Package {rWind}

Encoding: UTF-8
Type: Package
Title: Download, Edit and Include Wind and Sea Currents Data in Ecological and Evolutionary Analysis
Version: 1.2.0
BugReports: https://github.com/jabiologo/rWind/issues
Maintainer: Javier Fernández-López <jflopez.bio@gmail.com>
Description: Downloads and processes surface wind data from the Global Forecast System https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast and sea-current data from Ocean Surface Current Analyses Real-time (OSCAR). Converts flow speed and direction into spatial rasters and directed movement costs and conductances for connectivity and least-cost path analyses.
URL: https://github.com/jabiologo/rWind, https://allthiswasfield.blogspot.com/
License: GPL (≥ 3)
LazyData: true
Imports: raster (≥ 2.5-8), terra, gdistance, Matrix, lubridate
Suggests: testthat, rmarkdown, knitr, curl, shape
VignetteBuilder: knitr
Depends: R (≥ 3.5)
RoxygenNote: 7.3.3
NeedsCompilation: no
Packaged: 2026-10-08 12:08:29 UTC; javifl
Author: Javier Fernández-López [aut, cre], Klaus Schliep [aut], Yurena Arjona [aut]
Repository: CRAN
Date/Publication: 2026-10-18 16:30:02 UTC

Download, edit and include wind and sea currents data in ecological and evolutionary analysis

Description

rWind contains tools for downloading, editing and transforming wind and sea currents data from the Global Forecast System (GFS) and Ocean Surface Current Analyses Real-time (OSCAR). It also allows wind and sea currents data to be used to compute minimum-cost paths from flow speed and direction and to perform connectivity analysis.

Details

The complete list of functions can be displayed with library(help = rWind). For more information, please check: https://allthiswasfield.blogspot.com/.

Author(s)

Javier Fernández-López

Klaus Schliep

Yurena Arjona

Maintainer: Javier Fernández-López jflopez.bio@gmail.com

See Also

Useful links:

Arrow direction fitting for Arrowhead function from "shape" package

Description

arrowDir adapts wind direction value to be used by Arrowhead function from "shape" package to plot wind direction for each coordinate.

Usage

arrowDir(W)

Arguments

W

An object of class rWind or a data.frame which should content a column named "dir".

Details

Angle argument of Arrowhead function from "shape" package needs to be fed in an anti-clockwise way, relative to x-axis, in degrees 0,360. arrowDir function adapts wind direction provided by wind.fit (clockwise, relative to y-axis ) to requirements of Arrowhead.

Value

A numeric vector with one angle per grid cell, in degrees anticlockwise from the positive x-axis, for plotting with shape::Arrowhead(). For an rWind_series, only the first time step is used.

Note

arrowDir function works always together with Arrowhead function from "shape" package.

Author(s)

Javier Fernández-López

References

Karline Soetaert (2017). shape: Functions for Plotting Graphical Shapes, Colors. R package version 1.4.3. https://CRAN.R-project.org/package=shape

See Also

wind.dl

Examples

data(wind.data)

# Create a vector with wind direction (angles) adapted
alpha <- arrowDir(wind.data)
# Plot arrows if the optional shape package is available
if (requireNamespace("shape", quietly = TRUE)) {
plot(wind.data$lon, wind.data$lat, type = "n")
shape::Arrowhead(wind.data$lon, wind.data$lat,
  angle = alpha,
  arr.length = 0.1, arr.type = "curved"
)
}

Compute flow-based cost or conductance

Description

flow.dispersion computes movement conductance through a flow such as sea or wind currents. Passive movement uses a restricted version of the formula described in Felícisimo et al. (2008):

Usage

cost.FMGS(
  wind.direction,
  wind.speed,
  target,
  type = c("passive", "active"),
  speed.scale = NULL
)

flow.dispersion(
  x,
  fun = cost.FMGS,
  output = "transitionLayer",
  type = c("passive", "active"),
  speed.scale = NULL,
  ...
)

