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Real‐time rainfall prediction at small space‐time scales using a two‐dimensional stochastic advection‐diffusion model

Jinno, Kenji ; Kawamura, Akira ; Berndtsson, Ronny LU orcid ; Larson, Magnus LU and Niemczynowicz, Janusz LU (1993) In Water Resources Research 29(5). p.1489-1504
Abstract

A model based on the two‐dimensional stochastic advection‐diffusion equation is developed to forecast properties of individual rain cells in urban areas such as speed and spatial rainfall intensity. Two different modeling approaches are employed, and examples of the results are given. The first approach involves a Gaussian distribution as an analytic solution to the advection‐diffusion equation, whereas the second one entails a double Fourier series expansion of the rainfall intensity field. Both modeling approaches are used to predict the rainfall intensity field over a small 12‐gage urban catchment in southern Sweden. The model parameters are continuously updated by extended Kalman filtering. The Fourier series approach is shown to be... (More)

A model based on the two‐dimensional stochastic advection‐diffusion equation is developed to forecast properties of individual rain cells in urban areas such as speed and spatial rainfall intensity. Two different modeling approaches are employed, and examples of the results are given. The first approach involves a Gaussian distribution as an analytic solution to the advection‐diffusion equation, whereas the second one entails a double Fourier series expansion of the rainfall intensity field. Both modeling approaches are used to predict the rainfall intensity field over a small 12‐gage urban catchment in southern Sweden. The model parameters are continuously updated by extended Kalman filtering. The Fourier series approach is shown to be the most flexible for practical applications and to give the most accurate forecasts. This model approach gives acceptable forecasts for a lead time of 1–5 min. It gives consistently smaller prediction errors compared to both the Gaussian solution and simple extrapolation calculations. The effect of system noise level on the forecast accuracy and model performance is discussed. The model can be used not only to predict in real time the spatial rainfall, but also to parameterize the variability pattern of small‐scale spatial rainfall into a set of physically based parameters, thus separating the effects of advective velocity, turbulent diffusion, and development/decay.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Water Resources Research
volume
29
issue
5
pages
16 pages
publisher
American Geophysical Union (AGU)
external identifiers
  • scopus:0027426662
ISSN
0043-1397
DOI
10.1029/92WR02849
language
English
LU publication?
yes
id
551a1ea4-8b4d-4387-99dc-d63dde211a47
date added to LUP
2023-08-17 15:21:22
date last changed
2023-08-18 15:13:18
@article{551a1ea4-8b4d-4387-99dc-d63dde211a47,
  abstract     = {{<p>A model based on the two‐dimensional stochastic advection‐diffusion equation is developed to forecast properties of individual rain cells in urban areas such as speed and spatial rainfall intensity. Two different modeling approaches are employed, and examples of the results are given. The first approach involves a Gaussian distribution as an analytic solution to the advection‐diffusion equation, whereas the second one entails a double Fourier series expansion of the rainfall intensity field. Both modeling approaches are used to predict the rainfall intensity field over a small 12‐gage urban catchment in southern Sweden. The model parameters are continuously updated by extended Kalman filtering. The Fourier series approach is shown to be the most flexible for practical applications and to give the most accurate forecasts. This model approach gives acceptable forecasts for a lead time of 1–5 min. It gives consistently smaller prediction errors compared to both the Gaussian solution and simple extrapolation calculations. The effect of system noise level on the forecast accuracy and model performance is discussed. The model can be used not only to predict in real time the spatial rainfall, but also to parameterize the variability pattern of small‐scale spatial rainfall into a set of physically based parameters, thus separating the effects of advective velocity, turbulent diffusion, and development/decay.</p>}},
  author       = {{Jinno, Kenji and Kawamura, Akira and Berndtsson, Ronny and Larson, Magnus and Niemczynowicz, Janusz}},
  issn         = {{0043-1397}},
  language     = {{eng}},
  number       = {{5}},
  pages        = {{1489--1504}},
  publisher    = {{American Geophysical Union (AGU)}},
  series       = {{Water Resources Research}},
  title        = {{Real‐time rainfall prediction at small space‐time scales using a two‐dimensional stochastic advection‐diffusion model}},
  url          = {{http://dx.doi.org/10.1029/92WR02849}},
  doi          = {{10.1029/92WR02849}},
  volume       = {{29}},
  year         = {{1993}},
}