In MazamaScience/RAWSmet: Download and Process data from Remote Automatic Weather Stations

knitr::opts_chunk$set(echo = TRUE)

Background

The RAWSmet package was developed to make downloading and working with weather data gathered by RAWS stations simpler. Using this package in conjunction with the openair package can make some beautiful and informative visualizations of RAWS data.

openair is an R package designed specifically for modeling air quality data. The package provides many different tools for plotting wind and pollution roses, flexible time series plots, and more. Additionally, openair can easily group data and plot it by different periods such as by the hour, day, day of the week, season, and year. More information and documentation for openair may be found on its website: https://davidcarslaw.github.io/openair/.

The goal of this document is to introduce the use of RAWSmet and openair to create visualizations of weather data..

Setting up RAWSmet

Follow these instructions to set up RAWSmet correctly. These instruction may also be found on the package’s website: https://mazamascience.github.io/RAWSmet/.

To follow along with the rest of this document you may also install openair by running the following in the RStudio console:

install.packages("openair")

The RAWSmet package is designed to be used with R (>= 3.5) and RStudio so make sure you have those installed first.

Users will want to install the remotes package to have access to the latest version of the package from GitHub.

The following packages should be installed by typing the following at the RStudio console:

# Note that vignettes require knitr and rmarkdown
install.packages('knitr')
install.packages('rmarkdown')
install.packages('MazamaSpatialUtils')
remotes::install_github('MazamaScience/MazamaLocationUtils')
remotes::install_github('MazamaScience/RAWSmet')

Any work with spatial data, e.g. assigning states, counties and timezones, will require installation of required spatial datasets. To get these datasets you should type the following at the RStudio console:

library(MazamaSpatialUtils)
dir.create('~/Data/Spatial', recursive = TRUE)
setSpatialDataDir('~/Data/Spatial')
installSpatialData()

Data generated with package functions can be be saved to and reloaded from a dedicated directory much the same as the spatialDataDir used above. Set up a directory for RAWS data with:

library(RAWSmet)
dir.create('~/Data/RAWS', recursive = TRUE)
setRawsDataDir('~/Data/RAWS')

The rawsDataDir must be set up correctly to use its functionality. RAWS data takes a long time to download so data may be saved to this directory so it does not need to be downloaded again in the future.

Preparing data for openair

Throughout this section, we will be using RAWSmet's cefa_load() and cefa_loadMeta() functions. These functions will either load the specified data from rawsDataDir or download and save it to the rawsDataDir if it does not exist. You may also use wrcc_loadYear() and wrcc_loadMeta() for RAWS data from the WRCC.

Before using RAWS data with openair, we must ensure that the data is downloaded correctly and is in the correct format.

First, create or load all of the FW13 station metadata using cefa_loadMeta().

library(RAWSmet)
library(MazamaSpatialUtils)

setSpatialDataDir("~/Data/Spatial")
setRawsDataDir("~/Data/RAWS")

meta <- cefa_loadMeta()
head(meta)

The meta_leaflet() function provides an interactive map to help find the ID associated with a particular location. Just click on a dot to find the associated ID:

meta_leaflet(meta)

We will choose the station in Enumclaw, Washington (451702) and create or load a "timeseries object". This timeseries object contains two dataframes, one of station metadata and another of cleaned weather data.

# nwsID 451702 is Enumclaw, WA
Enumclaw <- cefa_load(nwsID = 451702, meta = meta)

names(Enumclaw)
# View station metadata
Enumclaw$meta
# View sample of raw data
head(Enumclaw$data)

Note that this RAWS timeseries object contains all of the data gathered by the specified station in its lifetime. We can filter the data to look at periods of interest using raws_filterDate(). This function can understand any date that is understood by lubridate::ymd().

Also note that the data stored in these RAWS timeseries objects are in UTC.

# 20050101 will be parsed as Jan. 1st, 2005
# 20060101 will be parsed as Jan 1st. 2006

# Get all observations between these dates
Enumclaw_2005 <- 
  raws_filterDate(Enumclaw, 
                  startdate = 20050101,
                  enddate = 20060101,
                  timezone = "America/Los_Angeles")

range(Enumclaw_2005$data$datetime)

# 20050801 will be parsed as Aug. 1st, 2005
# 20050901 will be parsed as Sep. 1st, 2005

# Get all observations between these dates
Enumclaw_200508 <- 
  raws_filterDate(Enumclaw,
                  startdate = 20050801,
                  enddate = 20060901,
                  timezone = "America/Los_Angeles")

range(Enumclaw_200508$data$datetime)

