Statistics of the Z–R Relationship for Strong Convective Weather over the Yangtze–Huaihe River Basin and Its Application to Radar Reflectivity Data Assimilation for a Heavy Rain Event

Fang, Xue; Shao, Aimei; Yue, Xinjian; Liu, Weicheng

doi:10.1007/s13351-018-7163-1

Cited by 12 publications

(1 citation statement)

References 23 publications

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“…They have small horizontal scales, short life history, and sudden bursts. Strong convective weather occurs in small and medium-scale weather systems with small spatial scales [4]. The general horizontal range is about a dozen to two or three hundred kilometers, but some horizontal ranges are only tens of meters to ten kilometers.…”

Section: Introductionmentioning

confidence: 99%

Notice of Retraction: Short-term and local rainfall probability prediction based on a dislocation support vector machine model using satellite and in-situ observational data

Chen

et al. 2024

IEEE Access

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Short-term and local rainfall commonly occurs in southern China, and can result in intensive precipitation in the local region over a very short time. Considerable precipitation could cause an intense flood within a city when the runoff exceeds the capacity of a city drainage system. Predicting rainfall probability accurately is among the important scientific problems in meteorology and remains a challenge. In this study, a dislocation machine learning method based on a support vector machine was used to predict short-term and local rainfall probability using Feng Yun II geostationary meteorological satellite imagery and ground in situ observational data. The results were validated by a traditional threat score, which is commonly used in evaluation of weather predictions. The results indicate that the proposed algorithm can achieve good performance for a 1-h to 6-h forecast. The result implies that prediction accuracy could be improved by using a machine

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Section: Introductionmentioning

confidence: 99%

Notice of Retraction: Short-term and local rainfall probability prediction based on a dislocation support vector machine model using satellite and in-situ observational data

Chen

et al. 2024

IEEE Access

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Evaluation of Remote Sensing Rainfall Products, Bias Correction and Temporal Disaggregation Approaches, for Improved Accuracy in Hydrologic Simulations

yaswanth

Kannan

Bindhu

et al. 2023

Water Resour Manage

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Flood risk management studies require reliable estimates of extreme precipitation at high spatial-temporal distribution to force hydrologic models. Recently, Remote Sensing Rainfall Products (RRPs) have gained signi cant importance in the eld of hydrometeorology, but their applicability in urban hydrologic predictions remains uncertain. The current study evaluates the accuracy of RRPs in comparison with observed rainfall and the signi cance of space-time representation of rain in simulating single and bimodal ood hydrographs. The current study is conducted for the Adyar river basin, a rapidly developing urban area in Chennai experiencing frequent oods. Sub-daily rainfall retrievals from three different satellites and Doppler Weather Radar (DWR) are the Remote sensing Rainfall products (RRPs) selected in the present study. Continuous and categorical statistical indices are selected to evaluate the performance of satellite rainfall estimates. Then the hydrologic utility of RRPs is conducted using the HEC-HMS model for ve extreme precipitation events. The RRPs simulated the rising and recession portion of ood hydrographs accurately with a bias in peak discharge. Then, two approaches are selected to further improve the ood hydrograph simulations in the current study; 1) Hydrologic model simulations after disaggregating the daily station data to sub-daily scale using time characteristics of RRPs, 2) Hydrologic simulations after bias adjusting the RRPs with station data. We found substantial improvements in model results in the two approaches. The disaggregation approach using satellite rainfall estimates has overcome the insu ciency of sub-daily rainfall observations. The bias adjusted radar rainfall data is found as best performing for the ood hydrograph simulations.

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Improving the Nowcasting of Strong Convection by Assimilating Both Wind and Reflectivity Observations of Phased Array Radar: A Case Study

Lin¹,

Feng²,

Xu³

et al. 2022

J Meteorol Res

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Statistics of the Z–R Relationship for Strong Convective Weather over the Yangtze–Huaihe River Basin and Its Application to Radar Reflectivity Data Assimilation for a Heavy Rain Event

Cited by 12 publications

References 23 publications

Notice of Retraction: Short-term and local rainfall probability prediction based on a dislocation support vector machine model using satellite and in-situ observational data

Notice of Retraction: Short-term and local rainfall probability prediction based on a dislocation support vector machine model using satellite and in-situ observational data

Evaluation of Remote Sensing Rainfall Products, Bias Correction and Temporal Disaggregation Approaches, for Improved Accuracy in Hydrologic Simulations

Improving the Nowcasting of Strong Convection by Assimilating Both Wind and Reflectivity Observations of Phased Array Radar: A Case Study

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