In Kinekenneth48/rdailychange: Daily Change Method for Spatial Data

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rdailychange

Overview

The goal of rdailychange is to allow the user to mimic the shedding phenomenon of snow on solar panels by extracting sequential changes in snow water equivalent (SWE). Day-1 observations are sequential daily changes in SWE. Day-2 observations assume that the shedding phenomenon occurs after two days. Hence, Day-2 observations are a sum of two consecutive days of positive sequential changes. Day-3, Day-4 and Day-5 observations are a sum of three, four and five consecutive sequential daily changes. In addition, they allow for at most one middle sequential change to be a negative value as long as the sum of the other sequential daily changes are greater than the negative change.

Installation

Development version

This package is still under development, to get a bug fix or to use a feature from the development version, you can install and load the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("Kinekenneth48/rdailychange")
library(rdailychange)

Package Contents

The package revolves around three main function named extract_observations, fit_observations, and plot_events. Documentation about these functions can be obtained by entering the following in the R console

?rdailychange::extract_observations
?rdailychange::fit_observations
?rdailychange::plot

The package also contains a sample dataset of snow load measurements(SWE) of 9 weather stations from 1951 to 2020 snow year. The data is available by installing the rdailychange package and invoking the command.

data("sample_data")
sample_data = force(sample_data)

dim(sample_data)

Package Usage

Once installed, this package can extract snow load values that mimic shedding phenomenon of snow on solar panel and plot the mean recurrence interval (MRI) per weather stations. The following example makes use of the sample_data dataset. The example will following the workflow of extracting the Day-X method observations, fitting those observations to either a generalized extreme value distribution or generalized Pareto distribution, and finally visualizing the MRI values from the weather stations. This example will focus on Day-1 method, however, the same worflow can be applied to Day-2,-3,-4, and -5 methods.

Extract Day-1 method observations from the 7 weather stations

After extracting Day-1 method observations, a information about a typical weather stations will include the Day-1 observations and the annual maximum load for each snow year.

d1 = rdailychange::extract_observations(sample_data, day=1)

#after
d1$USW00023062

Fit the observations

Once the observations are extracted, we can now fit a extreme value distribution to the observations. The package can fit the Generalized Extreme Value(GEV) or the Generalized Pareto distribution (GP), which are the two main distributions for fitting extreme values. These distributions are preferred because of their ability to better fit extreme values of observations. The return value will include 50-year, 100-year, and 500-year MRI value from the Day-1 method along side their ratio with respective to the annual data.

d1_parm = rdailychange::fit_observations(d1, type = "GEV")
d1_parm 

Visualize MRI values

Finally, we can visualize the 50-year MRI values for each weather station.

plot(d1_parm)

Getting help

If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub.

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Please note that this package is released under the MIT License. By participating or using this package you agree to abide by the terms of the license.

Kinekenneth48/rdailychange documentation built on Dec. 18, 2021, 3:34 a.m.