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README.md

rnoaa

Build Status Build status codecov.io rstudio mirror downloads cran version

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

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.

Dataset Description Start Date End Date Data Coverage
GHCND Daily Summaries 1763-01-01 2017-02-14 1.00
GSOM Global Summary of the Month 1763-01-01 2016-11-01 1.00
GSOY Global Summary of the Year 1763-01-01 2016-01-01 1.00
NEXRAD2 Weather Radar (Level II) 1991-06-05 2017-02-14 0.95
NEXRAD3 Weather Radar (Level III) 1994-05-20 2017-02-10 0.95
NORMAL_ANN Normals Annual/Seasonal 2010-01-01 2010-01-01 1.00
NORMAL_DLY Normals Daily 2010-01-01 2010-12-31 1.00
NORMAL_HLY Normals Hourly 2010-01-01 2010-12-31 1.00
NORMAL_MLY Normals Monthly 2010-01-01 2010-12-01 1.00
PRECIP_15 Precipitation 15 Minute 1970-05-12 2014-01-01 0.25
PRECIP_HLY Precipitation Hourly 1900-01-01 2014-01-01 1.00

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:

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')
#> $meta
#> $meta$totalCount
#> [1] 1980
#>
#> $meta$pageCount
#> [1] 25
#>
#> $meta$offset
#> [1] 1
#>
#>
#> $data
#>       mindate    maxdate                  name datacoverage            id
#> 1  1892-08-01 2016-12-31            Zwolle, NL       1.0000 CITY:NL000012
#> 2  1901-01-01 2017-02-08            Zurich, SZ       1.0000 CITY:SZ000007
#> 3  1957-07-01 2017-02-07         Zonguldak, TU       1.0000 CITY:TU000057
#> 4  1906-01-01 2017-02-08            Zinder, NG       0.9025 CITY:NG000004
#> 5  1973-01-01 2017-02-07        Ziguinchor, SG       1.0000 CITY:SG000004
#> 6  1938-01-01 2017-02-08         Zhytomyra, UP       0.9723 CITY:UP000025
#> 7  1948-03-01 2017-02-08        Zhezkazgan, KZ       0.9302 CITY:KZ000017
#> 8  1951-01-01 2017-02-08         Zhengzhou, CH       1.0000 CITY:CH000045
#> 9  1941-01-01 2016-12-31          Zaragoza, SP       1.0000 CITY:SP000021
#> 10 1936-01-01 2009-06-17      Zaporiyhzhya, UP       1.0000 CITY:UP000024
#> 11 1957-01-01 2017-02-10          Zanzibar, TZ       0.8016 CITY:TZ000019
#> 12 1973-01-01 2017-02-08            Zanjan, IR       0.9105 CITY:IR000020
#> 13 1893-01-01 2017-02-14     Zanesville, OH US       1.0000 CITY:US390029
#> 14 1912-01-01 2017-02-07             Zahle, LE       0.9819 CITY:LE000004
#> 15 1951-01-01 2017-02-08           Zahedan, IR       0.9975 CITY:IR000019
#> 16 1860-12-01 2017-02-08            Zagreb, HR       1.0000 CITY:HR000002
#> 17 1975-08-29 2017-02-08         Zacatecas, MX       0.9993 CITY:MX000036
#> 18 1947-01-01 2017-02-08 Yuzhno-Sakhalinsk, RS       1.0000 CITY:RS000081
#> 19 1893-01-01 2017-02-14           Yuma, AZ US       1.0000 CITY:US040015
#> 20 1942-02-01 2017-02-14   Yucca Valley, CA US       1.0000 CITY:US060048
#> 21 1885-01-01 2017-02-14      Yuba City, CA US       1.0000 CITY:US060047
#> 22 1998-02-01 2017-02-08            Yozgat, TU       1.0000 CITY:TU000056
#> 23 1893-01-01 2017-02-14     Youngstown, OH US       1.0000 CITY:US390028
#> 24 1894-01-01 2017-02-14           York, PA US       1.0000 CITY:US420024
#> 25 1876-01-01 2017-02-14        Yonkers, NY US       1.0000 CITY:US360031
#>
#> attr(,"class")
#> [1] "ncdc_locs"

