rnoaa
IMPORTANT - BUOY DATA
NOAA buoy data requires an R pacakage ncdf4 that is difficult to use on Windows. Therefore, we have moved functions for working with buoy data into a separate branch called buoy, and the CRAN version does not include buoy functions. Thus, if you're on a Linux machine or on OSX you should be able to use the buoy branch just fine after installing the netcdf as:
OSX
brew install netcdfLinux (Ubuntu)
sudo apt-get install netcdf*Then rnoaa with the buoy functions should install and load correctly. See this stackoverflow post and this blog post for more Linux/OSX netcdf installation help.
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 ERDDAP 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
Data sources used in rnoaa
The majority of functions in this package work with NOAA NCDC data.
- NOAA NCDC climate data:
- Severe weather data docs are here
- Sea ice data ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/shapefiles
- NOAA buoy data http://www.ndbc.noaa.gov/
- ERDDAP data http://upwell.pfeg.noaa.gov/erddap/index.html
- griddap - gridded data
- tabledap - tablular data
- Tornadoes! Data from the NOAA Storm Prediction Center http://www.spc.noaa.gov/gis/svrgis/
- HOMR - Historical Observing Metadata Repository - from the NOAA NCDC http://www.ncdc.noaa.gov/homr/api
- Storm data - from the International Best Track Archive for Climate Stewardship (IBTrACS) http://www.ncdc.noaa.gov/ibtracs/index.php?name=wmo-data
NOAA NCDC Datasets
There are many NOAA NCDC datasets. All data sources work, except NEXRAD2 and NEXRAD3, for an unkown reason.
| Dataset | Description | Start date | End date |
|---|---|---|---|
| ANNUAL | Annual Summaries | 1831-02-01 | 2013-11-01 |
| GHCND | Daily Summaries | 1763-01-01 | 2014-03-15 |
| GHCNDMS | Monthly Summaries | 1763-01-01 | 2014-01-01 |
| NORMAL_ANN | Normals Annual/Seasonal | 2010-01-01 | 2010-01-01 |
| NORMAL_DLY | Normals Daily | 2010-01-01 | 2010-12-31 |
| NORMAL_HLY | Normals Hourly | 2010-01-01 | 2010-12-31 |
| NORMAL_MLY | Normals Monthly | 2010-01-01 | 2010-12-01 |
| PRECIP_15 | Precipitation 15 Minute | 1970-05-12 | 2013-03-01 |
| PRECIP_HLY | Precipitation Hourly | 1900-01-01 | 2013-03-01 |
| NEXRAD2 | Nexrad Level II | 1991-06-05 | 2014-03-14 |
| NEXRAD3 | Nexrad Level III | 1994-05-20 | 2014-03-11 |
NOAA NCDC Attributes
Each NOAA dataset has a different set of attributes that you can potentially get back in your search. See the NOAA docs 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
Authentication
You'll need an API key to use the NOAA NCDC functions (those starting with ncdc*()) in this package (essentially a password). Go here 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-binreference - 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
install.packages("devtools")
devtools::install_github("ropensci/rnoaa")
library('rnoaa')or version with buoy functions on Github
install.packages("devtools")
