Assessments of Use of Blended Radar–Numerical Weather Prediction Product in Short-Range Warning of Intense Rainstorms in Localized Systems (SWIRLS) for Quantitative Precipitation Forecast of Tropical Cyclone Landfall on Vietnam’s Coast

Hung, Mai Khanh; Tien, Du Duc; Quan, Dang Dinh; Duc, Tran Anh; Dung, Pham Thi Phuong; Hole, Lars R.; Nam, Hoang Gia

doi:10.3390/atmos14081201

Cited by 3 publications

(1 citation statement)

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“…For precipitation nowcasting (zero to three hours), radar and satellites are the two tools that yield the best results, as evidenced in recent times in Vietnam (Hien and Thu 2000;Thanh et al 2010;Thanh and Thuc 2018). To address the challenge of forecasting heavy rainfall in small areas, forecast centers worldwide have adopted data assimilation from various observation sources (such as automatic rain gauges, radar, and satellite data) for high-resolution weather forecast methods (< 5 km) with deterministic and ensemble forecast products, probabilities (Milbrandt et al 2016;Gustafsson et al 2018), or blending radar data into numerical methods (Hung et al 2023). However, implementing this process takes considerable time and requires high-performance computer systems.…”

Section: Introductionmentioning

confidence: 99%

Application of optical flow technique to short-term rainfall forecast for some synoptic patterns in Vietnam

Thu,

Tri,

Hoa

et al. 2024

Preprint

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The occurrence of heavy rains can lead to human, economic, and ecological disasters with large-scale consequences. There are now many precipitation forecasting systems that use radar products with different algorithms and techniques to provide forecasts for up to one to three hours, such as McGill algorithm for precipitation nowcasting by Lagrange extrapolation (MAPLE), Short-Term Ensemble Prediction System (STEPS), and Short-range Warning of Intense Rainstorms in Localized Systems (SWIRLS). Optical flow engineering is an important technique in computer vision. Our aim was to apply optical flow techniques using the methods of DenseRotation_FCAFlow - this method is a small branch of the Rainymotion library system, belonging to the Dense method group DenseRotation_Farneback - this method uses the local optical flow and polynomial function extension techniques to calculate radar echo region motion, Real-time Optical Flow by Variational Methods for Echoes of Radar - this method is used with two sets of parameters for Hong Kong (ROVER_HKO) and Vietnam ( ROVER_VN) to predict rainfall quantitatively from 2019–2021 radar rainfall quantitative data in Vietnam. The results show the following: (i) Changing the parameterizations of the ROVER_HKO method to apply to conditions in Vietnam (ROVER_VN) gives better results than the remaining methods for the total accumulated rainfall of the entire rainfall events, and the ROVER_VN method gives the best results in case rain occurs due to a combination of many synoptic patterns; (ii) The performance of Quantitative Precipitation Forecasting (QPF) using a performance diagram with light and moderate rainfall thresholds is also better captured in a combined local and global optical flow method than when using either type of optical flow alone. The ROVER_VN method quantitatively forecasts the cumulative rainfall of the entire rain event in case the rain is caused by the interaction of many different synoptic patterns and the rainfall area develops to be wide and fast moving, playing an especially important role in providing input data for forecasting and warning of floods, flash floods, and landslides throughout the territory of Vietnam.

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Section: Introductionmentioning

confidence: 99%

Application of optical flow technique to short-term rainfall forecast for some synoptic patterns in Vietnam

Thu,

Tri,

Hoa

et al. 2024

Preprint

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An asymmetric tropical cyclone rainfall model in the Northern Vietnam coast

Angkanasirikul,

Jian,

2024

Meteorological Applications

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Rainfall associated with landfalling tropical cyclones (TCs) along the Northern Vietnam coast is examined to develop an asymmetric parametric TC‐induced rainfall model starting from the axisymmetric Rain‐Climatology and Persistence (R‐CLIPER) model. We recalibrated the R‐CLIPER model (original R‐CLIPER denoted as NHC) against observed rainfall patterns of 14 landfalling TCs from 2001 to 2021 in the Northern Vietnam coast, while relaxing the model's underlying linear relationships. The recalibrated R‐CLIPER (denoted as Fit‐Ax), still axisymmetric, suggests that some parameters are better correlated with the normalized maximum wind speed using logarithmic and exponential relationships. Fit‐Ax reduces the 12‐hr total rainfall overall root‐mean‐square errors (RMSEs) and Bias magnitudes in the before‐ and after‐landfall periods from NHC for the entire 500‐km TC domain. We further redistribute the Fit‐Ax rainfall intensity across the four quadrants with respect to the TC forward motion to account for the observed large asymmetry in quadrant rainfall (version denoted as Fit‐As). The vertical wind shear (VWS) and landfall (before or after) are considered in this redistribution. Fit‐As generally outperforms Fit‐Ax and NHC in reproducing the observed rainfall distribution for the 14 TCs. At the quadrant level, both Fit‐Ax and Fit‐As show significant improvement in Bias over NHC. Fit‐As is further better overall in RMSE and Skill when weighted by quadrant rainfall volume. In pattern matching, Fit‐As produces the best grid‐averaged Pearson correlation coefficients for 11 TCs. In addition, its equitable threat scores (ETSs) are best beyond the 20‐mm rainfall threshold, with the maximum of 0.299 at the 90‐mm rainfall threshold. Thus, our locally fitted asymmetric rainfall model demonstrates improved capability in reproducing the historical TC‐induced rainfall along the Northern Vietnam coast.

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