Introduction to pRecipe
In pRecipe: Precipitation R Recipes

knitr::opts_chunk$set(
  echo = TRUE,
  eval = TRUE,
  fig.width = 7,
  warning = FALSE,
  message = FALSE
)
library(pRecipe)
library(kableExtra)

pRecipe was conceived back in 2020 as part of MRVG's doctoral dissertation at the Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Czechia. Designed with reproducible science in mind, pRecipe facilitates the download, exploration, visualization, and analysis of multiple precipitation data products across various spatiotemporal scales [@vargas_godoy_precipe_2023].

~The Global Water Cycle Budget | @vargas_godoy_global_2021

"Like civilization and technology, our understanding of the global water cycle has been continuously evolving, and we have adapted our quantification methods to better exploit new technological resources. The accurate quantification of global water fluxes and storage is crucial in studying the global water cycle."

Before We Start

Like many other R packages, pRecipe has some system requirements:

PROJ
Geospatial Data Abstraction Library (GDAL)
Network Common Data Form (NetCDF)

Data

pRecipe database hosts 27 different precipitation datasets; six gauge-based, eight satellite-based, eight reanalysis, and five hydrological model precipitation products. Their specifications as available in the database, as well as links to their providers, and their respective references are detailed in the following subsections. We have already homogenized, compacted to a single file, and stored them in Zenodo repositories under the following naming convention:

<dataset>-<version>_<variable>_<units>_<coverage>_<start date>_<end date>_<resolution>_<time step>.nc

The pRecipe data collection was homogenized to these specifications:

<variable> = total precipitation (tp)
<units> = millimeters (mm)
<resolution> = 0.25°

E.g., Daily GPCP v3.2 [@adler_global_2018] would be:

gpcp-v3-2_tp_mm_global_197901_202109_025_daily.nc

Gauge-Based Products

tibble::tribble(
  ~"Dataset", ~"Spatial Coverage", ~"Highest Temporal Resolution Available", ~"Record Length", ~"Get Data", ~"Reference",
"CPC-Global", "Land", "Daily", "1979/01-2023/09", "[Download](https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html)", "@xie_cpc_2010",
"CRU TS v4.08", "Land", "Monthly", "1901/01-2023/12", "[Download](https://crudata.uea.ac.uk/cru/data/hrg/)", "@harris_version_2020",
"EM-Earth", "Land", "Daily", "1950/01-2019/12", "[Download](https://www.frdr-dfdr.ca/repo/dataset/8d30ab02-f2bd-4d05-ae43-11f4a387e5ad)", "@tang_em-earth_2022",
"GHCN v2", "Land", "Monthly", "1900/01-2015/05", "[Download](https://psl.noaa.gov/data/gridded/data.ghcngridded.html)", "@peterson_overview_1997",
"GPCC v2022", "Land", "Daily", "1891/01-2020/10", "[Download](https://psl.noaa.gov/data/gridded/data.gpcc.html)", "@schneider_gpcc_2011",
"PREC/L", "Land", "Monthly", "1948/01-2024/10", "[Download](https://psl.noaa.gov/data/gridded/data.precl.html)", "@chen_global_2002"
) |>
  kbl(align = 'lccccr') |>
  kable_styling("striped") |>
  unclass() |> cat()