Arguments

wind.direction

A vector or scalar containing wind directions.

wind.speed

A vector or scalar containing wind speeds.

target

direction of the target cell

type

Movement model. In "passive" mode, deviations of 90 degrees or more from the flow direction are forbidden. In "active" mode, supportive flow lowers relative cost and opposing flow raises it; movement against the flow remains possible.

speed.scale

Positive reference speed for the active model. If NULL, the median positive speed of the complete raster layer is used. For comparisons among dates or regions, supply one fixed value in the same units as wind.speed.

x

A SpatRaster returned by wind2raster() with direction and speed layers, a legacy RasterStack, or a list of either type.

fun

A function to compute the cost to move between cells. The default is cost.FMGS from Felicísimo et al. (2008), see details.

output

This argument allows to select different kinds of output. "raw" mode creates a matrix (class "dgCMatrix") with transition costs between all cells in the raster. "transitionLayer" creates a TransitionLayer object with conductance values to be used with "gdistance" package.

...

Further arguments passed to or from other methods.

Details

Cost=(1/Speed)*(HorizontalFactor)

where HorizontalFactor incrementally penalizes angular deviations from the flow direction and prevents passive movement at deviations of 90 degrees or more. Active movement uses a general relative index based on signed flow support rather than the original formula.

Value

flow.dispersion() returns a gdistance::TransitionLayer for output = "transitionLayer", with directed conductances (1/cost) between the eight neighbouring cells, or a sparse matrix of class dgCMatrix containing edge costs for output = "raw". Rows index origins and columns index destinations. Costs average local costs at both cells and are multiplied by sqrt(2) for diagonal moves. In the raw matrix, Inf marks forbidden neighbouring moves; zeros represent absent edges, not free moves. In the conductance matrix, forbidden moves have zero conductance. A list input returns a list of the corresponding output objects.

cost.FMGS() returns numeric local movement costs for the supplied flow values and target direction (a vector, or a matrix for matrix inputs). Passive costs are the horizontal factor divided by speed, with Inf for forbidden moves. Active costs follow cost.active().

Note

The active model is a relative connectivity index, not a mechanistic model of animal movement. It ignores the component of flow perpendicular to movement.

Costs are relative per grid step. The diagonal correction assumes a square grid, but no correction is made for the changing physical distances of longitude/latitude cells. Do not interpret results as physical time, energy, or distance or compare them directly across grid resolutions or distant latitude ranges.

Note that for large data sets, it could take a while. For large study areas is strongly advised perform the analysis in a remote computer or a cluster.

Author(s)

Javier Fernández-López; Klaus Schliep; Yurena Arjona

References

Felicísimo, Á. M., Muñoz, J., & González-Solis, J. (2008). Ocean surface winds drive dynamics of transoceanic aerial movements. PLoS One, 3(8), e2928.

Jacob van Etten (2017). R Package gdistance: Distances and Routes on Geographical Grids. Journal of Statistical Software, 76(13), 1-21. doi:10.18637/jss.v076.i13

See Also

wind.dl, wind2raster

Examples


require(gdistance)

data(wind.data)

wind <- wind2raster(wind.data)

Conductance <- flow.dispersion(wind, type = "passive")

conductance_matrix <- gdistance::transitionMatrix(Conductance)
conductance_matrix[1:6, 1:6]
Matrix::image(conductance_matrix)

Relative cost of active movement through a flow

Description

cost.active() is a deliberately simple, general index based on signed flow support. The flow component parallel to movement is speed * cos(angle). Supportive flow lowers cost, opposing flow raises it, and perpendicular flow has no effect. This is a relative index, not physical travel time or energy.

Usage

cost.active(flow.direction, flow.speed, target, speed.scale = NULL)

Arguments

flow.direction

Flow direction in degrees, pointing towards where the flow moves.

flow.speed

Non-negative flow speed.

target

Movement direction in degrees.

speed.scale

Positive reference speed used to make flow support dimensionless. By default, the median positive input speed is used.

