In leighseverson/rnoaa: 'NOAA' Weather Data from R

rnoaa

library("knitr")
hook_output <- knitr::knit_hooks$get("output")
knitr::knit_hooks$set(output = function(x, options) {
   lines <- options$output.lines
   if (is.null(lines)) {
     return(hook_output(x, options))  # pass to default hook
   }
   x <- unlist(strsplit(x, "\n"))
   more <- "..."
   if (length(lines)==1) {        # first n lines
     if (length(x) > lines) {
       # truncate the output, but add ....
       x <- c(head(x, lines), more)
     }
   } else {
     x <- c(if (abs(lines[1])>1) more else NULL,
            x[lines],
            if (length(x)>lines[abs(length(lines))]) more else NULL
           )
   }
   # paste these lines together
   x <- paste(c(x, ""), collapse = "\n")
   hook_output(x, options)
 })

knitr::opts_chunk$set(
  comment = "#>",
  collapse = TRUE,
  warning = FALSE,
  message = FALSE,
  fig.width = 10,
  fig.path = "tools/",
  cache.path = "inst/cache/"
)

rnoaa is an R interface to many NOAA data sources. We don't cover all of them, but we include many commonly used sources, and add we are always adding new sources. We focus on easy to use interfaces for getting NOAA data, and giving back data in easy to use formats downstream. We currently don't do much in the way of plots or analysis.

Data sources in rnoaa

• NOAA NCDC climate data:
  • We are using the NOAA API version 2
  • Docs for the NCDC API are at http://www.ncdc.noaa.gov/cdo-web/webservices/v2
  • GHCN Daily data is available at http://www.ncdc.noaa.gov/oa/climate/ghcn-daily/ via FTP and HTTP
• Severe weather data docs are at http://www.ncdc.noaa.gov/swdiws/
• Sea ice data
• NOAA buoy data
• ERDDAP data
• Now in package rerddap
• Tornadoes! Data from the NOAA Storm Prediction Center
• HOMR - Historical Observing Metadata Repository - from NOAA NCDC
• Storm data - from the International Best Track Archive for Climate Stewardship (IBTrACS)
• GHCND FTP data - NOAA NCDC API has some/all (not sure really) of this data, but FTP allows to get more data more quickly
• Global Ensemble Forecast System (GEFS) data
• Extended Reconstructed Sea Surface Temperature (ERSST) data
• Argo buoys - a global array of more than 3,000 free-drifting profiling floats that measures thetemperature and salinity of the upper 2000 m of the ocean
• NOAA CO-OPS - tides and currents data
• NOAA Climate Prediction Center (CPC)
• Africa Rainfall Climatology version 2

Help

There is a tutorial on the rOpenSci website, and there are many tutorials in the package itself, available in your R session, or on CRAN. The tutorials:

• NOAA Buoy vignette
• NOAA National Climatic Data Center (NCDC) vignette (examples)
• NOAA NCDC attributes vignette
• NOAA NCDC workflow vignette
• Sea ice vignette
• Severe Weather Data Inventory (SWDI) vignette
• Historical Observing Metadata Repository (HOMR) vignette
• Storms (IBTrACS) vignette

netcdf data

Functions to work with buoy data use netcdf files. You'll need the ncdf package for those functions, and those only. ncdf is in Suggests in this package, meaning you only need ncdf if you are using the buoy functions. You'll get an informative error telling you to install ncdf if you don't have it and you try to use the buoy functions. Installation of ncdf should be straightforward on Mac and Windows, but on Linux you may have issues. See http://cran.r-project.org/web/packages/ncdf/INSTALL

NOAA NCDC Datasets

There are many NOAA NCDC datasets. All data sources work, except NEXRAD2 and NEXRAD3, for an unknown reason. This relates to ncdc_*() functions only.

dat <- ncdc_datasets()$data
dat <- dat[, !names(dat) %in% 'uid']
dat <- dat[, c('id', 'name', 'mindate', 'maxdate', 'datacoverage')]
names(dat) <- c('Dataset', 'Description', 'Start Date', 'End Date', 'Data Coverage')
knitr::kable(dat)

NOAA NCDC Attributes

Each NOAA dataset has a different set of attributes that you can potentially get back in your search. See http://www.ncdc.noaa.gov/cdo-web/datasets for detailed info on each dataset. We provide some information on the attributes in this package; see the vignette for attributes to find out more

NCDC Authentication

You'll need an API key to use the NOAA NCDC functions (those starting with ncdc*()) in this package (essentially a password). Go to http://www.ncdc.noaa.gov/cdo-web/token to get one. You can't use this package without an API key.

