EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

A Dynamic and Flexible State Model for Rainfall Nowcasting

Marc Schleiss and Venkat Roy
Marc Schleiss and Venkat Roy
  • TU Delft, Geoscience & Remote Sensing, Delft, Netherlands (

We present a dynamic state model estimation method for rainfall nowcasting in which we assume that the short term spatio-temporal evolution of rainfall can be approximated by a linear state model with stochastic perturbations.  We estimate the model parameters using radar reflectivity measurements for one-step as well as multiple-step ahead rainfall nowcasting. If the rainfall intensity at location x and time index t is given by ut(x), then the overall rainfall field intensity vector at any time t over N pixels (of the target area) can be represented by ut = [ut(xN),...ut(xN)]T. Following the aforementioned formalism, the spatio-temporal evolution of the rainfall field can be described by the following linear state model given by
ut = Htut-1 + qt
where Ht is an unknown time-varying state-transition matrix of dimensions NxN and qt is a stochastic process noise vector of length N. We present an iterative least squares based method to estimate Ht and explore simpler algebraic structures (e.g., scaled affine transformations) to reduce the numbers of unknown parameters during estimation. We evaluate the performances of the proposed model using simulations and radar reflectivity data from the Royal Netherlands Meteorological Institute (KNMI). We observe that the nowcasting performances strongly depend on the size of the target area (number of pixels N), the type of events as well as the parameterization of Ht. The key advantage of the proposed approach over classical nowcasting methods based on Lagrangian persistence is the possibility to incorporate prior information about future rainfall evolution from external sources of information such as satellites or numerical weather prediction models during the estimation of the parameters.

How to cite: Schleiss, M. and Roy, V.: A Dynamic and Flexible State Model for Rainfall Nowcasting, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21400,, 2020


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