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

ncdc_stations(datasetid='GHCND', locationid='FIPS:12017', stationid='GHCND:USC00084289')
#> $meta
#> NULL
#>
#> $data
#>   elevation    mindate    maxdate latitude                  name
#> 1      12.2 1899-02-01 2017-02-13  28.8029 INVERNESS 3 SE, FL US
#>   datacoverage                id elevationUnit longitude
#> 1            1 GHCND:USC00084289        METERS  -82.3126
#>
#> attr(,"class")
#> [1] "ncdc_stations"

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 )
#>                  date        datatype           station value fl_c
#> 1 2010-05-01T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   652    S
#> 2 2010-05-02T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   655    S
#> 3 2010-05-03T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   658    S
#> 4 2010-05-04T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   661    S
#> 5 2010-05-05T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   663    S
#> 6 2010-05-06T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   666    S

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")

plot of chunk unnamed-chunk-13

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")

plot of chunk unnamed-chunk-14

Get table of all datasets

ncdc_datasets()
#> $meta
#> $meta$offset
#> [1] 1
#>
#> $meta$count
#> [1] 11
#>
#> $meta$limit
#> [1] 25
#>
#>
#> $data
#>                     uid    mindate    maxdate                        name
#> 1  gov.noaa.ncdc:C00861 1763-01-01 2017-02-14             Daily Summaries
#> 2  gov.noaa.ncdc:C00946 1763-01-01 2016-11-01 Global Summary of the Month
#> 3  gov.noaa.ncdc:C00947 1763-01-01 2016-01-01  Global Summary of the Year
#> 4  gov.noaa.ncdc:C00345 1991-06-05 2017-02-14    Weather Radar (Level II)
#> 5  gov.noaa.ncdc:C00708 1994-05-20 2017-02-10   Weather Radar (Level III)
#> 6  gov.noaa.ncdc:C00821 2010-01-01 2010-01-01     Normals Annual/Seasonal
#> 7  gov.noaa.ncdc:C00823 2010-01-01 2010-12-31               Normals Daily
#> 8  gov.noaa.ncdc:C00824 2010-01-01 2010-12-31              Normals Hourly
#> 9  gov.noaa.ncdc:C00822 2010-01-01 2010-12-01             Normals Monthly
#> 10 gov.noaa.ncdc:C00505 1970-05-12 2014-01-01     Precipitation 15 Minute
#> 11 gov.noaa.ncdc:C00313 1900-01-01 2014-01-01        Precipitation Hourly
#>    datacoverage         id
#> 1          1.00      GHCND
#> 2          1.00       GSOM
#> 3          1.00       GSOY
#> 4          0.95    NEXRAD2
#> 5          0.95    NEXRAD3
#> 6          1.00 NORMAL_ANN
#> 7          1.00 NORMAL_DLY
#> 8          1.00 NORMAL_HLY
#> 9          1.00 NORMAL_MLY
#> 10         0.25  PRECIP_15
#> 11         1.00 PRECIP_HLY
#>
#> attr(,"class")
#> [1] "ncdc_datasets"

Get data category data and metadata

ncdc_datacats(locationid = 'CITY:US390029')
#> $meta
#> $meta$totalCount
#> [1] 38
#>
#> $meta$pageCount
#> [1] 25
#>
#> $meta$offset
#> [1] 1
#>
#>
#> $data
#>                     name            id
#> 1    Annual Agricultural        ANNAGR
#> 2     Annual Degree Days         ANNDD
#> 3   Annual Precipitation       ANNPRCP
#> 4     Annual Temperature       ANNTEMP
#> 5    Autumn Agricultural         AUAGR
#> 6     Autumn Degree Days          AUDD
#> 7   Autumn Precipitation        AUPRCP
#> 8     Autumn Temperature        AUTEMP
#> 9               Computed          COMP
#> 10 Computed Agricultural       COMPAGR
#> 11           Degree Days            DD
#> 12      Dual-Pol Moments DUALPOLMOMENT
#> 13             Echo Tops       ECHOTOP
#> 14      Hydrometeor Type   HYDROMETEOR
#> 15            Miscellany          MISC
#> 16                 Other         OTHER
#> 17               Overlay       OVERLAY
#> 18         Precipitation          PRCP
#> 19          Reflectivity  REFLECTIVITY
#> 20    Sky cover & clouds           SKY
#> 21   Spring Agricultural         SPAGR
#> 22    Spring Degree Days          SPDD
#> 23  Spring Precipitation        SPPRCP
#> 24    Spring Temperature        SPTEMP
#> 25   Summer Agricultural         SUAGR
#>
#> attr(,"class")
#> [1] "ncdc_datacats"

