README.md
In Kinekenneth48/rdailychange: Daily Change Method for Spatial Data
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
#> No documentation for 'plot' in specified packages and libraries:
#> you could try '??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)
#> [1] 158627 7
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
#> $d1
#> ID NAME STATE LONGITUDE LATITUDE DIFF
#> 1: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 22.55386229
#> 2: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 7.34369854
#> 3: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 13.67618618
#> 4: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 0.06114788
#> 5: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 5.27577624
#> ---
#> 1853: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 5.73059937
#> 1854: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 5.77991830
#> 1855: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 5.79062263
#> 1856: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 5.80123780
#> 1857: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 12.64307390
#>
#> $annual
#> ID NAME STATE LONGITUDE LATITUDE YEAR ANNUAL
#> 1: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1951 37.66844
#> 2: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1952 67.85129
#> 3: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1953 26.70000
#> 4: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1954 16.50000
#> 5: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1955 16.50000
#> 6: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1956 18.01812
#> 7: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1957 59.70000
#> 8: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1958 14.00000
#> 9: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1959 33.00000
#> 10: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1960 19.10000
#> 11: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1961 22.40000
#> 12: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1962 27.70000
#> 13: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1963 18.50000
#> 14: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1964 25.10000
#> 15: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1965 12.40000
#> 16: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1966 15.20000
#> 17: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1967 20.85296
#> 18: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1968 13.00000
#> 19: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1969 12.70000
#> 20: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1970 30.50000
#> 21: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1971 30.50000
#> 22: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1972 63.50000
#> 23: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1973 40.60000
#> 24: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1974 48.30000
#> 25: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1975 22.90000
#> 26: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1976 30.50000
#> 27: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1977 27.90000
#> 28: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1978 25.40000
#> 29: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1979 20.30000
#> 30: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1980 66.00000
#> 31: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1981 38.10000
#> 32: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1982 17.80000
#> 33: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1983 106.70000
#> 34: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1984 58.40000
#> 35: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1985 22.90000
#> 36: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1986 55.90000
#> 37: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1987 12.59739
#> 38: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1988 45.70000
#> 39: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1989 20.30000
#> 40: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1990 30.50000
#> 41: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1991 15.20000
#> 42: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1992 30.50000
#> 43: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1993 25.40000
#> 44: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1994 12.70000
#> 45: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1995 17.80000
#> 46: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1996 23.73134
#> 47: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1997 18.32555
#> 48: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1998 53.08316
#> 49: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1999 30.98088
#> 50: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2000 29.31596
#> 51: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2001 57.67667
#> 52: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2002 28.57546
#> 53: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2003 119.88237
#> 54: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2004 18.39438
#> 55: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2005 64.23560
#> 56: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2006 18.14317
#> 57: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2007 92.43089
#> 58: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2008 51.26619
#> 59: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2009 24.22318
#> 60: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2010 46.28233
#> 61: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2011 32.45188
#> 62: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2012 61.95337
#> 63: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2013 40.86082
#> 64: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2014 22.08451
#> 65: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2015 39.05023
#> 66: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2016 73.48308
#> 67: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2017 32.19965
#> 68: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2018 21.24628
#> 69: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2019 38.74096
#> 70: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2020 46.04772
#> ID NAME STATE LONGITUDE LATITUDE YEAR ANNUAL
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
#> ID NAME STATE LONGITUDE LATITUDE
#> 1: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633
#> 2: USW00014933 DES MOINES INTL AP IA -93.6531 41.5339
#> 3: USW00094910 WATERLOO MUNI AP IA -92.4011 42.5544
#> 4: USW00094846 CHICAGO OHARE INTL AP IL -87.9336 41.9950
#> 5: USW00014842 PEORIA GTR PEORIA AP IL -89.6839 40.6675
#> 6: USW00014739 BOSTON LOGAN INTL AP MA -71.0106 42.3606
#> 7: USW00094847 DETROIT METRO AP MI -83.3308 42.2314
#> 8: USW00014837 MADISON DANE RGNL AP WI -89.3453 43.1406
#> 9: USW00024089 CASPER NATRONA CO AP WY -106.4636 42.8975
#> NO_OF_OBSERVATIONS DAY_CHANGE EVENT50 ANNUAL_RATIO50 EVENT100
#> 1: 1857 1 5.902894 0.05656550 7.823793
#> 2: 2026 1 5.711328 0.05217598 8.191689
#> 3: 2391 1 5.095065 0.04867620 7.799867
#> 4: 1533 1 5.950440 0.04582840 8.288424
#> 5: 1565 1 5.400767 0.05612399 7.498983
#> 6: 887 1 9.468161 0.06267224 12.659130
#> 7: 1689 1 7.239606 0.04763226 10.039456
#> 8: 2769 1 5.090886 0.03849085 7.348875
#> 9: 2329 1 4.581452 0.05540722 6.177016
#> ANNUAL_RATIO100 EVENT500 ANNUAL_RATIO500
#> 1: 0.06118689 14.34948 0.07207539
#> 2: 0.06632659 18.13450 0.11635474
#> 3: 0.06951973 20.19597 0.16017404
#> 4: 0.05207348 17.09658 0.06913993
#> 5: 0.06503502 15.33826 0.09072052
#> 6: 0.07053219 23.73836 0.09209757
#> 7: 0.05484056 20.53219 0.07582727
#> 8: 0.04921063 16.51872 0.08752418
#> 9: 0.06241863 11.78661 0.08139972
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.
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.
