in capturing the observed rainfall patterns, with Pearson’s r values of up to 0.84, whereas the radar-derived HSR data had the lowest correlations (Pearson’s r below 0.2), highlighting substantial discrepancies. For runoff simulation, the P-ASOS and ASOS-AWS combined interpolated dataset (R-AWS) achieved relatively accurate predictions, with P-ASOS and R-AWS exhibiting Normalized Peak Error (NPE) values of approximately 0.03 and Peak Time Error (PTE) within 20 min. In contrast, the HSR data produced large errors, with NPE up to 4.66 and PTE deviations exceeding 200 min, indicating poor temporal accuracy. Although input-specific calibration improved performance, significant errors persisted because of the inherent uncertainty of rainfall data. These findings underscore the importance of selecting and calibrating appropriate rainfall inputs to enhance the reliability of short-term flood modeling, particularly in ungauged and data-sparse basins.

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Accuracy Evaluation of Composite Hybrid Surface Rainfall (HSR) Using KMA Weather Radar Network

Cited by 2 publications

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Advancing very short-term rainfall prediction with blended U-Net and partial differential approaches

Advancing very short-term rainfall prediction with blended U-Net and partial differential approaches

Assessing the Impact of Rainfall Inputs on Short-Term Flood Simulation with Cell2Flood: A Case Study of the Waryong Reservoir Basin

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