devtools::install_github("ropensci/rnoaa", ref="buoy")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] 1656
#>
#> $meta$pageCount
#> [1] 25
#>
#> $meta$offset
#> [1] 1
#>
#>
#> $data
#> id name datacoverage mindate maxdate
#> 1 CITY:NL000012 Zwolle, NL 1.0000 1892-08-01 2014-11-30
#> 2 CITY:SZ000007 Zurich, SZ 1.0000 1901-01-01 2014-12-16
#> 3 CITY:NG000004 Zinder, NG 0.8678 1906-01-01 1980-12-31
#> 4 CITY:UP000025 Zhytomyra, UP 0.9729 1938-01-01 2014-12-16
#> 5 CITY:KZ000017 Zhezkazgan, KZ 0.9288 1948-03-01 2014-12-16
#> 6 CITY:CH000045 Zhengzhou, CH 1.0000 1951-01-01 2014-12-16
#> 7 CITY:SP000021 Zaragoza, SP 1.0000 1941-01-01 2012-08-31
#> 8 CITY:UP000024 Zaporiyhzhya, UP 0.9739 1936-01-01 2009-06-16
#> 9 CITY:US390029 Zanesville, OH US 1.0000 1893-01-01 2014-12-18
#> 10 CITY:LE000004 Zahle, LE 0.7811 1912-01-01 1971-12-31
#> 11 CITY:IR000019 Zahedan, IR 0.9930 1951-01-01 2010-05-19
#> 12 CITY:HR000002 Zagreb, HR 1.0000 1860-12-01 2013-12-31
#> 13 CITY:RS000081 Yuzhno-Sakhalinsk, RS 1.0000 1947-01-01 2014-12-16
#> 14 CITY:US040015 Yuma, AZ US 1.0000 1893-01-01 2014-12-18
#> 15 CITY:US060048 Yucca Valley, CA US 1.0000 1942-02-01 2014-12-18
#> 16 CITY:US060047 Yuba City, CA US 1.0000 1893-01-01 2014-12-18
#> 17 CITY:US390028 Youngstown, OH US 1.0000 1893-01-01 2014-12-18
#> 18 CITY:US420024 York, PA US 1.0000 1941-01-01 2014-12-18
#> 19 CITY:US360031 Yonkers, NY US 1.0000 1876-01-01 2014-12-18
#> 20 CITY:JA000017 Yokohama, JA 1.0000 1949-01-01 2014-12-16
#> 21 CITY:CH000044 Yinchuan, CH 1.0000 1951-01-01 2014-12-16
#> 22 CITY:AM000001 Yerevan, AM 0.9751 1885-06-01 2014-12-16
#> 23 CITY:US280020 Yazoo City, MS US 1.0000 1948-01-01 2014-12-18
#> 24 CITY:RS000080 Yaroslavl, RS 0.9850 1959-07-01 1987-05-20
#> 25 CITY:US460009 Yankton, SD US 1.0000 1932-01-01 2014-12-18
#>
#> 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
#>
#> $data
#> id elevation name elevationUnit
#> 1 GHCND:USC00084289 12.2 INVERNESS 3 SE, FL US METERS
#> datacoverage longitude mindate latitude maxdate
#> 1 1 -82.3126 1899-02-01 28.8029 2014-12-17
#>
#> attr(,"class")
#> [1] "ncdc_stations"Search for data
out <- ncdc(datasetid='NORMAL_DLY', stationid='GHCND:USW00014895', datatypeid='dly-tmax-normal', startdate = '2010-05-01', enddate = '2010-05-10')See a data.frame
head( out$data )
#> station value datatype date fl_c
#> 1 GHCND:USW00014895 652 DLY-TMAX-NORMAL 2010-05-01T00:00:00 S
#> 2 GHCND:USW00014895 655 DLY-TMAX-NORMAL 2010-05-02T00:00:00 S
#> 3 GHCND:USW00014895 658 DLY-TMAX-NORMAL 2010-05-03T00:00:00 S
#> 4 GHCND:USW00014895 661 DLY-TMAX-NORMAL 2010-05-04T00:00:00 S
#> 5 GHCND:USW00014895 663 DLY-TMAX-NORMAL 2010-05-05T00:00:00 S
#> 6 GHCND:USW00014895 666 DLY-TMAX-NORMAL 2010-05-06T00:00:00 SPlot 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()
#> $meta
#> $meta$limit
#> [1] 25
#>
#> $meta$count
#> [1] 11
#>
#> $meta$offset
#> [1] 1
#>
#>
#> $data
#> uid id name datacoverage