Details

Let z = speed * cos(angle) / speed.scale. When z >= 0, cost is 1 / (1 + z); when z < 0, cost is 1 - z. Thus support equal to the reference speed halves cost, opposition equal to the reference speed doubles it, and cost(-z) = 1 / cost(z).

Value

A numeric vector of relative movement costs, or a numeric array when the input supplying the output shape is an array. Scalar inputs are recycled to match the longer input. A value below one indicates support, one indicates calm or perpendicular flow, and a value above one indicates opposition. Invalid flow values give Inf, except that zero speed gives a cost of one even when direction is missing.

Compute flow-based cost or conductance

Description

flow.dispersion_int computes movement conductance through a flow such as sea or wind currents. Passive movement uses a restricted version of the formula described in Felícisimo et al. (2008):

Usage

flow.dispersion_int(
  stack,
  fun = cost.FMGS,
  output = "transitionLayer",
  type = c("passive", "active"),
  speed.scale = NULL,
  ...
)

Arguments

stack

A SpatRaster returned by wind2raster() with direction and speed layers. Legacy RasterStack objects are also accepted.

fun

A function to compute the cost to move between cells. The default is cost.FMGS from Felicísimo et al. (2008), see details.

output

This argument allows to select different kinds of output. "raw" mode creates a matrix (class "dgCMatrix") with transition costs between all cells in the raster. "transitionLayer" creates a TransitionLayer object with conductance values to be used with "gdistance" package.

type

Movement model: "passive" or "active".

speed.scale

Positive reference speed for the active model. If NULL, the median positive speed of the complete raster layer is used.

...

Further arguments passed to or from other methods.

Details

Cost=(1/Speed)*(HorizontalFactor)

where HorizontalFactor incrementally penalizes angular deviations from the flow direction and prevents passive movement at deviations of 90 degrees or more. Active movement instead uses the signed flow component parallel to the movement direction; supportive flow lowers cost and opposing flow raises it.

Value

With output = "transitionLayer", a gdistance::TransitionLayer whose sparse matrix stores directed conductances (1/cost) between the eight neighbouring cells. With output = "raw", a sparse matrix of class dgCMatrix containing edge costs. Rows index origins and columns index destinations. Each edge cost averages local costs at both cells and is multiplied by sqrt(2) for diagonal moves. In the raw matrix, Inf marks forbidden neighbouring moves; zeros represent absent edges, not free moves. In the conductance matrix, forbidden moves have zero conductance.

Note

Costs are relative per grid step. The diagonal correction assumes a square grid, but no correction is made for the changing physical distances of longitude/latitude cells. Results should not be interpreted as physical time, energy, or distance and should not be compared directly across grid resolutions or distant latitude ranges.

Note that for large data sets, it could take a while. For large study areas is strongly advised perform the analysis in a remote computer or a cluster.

Author(s)

Javier Fernández-López; Klaus Schliep; Yurena Arjona

References

Felicísimo, Á. M., Muñoz, J., & González-Solis, J. (2008). Ocean surface winds drive dynamics of transoceanic aerial movements. PLoS One, 3(8), e2928.

Jacob van Etten (2017). R Package gdistance: Distances and Routes on Geographical Grids. Journal of Statistical Software, 76(13), 1-21. doi:10.18637/jss.v076.i13

See Also

wind.dl, wind2raster

Examples


data(wind.data)
wind <- wind2raster(wind.data)
Conductance <- flow.dispersion(wind, type = "passive")
conductance_matrix <- gdistance::transitionMatrix(Conductance)
conductance_matrix[1:6, 1:6]
Matrix::image(conductance_matrix)

OSCAR Sea currents data download

Description

seaOscar.dl downloads sea currents data from the Ocean Surface Current Analyses Real-time (OSCAR) (PO.DAAC, doi:10.5067/OSCAR-03D01). Geospatial resolution is 0.33 degrees and sea currents are calculated for 15 m depth. Longitudes in the usual -180 to 180 notation are translated to the 20 to 420 degree axis used by the service.

