Environmental variables

Download and plot SST data from a specific period.

Intro

This post is to create a map with environmental variables.

1. Identify the data you want to download
2. Download data from the server
3. Create a basic map with the values

Define

Define the area you want to download the data from.

xlim1<--125.
xlim2<--88.
ylim1<-14.
ylim2<-35.

Define also the time frame you are interested in.

time1<-'2017-03-01T00:00:00Z'
time2<-'2017-03-30T00:00:00Z'

Load data

Load the package rerddap

library("rerddap")

Check the list of available servers.

For the example, we are interested in the Pacific Ocean, therefore I would use NOAA.

servers()

I want to download sea surface temperature (SST) data, therefore I will use erdMH1sstdmday

sstInfo <- info('erdMH1sstdmday')

To check the specifications use browse

browse('erdMH1sstdmday') 

It takes some time to download.

SST03.2017<-griddap(sstInfo, latitude = c(ylim1, ylim2), longitude = c(xlim1, xlim2), time = c(time1,time2), fields = 'sst')

To use ggplot, the format in dataframe works better.

SST03.2017dt<-SST03.2017$data

Clear NaNs

library(tidyverse)

SST03.2017dt_clean<-SST03.2017dt %>%
  filter(sst!='NaN')

Create plot

ggplot(SST03.2017dt_clean) +
 geom_raster(aes(x=lon, y=lat, fill = sst))+ 
  scale_fill_viridis_c(option = "H")

Add land for reference

Load the package rworldmap this includes shapefiles of countries

library(rworldmap)

The function getMap() loads the map in your environment

worldMap <- getMap()

Load the package tidyverse

Use the function fortify to be able to plot the map using ggplot

world.points <- fortify(worldMap)

To plot using ggplot a data frame is recommended

world.points$region <- world.points$id
world.df <- world.points[,c("long","lat","group", "region")]

Create plot using ggplot

ggplot(SST03.2017dt) +
  geom_raster(aes(x=lon, y=lat, fill = sst))+
  geom_polygon(data = world.df, aes(x = long, y = lat, group = group), colour = '#403d39', fill = "#e5e5e5") +
  theme(
    axis.title = element_blank(),
    panel.border = element_rect(colour = "#495057", fill=NA, size=1),
    panel.grid = element_blank(), 
    panel.spacing = unit(0, "lines"), 
    plot.background = element_blank())+
    coord_sf(xlim = c(xlim1+2,xlim2-2), 
             ylim = c(ylim1+2,ylim2-2))+ 
  scale_fill_viridis_c(option = "H")

There you go!