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."
Like many other R packages, pRecipe
has some system requirements:
pRecipe
database hosts 27 different precipitation data sets; seven gauge-based, eight satellite-based, seven reanalysis, and five hydrological model precipitation products. Their native specifications, 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 a Zenodo repository under the following naming convention:
<data set>_<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., GPCP v2.3 [@adler_global_2018] would be:
gpcp_tp_mm_global_197901_202205_025_monthly.nc
tibble::tribble( ~"Data Set", ~"Spatial Resolution", ~Global, ~Land, ~Ocean, ~"Temporal Resolution", ~"Record Length", ~"Get Data", ~"Reference", "CPC-Global", "0.5°", "", "x", "", "Daily", "1979/01-2022/08", "[Download](https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html)", "@xie_cpc_2010", "CRU TS v4.06", "0.5°", "", "x", "", "Monthly", "1901/01-2021/12", "[Download](https://crudata.uea.ac.uk/cru/data/hrg/)", "@harris_version_2020", "EM-EARTH", "0.1°", "", "x", "", "Daily", "1950/01-2019/12", "[Download](https://www.frdr-dfdr.ca/repo/dataset/8d30ab02-f2bd-4d05-ae43-11f4a387e5ad)", "@tang_em-earth_2022", "GHCN v2", "5°", "", "x", "", "Monthly", "1900/01-2015/05", "[Download](https://psl.noaa.gov/data/gridded/data.ghcngridded.html)", "@peterson_overview_1997", "GPCC v2020", "0.25°", "", "x", "", "Monthly", "1891/01-2022/08", "[Download](https://psl.noaa.gov/data/gridded/data.gpcc.html)", "@schneider_gpcc_2011", "PREC/L", "0.5°", "", "x", "", "Monthly", "1948/01-2022/08", "[Download](https://psl.noaa.gov/data/gridded/data.precl.html)", "@chen_global_2002", "UDel v5.01", "0.5°", "", "x", "", "Monthly", "1901/01-2017/12", "[Download](https://psl.noaa.gov/data/gridded/data.UDel_AirT_Precip.html)", "@willmott_terrestrial_2001" ) |> kbl(align = 'lcccccccr') |> kable_styling("striped") |> add_header_above(c(" " = 1, " " = 1, "Spatial Coverage" = 3, " " = 1, " " = 1, " " = 1, " " = 1)) |> unclass() |> cat()
tibble::tribble( ~"Data Set", ~"Spatial Resolution", ~Global, ~Land, ~Ocean, ~"Temporal Resolution", ~"Record Length", ~"Get Data", ~Reference, "CHIRPS v2.0", "0.05°", "", "50°SN", "", "Monthly", "1981/01-2022/07", "[Download](https://www.chc.ucsb.edu/data/chirps)", "@funk_climate_2015", "CMAP", "2.5°", "x", "x", "x", "Monthly", "1979/01-2022/07", "[Download](https://psl.noaa.gov/data/gridded/data.cmap.html)", "@xie_global_1997", "CMORPH", "0.25°", "60°SN", "60°SN", "60°SN", "Daily", "1998/01-2021/12", "[Download](https://www.ncei.noaa.gov/data/cmorph-high-resolution-global-precipitation-estimates/)", "@joyce_cmorph_2004", "GPCP v2.3", "0.5°", "x", "x", "x", "Monthly", "1979/01-2022/05", "[Download](https://psl.noaa.gov/data/gridded/data.gpcp.html)", "@adler_global_2018", "GPM IMERGM v06", "0.1°", "x", "x", "x", "Monthly", "2000/06-2020/12", "[Download](https://doi.org/10.5067/GPM/IMERG/3B-MONTH/06)", "@huffman_gpm_2019", "MSWEP