Usage

seaOscar.dl(
  yyyy,
  mm,
  dd,
  lon1,
  lon2,
  lat1,
  lat2,
  type = "read-data",
  trace = 1
)

Arguments

yyyy

Selected year.

mm

Selected month.

dd

Selected day.

lon1

Western longitude

lon2

Eastern longitude

lat1

Northern latitude

lat2

Southern latitude

type

Output type. "read-data" is selected by default, creating an R object. If you choose "csv", seaOscar.dl create a a CSV file in your working directory named "oscar_yyyy_mm_dd.csv".

trace

if trace = 1 (by default) track downloaded files

Details

The output type is determined by type="csv" or type="read-data". If type="csv" is selected, the function creates an "oscar_yyyy_mm_dd.csv" file that is downloaded at the work directory. If type="read-data" is selected, an R object (data.frame) is created.

Value

An object of classes rWind and data.frame, with one row per grid cell and columns time (POSIXct, UTC), lat and lon (degrees), u and v (eastward and northward current components, m/s), dir (direction of movement, in degrees clockwise from north), and speed (m/s). These values describe currents at the OSCAR reference depth. With type = "csv", a CSV file is also written in the working directory and the same object is returned.

Note

The NOAA ERDDAP endpoint used here provides a historical subset of OSCAR, not its full temporal coverage. Check the available dates in the dataset metadata before requesting data. Requests outside that coverage fail.

Author(s)

Javier Fernández-López (jflopez.bio@gmail.com)

References

doi:10.5067/OSCAR-03D01

See Also

wind.dl_2, wind2raster

Examples


# Download historical sea currents near the Galapagos Islands


seaOscar.dl(2014, 9, 26, -93, -88, 2, -3)

Transforming a rWind_series object into a data.frame

Description

The output of tidy is always a data.frame. It is therefore suited for further manipulation by packages like dplyr, reshape2, ggplot2 and ggvis.

Usage

tidy(x, ...)

## S3 method for class 'rWind_series'
tidy(x, ...)

Arguments

x

An object to be converted into a tidy data.frame

...

extra arguments

Value

For an rWind_series, an object of classes rWind and data.frame, formed by stacking the time steps in their original order. Each row represents one grid cell at one time. The seven columns are time (POSIXct, UTC), lat and lon (numeric coordinates in degrees), ugrd10m and vgrd10m (numeric eastward and northward wind components in m/s), dir (numeric direction of movement, in degrees clockwise from north), and speed (numeric wind speed in m/s). Values are retained for each time step; no temporal averaging is performed.

Examples

data(wind.series)
df <- tidy(wind.series)
head(df)
# Compute mean speed at each location using the combined data frame
mean_speed <- stats::aggregate(speed ~ lat + lon, data = df, FUN = mean)
head(mean_speed)

Transform U and V components in direction and speed and vice versa

Description

Transform U and V components in direction and speed and vice versa

Usage

uv2ds(u, v)

ds2uv(d, s)

Arguments

u

U component.

v

U component.

d

direction (degrees).

s

speed (m/s).

Value

uv2ds() returns a numeric matrix with columns dir (degrees clockwise from north, towards where the flow moves) and speed (in the same units as U and V), with one row per input pair.

ds2uv() returns a numeric matrix with columns u (eastward component) and v (northward component), in the same units as s, with one row per direction-speed pair.

Note

Multiple U and V values can be processed. "dir" denotes where the wind/sea current is going (toward), not from where is coming.