Satellite-Based Products

tibble::tribble(
  ~"Dataset", ~"Spatial Coverage", ~"Highest Temporal Resolution Available", ~"Record Length", ~"Get Data", ~"Reference",
"CHIRPS v2.0", "Land 50°SN", "Daily", "1981/01-2023/08", "[Download](https://www.chc.ucsb.edu/data/chirps)", "@funk_climate_2015",
"CMAP", "Global", "Monthly", "1979/01-2024/10", "[Download](https://psl.noaa.gov/data/gridded/data.cmap.html)", "@xie_global_1997",
"CMORPH-CDR", "Global 60°SN", "Daily", "1998/01-2023/04", "[Download](https://www.ncei.noaa.gov/data/cmorph-high-resolution-global-precipitation-estimates/)", "@joyce_cmorph_2004",
"GPCP v3.2", "Global", "Daily", "1979/01-2021/09", "[Download](https://psl.noaa.gov/data/gridded/data.gpcp.html)", "@adler_global_2018",
"GPM IMERGM Final v07", "Global", "Daily", "1998/01-2024/06", "[Download](https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDF_07/summary?keywords=GPM_3IMERGDF_07)", "@huffman_gpm_2019",
"GSMaP v8", "Global", "Daily", "1998/01-2023/06", "[Download](https://sharaku.eorc.jaxa.jp/GSMaP/)", "@kubota_global_2020",
"MSWEP v2.8", "Global", "Daily", "1979/01-2024/11", "[Download](https://www.gloh2o.org/mswep/)", "@beck_mswep_2019",
"PERSIANN-CDR", "Global 60°SN", "Daily", "1983/01-2023/12", "[Download](https://chrsdata.eng.uci.edu/)", "@ashouri_persiann-cdr_2015"
) |>
  kbl(align = 'lccccr') |>
  kable_styling("striped") |>
  unclass() |> cat()

Reanalysis Products

tibble::tribble(
  ~"Dataset", ~"Spatial Coverage", ~"Highest Temporal Resolution Available", ~"Record Length", ~"Get Data", ~"Reference",
"20CR v3", "Global", "Daily", "1836/01-2015/12", "[Download](https://psl.noaa.gov/data/gridded/data.20thC_ReanV3.html)", "@slivinski_towards_2019",
"ERA-20C", "Global", "Daily", "1900/01-2010/12", "[Download](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-20th-century)", "@poli_era-20c_2016",
"ERA5", "Global", "Monthly", "1959/01-2021/12", "[Download](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5)", "@hersbach_era5_2020",
"ERA5-Land", "Land", "Monthly", "1959/01-2021/12", "[Download](https://www.ecmwf.int/en/era5-land)", "@munoz_era5_2021",
"JRA-55", "Global", "Daily", "1958/01-2023/09", "[Download](https://rda.ucar.edu/datasets/ds628.1/dataaccess/)", "@kobayashi_jra-55_2015",
"MERRA-2", "Global", "Daily", "1980/01-2024/10", "[Download](https://disc.gsfc.nasa.gov/datasets?page=1&project=MERRA-2)", "@gelaro_modern-era_2017",
"NCEP/NCAR R1", "Global", "Daily", "1948/01-2023/12", "[Download](https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.derived.html)", "@kalnay_ncepncar_1996",
"NCEP/DOE R2", "Global", "Daily", "1979/01-2023/12", "[Download](https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html)", "@kanamitsu_ncepdoe_2002"
) |>
  kbl(align = 'lccccr') |>
  kable_styling("striped") |>
  unclass() |> cat()

Hydrological Model Forcing

tibble::tribble(
  ~"Dataset", ~"Spatial Coverage", ~"Highest Temporal Resolution Available", ~"Record Length", ~"Get Data", ~"Reference",
"FLDAS", "Land", "Monthly", "1982/01-2024/10", "[Download](https://ldas.gsfc.nasa.gov/fldas/fldas-data-download)", "@mcnally_land_2017",
"GLDAS CLSM v2.0", "Land", "Daily", "1948/01-2014/12", "[Download](https://ldas.gsfc.nasa.gov/gldas/gldas-get-data)", "@rodell_global_2004",
"GLDAS NOAH v2.0", "Land", "Monthly", "1948/01-2014/12", "[Download](https://ldas.gsfc.nasa.gov/gldas/gldas-get-data)", "@rodell_global_2004",
"GLDAS VIC v2.0", "Land", "Monthly", "1948/01-2014/12", "[Download](https://ldas.gsfc.nasa.gov/gldas/gldas-get-data)", "@rodell_global_2004",
"TerraClimate", "Land", "Monthly", "1958/01-2023/12", "[Download](https://www.climatologylab.org/terraclimate.html)", "@abatzoglou_terraclimate_2018"
) |>
  kbl(align = 'lccccr') |>
  kable_styling("striped") |>
  unclass() |> cat()

Introduction to pRecipe

In this introductory demo we will first download the GPM-IMERGM dataset. We will then subset the downloaded data over South America for the 2001-2015 period, and crop it to the national scale for Bolivia. In the next step, we will generate time series for our datasets and conclude with the visualization of our data.

