Package 'chirps' reference manual

Title:	Access Climate Hazards Center Datasets
Description:	Provides access to Climate Hazards Center (CHC) datasets including CHIRPS v3, CHIRPS v2, CHIRTS-daily and CHIRTS-ERA5, read directly from the CHC server. 'CHIRPS' is a quasi-global (60°S to 60°N for v3) high-resolution (0.05 arc-degrees) rainfall data set, which incorporates satellite imagery and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. 'CHIRTS' is a quasi-global (60°S to 70°N), high-resolution data set of daily maximum and minimum temperatures. Data are returned as rasters or as time series for points and polygons, ready for climate indices with the 'climatrends' package. For more details on CHC datasets please visit their official home page <https://www.chc.ucsb.edu/data>.
Authors:	Kauê de Sousa [aut, cre] (ORCID: <https://orcid.org/0000-0002-7571-7845>), Adam H. Sparks [aut] (ORCID: <https://orcid.org/0000-0002-0061-8359>), Aniruddha Ghosh [aut] (ORCID: <https://orcid.org/0000-0003-3667-8019>), Pete Peterson [ctb] (API Client implementation), William Ashmall [ctb] (API Client implementation), Jacob van Etten [ths] (ORCID: <https://orcid.org/0000-0001-7554-2558>), Svein Ø. Solberg [ths] (ORCID: <https://orcid.org/0000-0002-4491-4483>), Emanuele Cordano [aut] (ORCID: <https://orcid.org/0000-0002-3508-5898>)
Maintainer:	Kauê de Sousa <[email protected]>
License:	MIT + file LICENSE
Version:	2.0.0
Built:	2026-11-03 18:18:02 UTC
Source:	https://github.com/ropensci/chirps

Methods to coerce geographical coordinates into a geojson polygon

Description

Take single points from geographical coordinates and coerce into a geojson of geometry 'Polygon'

Usage

as.geojson(lonlat, dist = 1e-05, nQuadSegs = 2L, ...)

## Default S3 method:
as.geojson(lonlat, dist = 1e-05, nQuadSegs = 2L, ...)

## S3 method for class 'sf'
as.geojson(lonlat, dist = 1e-05, nQuadSegs = 2L, ...)
as.geojson(lonlat, dist = 1e-05, nQuadSegs = 2L, ...)

## Default S3 method:
as.geojson(lonlat, dist = 1e-05, nQuadSegs = 2L, ...)

## S3 method for class 'sf'
as.geojson(lonlat, dist = 1e-05, nQuadSegs = 2L, ...)

Arguments

lonlat

a data.frame or matrix with geographical coordinates 'lonlat', in that order, or an object of class sf with geometry type 'POINT' or 'POLYGON'

dist

numeric, buffer distance for all lonlat

nQuadSegs

integer, number of segments per quadrant

...

further arguments passed to sf methods

Value

An object of class 'geosjon' for each row in lonlat

Examples


# Default S3 Method
# random geographic points within bbox(10, 12, 45, 47)
library("sf")

set.seed(123)
lonlat = data.frame(lon = runif(1, 10, 12),
                     lat = runif(1, 45, 47))

gjson = as.geojson(lonlat)

#################

# S3 Method for objects of class 'sf'
# random geographic points within bbox(10, 12, 45, 47)
library("sf")

set.seed(123)
lonlat = data.frame(lon = runif(5, 10, 12),
                     lat = runif(5, 45, 47))

lonlat = st_as_sf(lonlat, coords = c("lon","lat"))

gjson = as.geojson(lonlat)

# Default S3 Method
# random geographic points within bbox(10, 12, 45, 47)
library("sf")

set.seed(123)
lonlat = data.frame(lon = runif(1, 10, 12),
                     lat = runif(1, 45, 47))

gjson = as.geojson(lonlat)

#################

# S3 Method for objects of class 'sf'
# random geographic points within bbox(10, 12, 45, 47)
library("sf")

set.seed(123)
lonlat = data.frame(lon = runif(5, 10, 12),
                     lat = runif(5, 45, 47))

lonlat = st_as_sf(lonlat, coords = c("lon","lat"))

gjson = as.geojson(lonlat)

API Client for CHIRPS and CHIRTS

Description

logo

Provides access to Climate Hazards Center (CHC) datasets including CHIRPS v3, CHIRPS v2, CHIRTS-daily and CHIRTS-ERA5, read directly from the CHC server. 'CHIRPS' is a quasi-global (60°S to 60°N for v3) high-resolution (0.05 arc-degrees) rainfall data set, which incorporates satellite imagery and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. 'CHIRTS' is a quasi-global (60°S to 70°N), high-resolution data set of daily maximum and minimum temperatures. Data are returned as rasters or as time series for points and polygons, ready for climate indices with the 'climatrends' package. For more details on CHC datasets please visit their official home page https://www.chc.ucsb.edu/data.

