BL.sim: Simulating storms and cells

In BLRPM: Stochastic Rainfall Generator Bartlett-Lewis Rectangular Pulse Model

Description

Bartlett-Lewis Rectangular Pulse Model

Usage

BL.sim(lambda = 4/240, gamma = 1/10, beta = 0.3, eta = 2, mux = 4,
  t.sim = 240)

Arguments

lambda	value specifying the generation rate of storms [1/h]
gamma	value specifying the storm duration [1/h]
beta	value specifying the generation rate of cells [1/h]
eta	value specifying the cell duration [1/h]
mux	value specifying the cell intensity [mm/h]
t.sim	value specifying the simulation time [h]

Details

Model description (Rodriguez-Iturbe et al., 1987):

The model is a combination of 2 poisson processes and simulates storms and cells. During the given simulation time storms are generated in a poisson process with rate lambda. Those storms are given a exponetially distributed duration with parameter gamma. During its duration the storm generates in a second poisson process cells with rate beta. The first cell has to be instantaneous at the time of the storm arrival. The cell duration is exponentially distributed with parameter eta. For the whole lifetime each cell is given a constant intensity which is exponentially distributed with parameter 1/mux.

Aggregation:

The intensities of all cells alive at time t are summed up for total precipitation at time t.

Parameter estimation:

The model parameters (lambda,gamma,beta,eta,mux) can be estimated from simulated or observed precipitation time series using the method of moments. Certain moments, e.g. mean, variance can be calculated from the time series at different aggregation levels. These moments can also be calculated theoretically from model parameters. Both sets of statistics can be compared in an objective function, similar to a squared error estimator. By numerical optimization the model parameters can be tuned to match the time series characteristics. BL.sim generates model realisations of storms and cells by using given model parameters lambda,gamma,beta,eta,mux for a given simulation time t.sim

Value

BL.sim returns storms; data.frame of all storms containing information about occurence time, end time and number of cells

BL.sim returns cells; data.frame of all cells containing information about occurence time, end time, intensity and storm index

Author(s)

Christoph Ritschel [e-mail]

Examples

lambda <- 4/240
gamma <- 1/10
beta <- 0.3
eta <- 2
mux <- 4
t.sim <- 240
simulation <- BL.sim(lambda,gamma,beta,eta,mux,t.sim)

Example output

Loading required package: R6

BLRPM documentation built on May 2, 2019, 9:23 a.m.