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()
#> OGR data source with driver: ESRI Shapefile
#> Source: "/Users/sacmac/.rnoaa/tornadoes/tornadoes", layer: "tornado"
#> with 57988 features
#> It has 21 fields
library('sp')
plot(shp)

plot of chunk unnamed-chunk-17

HOMR metadata

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

homr(qid = 'COOP:046742')
#> $`20002078`
#> $`20002078`$id
#> [1] "20002078"
#>
#> $`20002078`$head
#>                  preferredName latitude_dec longitude_dec precision
#> 1 PASO ROBLES MUNICIPAL AP, CA      35.6697     -120.6283    DDMMSS
#>             por.beginDate por.endDate
#> 1 1949-10-05T00:00:00.000     Present
#>
#> $`20002078`$namez
#>                         name  nameType
#> 1   PASO ROBLES MUNICIPAL AP      COOP
#> 2   PASO ROBLES MUNICIPAL AP PRINCIPAL
#> 3 PASO ROBLES MUNICIPAL ARPT       PUB
#>
#> $`20002078`$identifiers
#>      idType          id
#> 1     GHCND USW00093209
#> 2   GHCNMLT USW00093209
...

Storm data

Get storm data for the year 2010

storm_data(year = 2010)
#> # A tibble: 2,855 × 195
#>       serial_num season   num basin sub_basin  name            iso_time
#>            <chr>  <int> <int> <chr>     <chr> <chr>               <chr>
#> 1  2009317S10073   2010     1    SI        MM  ANJA 2009-11-13 06:00:00
#> 2  2009317S10073   2010     1    SI        MM  ANJA 2009-11-13 12:00:00
#> 3  2009317S10073   2010     1    SI        MM  ANJA 2009-11-13 18:00:00
#> 4  2009317S10073   2010     1    SI        MM  ANJA 2009-11-14 00:00:00
#> 5  2009317S10073   2010     1    SI        MM  ANJA 2009-11-14 06:00:00
#> 6  2009317S10073   2010     1    SI        MM  ANJA 2009-11-14 12:00:00
#> 7  2009317S10073   2010     1    SI        MM  ANJA 2009-11-14 18:00:00
#> 8  2009317S10073   2010     1    SI        MM  ANJA 2009-11-15 00:00:00
#> 9  2009317S10073   2010     1    SI        MM  ANJA 2009-11-15 06:00:00
#> 10 2009317S10073   2010     1    SI        MM  ANJA 2009-11-15 12:00:00
#> # ... with 2,845 more rows, and 188 more variables: nature <chr>,
#> #   latitude <dbl>, longitude <dbl>, wind.wmo. <dbl>, pres.wmo. <dbl>,
#> #   center <chr>, wind.wmo..percentile <dbl>, pres.wmo..percentile <dbl>,
#> #   track_type <chr>, latitude_for_mapping <dbl>,
#> #   longitude_for_mapping <dbl>, current.basin <chr>,
#> #   hurdat_atl_lat <dbl>, hurdat_atl_lon <dbl>, hurdat_atl_grade <dbl>,
#> #   hurdat_atl_wind <dbl>, hurdat_atl_pres <dbl>, td9636_lat <dbl>,
...

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)
#>   Total_precipitation_surface_6_Hour_Accumulation_ens lon lat ens time1
#> 1                                                   0 244  46   0     6
#> 2                                                   0 244  46   1    12
#> 3                                                   0 244  46   2    18
#> 4                                                   0 244  46   3    24
#> 5                                                   0 244  46   4    30
#> 6                                                   0 244  46   5    36

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")
#> $metadata
#> $metadata$id
#> [1] "9063053"
#>
#> $metadata$name
#> [1] "Fairport"
#>
#> $metadata$lat
#> [1] "41.7598"
#>
#> $metadata$lon
#> [1] "-81.2811"
#>
#>
#> $data
#>            t       v   f
#> 1 2015-09-27 174.430 0,0
#> 2 2015-09-28 174.422 0,0

Contributors

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.

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