Uncertainty analysis of radar rainfall estimates induced by atmospheric conditions using long short-term memory networks

Yang, Qiqi; Dai, Qiang; Han, Dawei; Zhu, Zhizhou; Zhang, Shuliang

doi:10.1016/j.jhydrol.2020.125482

Cited by 13 publications

(4 citation statements)

References 52 publications

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“…Rain gauges, radar and satellite observations are the three main sources of data characterizing rainfall. These three sources of data have advantages and disadvantages in terms of spatial and temporal resolution, observation accuracy and coverage [94]. Satellite and radar data can be less accurate than rain gauges, which measure rainfall directly.…”

Section: Issues Related To Flood Simulationsmentioning

confidence: 99%

Assessment of the Feasibility of Implementing a Flash Flood Early Warning System in a Small Catchment Area

2023

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The occurrence of flash floods is an increasingly common phenomenon. In many parts of the world, it is associated with an increase in the intensity of rainfall. Reducing the financial and social losses caused by the occurrence of local urban floods is possible through the use of hydrodynamic modeling and real-time flood forecasting. The purpose of this study is to assess the ability of the modeling technique to simulate the flow in a small catchment area and to determine the time remaining to reach the set warning and danger levels. SWMM 5.2 and QuantumGIS software were used in the study. The analysis showed that for the considered catchment area with a short length of the main stream (1612 m), the time possible for implementing countermeasures and evacuating the population is 70 to 120 min. The study also confirmed that short-term rainfall requires less depth to reach high stormwater elevations than long-term rainfall. In addition, a relationship was noted between the preceding rainfall and the height of stormwater and the forecast time. There was an unfavorable reduction in forecasting time as the depth of rainfall increased and its duration shortened. In the case of the analyzed catchment, the maximum elevation of stormwater (Esw,max) is generated by rainfall that is characterized by the highest intensity in the final phase of their duration. Similarly, the longest forecast time (tf) for the maximum stormwater elevation is caused by rainfall, which is characterized by the highest intensity in its final phase. The results of the study can significantly assist local governments when developing a catchment management plan and when trying to implement practices to minimize the negative effects of flash floods.

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Section: Issues Related To Flood Simulationsmentioning

confidence: 99%

Assessment of the Feasibility of Implementing a Flash Flood Early Warning System in a Small Catchment Area

2023

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“…Dai and Han (Dai and Han, 2014) were the first to incorporate the wind field into the construction of a radar rainfall uncertainty model, and the proposed model improved the correlation coefficients of most rainfall events by over 10%. Yang et al (2020) considered multiple atmospheric fields to build a radar rainfall uncertainty adjustment model, and the results indicated a satisfactory performance of the model under high relative humidity and wind speed.…”

Section: Introductionmentioning

confidence: 99%

Long lead-time radar rainfall nowcasting method incorporating atmospheric conditions using long short-term memory networks

Zhu

Yang

Zhang

et al. 2023

Front. Environ. Sci.

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High-resolution radar rainfall data have great potential for rainfall predictions up to 6 h ahead (nowcasting); however, conventional extrapolation approaches based on in-built physical assumptions yield poor performance at longer lead times (3–6 h), which limits their operational utility. Moreover, atmospheric factors in radar estimate errors are often ignored. This study proposed a radar rainfall nowcasting method that attempts to achieve accurate nowcasting of 6 h using long short-term memory (LSTM) networks. Atmospheric conditions were considered to reduce radar estimate errors. To build radar nowcasting models based on LSTM networks (LSTM-RN), approximately 11 years of radar, gauge rainfall, and atmospheric data from the UK were obtained. Compared with the models built on optical flow (OF-RN) and random forest (RF-RN), LSTM-RN had the lowest root-mean-square errors (RMSE), highest correlation coefficients (COR), and mean bias errors closest to 0. Furthermore, LSTM-RN showed a growing advantage at longer lead times, with the RMSE decreasing by 17.99% and 7.17% compared with that of OF-RN and RF-RN, respectively. The results also revealed a strong relationship between LSTM-RN performance and weather conditions. This study provides an effective solution for nowcasting radar rainfall at long lead times, which enhances the forecast value and supports practical utility.

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“…Most correction schemes rely on the use of 4D wind profiles derived from numerical prediction models (Mittermaier et al, 2004;Lack and Fox, 2007;Lauri et al, 2012;Sandford, 2015) or a combination of them with reanalysis (Dai et al, 2013(Dai et al, , 2019Yang et al, 2020). The latter also accounts for drop size distribution (DSD).…”

Section: Introductionmentioning

confidence: 99%

3D trajectories and velocities of rainfall drops in a multifractal turbulent wind field

2022

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Abstract. Weather radars measure rainfall in altitude, whereas hydro-meteorologists are mainly interested in rainfall at ground level. During their fall, drops are advected by the wind, which affects the location of the measured field. The governing equation of a rain drop's motion relates the acceleration to the forces of gravity and buoyancy along with the drag force. It depends non-linearly on the instantaneous relative velocity between the drop and the local wind, which yields complex behaviour. Here, the drag force is expressed in a standard way with the help of a drag coefficient expressed as a function of the Reynolds number. Corrections accounting for the oblateness of drops greater than 1–2 mm are suggested and validated through a comparison of the retrieved “terminal fall velocity” (i.e. without wind) with commonly used relationships in the literature. An explicit numerical scheme is then implemented to solve this equation for a 3+1D turbulent wind field, and hence analyse the temporal evolution of the velocities and trajectories of rain drops during their fall. It appears that multifractal features of the input wind are simply transferred to the drop velocity with an additional fractional integration whose level depends on the drop size, and a slight time shift. Using an actual high-resolution 3D sonic anemometer and a scale invariant approach to simulate realistic fluctuations of wind in space, trajectories of drops of various sizes falling form 1500 m are studied. For a strong wind event, drops located within a radar gate in altitude during 5 min are spread on the ground over an area of the size of a few kilometres. The spread for drops of a given diameter is found to cover a few radar pixels. Consequences on measurements of hydro-meteorological extremes that are needed to improve the resilience of urban areas are discussed.

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Uncertainty analysis of radar rainfall estimates induced by atmospheric conditions using long short-term memory networks

Cited by 13 publications

References 52 publications

Assessment of the Feasibility of Implementing a Flash Flood Early Warning System in a Small Catchment Area

Assessment of the Feasibility of Implementing a Flash Flood Early Warning System in a Small Catchment Area

Long lead-time radar rainfall nowcasting method incorporating atmospheric conditions using long short-term memory networks

3D trajectories and velocities of rainfall drops in a multifractal turbulent wind field

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