Abstract
A framework for analysing weather radar (DBz) images as spatial point processes is presented. Weather radar images are modelled for the purpose of predicting their evolution in time and thereby providing a basis for short-period precipitation forecasts. An observed image sequence is modelled as a set of individual rain cells that are the outcome of a marked 2+1D spatial point process. To each point giving the place and time of maturation of a rain cell is assigned a vector of possibly time-varying features such as intensity, duration, extent, shape and velocity. The point process is a doubly stochastic spatial point process with a clustering mechanism determined by the mesoscale situation. Also determined by the mesoscale situation are prior distributions for the elements of the feature vector. A scheme for fitting this type of model to an observed sequence of weather radar images is presented. >
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