Note

While chirps does not redistribute the data or provide it in any way, we encourage users to cite Funk et al. (2015) when using CHIRPS and Funk et al. (2019) when using CHIRTS.

Funk et al. (2015). Scientific Data, 2, 150066. doi:10.1038/sdata.2015.66

Funk et al. (2019). Journal of Climate, 32(17), 5639-5658. doi:10.1175/JCLI-D-18-0698.1

Author(s)

Kauê de Sousa and Adam H. Sparks and Aniruddha Ghosh

Get CHIRPS precipitation data

Description

Get daily rainfall from CHIRPS, produced by the Climate Hazards Center (CHC), for points, polygons or an area. The data are read from the CHC server, which is the main source and serves both CHIRPS v3 (default) and CHIRPS v2.

Usage

get_chirps(object, dates, server = "CHC", ...)

## Default S3 method:
get_chirps(object, dates, server = "CHC", as.matrix = FALSE, ...)

## S3 method for class 'SpatVector'
get_chirps(object, dates, server = "CHC", as.raster = TRUE, ...)

## S3 method for class 'SpatRaster'
get_chirps(object, dates, server = "CHC", as.raster = TRUE, ...)

## S3 method for class 'SpatExtent'
get_chirps(object, dates, server = "CHC", as.raster = TRUE, ...)

## S3 method for class 'sf'
get_chirps(object, dates, server = "CHC", as.sf = FALSE, ...)

## S3 method for class 'sfc'
get_chirps(object, dates, server = "CHC", as.sf = FALSE, ...)

## S3 method for class 'geojson'
get_chirps(object, dates, server = "CHC", as.geojson = FALSE, ...)
get_chirps(object, dates, server = "CHC", ...)

## Default S3 method:
get_chirps(object, dates, server = "CHC", as.matrix = FALSE, ...)

## S3 method for class 'SpatVector'
get_chirps(object, dates, server = "CHC", as.raster = TRUE, ...)

## S3 method for class 'SpatRaster'
get_chirps(object, dates, server = "CHC", as.raster = TRUE, ...)

## S3 method for class 'SpatExtent'
get_chirps(object, dates, server = "CHC", as.raster = TRUE, ...)

## S3 method for class 'sf'
get_chirps(object, dates, server = "CHC", as.sf = FALSE, ...)

## S3 method for class 'sfc'
get_chirps(object, dates, server = "CHC", as.sf = FALSE, ...)

## S3 method for class 'geojson'
get_chirps(object, dates, server = "CHC", as.geojson = FALSE, ...)

Arguments

object

input, an object of class data.frame (or any other object that can be coerced to data.frame) with longitude and latitude in the first two columns, SpatVector, SpatRaster, SpatExtent, sf, sfc or geojson

dates

a character or Date vector "YYYY-MM-DD", with one date for a single day or two dates for the start and end of the period

server

a character, "CHC" (default and recommended) or "ClimateSERV". See the section on servers

...

additional arguments passed to get_chirps_raw and extract. See details

as.matrix

logical, returns a matrix with one row per location and one column per day, named "YYYY-MM-DD"

as.raster

logical, returns an object of class SpatRaster

as.sf

logical, returns an object of class sf

as.geojson

logical, returns an object of class geojson

Details

Additional arguments

version: CHIRPS version, "3.0" (default) or "2.0". CHC retires CHIRPS v2 after the December 2026 release

type: CHIRPS v3 product, "rnl" (default) or "sat". See get_chirps_raw

resolution: numeric, resolution of CHIRPS v2, 0.05 (default) or 0.25 degrees

operation: for polygons, the function used to summarise the cells of each polygon on each day

operation		value
max	=	0
min	=	1
median	=	2
sum	=	4
average	=	5 (default value)

Coverage: CHIRPS v3 covers 60°S to 60°N and CHIRPS v2 covers 50°S to 50°N, from 1981 to near present. Final data for a month arrive some weeks after it ends.

Polygons: with sf, SpatVector or geojson polygons, each day is summarised over the cells that touch the polygon, with the function set by operation. The lon and lat columns give the centroid of each polygon.

Messages: each request states what it downloads, here daily rainfall from CHIRPS, with the period, so it is not mistaken for temperature from get_chirts. Set options(chirps.quiet = TRUE) to hide these messages.