NOTE: While the functions in pRecipe are intended to work directly with its data inventory. pRecipe can handle most other datasets in ".nc" format, as well as any other ".nc" file generated by its functions.

Installation

install.packages('pRecipe')
library(pRecipe)

Data Download

Downloading the entire data collection or only a few datasets is quite straightforward. You just call the download_data function, which has four arguments dataset, path, domain, and timestep.

dataset is set to "all" by default, but you can specify the names of your datasets of interest only.
path is set to "." by default. I.e., the current working directory. By replacing it for [your_project_folder], the downloaded files will be stored in [your_project_folder] instead.
domain is set to "raw" by default, but you can specify the domain of your interest only. E.g., "ocean" for ocean only datasets (For availability please check the [Data] section).
timestep is set to "monthly" by default, but if you prefer you can also download annual data with "yearly".

Let's download the GPM-IMERGM dataset and inspect its content with infoNC:

download_data(dataset = 'gpm-imerg')
gpm_global <- raster::brick('gpm-imerg-v7_tp_mm_global_199801_202406_025_monthly.nc')
infoNC(gpm_global)

[1] "class      : RasterBrick "
[2] "dimensions : 720, 1440, 1036800, 318  (nrow, ncol, ncell, nlayers)"
[3] "resolution : 0.25, 0.25  (x, y)"
[4] "extent     : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)"
[5] "crs        : +proj=longlat +datum=WGS84 +no_defs "
[6] "source     : gpm-imerg_tp_mm_global_200006_202012_025_monthly.nc "
[7] "names      : X1998.01.01, X1998.02.01, X1998.03.01, X1998.04.01, X1998.05.01, X1998.06.01, X1998.07.01, X1998.08.01, X1998.09.01, X1998.10.01, X1998.11.01, X1998.12.01, X1999.01.01, X1999.02.01, X1999.03.01, ... "
[8] "Date/time  : 1998-01-01, 2024-06-01 (min, max)"
[9] "varname    : tp "

Processing

Once we have downloaded our database, we can start processing the data with:

crop_data to crop the data using a shapefile.
fldmean to generate a time series by taking the area weighted average over each timestep.
remap to go from the native resolution (0.25°) to coarser ones (e.g., 0.5°, 1°, 1.5°, ...).
subset_data to subset the data in time and/or space.
yearstat to aggregate the data from monthly into annual.

Subset

To subset our data to a desired region and period of interest, we use the subset_data function, which has three arguments x, box, and yrs.

x Raster* object or a data.table or a filename (character).
box is the bounding box of the region of interest with the coordinates in degrees in the form (xmin, xmax, ymin, ymax).
yrs is the period of interest with years in the form (start_year, end_year).

Let's subset the GPM-IMERGM dataset over South America (-96, -30, -56, 24) for the 2001-2020 period, and inspect its content with infoNC:

gpm_subset <- subset_data(gpm_global, box = c(-96, -30, -56, 24), yrs = c(2001, 2020))
infoNC(gpm_subset)

[1] "class      : RasterBrick "
[2] "dimensions : 320, 264, 84480, 240  (nrow, ncol, ncell, nlayers)"
[3] "resolution : 0.25, 0.25  (x, y)"
[4] "extent     : -96, -30, -56, 24  (xmin, xmax, ymin, ymax)"
[5] "crs        : +proj=longlat +datum=WGS84 +no_defs "
[6] "source     : r_tmp_2024-12-05_204859.40679_5927_83505.grd "
[7] "names      :  X2001.01.01,  X2001.02.01,  X2001.03.01,  X2001.04.01,  X2001.05.01,  X2001.06.01,  X2001.07.01,  X2001.08.01,  X2001.09.01,  X2001.10.01,  X2001.11.01,  X2001.12.01,  X2002.01.01,  X2002.02.01,  X2002.03.01, ... "
[8] "min values :           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0, ... "
[9] "max values :    877.0017,    830.7960,    926.5452,    879.0210,   1614.3760,   1347.4813,   1298.2778,   1030.6008,   2121.5745,   1154.9041,   1012.2653,    937.1544,    983.7074,    828.4057,    712.0858, ... "
[10] "time       : 2001-01-01, 2020-12-01 (min, max)"