#> 1 gov.noaa.ncdc:C00040 ANNUAL Annual Summaries 1.00
#> 2 gov.noaa.ncdc:C00861 GHCND Daily Summaries 1.00
#> 3 gov.noaa.ncdc:C00841 GHCNDMS Monthly Summaries 1.00
#> 4 gov.noaa.ncdc:C00345 NEXRAD2 Weather Radar (Level II) 0.95
#> 5 gov.noaa.ncdc:C00708 NEXRAD3 Weather Radar (Level III) 0.95
#> 6 gov.noaa.ncdc:C00821 NORMAL_ANN Normals Annual/Seasonal 1.00
#> 7 gov.noaa.ncdc:C00823 NORMAL_DLY Normals Daily 1.00
#> 8 gov.noaa.ncdc:C00824 NORMAL_HLY Normals Hourly 1.00
#> 9 gov.noaa.ncdc:C00822 NORMAL_MLY Normals Monthly 1.00
#> 10 gov.noaa.ncdc:C00505 PRECIP_15 Precipitation 15 Minute 0.25
#> 11 gov.noaa.ncdc:C00313 PRECIP_HLY Precipitation Hourly 1.00
#> mindate maxdate
#> 1 1831-02-01 2014-07-01
#> 2 1763-01-01 2014-12-18
#> 3 1763-01-01 2014-11-01
#> 4 1991-06-05 2014-12-18
#> 5 1994-05-20 2014-12-14
#> 6 2010-01-01 2010-01-01
#> 7 2010-01-01 2010-12-31
#> 8 2010-01-01 2010-12-31
#> 9 2010-01-01 2010-12-01
#> 10 1970-05-12 2013-07-01
#> 11 1900-01-01 2013-10-01
#>
#> attr(,"class")
#> [1] "ncdc_datasets"Get data category data and metadata
ncdc_datacats(locationid='CITY:US390029')
#> $meta
#> $meta$totalCount
#> [1] 37
#>
#> $meta$pageCount
#> [1] 25
#>
#> $meta$offset
#> [1] 1
#>
#>
#> $data
#> id name
#> 1 ANNAGR Annual Agricultural
#> 2 ANNDD Annual Degree Days
#> 3 ANNPRCP Annual Precipitation
#> 4 ANNTEMP Annual Temperature
#> 5 AUAGR Autumn Agricultural
#> 6 AUDD Autumn Degree Days
#> 7 AUPRCP Autumn Precipitation
#> 8 AUTEMP Autumn Temperature
#> 9 COMP Computed
#> 10 COMPAGR Computed Agricultural
#> 11 DD Degree Days
#> 12 DUALPOLMOMENT Dual-Pol Moments
#> 13 ECHOTOP Echo Tops
#> 14 HYDROMETEOR Hydrometeor Type
#> 15 OTHER Other
#> 16 OVERLAY Overlay
#> 17 PRCP Precipitation
#> 18 REFLECTIVITY Reflectivity
#> 19 SKY Sky cover & clouds
#> 20 SPAGR Spring Agricultural
#> 21 SPDD Spring Degree Days
#> 22 SPPRCP Spring Precipitation
#> 23 SPTEMP Spring Temperature
#> 24 SUAGR Summer Agricultural
#> 25 SUDD Summer Degree Days
#>
#> 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 and 21 fields
#> Feature type: wkbLineString with 2 dimensions
library('sp')
plot(shp)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
#> 3 COOP 046742
#> 4 WBAN 93209
#> 5 FAA PRB
#> 6 ICAO KPRB
#> 7 NWSLI PRB
#> 8 NCDCSTNID 20002078
#>
#> $`20002078`$status
#> NULL
#>
#> $`20002078`$platform
#> [1] "COOP"
#>
#> $`20002078`$relocations
#> relocation date
#> 1 5.1 mi NNE 1949-10-05T00:00:00.000
#>
#> $`20002078`$remarks
#> type
#> 1 NWS COOP INGEST
#> 2 GENERAL
#> 3 RIVER BASIN (COOP NETWORK)
#> remark
#> 1 REQUIRED 5 YEAR UPDATE\r\nASOS COMMISSIONED 01/18/2001\r\nSERVICE LEVEL "D" ASOS MAINTAINED BY LOX NWS ETS
#> 2 PHOTOS ADDED FOR ASOS EXPOSURE DOCUMENTATION EFFORT (CIRCA 1999+) WHICH TOOK PHOTOS OF THE AREA SURROUNDING COASTAL AND INLAND ASOS SITES IN AREAS SUBJECT TO HURRICANES. THE PHOTOS HELP DOCUMENT STATION EXPOSURE AND IDENTIFY OBSTACLES THAT COULD REDUCE THE WINDS AT THE SITE.