Indices: for rainfall indices (dry spells, heavy rain days, intensity) use climatrends::rainfall(). The matrix returned with as.matrix = TRUE is its input, see the examples.

Value

A data frame of CHIRPS data in the long format, ordered by id and date, or a matrix, raster, sf or geojson object as requested:

id: the index for the rows in object
date: the date of each value
lon: the longitude as provided in object
lat: the latitude as provided in object
chirps: the CHIRPS value in mm

Servers

CHC (default) reads the CHIRPS files from https://data.chc.ucsb.edu through terra. It serves CHIRPS v3 and v2, gives the value of the cell that holds each point, and handles many points and long periods in one call. It is the recommended source.

ClimateSERV is kept for backward compatibility. It serves CHIRPS v2 only, averaged over a small buffer around each point, so its values can differ from the CHC values. It sends several requests per location and is slow for more than a few points. ClimateSERV support for CHIRPS may be removed in a future release. Its extra arguments are dist, the buffer distance around each point, nQuadSegs, the number of segments per buffer quadrant, and operation as above.

SpatVector, SpatRaster and SpatExtent input always uses CHC.

References

Funk, C., Peterson, P., Harrison, L. et al. (2026). Scientific Data, 13, 718.
doi:10.1038/s41597-026-07096-4

Funk C. et al. (2015). Scientific Data, 2, 150066.
doi:10.1038/sdata.2015.66

Examples


library("chirps")
library("terra")

dates = c("2017-12-15", "2017-12-31")
lonlat = data.frame(lon = c(-55.0281, -54.9857), lat = c(-2.8094, -2.8756))

# Case 1: return a data.frame in the long format
r1 = get_chirps(lonlat, dates)

# Case 2: a single day, from the CHIRPS v3 product based on IMERG
r2 = get_chirps(lonlat, "2017-12-15", type = "sat")

# Case 3: return a matrix, one column per day
r3 = get_chirps(lonlat, dates, as.matrix = TRUE)

# Case 4: input SpatVector and return raster
f = system.file("ex/lux.shp", package = "terra")
v = vect(f)
r4 = get_chirps(v, dates, as.raster = TRUE)

# Case 5: input SpatExtent and return a raster within the extent
area = ext(c(-66, -64, -6, -4))
r5 = get_chirps(area, dates)

# Case 6: total rainfall per polygon and day
r6 = get_chirps(v, dates, as.raster = FALSE, operation = 4)

# Case 7: rainfall indices with climatrends
if (requireNamespace("climatrends", quietly = TRUE)) {
  climatrends::rainfall(r3, day.one = dates[1], span = ncol(r3))
}

library("chirps")
library("terra")

dates = c("2017-12-15", "2017-12-31")
lonlat = data.frame(lon = c(-55.0281, -54.9857), lat = c(-2.8094, -2.8756))

# Case 1: return a data.frame in the long format
r1 = get_chirps(lonlat, dates)

# Case 2: a single day, from the CHIRPS v3 product based on IMERG
r2 = get_chirps(lonlat, "2017-12-15", type = "sat")

# Case 3: return a matrix, one column per day
r3 = get_chirps(lonlat, dates, as.matrix = TRUE)

# Case 4: input SpatVector and return raster
f = system.file("ex/lux.shp", package = "terra")
v = vect(f)
r4 = get_chirps(v, dates, as.raster = TRUE)

# Case 5: input SpatExtent and return a raster within the extent
area = ext(c(-66, -64, -6, -4))
r5 = get_chirps(area, dates)

# Case 6: total rainfall per polygon and day
r6 = get_chirps(v, dates, as.raster = FALSE, operation = 4)

# Case 7: rainfall indices with climatrends
if (requireNamespace("climatrends", quietly = TRUE)) {
  climatrends::rainfall(r3, day.one = dates[1], span = ncol(r3))
}

Get raw CHIRPS raster data

Description

Download Global CHIRPS precipitation data in its original raster format.

Usage

get_chirps_raw(
  dates,
  version = "3.0",
  resolution = 0.05,
  type = c("rnl", "sat"),
  verbose = FALSE,
  ...
)
get_chirps_raw(
  dates,
  version = "3.0",
  resolution = 0.05,
  type = c("rnl", "sat"),
  verbose = FALSE,
  ...
)

Arguments

dates

Character vector of length two with start and end dates in "YYYY-MM-DD" format.

version

Character or numeric. CHIRPS version. Supported values are "3.0" (default) and "2.0". CHC retires CHIRPS v2 after the December 2026 release.

resolution

Numeric. Spatial resolution for CHIRPS v2. Supported values are 0.05 and 0.25. Ignored for CHIRPS v3.

type

Character. CHIRPS v3 product type. Supported values are "rnl" and "sat". Ignored for CHIRPS v2.

verbose

Logical. If TRUE, prints the URLs used.

