Tuesday, December 4, 2018 From rOpenSci (https://ropensci.org/blog/2018/12/04/rnoaa-update/). Except where otherwise noted, content on this site is licensed under the CC-BY license.
We’ve just released a new version of rnoaa with A LOT of changes. Check out the release notes for a complete list of changes.
We’ll highlight a few things in this post:
ncdc()Links:
Install the lastest from CRAN
install.packages("rnoaa")
Some binaries are not up yet on CRAN - you can also install from GitHub:
remotes::install_github("ropensci/[email protected]")
Load rnoaa
library(rnoaa)
There are three new data sources in the package. There’s more in the works. You
can keep track of new data sources in the package via the newdatasource
label in the issue tracker.
Blended Sea Winds contains
globally gridded, high-resolution ocean surface vector winds and wind stresses on a global 0.25° grid, and multiple time resolutions of six-hourly, daily, monthly, and 11-year (1995-2005) climatological monthlies.
Data is available from July 9, 1987 to present.
Check out the link above for more information on the data.
In a brief example of getting the data in rnoaa, we get data for the date
October 1st, 2017, and we get daily resolution. The data is provided in
netcdf format.
bsw(date = "2017-10-01", resolution = "daily")
#> File /Users/sckott/Library/Caches/R/noaa_bsw/uv/daily/uv20171001rt.nc (NC_FORMAT_CLASSIC):
#>
#> 3 variables (excluding dimension variables):
#> float u[lon,lat,zlev,time]
#> long_name: Sea Surface Wind: x-component
#> units: m/s
#> _FillValue: -9999
#> float v[lon,lat,zlev,time]
#> long_name: Sea Surface Wind: y-component
#> units: m/s
#> _FillValue: -9999
#> float w[lon,lat,zlev,time]
#> long_name: Sea Surface Wind: speed as scalar means
#> units: m/s
#> _FillValue: -9999
#>
#> 4 dimensions:
#> zlev Size:1
#> long_name: height above sea level
#> units: meters
#> time Size:1
#> long_name: Center Time of the Data
#> units: hours since 1978-01-01 00:00:00
#> lat Size:719
#> long_name: latitude
#> units: degrees_north
#> grids: uniform grids from -89.75 to 89.75 by 0.25
#> lon Size:1440
#> long_name: longitude
#> units: degrees_east
#> grids: uniform grids from 0.00 to 359.75 by 0.25
#>
#> 11 global attributes:
#> Conventions: COARDS, CF-1.0, Unidata Dataset Discovery v1.0
#> title: NOAA/NCDC Blended daily 0.25-degree Sea Surface Winds
#> source: Multiple satellite observations: DMSP SSMI F08, F10, F11, F13,F14 F15; TMI; QuikSCAT; AMSR-E; Direction from NCEP Reanalysis-2
#> summary: Gridded and blended sea surface vector winds from multiple satellites with direction from NCEP Reanalysis-2; Global ocean coverage with a 0.25-degree resolution; The whole datasets covers from July 1987 to present, daily resolution in this dataset; 6-hourly and monthly are also available in other directories; See http://www.ncdc.noaa.gov/oa/rsad/blendedseawinds.html and links within for details. Include (u,v) means and scalar mean speed w for comparison
#> Keywords: sea winds, ocean winds, sea surface winds, air-sea interaction, air-sea flux, wind-driven circulation, Ekman pumping, Ekman transport, ocean upwelling, wind stress, windstress
#> references: links at http://www.ncdc.noaa.gov/oa/rsad/blendedseawinds.html
#> History: Simple spatiotemporally weighted Interpolation (SI), V.1.2. Version 1.2 uses updated satellite retrievals by Remote Sensing System, released in September 2006: SSMI V06, TMI V04, QSCAT V03a. AMSRE V05 was also updated using the new SSMI rain rate
#> institution: NOAA NESDIS National Climatic Data Center
#> Contact: Huai-Min.Zhang AT noaa.gov or satorder AT noaa.gov; ph:1+828-271-4090
#> Acknowledgment: The gridded data were generated from the multiple satellite observations of DOD, NOAA and NASA (and future others) and wind retrievals of the Remote Sensing Systems, Inc. (http://www.remss.com), using scientific methods such as objective analysis (OA). The OA is only truly objective when the needed statistics are completely known, which may not be always the case.
#> Data_Calendar_Date: 2017-10-01
Local Climatological Data consists of
hourly, daily, and monthly summaries for approximately 1,600 U.S. locations.