#> 3 SALINAS
#>
#> $`20002078`$updates
#> effectiveDate providedBy updateSource version
#> 1 2014-08-14T00:00:00.000 NCDC\\KTHOMAS AD HOC NONE
#> description
#> 1 ADDING ANEMOMETER HEIGHTS FROM NWS SURFACE OBSERVATIONS PROGRAM LISTING DATED FEBRUARY 11, 2009
#> enteredBy enteredDate modifiedBy modifiedDate
#> 1 KTHOMAS 2014-08-14T10:07:52-04:00 SMCNEILL 2014-09-24T14:56:23-04:00
#>
#> $`20002078`$elements
#> dataProgram element frequency observationTime publishedFlag receiver
#> 1 COOP HPD PRECIP HOURLY 2400 HPD NCDC
#> 2 COOP SOD PRECIP DAILY 2400 CD NCDC
#> 3 COOP SOD TEMP DAILY 2400 CD NCDC
#> 4 COOP SOD WIND HOURLY UNKN <NA> <NA>
#> reportingMethod equipment.equipment equipment.equipmentMods
#> 1 ADP AHTB RCRD;HTD
#> 2 ADP PCPNX <NA>
#> 3 ADP TEMPX <NA>
#> 4 <NA> <NA> <NA>
#> equipment.equipmentAzimuth equipment.equipmentDistance
#> 1 000 0
#> 2 000 0
#> 3 090 4
#> 4 <NA> <NA>
#> equipment.equipmentDistanceUnits date.beginDate date.endDate
#> 1 ft 2010-02-11T00:00:00.000 Present
#> 2 ft 2010-02-11T00:00:00.000 Present
#> 3 ft 2010-02-11T00:00:00.000 Present
#> 4 <NA> 2010-02-11T00:00:00.000 Present
#> equipment
#> 1 <NA>
#> 2 <NA>
#> 3 <NA>
#> 4 ANEMSONIC
#>
#> $`20002078`$location
#> $`20002078`$location$id
#> [1] "20002078"
#>
#> $`20002078`$location$description
#> description
#> 1 PASO ROBLES MUNICIPAL AIRPORT OUTSIDE AND 5 MILES NE OF PO AT PASO ROBLES CA
#>
#> $`20002078`$location$latlon
#> latitude_dec longitude_dec latitude_dms longitude_dms precision
#> 1 35.6697 -120.6283 35,40,11,N 120,37,42,W DDMMSS
#> datum_horiz source
#> 1 NAD83 ASOS SITE SURVEY
#>
#> $`20002078`$location$elevation
#> elevationType elevationFeet elevationMeters groundElevDatum
#> 1 GROUND 810 246.9 NAVD88
#>
#> $`20002078`$location$topography
#> description
#> "LEVEL VALLEY LOCATION (S-N)"
#>
#> $`20002078`$location$obstructions
#> description
#> 1 HYGR 090/04 NO OBSTRUCTIONS
#>
#> $`20002078`$location$geoinfo
#> ncdstnId country state county utcoffset
#> 1 20002078 UNITED STATES CA SAN LUIS OBISPO -8
#>
#> $`20002078`$location$nwsinfo
#> ncdstnId climateDivisions.stateProvince climateDivisions.climateDivision
#> 1 20002078 CA 04
#> climateDivisions.displayName nwsRegion nwsWfos.nwsWfo
#> 1 Central Coast Drainage WESTERN LOX
#> nwsWfos.displayName
#> 1 LOS ANGELES, CA
#>
#>
#>
#> attr(,"class")
#> [1] "homr"
#> attr(,"combined")
#> [1] FALSEStorm data
Get storm data for the year 2010
storm_data(year=2010)
#> <NOAA Storm Data>
#> Size: 2855 X 195
#>
#> serial_num season num basin sub_basin name iso_time
#> 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
#> .. ... ... ... ... ... ... ...