...

Additional arguments reserved for future use.

Details

This function returns CHIRPS raster layers as a SpatRaster without extracting values, reshaping the data, or converting it to a data frame. It is intended for users who need direct access to the original raster product for custom spatial workflows.

CHIRPS v3 (Climate Hazards Center Infrared Precipitation with Stations) is a high-resolution (0.05°), quasi-global rainfall dataset spanning 60°N to 60°S from 1981 to near-present.

CHIRPS v3 combines satellite-based thermal infrared rainfall estimates with in-situ station observations to produce gridded precipitation estimates over land.

Two daily products are available:

rnl: Daily values derived using ECMWF ERA5 precipitation to partition pentadal CHIRPS totals into daily amounts.
sat: Daily values derived using NASA IMERG precipitation to partition pentadal CHIRPS totals into daily amounts.

Additional information is available at: https://www.chc.ucsb.edu/data/chirps3

Daily product documentation: https://data.chc.ucsb.edu/products/CHIRPS/v3.0/daily/readme.txt

Value

A SpatRaster with one layer per day.

References

Climate Hazards Center Infrared Precipitation with Stations version 3 (CHIRPS3) Data Repository. doi:10.15780/G2JQ0P

Version 3 Funk, C., Peterson, P., Harrison, L. et al. (2026). The Climate Hazards Center Infrared Precipitation with Stations, Version 3. Scientific Data, 13, 718. doi:10.1038/s41597-026-07096-4

Version 2 Funk C. et al. (2015). Scientific Data, 2, 150066. doi:10.1038/sdata.2015.66

Examples

if (interactive()) {
r1 = get_chirps_raw(dates = c("2017-12-15", "2017-12-31"),
                    version = "3.0",
                    type = "rnl")

r2 = get_chirps_raw(dates = c("2017-12-15", "2017-12-31"),
                    version = "3.0",
                    type = "sat")
}

if (interactive()) {
r1 = get_chirps_raw(dates = c("2017-12-15", "2017-12-31"),
                    version = "3.0",
                    type = "rnl")

r2 = get_chirps_raw(dates = c("2017-12-15", "2017-12-31"),
                    version = "3.0",
                    type = "sat")
}

Get CHIRTS temperature data

Description

Get daily temperature from the Climate Hazards Center (CHC) for points, polygons or an area. Two products are available. CHIRTS-daily (default) covers 1983 to 2016 and has maximum and minimum temperature, relative humidity and heat index. CHIRTS-ERA5 extends maximum and minimum temperature from 1980 to near present.

Usage

get_chirts(object, dates, var, ...)

## Default S3 method:
get_chirts(object, dates, var, as.matrix = FALSE, ...)

## S3 method for class 'SpatVector'
get_chirts(object, dates, var, as.raster = TRUE, ...)

## S3 method for class 'SpatRaster'
get_chirts(object, dates, var, as.raster = TRUE, ...)

## S3 method for class 'SpatExtent'
get_chirts(object, dates, var, as.raster = TRUE, ...)
get_chirts(object, dates, var, ...)

## Default S3 method:
get_chirts(object, dates, var, as.matrix = FALSE, ...)

## S3 method for class 'SpatVector'
get_chirts(object, dates, var, as.raster = TRUE, ...)

## S3 method for class 'SpatRaster'
get_chirts(object, dates, var, as.raster = TRUE, ...)

## S3 method for class 'SpatExtent'
get_chirts(object, dates, var, as.raster = TRUE, ...)

Arguments

object

an object of class data.frame (or any other object that can be coerced to a data.frame) with longitude and latitude in the first two columns, sf points, SpatVector, SpatRaster or SpatExtent

dates

a character or Date vector "YYYY-MM-DD", with one date for a single day or two dates for the start and end of the period

var

character, A valid variable from the options: “Tmax”, “Tmin”, “RHum” and “HeatIndex”. CHIRTS-ERA5 has “Tmax” and “Tmin” only

...

further arguments passed to get_chirts_raw and extract. See details

as.matrix

logical, returns a matrix with one row per location and one column per day, named "YYYY-MM-DD"

as.raster

logical, returns an object of class SpatRaster

Details

Variable description from https://data.chc.ucsb.edu/products/CHIRTS/daily/aaa.Readme.txt

Tmax: Daily average maximum air temperature at 2 m above ground
Tmin: Daily average minimum air temperature at 2 m above ground
RHum: Daily average relative humidity
HeatIndex: Daily average heat index

Additional arguments

product: “CHIRTS-daily” (default, 1983 to 2016) or “CHIRTS-ERA5” (1980 to near present, Tmax and Tmin)

operation: for polygons, the function used to summarise the cells of each polygon on each day, with the codes listed in get_chirps (average by default)

Coverage: both products cover 60°S to 70°N.

