Publication

Two-site modeling of rainfall based on the Neyman-Scott process

André Musy, Stephan Morgenthaler, Anne-Catherine Favre
2002
Journal Articles

Abstract

On the basis of the Neyman-Scott process a model is proposed to generate rainfall at two sites simultaneously. This model takes into account the correlation, which is produced by generating bivariate correlated variates. The cross correlation between the sites has been computed in order to estimate the parameters. Finally, the model is validated for three pairs of stations situated in the Swiss Plateau region.

Official source

https://infoscience.epfl.ch/entities/publication/c4a3f34e-663e-451e-a15a-e615ff881146

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

André Musy, Stephan Morgenthaler, Anne-Catherine Favre
2002
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/c4a3f34e-663e-451e-a15a-e615ff881146

About this result

Related concepts (24)

Correlation

In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the so-called demand curve.

Cross-correlation

In signal processing, cross-correlation is a measure of similarity of two series as a function of the displacement of one relative to the other. This is also known as a sliding dot product or sliding inner-product. It is commonly used for searching a long signal for a shorter, known feature. It has applications in pattern recognition, single particle analysis, electron tomography, averaging, cryptanalysis, and neurophysiology. The cross-correlation is similar in nature to the convolution of two functions.

Correlation coefficient

A correlation coefficient is a numerical measure of some type of correlation, meaning a statistical relationship between two variables. The variables may be two columns of a given data set of observations, often called a sample, or two components of a multivariate random variable with a known distribution. Several types of correlation coefficient exist, each with their own definition and own range of usability and characteristics. They all assume values in the range from −1 to +1, where ±1 indicates the strongest possible agreement and 0 the strongest possible disagreement.

Related publications (12)

Two-site modeling of rainfall based on the Neyman-Scott process

Chat with Graph Search

Structural reliability and ageing of multistage pumps during start-up: A fracture mechanics-based experimental assessment

Exploring Causal Information Bottleneck for Adversarial Defense

Ascent Fails to Forget