Match up satellite to buoy data

Author

NOAA CoastWatch

Published

March 1, 2024

Match up satellite and buoy data

history | Updated March 2024

Background

There are buoys in many locations around the world that provide data streams of oceanic and atmospheric parameters. The data are often available through data centers like the National Data Buoy Center (NDBC https://www.ndbc.noaa.gov) and the ARGO floats program (http://www.argo.ucsd.edu). In situ buoy data are widely used to monitor environmental conditions.

In-situ buoy data can be used to evaluate the accuracy of satellite data.

Objective

In this exercise, we will learn how to match up satellite data to in situ buoy data using rerddap and rxtracto R packages.

The exercise demonstrates the following techniques:

Downloading tabular data (buoy data) from CoastWatch ERDDAP data server
Retrieving information about a dataset from ERDDAP
Subsetting satellite data within a rectangular boundary
Matching satellite data with the buoy data
Running statistical analysis to compare buoy and satellite data
Producing satellite maps and overlaying buoy data

Datasets used:

The sea surface temperature (SST) satellite data from the NOAA Geo-polar blended analysis
The NDBC Standard Meteorological Buoy Data (dataset ID: cwwcNDBCMet) is used for validating or ground truthing the satellite SST data

Install required packages and load libraries

# Function to check if pkgs are installed, install missing pkgs, and load

pkgTest <- function(x)
{
  if (!require(x,character.only = TRUE))
  {
    install.packages(x,dep=TRUE)
    if(!require(x,character.only = TRUE)) stop("Package not found")
  }
}

list.of.packages <- c( "ncdf4", "rerddap", "plotdap", "httr",
                       "lubridate", "gridGraphics",  "mapdata",
                       "ggplot2", "RColorBrewer", "grid", "PBSmapping", 
                       "rerddapXtracto","dplyr","viridis","cmocean")

# create list of installed packages
pkges = installed.packages()[,"Package"]

for (pk in list.of.packages) {
  pkgTest(pk)
}

Downloading buoy data from ERDDAP

Extract data using the rerddap::tabledap function

Using rerddap::tabledap function, we will request and download data with the following specifications:

Buoy dataset ID: cwwcNDBCMet
Region boundaries: 35 to 40 north latitude and -125 to -120 east longitude
Time span: 08/01/2023 to 08/10/2023
Variables: station, latitude, longitude, time, and water temperature parameters

# Subset and download tabular data from ERDDAP 

ERDDAP_Node = "https://coastwatch.pfeg.noaa.gov/erddap"

NDBC_id = 'cwwcNDBCMet'
NDBC_info=info(datasetid = NDBC_id,url = ERDDAP_Node)

buoy <- rerddap::tabledap( url = ERDDAP_Node, NDBC_id,
  fields=c('station', 'latitude',  'longitude', 'time', 'wtmp'), 
  'time>=2023-08-01',   'time<=2023-08-10', 
  'latitude>=35','latitude<=40', 'longitude>=-125','longitude<=-120',
  'wtmp>0'
)

#Create data frame with the downloaded data
buoy.df <-data.frame(station=buoy$station,
                     longitude=as.numeric(buoy$longitude),
                     latitude=as.numeric(buoy$latitude),
                     time=strptime(buoy$time, "%Y-%m-%d %H:%M:%S"),
                     date=as.Date(buoy$time),
                     temp=as.numeric(buoy$wtmp))

# Check for unique stations
unique.sta <- unique(buoy$sta)
n.sta <- length(unique.sta)
n.sta

[1] 24

Plot the buoy data for the first 10 stations in buoy.df

Let’s see what the buoy data looks like for our time period.

plot(buoy.df$time,buoy.df$temp,type='n', xlab='Date', ylab='SST (ºC)',main='SST from the first 10 stations')


for (i in 1:10){
  I=which(buoy.df$station==unique.sta[i])
  lines(buoy.df$time[I],buoy.df$temp[I])
}

Select buoy data closest in time to satellite data

Since buoy data are hourly and the satellite data are daily, the buoy data needs to be averaged daily for each station.

buoy.df.day=buoy.df %>%
  group_by(station, date) %>%
  summarize(
    lon=mean(longitude),
    lat=mean(latitude),
    temp.day=mean(temp),
    .groups="drop"
    )

head(buoy.df.day)

# A tibble: 6 × 5
  station date         lon   lat temp.day
  <chr>   <date>     <dbl> <dbl>    <dbl>
1 46013   2023-08-01 -123.  38.2     10.4
2 46013   2023-08-02 -123.  38.2     10.7
3 46013   2023-08-03 -123.  38.2     10.6
4 46013   2023-08-04 -123.  38.2     10.4
5 46013   2023-08-05 -123.  38.2     10.7
6 46013   2023-08-06 -123.  38.2     10.6

Download Satellite SST (sea surface temperature) data

We will use Sea surface temperature (SST) satellite data from CoastWatch West code node ERDDAP server.