Messages: each request states what it downloads, here daily temperature or humidity from CHIRTS, with the period, so it is not mistaken for rainfall from get_chirps. Set options(chirps.quiet = TRUE) to hide these messages.

Indices: for temperature indices (growing degree days, crop sensitive indices, frost and heat days) use the climatrends package, which takes the matrices returned with as.matrix = TRUE.

Value

A data frame in the long format, ordered by id and date, with the value in the column chirts. A SpatRaster if as.raster = TRUE, or a matrix with one column per day, named "YYYY-MM-DD", if as.matrix = TRUE

Examples


library("chirps")
library("terra")

# Case 1: input a data frame return a data frame in the long format
dates = c("2010-12-15","2010-12-31")
lonlat = data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

temp1 = get_chirts(lonlat, dates, var = "Tmax")

# Case 2: input a data frame return a matrix
temp2 = get_chirts(lonlat, dates, "Tmax", as.matrix = TRUE)

# Case 3: recent dates from CHIRTS-ERA5
temp3 = get_chirts(lonlat, c("2024-01-01", "2024-01-31"), var = "Tmin",
                   product = "CHIRTS-ERA5")

# Case 4: input a SpatVector and return raster
f = system.file("ex/lux.shp", package="terra")
v = vect(f)
temp4 = get_chirts(v, dates, var = "Tmax", as.raster = TRUE)

# Case 5: average per polygon and day
temp5 = get_chirts(v, dates, var = "Tmax", as.raster = FALSE)

# Case 6: indices with climatrends
if (requireNamespace("climatrends", quietly = TRUE)) {
  tmin = get_chirts(lonlat, dates, var = "Tmin", as.matrix = TRUE)
  temp = array(c(temp2, tmin), dim = c(dim(temp2), 2),
               dimnames = list(NULL, colnames(temp2), NULL))
  climatrends::temperature(temp, day.one = dates[1], span = ncol(temp2))
}

library("chirps")
library("terra")

# Case 1: input a data frame return a data frame in the long format
dates = c("2010-12-15","2010-12-31")
lonlat = data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

temp1 = get_chirts(lonlat, dates, var = "Tmax")

# Case 2: input a data frame return a matrix
temp2 = get_chirts(lonlat, dates, "Tmax", as.matrix = TRUE)

# Case 3: recent dates from CHIRTS-ERA5
temp3 = get_chirts(lonlat, c("2024-01-01", "2024-01-31"), var = "Tmin",
                   product = "CHIRTS-ERA5")

# Case 4: input a SpatVector and return raster
f = system.file("ex/lux.shp", package="terra")
v = vect(f)
temp4 = get_chirts(v, dates, var = "Tmax", as.raster = TRUE)

# Case 5: average per polygon and day
temp5 = get_chirts(v, dates, var = "Tmax", as.raster = FALSE)

# Case 6: indices with climatrends
if (requireNamespace("climatrends", quietly = TRUE)) {
  tmin = get_chirts(lonlat, dates, var = "Tmin", as.matrix = TRUE)
  temp = array(c(temp2, tmin), dim = c(dim(temp2), 2),
               dimnames = list(NULL, colnames(temp2), NULL))
  climatrends::temperature(temp, day.one = dates[1], span = ncol(temp2))
}

Get raw CHIRTS raster data

Description

Download CHIRTS daily temperature data in its original raster format, from CHIRTS-daily (default) or CHIRTS-ERA5.

Usage

get_chirts_raw(
  dates,
  var,
  product = c("CHIRTS-daily", "CHIRTS-ERA5"),
  resolution = 0.05,
  coverage = "global",
  interval = "daily",
  format = "tifs",
  version = "1.0",
  use_vsicurl = TRUE,
  verbose = FALSE,
  ...
)
get_chirts_raw(
  dates,
  var,
  product = c("CHIRTS-daily", "CHIRTS-ERA5"),
  resolution = 0.05,
  coverage = "global",
  interval = "daily",
  format = "tifs",
  version = "1.0",
  use_vsicurl = TRUE,
  verbose = FALSE,
  ...
)

Arguments

dates

Character or Date vector "YYYY-MM-DD", with one date for a single day or two dates for the start and end of the period.