Author(s)

Javier Fernández-López (jflopez.bio@gmail.com)

See Also

wind.mean, wind2raster

Examples


(ds <- uv2ds(c(1, 1, 3, 1), c(1, 1.7, 3, 1)))
ds2uv(ds[, 1], ds[, 2])

Wind data example

Description

This is an example of wind data obtained with wind.dl function for the Iberian Peninsula coordinates on 12/February/2015 at 00:00 (UTC)

Format

A list with one data.frame with 651 observations on the following 7 variables:

list("time (UTC)")

a numeric with selected time of wind data

list("latitude (degrees_north)")

a numeric with latitude values

list("longitude (degrees_east)")

a numeric with longitude values

list("ugrd10m (m s-1)")

a numeric with U component of wind data

list("vgrd10m (m s-1)")

a numeric with V component of wind data

list("dir")

a numeric with direction of wind data

list("speed")

a numeric with speed of wind data

Details

This data set is the result of:

wind.data <- wind.dl(2015,2,12,0,-10,5,35,45)

Source

https://allthiswasfield.blogspot.com/2016/12/rwind-r-package-released.html

References

https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast

Examples


data(wind.data)
str(wind.data)
head(wind.data[[1]])

Wind-data download

Description

wind.dl downloads wind data from the Global Forecast System (GFS) of the USA's National Weather Service (NWS) (https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast). Wind data are taken from NOAA/NCEP Global Forecast System (GFS) Atmospheric Model collection. Geospatial resolution is 0.5 degrees (approximately 50 km), and wind is calculated for Earth surface, at 10 m. More metadata information for the current PacIOOS data: https://pae-paha.pacioos.hawaii.edu/erddap/griddap/ncep_global.graph Historical data are obtained from the official NOAA/NCEI GFS 0.5 degree archive: https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00634

Usage

wind.dl(
  yyyy,
  mm,
  dd,
  tt,
  lon1,
  lon2,
  lat1,
  lat2,
  type = "read-data",
  trace = 1,
  source = c("auto", "pacioos", "ncei")
)

read.rWind(file)

Arguments

yyyy

Selected year.

mm

Selected month.

dd

Selected day.

tt

Selected time. There are currently several options at the GFS database: 00:00 - 03:00 - 06:00 - 09:00 - 12:00 - 15:00 - 18:00 - 21:00 (UTC).

lon1

Western longitude

lon2

Eastern longitude

lat1

Southern latitude

lat2

Northern latitude

type

Output type. "read-data" is selected by default, creating an R object. If you choose "csv", wind.dl create a a CSV file in your working directory named "wind_yyyy_mm_dd_tt.csv".

trace

if trace = 1 (by default) track downloaded files

source

Data provider. "auto" selects PacIOOS for dates in its current coverage and NOAA/NCEI for older dates. Use "pacioos" or "ncei" to force one provider.

file

file name of the saved ".csv" files.

Details

The output type is determined by type="csv" or type="read-data". If type="csv" is selected, the function creates a "wind_yyyy_mm_dd_tt.csv" file that is downloaded at the work directory. If type="read-data" is selected, an R object (data.frame) is created.

Value

wind.dl() and read.rWind() return an object of classes rWind and data.frame, with one row per grid cell. Columns are time (POSIXct, UTC), lat and lon (degrees), ugrd10m and vgrd10m (eastward and northward wind components in m/s), dir (direction of movement, in degrees clockwise from north), and speed (m/s). With type = "csv", wind.dl() also writes a CSV file in the working directory and returns the same object.

Note

Longitude coordinate are provided by GFS dataset in 0/360 notation and transformed internally into -180/180. Wind "dir" denotes where the wind is going (toward), not from where is coming. With source = "auto", dates before the current PacIOOS coverage are requested from NOAA/NCEI.