URL: https://coastwatch.pfeg.noaa.gov/erddap/ Dataset ID:nesdisBLENDEDsstDNDaily

url=  'https://coastwatch.pfeg.noaa.gov/erddap/'
datasetid = 'nesdisBLENDEDsstDNDaily'

# Get Data Information given dataset ID and URL
dataInfo <- rerddap::info(datasetid, url)

# Show data Info
dataInfo

<ERDDAP info> nesdisBLENDEDsstDNDaily 
 Base URL: https://coastwatch.pfeg.noaa.gov/erddap 
 Dataset Type: griddap 
 Dimensions (range):  
     time: (2019-07-22T12:00:00Z, 2024-09-12T12:00:00Z) 
     latitude: (-89.975, 89.975) 
     longitude: (-179.975, 179.975) 
 Variables:  
     analysed_sst: 
         Units: degree_C 
     analysis_error: 
         Units: degree_C 
     mask: 
     sea_ice_fraction: 
         Units: 1

Extract the matchup data using rxtracto

We will extract satellite data for each buoy station.
1. get coordinates of each buoy station 2. use the rxtracto function and the buoy coordinates to download satellite data closest to each station

# Set the variable name of interest from the satellite data
parameter <- 'analysed_sst'

# Set x,y,t,z coordinates based on buoy data
xcoord <- buoy.df.day$lon
ycoord <- buoy.df.day$lat
tcoord <- buoy.df.day$date

# Set the variable name of interest from the satellite data
parameter <- 'analysed_sst'

# Set x,y,t,z coordinates based on buoy data
xcoord <- buoy.df.day$lon
ycoord <- buoy.df.day$lat
tcoord <- buoy.df.day$date

# Extract satellite data 
extract <- rxtracto(dataInfo, parameter=parameter, 
                    tcoord=tcoord,
                    xcoord=xcoord,
                    ycoord=ycoord,
                    xlen=.01,ylen=.01)
                     
buoy.df.day$sst<-extract$`mean analysed_sst`

Get subset of data where a satellite value was found

Our satellite product is gap-free (gaps due to clouds were filled using some interpolation) but it has a spatial resolution of 5km. Some buoy stations may be so close to shore that they end up in the landmask of the satellite data. So let’s find the stations that were matched up to an SST pixel.

# Get subset of data where there is a satellite value 
goodbuoy<-subset(buoy.df.day, sst > 0)
unique.sta<-unique(goodbuoy$station)
nbuoy<-length(unique.sta)
ndata<-length(goodbuoy$station)

Compare results for satellite and buoy

Plot the satellite SST verses the buoy temperature to visualize how well the two datasets match each other.

# Set up map title 
main="California coast, 8/1/23-8/10/23"   

p <- ggplot(goodbuoy, aes(temp.day, sst,color=lat)) + 
     coord_fixed(xlim=c(8,25),ylim=c(8,25)) 
p + geom_point() + 
  ylab('Satellite SST')  + 
  xlab('Buoy average daily SST') +
  scale_x_continuous(minor_breaks = seq(8, 25)) + 
  scale_y_continuous(minor_breaks = seq(8, 25)) + 
  #geom_abline(a=fit[1],b=fit[2]) +
  #annotation_custom(my_grob) + 
  #scale_color_gradientn(colours = "viridis", name="Buoy\nLatitude") +
  scale_color_viridis(discrete = FALSE, name="Buoy\nLatitude") +
  labs(title=main) + theme(plot.title = element_text(size=20, face="bold", vjust=2))

Run a linear regression of Blended SST versus the buoy data. * The R squared is close to 1 (0.8733) * The slope is 0.7151

lmHeight = lm(sst~temp.day, data = goodbuoy)
summary(lmHeight)


Call:
lm(formula = sst ~ temp.day, data = goodbuoy)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.21936 -0.47676  0.02142  0.40927  2.19949 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  3.65951    0.27549   13.28   <2e-16 ***
temp.day     0.71469    0.01976   36.17   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.7322 on 195 degrees of freedom
Multiple R-squared:  0.8703,    Adjusted R-squared:  0.8696 
F-statistic:  1309 on 1 and 195 DF,  p-value: < 2.2e-16

Create a map of SST and overlay the buoy data

Extract blended SST data for August 1st, 2023

# First define the box and time limits of the requested data 
ylim<-c(32,42)
xlim<-c(-127,-118)

# Extract the monthly satellite data
SST <- rxtracto_3D(dataInfo,xcoord=xlim,ycoord=ylim,parameter=parameter, 
                   tcoord=c('2023-08-01','2023-08-01'))

SST$sst <- drop(SST$analysed_sst)

Create the map frame for the satellite data and buoy SST overlay

mapFrame<- function(longitude,latitude,sst){
  dims<-dim(sst)
  sst<-array(sst,dims[1]*dims[2])
  sstFrame<-expand.grid(x=longitude,y=latitude)
  sstFrame$sst<-sst
  return(sstFrame)
}

sstFrame<-mapFrame(SST$longitude,SST$latitude,SST$sst)
coast <- map_data("worldHires", ylim = ylim, xlim = xlim)
my.col <- colorRampPalette(rev(brewer.pal(11, "RdYlBu")))(22-13) 

buoy2<-subset(buoy.df.day, month(date)==8 &day(date)==1 & temp.day > 0)

Create the map

myplot<-ggplot(data = sstFrame, aes(x = x, y = y, fill = sst)) +
  geom_tile(na.rm=T) +
  geom_polygon(data = coast, aes(x=long, y = lat, group = group), fill = "grey80") +
  theme_bw(base_size = 15) + ylab("Latitude") + xlab("Longitude") +
  coord_fixed(1.3,xlim = xlim, ylim = ylim) +
  scale_fill_cmocean(name = 'thermal',limits=c(10,25),na.value = NA) +
  ggtitle(paste("Satellite SST and buoy temperature (circles) \n", unique(as.Date(SST$time)))) +
  geom_point(data=buoy2, aes(x=lon,y=lat,color=temp.day),size=3,shape=21,color="black") + 
  scale_color_cmocean(name = 'thermal',limits=c(10,25),na.value ="grey20") 
  
myplot