var

Character. CHIRTS variable. Supported values are "Tmax", "Tmin", "RHum", and "HeatIndex". CHIRTS-ERA5 has "Tmax" and "Tmin" only.

product

Character. "CHIRTS-daily" (default, 1983 to 2016) or "CHIRTS-ERA5" (1980 to near present).

resolution

Numeric. Spatial resolution. Default is 0.05. CHIRTS-daily only.

coverage

Character. Spatial coverage. Default is "global". CHIRTS-daily only.

interval

Character. Temporal interval. Default is "daily". Currently retained for API consistency.

format

Character. File format folder. Default is "tifs". CHIRTS-daily only.

version

Character or numeric. CHIRTS-daily version. Default is "1.0".

use_vsicurl

Logical. If TRUE, prepends "/vsicurl" to remote URLs.

verbose

Logical. If TRUE, prints the URLs used.

...

Additional arguments reserved for future use.

Details

This function returns CHIRTS raster layers as a SpatRaster without extracting values, reshaping the data, or converting it to a data frame.

CHIRTS-daily (Climate Hazards Center Infrared Temperature with Stations) is a quasi-global (60°S to 70°N), high-resolution (0.05 degrees) dataset of daily maximum and minimum temperatures, relative humidity and heat index covering 1983-2016. It combines monthly CHIRTS temperature fields with daily temperature variability from the ERA5 reanalysis. Daily minimum temperatures are derived using the ERA5 diurnal temperature range. See https://www.chc.ucsb.edu/data/chirtsdaily.

CHIRTS-ERA5 is a quasi-global (60°S to 70°N), high-resolution (0.05 degrees) dataset of daily maximum and minimum temperatures from 1980 to near present. It combines CHIRTS (1983-2016) with the regularly updated ERA5 reanalysis, downscaled and bias-corrected relative to CHIRTS. It is an experimental CHC product. Users should cite "CHIRTS-ERA5 Data Repository. https://doi.org/10.15780/G2F08J. Data accessed on [DATE]." See https://www.chc.ucsb.edu/data/chirts-era5.

Value

A SpatRaster with one layer per day.

References

Verdin, A., Funk, C., Peterson, P., Landsfeld, M., Tuholske, C., and Grace, K. (2020). Development and validation of the CHIRTS-daily quasi-global high-resolution daily temperature data set. Scientific Data, 7, 303. doi:10.1038/s41597-020-00643-7

Funk, C., Peterson, P., Peterson, S., Shukla, S., Davenport, F., Michaelsen, J., et al. (2019). A High-Resolution 1983-2016 Tmax Climate Data Record Based on Infrared Temperatures and Stations by the Climate Hazards Center. Journal of Climate, 32, 5639-5658. doi:10.1175/JCLI-D-18-0698.1

Climate Hazards Center (CHC). CHIRTS-ERA5 Data Repository. doi:10.15780/G2F08J

Examples

if (interactive()) {
  tmax = get_chirts_raw(dates = c("2015-01-01", "2015-01-31"),
                        var = "Tmax")

  # recent dates from CHIRTS-ERA5
  tmin = get_chirts_raw(dates = c("2024-01-01", "2024-01-31"),
                        var = "Tmin",
                        product = "CHIRTS-ERA5")
}

if (interactive()) {
  tmax = get_chirts_raw(dates = c("2015-01-01", "2015-01-31"),
                        var = "Tmax")

  # recent dates from CHIRTS-ERA5
  tmin = get_chirts_raw(dates = c("2024-01-01", "2024-01-31"),
                        var = "Tmin",
                        product = "CHIRTS-ERA5")
}

Get evaporative stress index (ESI) data

Description

Get evaporative stress index (ESI) from SERVIR Global via ClimateSERV API Client. ESI is available every four (or twelve) weeks from 2001 to present. The dataset may contain cloudy data which is returned as NAs. ClimateSERV works with 'geojson' of type 'Polygon'. The input object is then transformed into polygons with a small buffer area around the point.

Usage

get_esi(object, dates, operation = 5, period = 1, ...)

## Default S3 method:
get_esi(object, dates, operation = 5, period = 1, ...)

## S3 method for class 'sf'
get_esi(object, dates, operation = 5, period = 1, as.sf = FALSE, ...)

## S3 method for class 'geojson'
get_esi(object, dates, operation = 5, period = 1, as.geojson = FALSE, ...)
get_esi(object, dates, operation = 5, period = 1, ...)

