Improved rainfall nowcasting using Burgers’ equation

Ryu, Soorok; Lyu, Geunsu; Do, Younghae; Lee, GyuWon

doi:10.1016/j.jhydrol.2019.124140

Cited by 23 publications

(19 citation statements)

References 27 publications

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“…The blending scheme performed especially well after a typhoon made landfall in Taiwan [20]. Moreover, some nowcasting rainfall models based on the advection-diffusion equation with non-stationary motion vectors are proposed in [22] to obtain smoother rainfall predictions for lead times and increase skill scores. The motion vectors are updated in each time step by solving the two-dimensional (2-D) Burgers' equations [22].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, some nowcasting rainfall models based on the advection-diffusion equation with non-stationary motion vectors are proposed in [22] to obtain smoother rainfall predictions for lead times and increase skill scores. The motion vectors are updated in each time step by solving the two-dimensional (2-D) Burgers' equations [22]. The optical flow-based methods are proposed and widely utilized by various observatory stations [17].…”

Section: Introductionmentioning

confidence: 99%

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Two-Stage Spatiotemporal Context Refinement Network for Precipitation Nowcasting

et al. 2021

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Precipitation nowcasting by radar echo extrapolation using machine learning algorithms is a field worthy of further study, since rainfall prediction is essential in work and life. Current methods of predicting the radar echo images need further improvement in prediction accuracy as well as in presenting the predicted details of the radar echo images. In this paper, we propose a two-stage spatiotemporal context refinement network (2S-STRef) to predict future pixel-level radar echo maps (deterministic output) more accurately and with more distinct details. The first stage is an efficient and concise spatiotemporal prediction network, which uses the spatiotemporal RNN module embedded in an encoder and decoder structure to give a first-stage prediction. The second stage is a proposed detail refinement net, which can preserve the high-frequency detailed feature of the radar echo images by using the multi-scale feature extraction and fusion residual block. We used a real-world radar echo map dataset of South China to evaluate the proposed 2S-STRef model. The experiments showed that compared with the PredRNN++ and ConvLSTM method, our 2S-STRef model performs better on the precipitation nowcasting, as well as at the image quality evaluating index and the forecasting indices. At a given 45dBZ echo threshold (heavy precipitation) and with a 2 h lead time, the widely used CSI, HSS, and SSIM indices of the proposed 2S-STRef model are found equal to 0.195, 0.312, and 0.665, respectively. In this case, the proposed model outperforms the OpticalFlow method and PredRNN++ model.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two-Stage Spatiotemporal Context Refinement Network for Precipitation Nowcasting

et al. 2021

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“…Later, various aspects of turbulence were investigated by Burgers 2,3 and proposed Burgers' equation as a mathematical model of turbulence. Besides the modeling of turbulence, Burgers' equation appears frequently in several fields of engineering and science which include gas dynamics, traffic flow, nonlinear acoustics, and heat conduction (see previous works 4–13 ). Burgers' equation is similar to Navier–Stokes equation as it contains nonlinear convection term and diffusion term with viscous coefficient.…”

Section: Introductionmentioning

confidence: 99%

On a weakly L‐stable time integration formula coupled with nonstandard finite difference scheme with application to nonlinear parabolic partial differential equations

Rawani

Verma

et al. 2021

Math Methods in App Sciences

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In the present paper, we establish an efficient numerical scheme based on weakly L‐stable time integration convergent formula and nonstandard finite difference (NSFD) scheme. We solve Burgers' equation with Dirichlet boundary conditions as well as Neumann boundary conditions. We also solve the Fisher equation. We use Hermite approximation polynomial of order five and backward explicit Taylor's series approximation of order six to derive the numerical integration formula for the initial value problem (IVP) y′false(tfalse)=gfalse(t,yfalse),0.1emyfalse(t0false)=ρ0. We combine this method with the NSFD scheme and convert the problem into the system of algebraic equations. We discuss the convergence, truncation error, and stability of the developed method. To demonstrate the efficiency of the developed method, we compare the numerical results with some existing numerical results and exact solutions.

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“…When focusing on strong convection in warm seasons, their results demonstrated that the forecast skill is useful up to 60 min. Ryu et al (2019) introduced an advection-diffusion model to modify motion vectors, and concluded that when motion vectors are time dependent in the nowcast lead time, better nowcast performance can be achieved.…”

Section: Introductionmentioning

confidence: 99%

Improving Radar Echo Lagrangian Extrapolation Nowcasting by Blending Numerical Model Wind Information: Statistical Performance of 16 Typhoon Cases

Chung

Yao

2020

Monthly Weather Review

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Severe weather nowcasting is a crucial mission of atmospheric science for the betterment of society to save life, limb, and property. In this study, composite radar data from the Central Weather Bureau of 16 typhoons are collected to examine the statistical performance of the McGill Algorithm for Precipitation nowcasting using Lagrangian Extrapolation (MAPLE) over Taiwan, an extrapolation algorithm that predicts future precipitation based on current radar echoes. In addition, instead of mixing the precipitation between radar extrapolation and numerical model forecast as in previous studies, a blending system is formed by synthesizing the wind information from model forecast with the echo extrapolation motion field via a variational algorithm to improve the nowcasting system. The statistical results of the radar echo extrapolation for 16 typhoon cases show that while the quantitative precipitation nowcasting skill can persist for up to 2 h, significant distortion for the rotational system is found after 2 h. On the other hand, the blending system helps to capture and maintain the rotation of typhoon rainband structures. The blending system extends the nowcasting skill by 1 h to a total of 3 h. Furthermore, the blending scheme performs especially well after the typhoon makes landfall in Taiwan. For disaster prevention and mitigation, this blending nowcasting technique may provide effective weather information immediately.

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Improved rainfall nowcasting using Burgers’ equation

Cited by 23 publications

References 27 publications

Two-Stage Spatiotemporal Context Refinement Network for Precipitation Nowcasting

Two-Stage Spatiotemporal Context Refinement Network for Precipitation Nowcasting

On a weakly L‐stable time integration formula coupled with nonstandard finite difference scheme with application to nonlinear parabolic partial differential equations

Improving Radar Echo Lagrangian Extrapolation Nowcasting by Blending Numerical Model Wind Information: Statistical Performance of 16 Typhoon Cases

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