#> Variables not shown: 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), td9636_lon (dbl), td9636_grade (dbl), td9636_wind
#> (dbl), td9636_pres (dbl), reunion_lat (dbl), reunion_lon (dbl),
#> reunion_grade (dbl), reunion_wind (dbl), reunion_pres (dbl), atcf_lat
#> (dbl), atcf_lon (dbl), atcf_grade (dbl), atcf_wind (dbl), atcf_pres
#> (dbl), ds824_sh_lat (dbl), ds824_sh_lon (dbl), ds824_sh_grade (dbl),
#> ds824_sh_wind (dbl), ds824_sh_pres (dbl), ds824_ni_lat (dbl),
#> ds824_ni_lon (dbl), ds824_ni_grade (dbl), ds824_ni_wind (dbl),
#> ds824_ni_pres (dbl), bom_lat (dbl), bom_lon (dbl), bom_grade (dbl),
#> bom_wind (dbl), bom_pres (dbl), ds824_au_lat (dbl), ds824_au_lon
#> (dbl), ds824_au_grade (dbl), ds824_au_wind (dbl), ds824_au_pres
#> (dbl), jtwc_sh_lat (dbl), jtwc_sh_lon (dbl), jtwc_sh_grade (dbl),
#> jtwc_sh_wind (dbl), jtwc_sh_pres (dbl), jtwc_wp_lat (dbl),
#> jtwc_wp_lon (dbl), jtwc_wp_grade (dbl), jtwc_wp_wind (dbl),
#> jtwc_wp_pres (dbl), td9635_lat (dbl), td9635_lon (dbl), td9635_grade
#> (dbl), td9635_wind (dbl), td9635_pres (dbl), ds824_wp_lat (dbl),
#> ds824_wp_lon (dbl), ds824_wp_grade (dbl), ds824_wp_wind (dbl),
#> ds824_wp_pres (dbl), jtwc_io_lat (dbl), jtwc_io_lon (dbl),
#> jtwc_io_grade (dbl), jtwc_io_wind (dbl), jtwc_io_pres (dbl), cma_lat
#> (dbl), cma_lon (dbl), cma_grade (dbl), cma_wind (dbl), cma_pres
#> (dbl), hurdat_epa_lat (dbl), hurdat_epa_lon (dbl), hurdat_epa_grade
#> (dbl), hurdat_epa_wind (dbl), hurdat_epa_pres (dbl), jtwc_ep_lat
#> (dbl), jtwc_ep_lon (dbl), jtwc_ep_grade (dbl), jtwc_ep_wind (dbl),
#> jtwc_ep_pres (dbl), ds824_ep_lat (dbl), ds824_ep_lon (dbl),
#> ds824_ep_grade (dbl), ds824_ep_wind (dbl), ds824_ep_pres (dbl),
#> jtwc_cp_lat (dbl), jtwc_cp_lon (dbl), jtwc_cp_grade (dbl),
#> jtwc_cp_wind (dbl), jtwc_cp_pres (dbl), tokyo_lat (dbl), tokyo_lon
#> (dbl), tokyo_grade (dbl), tokyo_wind (dbl), tokyo_pres (dbl),
#> neumann_lat (dbl), neumann_lon (dbl), neumann_grade (dbl),
#> neumann_wind (dbl), neumann_pres (dbl), hko_lat (dbl), hko_lon (dbl),
#> hko_grade (dbl), hko_wind (dbl), hko_pres (dbl), cphc_lat (dbl),
#> cphc_lon (dbl), cphc_grade (dbl), cphc_wind (dbl), cphc_pres (dbl),
#> wellington_lat (dbl), wellington_lon (dbl), wellington_grade (dbl),
#> wellington_wind (dbl), wellington_pres (dbl), newdelhi_lat (dbl),
#> newdelhi_lon (dbl), newdelhi_grade (dbl), newdelhi_wind (dbl),
#> newdelhi_pres (dbl), nadi_lat (dbl), nadi_lon (dbl), nadi_grade
#> (dbl), nadi_wind (dbl), nadi_pres (dbl), reunion_rmw (dbl),
#> reunion_wind_radii_1_ne (dbl), reunion_wind_radii_1_se (dbl),
#> reunion_wind_radii_1_sw (dbl), reunion_wind_radii_1_nw (dbl),
#> reunion_wind_radii_2_ne (dbl), reunion_wind_radii_2_se (dbl),
#> reunion_wind_radii_2_sw (dbl), reunion_wind_radii_2_nw (dbl),
#> bom_mn_hurr_xtnt (dbl), bom_mn_gale_xtnt (dbl), bom_mn_eye_diam
#> (dbl), bom_roci (dbl), atcf_rmw (dbl), atcf_poci (dbl), atcf_roci
#> (dbl), atcf_eye (dbl), atcf_wrad34_rad1 (dbl), atcf_wrad34_rad2