In some examples, we get data for a single station for two different years. The data is returned in data.frame format
lcd(station = "01338099999", year = "2017")
#> # A tibble: 8,677 x 36
#> station date source latitude longitude elevation name report_type
#> <int> <chr> <int> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 1.34e9 2017… 4 61.2 6.65 51 VANG… FM-12
#> 2 1.34e9 2017… 4 61.2 6.65 51 VANG… FM-12
#> 3 1.34e9 2017… 4 61.2 6.65 51 VANG… FM-12
#> 4 1.34e9 2017… 4 61.2 6.65 51 VANG… FM-12
#> 5 1.34e9 2017… 4 61.2 6.65 51 VANG… FM-12
#> 6 1.34e9 2017… 4 61.2 6.65 51 VANG… FM-12
#> 7 1.34e9 2017… 4 61.2 6.65 51 VANG… FM-12
#> 8 1.34e9 2017… 4 61.2 6.65 51 VANG… FM-12
#> 9 1.34e9 2017… 4 61.2 6.65 51 VANG… FM-12
#> 10 1.34e9 2017… 4 61.2 6.65 51 VANG… FM-12
#> # … with 8,667 more rows, and 28 more variables: call_sign <int>,
#> # quality_control <chr>, wnd <chr>, cig <chr>, vis <chr>, tmp <chr>,
#> # dew <chr>, slp <chr>, aa1 <chr>, aa2 <chr>, aj1 <chr>, ay1 <chr>,
#> # ay2 <chr>, ga1 <chr>, ga2 <chr>, ga3 <chr>, ge1 <chr>, gf1 <chr>,
#> # ia1 <chr>, ka1 <chr>, ka2 <chr>, ma1 <chr>, md1 <chr>, mw1 <chr>,
#> # oc1 <chr>, od1 <chr>, rem <chr>, eqd <chr>
lcd(station = "01338099999", year = "2015")
#> # A tibble: 8,097 x 36
#> station date source latitude longitude elevation name report_type
#> <int> <chr> <int> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 1.34e9 2015… 4 61.2 6.65 51 VANG… FM-12
#> 2 1.34e9 2015… 4 61.2 6.65 51 VANG… FM-12
#> 3 1.34e9 2015… 4 61.2 6.65 51 VANG… FM-12
#> 4 1.34e9 2015… 4 61.2 6.65 51 VANG… FM-12
#> 5 1.34e9 2015… 4 61.2 6.65 51 VANG… FM-12
#> 6 1.34e9 2015… 4 61.2 6.65 51 VANG… FM-12
#> 7 1.34e9 2015… 4 61.2 6.65 51 VANG… FM-12
#> 8 1.34e9 2015… 4 61.2 6.65 51 VANG… FM-12
#> 9 1.34e9 2015… 4 61.2 6.65 51 VANG… FM-12
#> 10 1.34e9 2015… 4 61.2 6.65 51 VANG… FM-12
#> # … with 8,087 more rows, and 28 more variables: call_sign <int>,
#> # quality_control <chr>, wnd <chr>, cig <chr>, vis <chr>, tmp <chr>,
#> # dew <chr>, slp <chr>, aa1 <chr>, aa2 <chr>, aj1 <chr>, ay1 <chr>,
#> # ay2 <chr>, ga1 <chr>, ga2 <chr>, ga3 <chr>, ge1 <chr>, gf1 <chr>,
#> # ia1 <chr>, ka1 <chr>, ka2 <chr>, ma1 <chr>, md1 <chr>, mw1 <chr>,
#> # oc1 <chr>, od1 <chr>, rem <chr>, eqd <chr>
The Storm Events Database contains records used to create the official NOAA Storm Data publication.
Data contains:
Data is available from January 1950 to August 2018.
There are two functions associated with this data: se_files() and se_data().
se_files() gives a data.frame of the details of each storm file, and
se_data() gets the data for a particular year and data type.
In a brief example, here we get data for year of 1988 and type “fatalities”
se_data(year = 1988, type = "fatalities")
#> # A tibble: 39 x 11
#> fat_yearmonth fat_day fat_time fatality_id event_id fatality_type
#> <int> <int> <int> <int> <int> <chr>
#> 1 198801 19 1130 1006297 10121593 D
#> 2 198801 19 1152 1006298 10121594 D
#> 3 198801 19 1158 1006299 10121595 D
#> 4 198803 29 1445 1006300 10044375 D
#> 5 198803 28 1805 1006301 10105510 D
#> 6 198803 17 1045 1006302 10139634 D
#> 7 198804 16 1430 1006303 10076954 D
#> 8 198804 19 230 1006304 10001120 D
#> 9 198805 7 2139 1006305 10082037 D
#> 10 198805 8 1500 1006306 10002626 D
#> # … with 29 more rows, and 5 more variables: fatality_date <chr>,
#> # fatality_age <lgl>, fatality_sex <lgl>, fatality_location <lgl>,
#> # event_yearmonth <int>
The main function for getting NCDC data via their API - ncdc() - gains the add_units parameter
(boolean) to toggle adding units to the output data.frame.
The default is add_units=FALSE - meaning that by default the results from the function
DO NOT CHANGE.
However, if add_units=TRUE we match by dataset id and data type id and return units if
we have them. The way we do this is by manually curating lists of metadata inside the package.
See any of the files in the source code
that start with units-.
Do be in touch if you see a problem with these units!
Note that we haven’t filled out all units for all datasets. If you’d like to help it would be much appreciated.
We should also note that ncdc() now returns tibbles in the data slot. This way if you
get back a huge number of rows it won’t print to console for a long time. You can revert
the tibbles back to data.frames just by using as.data.frame or similar.
There’s a huge list of bug fixes (and fixes listed in Minor Improvements ) in this version, and many people to thank for reporting them:
Get in touch if you have any rnoaa questions in the
issue tracker or the
rOpenSci discussion forum.