Crop

To further crop our data to a desired polygon other than a rectangle, we use the crop_data function, which has two arguments x, and y.

x Raster object or a data.table or a .nc filename (character).
y is a ".shp" filename (character).

Let's crop our GPM-IMERG subset to cover only Bolivia with the respective shape file, and inspect its content with infoNC:

gpm_bol <- crop_data(gpm_subset, "gadm41_BOL_0.shp")
infoNC(gpm_bol)

[1] "class      : RasterBrick "
[2] "dimensions : 54, 50, 2700, 180  (nrow, ncol, ncell, nlayers)"
[3] "resolution : 0.25, 0.25  (x, y)"
[4] "extent     : -69.75, -57.25, -23, -9.5  (xmin, xmax, ymin, ymax)"
[5] "crs        : +proj=longlat +datum=WGS84 +no_defs "
[6] "source     : memory"
[7] "names      :  X2001.01.01,  X2001.02.01,  X2001.03.01,  X2001.04.01,  X2001.05.01,  X2001.06.01,  X2001.07.01,  X2001.08.01,  X2001.09.01,  X2001.10.01,  X2001.11.01,  X2001.12.01,  X2002.01.01,  X2002.02.01,  X2002.03.01, ... "
[8] "min values : 2.402562e+01, 4.327217e+01, 8.482053e+00, 9.562346e-01, 3.222862e-02, 0.000000e+00, 0.000000e+00, 5.553878e-03, 1.055679e-02, 4.221552e-02, 2.083128e-01, 8.674479e+00, 2.208736e+00, 1.188102e+01, 6.304548e+00, ... "
[9] "max values :    512.37097,    585.90833,    509.95139,    418.54199,    243.92047,    124.44180,    201.84206,    109.64172,    167.08734,    303.71823,    439.69751,    497.84958,    485.17444,    565.73810,    572.18994, ... "
[10] "time       : 2001-01-01, 2020-12-01 (min, max)"

Generate Time series

To make a time series out of our data, we use the fldmean function, which has one argument x.

x Raster object or a data.table or a .nc filename (character).

Let's generate the time series for our three different GPM-IMERGM datasets (Global, South America, and Bolivia), and inspect its first 12 rows:

gpm_global_ts <- fldmean(gpm_global)
head(gpm_global_ts, 12)

          date    value
        <Date>    <num>
 1: 1998-01-01 82.64305
 2: 1998-02-01 78.81371
 3: 1998-03-01 87.46418
 4: 1998-04-01 86.26875
 5: 1998-05-01 89.34600
 6: 1998-06-01 83.88119
 7: 1998-07-01 87.55151
 8: 1998-08-01 87.38290
 9: 1998-09-01 82.47541
10: 1998-10-01 82.77823
11: 1998-11-01 80.51179
12: 1998-12-01 85.23061

gpm_subset_ts <- fldmean(gpm_subset)
head(gpm_subset_ts, 12)

          date     value
        <Date>     <num>
 1: 2001-01-01  95.95988
 2: 2001-02-01  85.44723
 3: 2001-03-01 108.46433
 4: 2001-04-01  99.11680
 5: 2001-05-01 114.35870
 6: 2001-06-01  87.50668
 7: 2001-07-01  95.68529
 8: 2001-08-01  84.40069
 9: 2001-09-01  90.51047
10: 2001-10-01 104.37209
11: 2001-11-01  98.31326
12: 2001-12-01 107.36328

gpm_bol_ts <- fldmean(gpm_bol)
head(gpm_bol_ts, 12)

          date     value
        <Date>     <num>
 1: 2001-01-01 218.27810
 2: 2001-02-01 177.55739
 3: 2001-03-01 154.74973
 4: 2001-04-01  82.46497
 5: 2001-05-01  56.24647
 6: 2001-06-01  23.71866
 7: 2001-07-01  27.05753
 8: 2001-08-01  17.00265
 9: 2001-09-01  51.99784
10: 2001-10-01  94.54848
11: 2001-11-01 151.14781
12: 2001-12-01 153.45496

