Sarah Tanja’s Open Lab Notebook - Environmental Landscape of an Intertidal Heat Wave

I searched the NDBC map for the stations closest to my collection site at Owens Beach, Point Defiance Park in Commencement Bay, Tacoma.

I found the following stations:

Station TCMW1 9446482 Tacoma, Met, WA - air temp

Historical data: https://www.ndbc.noaa.gov/station_history.php?station=tcmw1

Station TCNW1 9446484 Tacoma, WA - water temp - tide height

Historical Data: https://www.ndbc.noaa.gov/station_history.php?station=tcnw1

1 Install Packages

if ("tidyverse" %in% rownames(installed.packages()) == 'FALSE') install.packages('tidyverse')
if ("plotly" %in% rownames(installed.packages()) == 'FALSE') install.packages('plotly')

2 Load packages

library(tidyverse)
library(plotly)

# make a data directory, if there isn't one already (-p option will make one only if it isn't already there, otherwise it will do nothing)
mkdir -p data

3 Use `curl` to get NDBC data

Pull data from the year 2021 in which the June record-breaking heat wave occurred…

# navigate to data directory
cd data

# download 2021 data from station TCNW1 NDBC into the data directory
# this station has water temp in F
curl -O https://www.ndbc.noaa.gov/data/historical/stdmet/tcnw1h2021.txt.gz 

# download 2021 data from station TCMW1 NDBC into the data directory
# this station has air temp in F
curl -O https://www.ndbc.noaa.gov/data/historical/stdmet/tcmw1h2021.txt.gz

4 `gunzip` data files


gunzip data/tcnw1h2021.txt.gz 

gunzip data/tcmw1h2021.txt.gz

5 Read data into R and look at it!

read_tsv

# Read the first two rows separately to get column names
column_names <- read_table("data/tcnw1h2021.txt", n_max = 2, col_names = FALSE)

# Use the first row as column names
column_names <- column_names[1, ]

# read in data but skip the first two rows
tcnw12021 <- read_table("data/tcnw1h2021.txt", col_names = FALSE, skip = 2)

# Set column names
colnames(tcnw12021) <- column_names

# Print the first few rows of the data
print(tcnw12021)

Make the UTC timestamp work

# Concatenate into datetime stamp (these are UTC)
tcnw12021$datetimeUTC <- as.POSIXct(paste(tcnw12021$`#YY`, tcnw12021$MM, tcnw12021$DD, tcnw12021$hh, tcnw12021$mm),
                        format="%Y %m %d %H %M")

Get rid of NA data.. which appears to show up as 999

temps_subset <- tcnw12021 %>% 
  select(ATMP, WTMP, datetimeUTC)

temps_subset <- temps_subset %>% 
   mutate_all(~ replace(., . == 999, NA))

Graph air and water temps with datetimeUTC on X axis and air(ATMP) & water(WTMP) temp on Y axis

temps <- ggplot(na.omit(temps_subset), aes(x = datetimeUTC, y = WTMP)) +
  geom_point()+
  theme_bw()

ggplotly(temps)

Seriously!? The station data cuts out in May!?

What’s the next closest station that has June data?

Citation

BibTeX citation:

@online{tanja,
  author = {Tanja, Sarah},
  title = {Environmental {Landscape} of an {Intertidal} {Heat} {Wave}},
  date = {},
  url = {https://sarahtanja.github.io/quarto-blog//posts/projects/anemone/heat-stress/heat-wave.html},
  langid = {en}
}

For attribution, please cite this work as:

Tanja, Sarah. n.d. “Environmental Landscape of an Intertidal Heat Wave.” https://sarahtanja.github.io/quarto-blog//posts/projects/anemone/heat-stress/heat-wave.html.