## Default S3 method:
get_esi(object, dates, operation = 5, period = 1, ...)

## S3 method for class 'sf'
get_esi(object, dates, operation = 5, period = 1, as.sf = FALSE, ...)

## S3 method for class 'geojson'
get_esi(object, dates, operation = 5, period = 1, as.geojson = FALSE, ...)

Arguments

object

dates

a character or Date vector "YYYY-MM-DD", with one date for a single day or two dates for the start and end of the period

operation

optional, an integer that represents which type of statistical operation to perform on the dataset

period

an integer value for the period of ESI data, four weeks period = 1, twelve weeks = 2

...

additional arguments passed to get_chirps_raw and extract. See details

as.sf

logical, returns an object of class sf

as.geojson

logical, returns an object of class geojson

Details

operation: supported operations are:

operation		value
max	=	0
min	=	1
median	=	2
sum	=	4
average	=	5 (default value)

dist: numeric, buffer distance for each object coordinate

nQuadSegs: integer, number of segments per buffer quadrant

Value

A data frame of ESI data:

id

the index for the rows in object

dates

the dates from which ESI was requested

lon

the longitude as provided in object

lat

the latitude as provided in object

esi

the ESI value

Future deprecation

get_esi() reads from the ClimateSERV API, which chirps no longer recommends. get_esi() will be deprecated in a future release, and a notice says so once per session. CHIRPS rainfall is available from the CHC server with get_chirps().

Note

get_esi() may return some warning messages given by sf, please check the sf documentation for possible issues.

Examples



lonlat = data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

dates = c("2017-12-15","2018-06-20")

# by default the function sets a very small buffer around the points which
# can return NAs due to cloudiness in ESI data

dt = get_esi(lonlat, dates = dates)

# the argument dist passed through sf increase the buffer area

dt = get_esi(lonlat, dates = dates, dist = 0.1)

lonlat = data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

dates = c("2017-12-15","2018-06-20")

# by default the function sets a very small buffer around the points which
# can return NAs due to cloudiness in ESI data

dt = get_esi(lonlat, dates = dates)

# the argument dist passed through sf increase the buffer area

dt = get_esi(lonlat, dates = dates, dist = 0.1)

Get Integrated Multisatellite Retrievals for GPM (IMERG) data

Description

The IMERG dataset provides near-real time global observations of rainfall at 10km resolution, which can be used to estimate total rainfall accumulation from storm systems and quantify the intensity of rainfall and flood impacts from tropical cyclones and other storm systems. IMERG is a daily precipitation dataset available from 2015 to present within the latitudes 70 and -70 degrees.

Usage

get_imerg(object, dates, operation = 5, ...)

## Default S3 method:
get_imerg(object, dates, operation = 5, ...)

## S3 method for class 'sf'
get_imerg(object, dates, operation = 5, as.sf = FALSE, ...)

## S3 method for class 'geojson'
get_imerg(object, dates, operation = 5, as.geojson = FALSE, ...)
get_imerg(object, dates, operation = 5, ...)

## Default S3 method:
get_imerg(object, dates, operation = 5, ...)

## S3 method for class 'sf'
get_imerg(object, dates, operation = 5, as.sf = FALSE, ...)

## S3 method for class 'geojson'
get_imerg(object, dates, operation = 5, as.geojson = FALSE, ...)

Arguments

object

dates

a character or Date vector "YYYY-MM-DD", with one date for a single day or two dates for the start and end of the period

operation

optional, an integer that represents which type of statistical operation to perform on the dataset

...

additional arguments passed to get_chirps_raw and extract. See details

as.sf

logical, returns an object of class sf

as.geojson

logical, returns an object of class geojson

Details

operation: supported operations are:

operation		value
max	=	0
min	=	1
median	=	2
sum	=	4
average	=	5 (default value)

dist: numeric, buffer distance for each object coordinate

nQuadSegs: integer, number of segments per buffer quadrant

Value

A data frame of imerg data:

id

the index for the rows in object

dates

the dates from which imerg was requested

lon

the longitude as provided in object

lat

the latitude as provided in object

imerg

the IMERG value

Future deprecation

get_imerg() reads from the ClimateSERV API, which chirps no longer recommends. get_imerg() will be deprecated in a future release, and a notice says so once per session. CHIRPS rainfall is available from the CHC server with get_chirps().

Examples


lonlat = data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

dates = c("2017-12-15", "2017-12-31")

dt = get_imerg(lonlat, dates)

dt

lonlat = data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

dates = c("2017-12-15", "2017-12-31")

dt = get_imerg(lonlat, dates)

dt

Compute simple precipitation indices over a time series

Description

A simple example of how to compute rainfall indices from the data returned by get_chirps or get_imerg. For analysis, use the climatrends package instead. climatrends::rainfall() computes the same indices with tested definitions and flexible periods per location, and climatrends also has temperature, growing degree days, evapotranspiration and crop sensitive indices. See the examples for how to pass CHIRPS data to it.