#> (dbl), atcf_wrad34_rad3 (dbl), atcf_wrad34_rad4 (dbl),
#> atcf_wrad50_rad1 (dbl), atcf_wrad50_rad2 (dbl), atcf_wrad50_rad3
#> (dbl), atcf_wrad50_rad4 (dbl), atcf_wrad64_rad1 (dbl),
#> atcf_wrad64_rad2 (dbl), atcf_wrad64_rad3 (dbl), atcf_wrad64_rad4
#> (dbl), tokyo_dir50 (dbl), tokyo_long50 (dbl), tokyo_short50 (dbl),
#> tokyo_dir30 (dbl), tokyo_long30 (dbl), tokyo_short30 (dbl),
#> jtwc_.._rmw (dbl), jtwc_.._poci (dbl), jtwc_.._roci (dbl),
#> jtwc_.._eye (dbl), jtwc_.._wrad34_rad1 (dbl), jtwc_.._wrad34_rad2
#> (dbl), jtwc_.._wrad34_rad3 (dbl), jtwc_.._wrad34_rad4 (dbl),
#> jtwc_.._wrad50_rad1 (dbl), jtwc_.._wrad50_rad2 (dbl),
#> jtwc_.._wrad50_rad3 (dbl), jtwc_.._wrad50_rad4 (dbl),
#> jtwc_.._wrad64_rad1 (dbl), jtwc_.._wrad64_rad2 (dbl),
#> jtwc_.._wrad64_rad3 (dbl), jtwc_.._wrad64_rad4 (dbl)ERDDAP data
ERDDAP is a server built on top of OPenDAP, which serves some NOAA data. You can get gridded data (griddap), which lets you query from gridded datasets, or table data (tabledap) which lets you query from tabular datasets. In terms of how we interface with them, there are similarties, but some differences too. We try to make a similar interface to both data types in rnoaa.
First, you likely want to search for data, specify either griddadp or tabledap
erddap_search(query='size', which = "table")
#> 11 results, showing first 20
#> title
#> 1 Channel Islands, Kelp Forest Monitoring, Size and Frequency, Natural Habitat
#> 2 CalCOFI Fish Sizes
#> 3 CalCOFI Larvae Sizes
#> 4 OBIS - ARGOS Satellite Tracking of Animals
#> 5 GLOBEC NEP MOCNESS Plankton (MOC1) Data
#> 6 GLOBEC NEP Vertical Plankton Tow (VPT) Data
#> 7 NWFSC Observer Fixed Gear Data, off West Coast of US, 2002-2006
#> 8 NWFSC Observer Trawl Data, off West Coast of US, 2002-2006
#> 9 CalCOFI Larvae Counts Positive Tows
#> 10 CalCOFI Tows
#> 16 AN EXPERIMENTAL DATASET: Underway Sea Surface Temperature and Salinity Aboard the Oleander
#> dataset_id
#> 1 erdCinpKfmSFNH
#> 2 erdCalCOFIfshsiz
#> 3 erdCalCOFIlrvsiz
#> 4 aadcArgos
#> 5 erdGlobecMoc1
#> 6 erdGlobecVpt
#> 7 nwioosObsFixed2002
#> 8 nwioosObsTrawl2002
#> 9 erdCalCOFIlrvcntpos
#> 10 erdCalCOFItows
#> 16 nodcPJJUerddap_search(query='size', which = "grid")
#> 5 results, showing first 20
#> title
#> 11 NOAA Global Coral Bleaching Monitoring Products
#> 12 Coawst 4 use, Best Time Series [time][eta_u][xi_u]
#> 13 Coawst 4 use, Best Time Series [time][eta_v][xi_v]
#> 14 Coawst 4 use, Best Time Series [time][s_rho][eta_rho][xi_rho]
#> 15 Coawst 4 use, Best Time Series [time][Nbed][eta_rho][xi_rho]
#> dataset_id
#> 11 hawaii_3b41_0c0b_72bc
#> 12 whoi_61c3_0b5d_cd61
#> 13 whoi_62d0_9d64_c8ff
#> 14 whoi_7dd7_db97_4bbe
#> 15 whoi_a4fb_2c9c_16a7Then you can get information on a single dataset
erddap_info('hawaii_3b41_0c0b_72bc')
#> <ERDDAP Dataset> hawaii_3b41_0c0b_72bc
#> Dimensions (range):
#> time: (2000-11-28T00:00:00Z, 2014-12-04T00:00:00Z)
#> latitude: (85.0, -80.0)
