A Kernel-Based Spatio-Temporal Dynamical Model for Nowcasting Weather Radar Reflectivities

Xu, Ke; Wikle, Christopher K.; Fox, Neil I.

doi:10.1198/016214505000000682

Cited by 86 publications

(84 citation statements)

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“…This idea was reported in previous research [69] from a purely statistical aspect. It shows that a quantitative measurement of uncertainty improves accuracy in a small-scale storm, but a similar study on a large mesoscale system like a TC does not exist.…”

Section: Summary and Future Studymentioning

confidence: 72%

A Nowcasting Model for Tropical Cyclone Precipitation Regions Based on the TREC Motion Vector Retrieval with a Semi-Lagrangian Scheme for Doppler Weather Radar

Tang

Matyas

2018

Atmosphere

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Accurate observational data and reliable prediction models are both essential to improve the quality of precipitation forecasts. The spiraling trajectories of air parcels within a tropical cyclone (TC) coupled with the large sizes of these systems brings special challenges in making accurate short-term forecasts, or nowcasts. Doppler weather radars are ideal instruments to observe TCs when they move over land, and traditional nowcasts incorporate radar data. However, data from dozens of radars must be mosaicked together to observe the entire system. Traditional single-radar-based reflectivity tracking methods commonly employed in nowcasting are not suitable for TCs as they are not able to capture the circular motion of these systems. Thus, this paper focuses on improving short-term predictability of TC precipitation with Doppler weather radar observations based on: a multi-scale motion vector retrieval algorithm, an optimization technique and a semi-Lagrangian advection scheme. Motion fields of precipitation regions are obtained by a multi-level motion vector retrieval algorithm, then corrected and smoothed by the optimization technique using mass and smooth constraints. Predicted precipitation regions are then extrapolated using the semi-Lagrangian advection scheme. A case study of Hurricane Isabel (2003) shows that the combination of these methods may increase reliable rainfall prediction to about 5 h as the TC moves over land.

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Section: Summary and Future Studymentioning

confidence: 72%

A Nowcasting Model for Tropical Cyclone Precipitation Regions Based on the TREC Motion Vector Retrieval with a Semi-Lagrangian Scheme for Doppler Weather Radar

Tang

Matyas

2018

Atmosphere

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“…A detailed explanation of the methodology and rationale for the scheme can be seen in Xu et al (2005). A brief review of the underlying principles is presented herein.…”

Section: Methodsmentioning

confidence: 99%

“…As the method is Bayesian, the primary determinant of the parameters used for the nowcast are a result of the data that were used to establish the posterior distributions. Extensive details on this part of the methodology are available in Xu et al (2005). In particular, after a 1000-iteration burn-in, we consider 2000 realizations, available from the posterior distribution of parameters given the training data, as the nowcast samples.…”

Section: Methodsmentioning

confidence: 99%

“…Thereby, complete uncertainty measures can be produced to accompany the nowcast and examples of such products are displayed herein. The details of the methodology are available in two articles: Xu et al (2005) and Fox and Wikle (in press). The work presented here is designed to illustrate how distributed products, such as those produced through this methodology, may be of value to hydrologists.…”

Section: Introductionmentioning

confidence: 99%

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Providing distributed forecasts of precipitation using a Bayesian nowcast scheme

Fox¹,

Wikle²

2005

Atmospheric Science Letters

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“…Nevertheless, the model-based framework proposed in this paper may enable meaningful state tracking and connectivity estimation. The MRAIDE approach is not limited to neural fields; the framework can be applied to modeling other multi-resolution spatiotemporal dynamical systems such as weather systems, ecological systems, and others (Wikle, 2004;Xu et al, 2005). The key development is the multi-resolution decomposition forming the state-space model.…”

Section: Extensions To the Frameworkmentioning

confidence: 99%

Spatiotemporal multi-resolution approximation of the Amari type neural field model

Aram

Freestone

Dewar³

et al. 2013

NeuroImage

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Neural fields are spatially continuous state variables described by integro-differential equations, which are well suited to describe the spatiotemporal evolution of cortical activations on multiple scales. Here we develop a multi-resolution approximation (MRA) framework for the integro-difference equation (IDE) neural field model based on semi-orthogonal cardinal B-spline wavelets. In this way, a flexible framework is created, whereby both macroscopic and microscopic behavior of the system can be represented simultaneously. State and parameter estimation is performed using the expectation maximization (EM) algorithm. A synthetic example is provided to demonstrate the framework.

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A Kernel-Based Spatio-Temporal Dynamical Model for Nowcasting Weather Radar Reflectivities

Cited by 86 publications

References 1 publication

A Nowcasting Model for Tropical Cyclone Precipitation Regions Based on the TREC Motion Vector Retrieval with a Semi-Lagrangian Scheme for Doppler Weather Radar

A Nowcasting Model for Tropical Cyclone Precipitation Regions Based on the TREC Motion Vector Retrieval with a Semi-Lagrangian Scheme for Doppler Weather Radar

Providing distributed forecasts of precipitation using a Bayesian nowcast scheme

Spatiotemporal multi-resolution approximation of the Amari type neural field model

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