Usage

precip_indices(object, timeseries = FALSE, intervals = NULL)
precip_indices(object, timeseries = FALSE, intervals = NULL)

Arguments

object

an object of class chirps as provided by get_chirps

timeseries

logical, FALSE for a single point time series observation or TRUE for a time series based on intervals

intervals

integer no lower than 5, for the days intervals when timeseries = TRUE

Value

A data frame with precipitation indices:

MLDS

maximum length of consecutive dry day, rain < 1 mm (days)

MLWS

maximum length of consecutive wet days, rain >= 1 mm (days)

R10mm

number of heavy precipitation days 10 >= rain < 20 mm (days)

R20mm

number of very heavy precipitation days rain >= 20 (days)

Rx1day

maximum 1-day precipitation (mm)

Rx5day

maximum 5-day precipitation (mm)

R95p

total precipitation when rain > 95th percentile (mm), with the percentile taken over all days in the period, dry days included

R99p

total precipitation when rain > 99th percentile (mm), with the percentile taken over all days in the period, dry days included

Rtotal

total precipitation (mm) in wet days, rain >= 1 (mm)

SDII

simple daily intensity index, total precipitation in wet days divided by the number of wet days (mm/days)

References

Aguilar E., et al. (2005). Journal of Geophysical Research, 110(D23), D23107.

de Sousa K., van Etten J., Neby M., Solberg S. Ø. (2023). Climate variability indices for ecological and crop models in R: the climatrends package. Journal of Open Source Software, 8(85), 4405. doi:10.21105/joss.04405

Kehel Z., et al. (2016). In: Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits (eds Bari A., Damania A. B., Mackay M., Dayanandan S.), pp. 151-174. CRC Press.

Examples


lonlat = data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

dates = c("2017-12-15", "2017-12-31")

dt = get_chirps(lonlat, dates)

# take the indices for the entire period
precip_indices(dt, timeseries = FALSE)

# take the indices for periods of 7 days
precip_indices(dt, timeseries = TRUE, intervals = 7)

# recommended, the same data in climatrends
if (requireNamespace("climatrends", quietly = TRUE)) {
  rain = get_chirps(lonlat, dates, as.matrix = TRUE)
  climatrends::rainfall(rain, day.one = dates[1], span = ncol(rain))
}

lonlat = data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

dates = c("2017-12-15", "2017-12-31")

dt = get_chirps(lonlat, dates)

# take the indices for the entire period
precip_indices(dt, timeseries = FALSE)

# take the indices for periods of 7 days
precip_indices(dt, timeseries = TRUE, intervals = 7)

# recommended, the same data in climatrends
if (requireNamespace("climatrends", quietly = TRUE)) {
  rain = get_chirps(lonlat, dates, as.matrix = TRUE)
  climatrends::rainfall(rain, day.one = dates[1], span = ncol(rain))
}

Tapajós National Forest

Description

Geometries for the Tapajós National Forest, a protected area in the Brazilian Amazon

Usage

tapajos
tapajos

Format

An object of class 'sfc_POLYGON' within the bounding box xmin: -55.41127 ymin: -4.114584 xmax: -54.7973 ymax: -2.751706

Source

The data was provided by the Chico Mendes Institute for Biodiversity Conservation (ICMBio) via https://www.protectedplanet.net/en

Package 'chirps'

Help Index

Methods to coerce geographical coordinates into a geojson polygon

Description

Usage

Arguments

Value

Examples

API Client for CHIRPS and CHIRTS

Description

Note

Author(s)

See Also

Get CHIRPS precipitation data

Description

Usage

Arguments

Details

Value

Servers

References

See Also

Examples

Get raw CHIRPS raster data

Description

Usage

Arguments

Details

Value

References

Examples

Get CHIRTS temperature data

Description

Usage

Arguments

Details

Value

See Also

Examples

Get raw CHIRTS raster data

Description

Usage

Arguments

Details

Value

References

Examples

Get evaporative stress index (ESI) data

Description

Usage

Arguments

Details

Value

Future deprecation

Note

Examples

Get Integrated Multisatellite Retrievals for GPM (IMERG) data

Description

Usage

Arguments

Details

Value

Future deprecation

Examples

Compute simple precipitation indices over a time series

Description

Usage

Arguments

Value

References

See Also

Examples

Tapajós National Forest

Description

Usage

Format

Source