Once you obtain a key, there are two ways to use it.

a) Pass it inline with each function call (somewhat cumbersome)

ncdc(datasetid = 'PRECIP_HLY', locationid = 'ZIP:28801', datatypeid = 'HPCP', limit = 5, token =  "YOUR_TOKEN")

b) Alternatively, you might find it easier to set this as an option, either by adding this line to the top of a script or somewhere in your .rprofile

options(noaakey = "KEY_EMAILED_TO_YOU")

c) You can always store in permamently in your .Rprofile file.

Installation

GDAL

You'll need GDAL installed first. You may want to use GDAL >= 0.9-1 since that version or later can read TopoJSON format files as well, which aren't required here, but may be useful. Install GDAL:

• OSX - From http://www.kyngchaos.com/software/frameworks
• Linux - run sudo apt-get install gdal-bin reference
• Windows - From http://trac.osgeo.org/osgeo4w/

Then when you install the R package rgdal (rgeos also requires GDAL), you'll most likely need to specify where you're gdal-config file is on your machine, as well as a few other things. I have an OSX Mavericks machine, and this works for me (there's no binary for Mavericks, so install the source version):

install.packages("http://cran.r-project.org/src/contrib/rgdal_0.9-1.tar.gz", repos = NULL, type="source", configure.args = "--with-gdal-config=/Library/Frameworks/GDAL.framework/Versions/1.10/unix/bin/gdal-config --with-proj-include=/Library/Frameworks/PROJ.framework/unix/include --with-proj-lib=/Library/Frameworks/PROJ.framework/unix/lib")

The rest of the installation should be easy. If not, let us know.

Stable version from CRAN

install.packages("rnoaa")

or development version from GitHub

devtools::install_github("ropensci/rnoaa")

Load rnoaa

library('rnoaa')

NCDC v2 API data

Fetch list of city locations in descending order

ncdc_locs(locationcategoryid='CITY', sortfield='name', sortorder='desc')

Get info on a station by specifcying a dataset, locationtype, location, and station

ncdc_stations(datasetid='GHCND', locationid='FIPS:12017', stationid='GHCND:USC00084289')

Search for data

out <- ncdc(datasetid='NORMAL_DLY', stationid='GHCND:USW00014895', datatypeid='dly-tmax-normal', startdate = '2010-05-01', enddate = '2010-05-10')

See a data.frame

head( out$data )

Plot data, super simple, but it's a start

out <- ncdc(datasetid='GHCND', stationid='GHCND:USW00014895', datatypeid='PRCP', startdate = '2010-05-01', enddate = '2010-10-31', limit=500)
ncdc_plot(out, breaks="1 month", dateformat="%d/%m")

More plotting

You can pass many outputs from calls to the noaa function in to the ncdc_plot function.

out1 <- ncdc(datasetid='GHCND', stationid='GHCND:USW00014895', datatypeid='PRCP', startdate = '2010-03-01', enddate = '2010-05-31', limit=500)
out2 <- ncdc(datasetid='GHCND', stationid='GHCND:USW00014895', datatypeid='PRCP', startdate = '2010-09-01', enddate = '2010-10-31', limit=500)
ncdc_plot(out1, out2, breaks="45 days")

Get table of all datasets

ncdc_datasets()

Get data category data and metadata

ncdc_datacats(locationid = 'CITY:US390029')

Tornado data

The function tornadoes() simply gets all the data. So the call takes a while, but once done, is fun to play with.

shp <- tornadoes()
library('sp')
plot(shp)

HOMR metadata

In this example, search for metadata for a single station ID

homr(qid = 'COOP:046742')

Storm data

Get storm data for the year 2010

storm_data(year = 2010)

GEFS data

Get forecast for a certain variable.

res <- gefs("Total_precipitation_surface_6_Hour_Accumulation_ens", lat = 46.28125, lon = -116.2188)
head(res$data)

Argo buoys data

There are a suite of functions for Argo data, a few egs:

# Spatial search - by bounding box
argo_search("coord", box = c(-40, 35, 3, 2))

# Time based search
argo_search("coord", yearmin = 2007, yearmax = 2009)

# Data quality based search
argo_search("coord", pres_qc = "A", temp_qc = "A")

# Search on partial float id number
argo_qwmo(qwmo = 49)

# Get data
argo(dac = "meds", id = 4900881, cycle = 127, dtype = "D")

CO-OPS data

Get daily mean water level data at Fairport, OH (9063053)

coops_search(station_name = 9063053, begin_date = 20150927, end_date = 20150928,
             product = "daily_mean", datum = "stnd", time_zone = "lst")

Contributors

• Scott Chamberlain
• Brooke Anderson
• Maëlle Salmon
• Adam Erickson
• Nicholas Potter
• Joseph Stachelek

Meta

• Please report any issues or bugs.
• License: MIT
• Get citation information for rnoaa in R doing citation(package = 'rnoaa')
• Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

leighseverson/rnoaa documentation built on May 21, 2019, 3:06 a.m.