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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
#> No documentation for 'plot' in specified packages and libraries:
#> you could try '??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)
#> [1] 158627 7
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
#> $d1
#> ID NAME STATE LONGITUDE LATITUDE DIFF
#> 1: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 22.55386229
#> 2: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 7.34369854
#> 3: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 13.67618618
#> 4: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 0.06114788
#> 5: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 5.27577624
#> ---
#> 1853: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 5.73059937
#> 1854: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 5.77991830
#> 1855: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 5.79062263
#> 1856: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 5.80123780
#> 1857: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 12.64307390
#>
#> $annual
#> ID NAME STATE LONGITUDE LATITUDE YEAR ANNUAL
#> 1: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1951 37.66844
#> 2: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1952 67.85129
#> 3: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1953 26.70000
#> 4: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1954 16.50000
#> 5: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1955 16.50000
#> 6: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1956 18.01812
#> 7: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1957 59.70000
#> 8: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1958 14.00000
#> 9: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1959 33.00000
#> 10: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1960 19.10000
#> 11: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1961 22.40000
#> 12: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1962 27.70000
#> 13: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1963 18.50000
#> 14: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1964 25.10000
#> 15: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1965 12.40000
#> 16: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1966 15.20000
#> 17: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1967 20.85296
#> 18: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1968 13.00000
#> 19: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1969 12.70000
#> 20: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1970 30.50000
#> 21: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1971 30.50000
#> 22: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1972 63.50000
#> 23: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1973 40.60000
#> 24: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1974 48.30000
#> 25: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1975 22.90000
#> 26: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1976 30.50000
#> 27: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1977 27.90000
#> 28: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1978 25.40000
#> 29: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1979 20.30000
#> 30: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1980 66.00000
#> 31: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1981 38.10000
#> 32: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1982 17.80000
#> 33: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1983 106.70000
#> 34: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1984 58.40000
#> 35: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1985 22.90000
#> 36: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1986 55.90000
#> 37: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1987 12.59739
#> 38: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1988 45.70000
#> 39: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1989 20.30000
#> 40: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1990 30.50000
#> 41: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1991 15.20000
#> 42: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1992 30.50000
#> 43: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1993 25.40000
#> 44: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1994 12.70000
#> 45: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1995 17.80000
#> 46: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1996 23.73134
#> 47: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1997 18.32555
#> 48: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1998 53.08316
#> 49: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 1999 30.98088
#> 50: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2000 29.31596
#> 51: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2001 57.67667
#> 52: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2002 28.57546
#> 53: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2003 119.88237
#> 54: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2004 18.39438
#> 55: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2005 64.23560
#> 56: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2006 18.14317
#> 57: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2007 92.43089
#> 58: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2008 51.26619
#> 59: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2009 24.22318
#> 60: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2010 46.28233
#> 61: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2011 32.45188
#> 62: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2012 61.95337
#> 63: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2013 40.86082
#> 64: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2014 22.08451
#> 65: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2015 39.05023
#> 66: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2016 73.48308
#> 67: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2017 32.19965
#> 68: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2018 21.24628
#> 69: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2019 38.74096
#> 70: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633 2020 46.04772
#> ID NAME STATE LONGITUDE LATITUDE YEAR ANNUAL
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
#> ID NAME STATE LONGITUDE LATITUDE
#> 1: USW00023062 DENVER-STAPLETON CO -104.8694 39.7633
#> 2: USW00014933 DES MOINES INTL AP IA -93.6531 41.5339
#> 3: USW00094910 WATERLOO MUNI AP IA -92.4011 42.5544
#> 4: USW00094846 CHICAGO OHARE INTL AP IL -87.9336 41.9950
#> 5: USW00014842 PEORIA GTR PEORIA AP IL -89.6839 40.6675
#> 6: USW00014739 BOSTON LOGAN INTL AP MA -71.0106 42.3606
#> 7: USW00094847 DETROIT METRO AP MI -83.3308 42.2314
#> 8: USW00014837 MADISON DANE RGNL AP WI -89.3453 43.1406
#> 9: USW00024089 CASPER NATRONA CO AP WY -106.4636 42.8975
#> NO_OF_OBSERVATIONS DAY_CHANGE EVENT50 ANNUAL_RATIO50 EVENT100
#> 1: 1857 1 5.902894 0.05656550 7.823793
#> 2: 2026 1 5.711328 0.05217598 8.191689
#> 3: 2391 1 5.095065 0.04867620 7.799867
#> 4: 1533 1 5.950440 0.04582840 8.288424
#> 5: 1565 1 5.400767 0.05612399 7.498983
#> 6: 887 1 9.468161 0.06267224 12.659130
#> 7: 1689 1 7.239606 0.04763226 10.039456
#> 8: 2769 1 5.090886 0.03849085 7.348875
#> 9: 2329 1 4.581452 0.05540722 6.177016
#> ANNUAL_RATIO100 EVENT500 ANNUAL_RATIO500
#> 1: 0.06118689 14.34948 0.07207539
#> 2: 0.06632659 18.13450 0.11635474
#> 3: 0.06951973 20.19597 0.16017404
#> 4: 0.05207348 17.09658 0.06913993
#> 5: 0.06503502 15.33826 0.09072052
#> 6: 0.07053219 23.73836 0.09209757
#> 7: 0.05484056 20.53219 0.07582727
#> 8: 0.04921063 16.51872 0.08752418
#> 9: 0.06241863 11.78661 0.08139972
Visualize MRI values
Finally, we can visualize the 50-year MRI values for each weather station.
plot(d1_parm)
Getting help
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