#> longitude: (-180.0, 179.5)
#> Variables:
#> CRW_DHW:
#> Units: Celsius weeks
#> CRW_HOTSPOT:
#> Units: Celsius
#> CRW_SST:
#> Units: Celsius
#> CRW_SSTANOMALY:
#> Units: Celsiusgriddap data
(out <- erddap_info('noaa_esrl_027d_0fb5_5d38'))
#> <ERDDAP Dataset> noaa_esrl_027d_0fb5_5d38
#> Dimensions (range):
#> time: (1850-01-01T00:00:00Z, 2014-05-01T00:00:00Z)
#> latitude: (87.5, -87.5)
#> longitude: (-177.5, 177.5)
#> Variables:
#> air:
#> Range: -20.9, 19.5
#> Units: degC(res <- erddap_grid(out,
time = c('2012-01-01','2012-06-12'),
latitude = c(21, 18),
longitude = c(-80, -75)
))
#> <NOAA ERDDAP griddap> noaa_esrl_027d_0fb5_5d38
#> Path: [/Users/sacmac/.rnoaa/erddap/noaa_esrl_027d_0fb5_5d38.csv]
#> Last updated: [2014-11-04 10:36:20]
#> File size: [0 mb]
#> Dimensions: [24 X 4]
#>
#> time latitude longitude air
#> 1 2012-01-01T00:00:00Z 22.5 -77.5 NaN
#> 2 2012-01-01T00:00:00Z 22.5 -72.5 NaN
#> 3 2012-01-01T00:00:00Z 17.5 -77.5 -0.1
#> 4 2012-01-01T00:00:00Z 17.5 -72.5 NaN
#> 5 2012-02-01T00:00:00Z 22.5 -77.5 NaN
#> 6 2012-02-01T00:00:00Z 22.5 -72.5 NaN
#> 7 2012-02-01T00:00:00Z 17.5 -77.5 0.4
#> 8 2012-02-01T00:00:00Z 17.5 -72.5 NaN
#> 9 2012-03-01T00:00:00Z 22.5 -77.5 NaN
#> 10 2012-03-01T00:00:00Z 22.5 -72.5 NaN
#> .. ... ... ... ...tabledap data
(out <- erddap_info('erdCalCOFIfshsiz'))
#> <ERDDAP Dataset> erdCalCOFIfshsiz
#> Variables:
#> calcofi_species_code:
#> Range: 19, 1550
#> common_name:
#> cruise:
#> fish_1000m3:
#> Units: Fish per 1,000 cubic meters of water sampled
#> fish_count:
#> fish_size:
#> Units: mm
#> itis_tsn:
#> latitude:
#> Range: 32.515, 38.502
#> Units: degrees_north
#> line:
#> Range: 46.6, 93.3
#> longitude:
#> Range: -128.5, -117.33
#> Units: degrees_east
#> net_location:
#> net_type:
#> order_occupied:
#> percent_sorted:
#> Units: %/100
#> sample_quality:
#> scientific_name:
#> ship:
#> ship_code:
#> standard_haul_factor:
#> station:
#> Range: 28.0, 114.9
#> time:
#> Range: 9.94464E8, 9.9510582E8
#> Units: seconds since 1970-01-01T00:00:00Z
#> tow_number:
#> Range: 2, 10
#> tow_type:
#> volume_sampled:
#> Units: cubic meterserddap_table(out, fields=c('longitude','latitude','fish_size','itis_tsn'),
'time>=2001-07-07','time<=2001-07-10')
#> <NOAA ERDDAP tabledap> erdCalCOFIfshsiz
#> Path: [~/.rnoaa/erddap/erdCalCOFIfshsiz.csv]
#> Last updated: [2014-12-19 09:14:01]
#> File size: [0.02 mb]
#> Dimensions: [558 X 4]
#>
#> longitude latitude fish_size itis_tsn
#> 2 -118.26 33.255 22.9 623745
#> 3 -118.26 33.255 22.9 623745
#> 4 -118.10667 32.738335 31.5 623625
#> 5 -118.10667 32.738335 48.3 623625
#> 6 -118.10667 32.738335 15.5 162221
#> 7 -118.10667 32.738335 16.3 162221
#> 8 -118.10667 32.738335 17.8 162221
#> 9 -118.10667 32.738335 18.2 162221
#> 10 -118.10667 32.738335 19.2 162221
#> 11 -118.10667 32.738335 20.0 162221
#> .. ... ... ... ...Meta
- Please report any issues or bugs.
- License: MIT
- Get citation information for
rnoaain R doingcitation(package = 'rnoaa')