Openair requires that dates and times of observations are stored in a column called date. However, RAWSmet names this column datetime. Use raws_getData() with forOpenair = TRUE to extract the data dataframe from a RAWS timeseries object with a new column called date containing the same values as datetime.

enumclawData_2005 <-raws_getData(Enumclaw_2005, forOpenair = TRUE)
enumclawData_200508 <-raws_getData(Enumclaw_2005, forOpenair = TRUE)

All of the functions used above may be used separately as demonstrated but they can also be strung neatly together by using the pipe %>% operator. The same data may be generated like so:

meta <- cefa_loadMeta()

enumclawData_2005 <- 
  cefa_load(nwsID = 451702, meta = meta) %>%
  raws_filterDate(20050101, 20060101, timezone = "America/Los_Angeles") %>%
  raws_getData(forOpenair = TRUE)

enumclawData_200508 <- 
  cefa_load(nwsID = 451702, meta = meta) %>%
  raws_filterDate(20050801, 20050901, timezone = "America/Los_Angeles") %>%
  raws_getData(forOpenair = TRUE)

# We will also extract a dataframe of ALL of the station's data
enumclawData_ALL <-
  cefa_load(nwsID = 451702, meta = meta) %>%
  raws_getData(forOpenair = TRUE)

After renaming the datetime column and filtering by dates of interest, we are now ready to create visualizations using openair.

Using openair

The RAWS timeseries data contains measurements for temperature, humidity, wind speed, wind direction, max gust speed, max gust direction, precipitation, and solar radiation. We can utilize various openair plots to gain insight from each of these parameters.

Wind rose plots

Let us first create some wind rose plots. Openair's windRose() function requires 3 arguments: the data to create the plot for, and the names of the wind speed and wind direction columns. By default, windRose() looks for columns named ws and wd for wind speed and direction respectively so it is important to specify these names when calling the function. (Remember that openair requires that dates and times of observations be stored in a column named date.)

library(openair)

openair::windRose(
  enumclawData_200508, 
  ws = "windSpeed", 
  wd = "windDirection",
  main = "Wind speed and direction in Enumclaw, August 2005", 
  key.footer = "(mph)"
)

windRose() can also group data and plot it by different periods. Lets look at the wind speed and directions by season in Enumclaw:

openair::windRose(
  enumclawData_2005, 
  ws = "windSpeed", 
  wd = "windDirection",
  main = "Wind speed and direction in Enumclaw, 2005", 
  type = "season", 
  key.footer = "(mph)"
)

Time-series plotting

Openair can also be used to create time-series plots and trends. Lets first take a look at the function timePlot(). This function requires 2 arguments: the data to create the plot for, and pollutant, the name of the column to plot with respect to time. (Again, timePlot() requires that dates and times of observations are stored in a column named date.)

openair::timePlot(
  enumclawData_200508, 
  pollutant = "temperature",
  avg.time = "hour",
  main = "Temperature in Enumclaw, August 2005", 
  key = FALSE, 
  xlab = "time",
  ylab = "temperature (°F)"
)

timePlot() can also plot multiple columns so they can be compared against each other. Lets compare temperature and humidity in Enumclaw in August 2005:

openair::timePlot(
  enumclawData_200508, 
  pollutant = c("temperature", "humidity"),
  avg.time = "hour",
  main = "Temperature and Humidity in Enumclaw, August 2005", 
  key = TRUE,
  name.pol = c("temperature (°F)", "humidity (%)"), 
  ylab = ""
)

Plotting trends in data is also very easy using openair. The smoothTrend() function plots monthly averages against the trend in the variable of interest. Lets look at the trend of solar radiation in Enumclaw in 2005:

openair::smoothTrend(
  enumclawData_2005, 
  pollutant = "solarRadiation",
  avg.time = "month",
  main = "Solar Radiation trend in Enumclaw, 2005", 
  statistic = "mean",
  xlab = "time",
  ylab = expression('solar radiation (W/m'^2*')')
)

Instead of comparing monthly averages to the trend of the data, openair can also compare different averages.

openair::smoothTrend(
  enumclawData_ALL,  
  pollutant = "solarRadiation",
  main = "Solar Radiation trend in Enumclaw", 
  statistic = "mean",
  xlab = "time", 
  ylab = expression('solar radiation (W/m'^2*')'), 
  avg.time = "year"
)

MazamaScience/RAWSmet documentation built on May 6, 2023, 6:57 a.m.