Visualize

Either after we have processed our data as required or right after downloaded, we have different options to visualize our data:

plot_box to see a seasonal boxplot.
plot_density to see the empirical density of monthly precipitation.
plot_heatmap to see a heatmap of all monthly values.
plot_line to see the average time series.
plot_map to see the Cartesian lon-lat map of the first raster layer.
plot_summary to see line, heatmap, box, and density plot together in a single plot.
plot_taylor to see a Taylor Diagram (requires a referential dataset).

Let's plot our three different GPM-IMERGM datasets (Global, South America, and Bolivia)

Maps

To see a map of any dataset raw or processed, we use plot_map.

plot_map(gpm_global)

{width=90%}

plot_map(gpm_subset)

{width=62%}

plot_map(gpm_bol)

{width=62%}

Time Series Visuals

Line

plot_line(gpm_global_ts)

{width=90%}

plot_line(gpm_subset_ts)

{width=90%}

plot_line(gpm_bol_ts)

{width=90%}

Heatmap

plot_heatmap(gpm_global_ts)

{width=90%}

plot_heatmap(gpm_subset_ts)

{width=90%}

plot_heatmap(gpm_bol_ts)

{width=90%}

Boxplot

plot_box(gpm_global_ts)

{width=90%}

plot_box(gpm_subset_ts)

{width=90%}

plot_box(gpm_bol_ts)

{width=90%}

Density

plot_density(gpm_global_ts)

{width=90%}

plot_density(gpm_subset_ts)

{width=90%}

plot_density(gpm_bol_ts)

{width=90%}

Summary

plot_summary(gpm_global_ts)
#plot_summary(gpm_subset_ts)
#plot_summary(gpm_cz_ts)

{width=90%}

Coming Soon

More functions for data processing and analysis.

Citation

If you acquire precipitation data products from pRecipe, we ask that you acknowledge us in your use of the data. We would also appreciate receiving a copy of the relevant publications. This will help pRecipe to justify keeping the data freely available online in the future. Thank you!

References

Any scripts or data that you put into this service are public.

pRecipe documentation built on April 4, 2025, 2:13 a.m.

rdrr.io home R language documentation Run R code online

CRAN packages Bioconductor packages R-Forge packages GitHub packages

Note that we can't provide technical support on individual packages. You should contact the package authors for that.

pRecipe
Precipitation R Recipes

Introduction to pRecipe
In pRecipe: Precipitation R Recipes

Before We Start

Data

Gauge-Based Products

Satellite-Based Products

Reanalysis Products

Hydrological Model Forcing

Introduction to pRecipe

Installation

Data Download

Processing

Subset

Crop

Generate Time series

Visualize

Maps

Time Series Visuals

Line

Heatmap

Boxplot

Density

Summary

Coming Soon

Citation

References

Try the pRecipe package in your browser

R Package Documentation

Browse R Packages

We want your feedback!

pRecipe Precipitation R Recipes

Introduction to pRecipe In pRecipe: Precipitation R Recipes

Before We Start

Data

Gauge-Based Products

Satellite-Based Products

Reanalysis Products

Hydrological Model Forcing

Introduction to pRecipe

Installation

Data Download

Processing

Subset

Crop

Generate Time series

Visualize

Maps

Time Series Visuals

Line

Heatmap

Boxplot

Density

Summary

Coming Soon

Citation

References

Try the pRecipe package in your browser

R Package Documentation

Browse R Packages

We want your feedback!

pRecipe
Precipitation R Recipes

Introduction to pRecipe
In pRecipe: Precipitation R Recipes