Author(s)

Javier Fernández-López (jflopez.bio@gmail.com)

References

http://www.digital-geography.com/cloud-gis-getting-weather-data/#.WDOWmbV1DCL

https://pae-paha.pacioos.hawaii.edu/erddap/griddap/ncep_global.graph

See Also

wind.dl_2, wind2raster

Examples


# Download wind for Iberian Peninsula region at 2015, February 12, 00:00


wind.dl(2015, 2, 12, 0, -10, 5, 35, 45)

Wind-data download

Description

wind.dl_2 downloads time-series wind data from the Global Forecast System (GFS) of the USA's National Weather Service (NWS) (https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast). Wind data are taken from NOAA/NCEP Global Forecast System (GFS) Atmospheric Model collection. Geospatial resolution is 0.5 degrees (approximately 50 km), and wind is calculated for Earth surface, at 10 m. More metadata information for current data: https://pae-paha.pacioos.hawaii.edu/erddap/griddap/ncep_global.html Historical data are obtained from the official NOAA/NCEI GFS 0.5 degree archive.

Usage

wind.dl_2(
  time,
  lon1,
  lon2,
  lat1,
  lat2,
  type = "read-data",
  trace = 1,
  source = c("auto", "pacioos", "ncei")
)

## S3 method for class 'rWind_series'
x[[i, exact = TRUE]]

Arguments

time

a scalar or vector of POSIXt or Date objects or an character which can transformed into those, see example below. There are currently these options at the GFS database for the hours: 00:00 - 03:00 - 06:00 - 09:00 - 12:00 - 15:00 - 18:00 - 21:00 (UTC) (TO).

lon1

Western longitude

lon2

Eastern longitude

lat1

Southern latitude

lat2

Northern latitude

type

Output type. "read-data" is selected by default, creating an R object. If you choose "csv", wind.dl create a a CSV file in your work directory named "wind_yyyy_mm_dd_tt.csv".

trace

if trace = 1 (by default) track downloaded files

source

Data provider. "auto" selects PacIOOS for dates in its current coverage and NOAA/NCEI for older dates. Use "pacioos" or "ncei" to force one provider.

x

object from which to extract element(s).

i

indices specifying elements to extract.

exact

Controls possible partial matching (not used yet).

Details

To get the same format as wind.dl, you should run tidy function from wind.dl_2 output. The output type is determined by type="csv" or type="read-data". If type="csv" is selected, the function creates a "wind_yyyy_mm_dd_tt.csv" file that is downloaded at the work directory. If type="read-data" is selected, an rWind_series object is created.

Value

With type = "read-data", wind.dl_2() returns an object of classes rWind_series and list, with one element per requested time. Each element stores a data frame of U and V wind components (m/s); element names encode UTC times and the lat_lon attribute stores the shared grid coordinates in degrees. Extracting a time step with [[ returns an object of classes rWind and data.frame, with columns time, lat, lon, ugrd10m, vgrd10m, dir, and speed, as described in wind.dl(). With type = "csv", wind.dl_2() writes one CSV file per time step in the working directory and returns NULL invisibly.

Note

wind.dl_2 requires two dates that represent the boundaries of the time lapse to download wind series data. U and V vector components allow you to create wind averages or tendencies for each coordinate at the study area. Longitude coordinates are provided by GFS dataset in 0/360 notation and transformed internally into -180/180. "dir" denotes where the wind/sea current is going (toward), not from where is coming. With source = "auto", dates before the current PacIOOS coverage are requested from NOAA/NCEI.

Author(s)

Javier Fernández-López (jflopez.bio@gmail.com)

References

http://www.digital-geography.com/cloud-gis-getting-weather-data/#.WDOWmbV1DCL

https://pae-paha.pacioos.hawaii.edu/erddap/griddap/ncep_global.graph

See Also

wind.mean, wind2raster, wind.dl, as_datetime, as.POSIXct

Examples


# Download two time steps for a small region in southern Iberia

dt <- as.POSIXct(c("2015-02-12 09:00:00", "2015-02-12 12:00:00"),
  tz = "UTC")
ww <- wind.dl_2(dt, -7, -4, 34.5, 37.5)
tidy(ww)

wind.fit_int

Description

wind.fit_int is used internally by wind.dl to transform downloaded data from GFS. wind.fit_int applies trigonometry tools to transform U and V vector wind components in wind direction and speed features. It also transforms 0-360 longitude notation obtained from GFS data into -180/180 longitude notation. Moreover, it cleans dates names and sorts the data by latitude.