--- title: "Environmental Landscape of an Intertidal Heat Wave" subtitle: "Using NOAA National Data Buoy Center (NDBC) to map air and water temperatures in Commencement Bay, Tacoma, WA, during the June 2021 Puget Sound heat wave" author: "Sarah Tanja" date: "`r format(Sys.time(), '%d %B, %Y')`" bibliography: heat.bib citation: true citation-location: margin format: html: df-print: paged toc: true toc-location: right smooth-scroll: true link-external-icon: true link-external-newwindow: true code-fold: false code-tools: true code-copy: true highlight-style: breeze code-overflow: wrap theme: minty --- ```{r setup, include=FALSE} knitr::opts_chunk$set( echo = TRUE, # Display code chunks eval = FALSE, # Evaluate code chunks warning = FALSE, # Hide warnings message = FALSE, # Hide messages fig.width = 6, # Set plot width in inches fig.height = 4, # Set plot height in inches fig.align = "center" # Align plots to the center ) ``` I searched the NDBC map for the stations closest to my collection site at Owens Beach, Point Defiance Park in Commencement Bay, Tacoma. I found the following stations: Station TCMW1 9446482 Tacoma, Met, WA - air temp ::: column-margin ![TCMW1: air temp](images/NDBC-TCMW1.jpg) ::: Historical data: <https://www.ndbc.noaa.gov/station_history.php?station=tcmw1> [Station TCNW1](https://tidesandcurrents.noaa.gov/stationhome.html?id=9446484) 9446484 Tacoma, WA - water temp - tide height ::: column-margin ![TCNW1 - water temp, tide height](images/NDCB-TCNW1.jpg) ::: Historical Data: <https://www.ndbc.noaa.gov/station_history.php?station=tcnw1> ## Install Packages ```{r install-packages, eval=TRUE, cache=TRUE} if ("tidyverse" %in% rownames(installed.packages()) == 'FALSE') install.packages('tidyverse') if ("plotly" %in% rownames(installed.packages()) == 'FALSE') install.packages('plotly') ``` ## Load packages ```{r load-packages, cache=TRUE} library(tidyverse) library(plotly) ``` ```{r, engine='bash'} # make a data directory, if there isn't one already (-p option will make one only if it isn't already there, otherwise it will do nothing) mkdir -p data ``` ## Use `curl` to get NDBC data Pull data from the year 2021 in which the June record-breaking heat wave occurred... ```{r, engine='bash'} # navigate to data directory cd data # download 2021 data from station TCNW1 NDBC into the data directory # this station has water temp in F curl -O https://www.ndbc.noaa.gov/data/historical/stdmet/tcnw1h2021.txt.gz # download 2021 data from station TCMW1 NDBC into the data directory # this station has air temp in F curl -O https://www.ndbc.noaa.gov/data/historical/stdmet/tcmw1h2021.txt.gz ``` ## `gunzip` data files ```{r, engine='bash'} gunzip data/tcnw1h2021.txt.gz gunzip data/tcmw1h2021.txt.gz ``` ## Read data into R and look at it! ```{r} read_tsv ``` ```{r} # Read the first two rows separately to get column names column_names <- read_table("data/tcnw1h2021.txt", n_max = 2, col_names = FALSE) # Use the first row as column names column_names <- column_names[1, ] # read in data but skip the first two rows tcnw12021 <- read_table("data/tcnw1h2021.txt", col_names = FALSE, skip = 2) # Set column names colnames(tcnw12021) <- column_names # Print the first few rows of the data print(tcnw12021) ``` Make the UTC timestamp work ```{r} # Concatenate into datetime stamp (these are UTC) tcnw12021$datetimeUTC <- as.POSIXct(paste(tcnw12021$`#YY`, tcnw12021$MM, tcnw12021$DD, tcnw12021$hh, tcnw12021$mm), format="%Y %m %d %H %M") ``` Get rid of NA data.. which appears to show up as 999 ```{r} temps_subset <- tcnw12021 %>% select(ATMP, WTMP, datetimeUTC) temps_subset <- temps_subset %>% mutate_all(~ replace(., . == 999, NA)) ``` Graph air and water temps with datetimeUTC on X axis and air(ATMP) & water(WTMP) temp on Y axis ```{r} temps <- ggplot(na.omit(temps_subset), aes(x = datetimeUTC, y = WTMP)) + geom_point()+ theme_bw() ggplotly(temps) ``` Seriously!? The station data cuts out in May!? What's the next closest station that has June data?