Usage

wind.fit_int(tmpx)

Arguments

tmpx

downloaded data produced by wind.dl.

Value

data.frame

Note

This function is used internally by wind.dl

Author(s)

Javier Fernández-López (jflopez.bio@gmail.com)

References

https://en.wikipedia.org/wiki/Cross_product

See Also

wind.dl, wind.mean, wind2raster

Wind-data mean

Description

wind.mean computes the mean (average) wind speed and wind direction of a time series dataset of winds of the same region. Summaries of time series are not trivial to compute. We compute the arithmetic mean for the wind speed. The direction as the circular mean, see https://en.wikipedia.org/wiki/Circular_mean for more details. The U and V components are afterwards transformed from these values.

Usage

wind.mean(x)

Arguments

x

An object of class rWind_series

Value

An object of classes rWind and data.frame, with one row per grid cell and columns time, lat, lon, ugrd10m, vgrd10m, dir, and speed. Speed is the arithmetic mean across time steps (m/s), and direction is the circular mean (degrees clockwise from north, towards where the wind moves). U and V (m/s) are derived from these mean values. Coordinates are in degrees, and time records the first time step (UTC).

Note

For large time series, it could take a while.

Author(s)

Javier Fernández-López (jflopez.bio@gmail.com)

References

https://en.wikipedia.org/wiki/Cross_product

See Also

wind.dl

Examples

data(wind.series)
wind_average <- wind.mean(wind.series)

Wind series example

Description

This is an example of a wind series data obtained with wind.dl function for New Zealand area on 3/January/2015 at all the available times: 00:00 - 03:00 - 06:00 - 09:00 - 12:00 - 15:00 - 18:00 - 21:00 (UTC)

Format

The format is an rWind list of 8 data.frame. Each data.frame contain 961 observations on the following 7 variables:

list("time (UTC)")

a factor with selected time of wind data

list("latitude (degrees_north)")

a factor with latitude values

list("longitude (degrees_east)")

a factor with longitude values

list("ugrd10m (m s-1)")

a factor with U component of wind data

list("vgrd10m (m s-1)")

a factor with V component of wind data

list("dir")

a numeric with direction of wind data

list("speed")

a numeric with speed of wind data

Details

This data set is the result of:

library(lubridate) dt <- seq(ymd_h(paste(2015,1,3,00, sep="-")), ymd_h(paste(2015,1,3,21, sep="-")),by="3 hours") wind.series <- wind.dl_2(dt, 164, 179, -48, -33)

Source

https://allthiswasfield.blogspot.com/2016/12/rwind-r-package-released.html

References

https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast

Examples


data(wind.series)
str(tidy(wind.series))

Wind data to terra rasters

Description

wind2raster() converts an rWind object to a two-layer terra::SpatRaster containing wind direction and speed. An rWind_series is converted to a list of SpatRaster objects, one per time step. All outputs use WGS 84 (EPSG:4326).

Usage

wind2raster(x)

Arguments

x

An object of class rWind or rWind_series.

Value

A terra::SpatRaster, or a list of them, with layers direction and speed.

Author(s)

Javier Fernández-López (jflopez.bio@gmail.com)

See Also

wind.dl

Examples


data(wind.data)

# Create a SpatRaster with wind direction and speed layers

wind2raster(wind.data)

Wind data to a terra raster

Description

wind2raster_int() converts one gridded rWind data frame to a two-layer terra::SpatRaster. Longitude and latitude define the grid and the output layers contain wind direction and speed. The coordinate reference system is WGS 84 (EPSG:4326).

Usage

wind2raster_int(x)

Arguments

x

an object of class rWind

Value

A two-layer terra::SpatRaster named direction and speed.

Author(s)

Javier Fernández-López (jflopez.bio@gmail.com)

See Also

wind.dl, wind2raster