v2.8", "0.1°", "x", "x", "x", "Monthly", "1979/02-2022/06", "[Download](https://www.gloh2o.org/mswep/)", "@beck_mswep_2019", "PERSIANN-CDR", "0.25°", "60°SN", "60°SN", "60°SN", "Monthly", "1983/01-2022/06", "[Download](https://chrsdata.eng.uci.edu/)", "@ashouri_persiann-cdr_2015", "TRMM 3B43 v7", "0.25°", "50°SN", "50°SN", "50°SN", "Monthly", "1998/01-2019/12", "[Download](https://doi.org/10.5067/TRMM/TMPA/MONTH/7)", "@huffman_trmm_2010" ) |> kbl(align = 'lcccccccr') |> kable_styling("striped") |> add_header_above(c(" " = 1, " " = 1, "Spatial Coverage" = 3, " " = 1, " " = 1, " " = 1, " " = 1)) |> unclass() |> cat()
tibble::tribble( ~"Data Set", ~"Spatial Resolution", ~Global, ~Land, ~Ocean, ~"Temporal Resolution", ~"Record Length", ~"Get Data", ~Reference, "20CR v3", "1°", "x", "x", "x", "Monthly", "1836/01-2015/12", "[Download](https://psl.noaa.gov/data/gridded/data.20thC_ReanV3.html)", "@slivinski_towards_2019", "ERA-20C", "1.125°", "x", "x", "x", "Monthly", "1900/01-2010/12", "[Download](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-20th-century)", "@poli_era-20c_2016", "ERA5", "0.25°", "x", "x", "x", "Monthly", "1959/01-2021/12", "[Download](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5)", "@hersbach_era5_2020", "JRA-55", "1.25°", "x", "x", "x", "Monthly", "1958/01-2021/12", "[Download](https://rda.ucar.edu/datasets/ds628.1/dataaccess/)", "@kobayashi_jra-55_2015", "MERRA-2", "0.5° x 0.625°", "x", "x", "x", "Monthly", "1980/01-2023/01", "[Download](https://disc.gsfc.nasa.gov/datasets?page=1&project=MERRA-2)", "@gelaro_modern-era_2017", "NCEP/NCAR R1", "1.875°", "x", "x", "x", "Monthly", "1948/01-2022/08", "[Download](https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.derived.html)", "@kalnay_ncepncar_1996", "NCEP/DOE R2", "1.875°", "x", "x", "x", "Monthly", "1979/01-2022/08", "[Download](https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html)", "@kanamitsu_ncepdoe_2002" ) |> kbl(align = 'lcccccccr') |> kable_styling("striped") |> add_header_above(c(" " = 1, " " = 1, "Spatial Coverage" = 3, " " = 1, " " = 1, " " = 1, " " = 1)) |> unclass() |> cat()
tibble::tribble( ~"Data Set", ~"Spatial Resolution", ~Global, ~Land, ~Ocean, ~"Temporal Resolution", ~"Record Length", ~"Get Data", ~Reference, "FLDAS", "0.1°", "", "x", "", "Monthly", "1982/01-2021/12", "[Download](https://ldas.gsfc.nasa.gov/fldas/fldas-data-download)", "@mcnally_land_2017", "GLDAS CLSM v2.0", "0.25°", "", "x", "", "Daily", "1948/01-2014/12", "[Download](https://ldas.gsfc.nasa.gov/gldas/gldas-get-data)", "@rodell_global_2004", "GLDAS NOAH v2.0", "0.25°", "", "x", "", "Monthly", "1948/01-2014/12", "[Download](https://ldas.gsfc.nasa.gov/gldas/gldas-get-data)", "@rodell_global_2004", "GLDAS VIC v2.0", "1°", "", "x", "", "Monthly", "1948/01-2014/12", "[Download](https://ldas.gsfc.nasa.gov/gldas/gldas-get-data)", "@rodell_global_2004", "TerraClimate", "4$km$", "", "x", "", "Monthly", "1958/01-2021/12", "[Download](https://www.climatologylab.org/terraclimate.html)", "@abatzoglou_terraclimate_2018" ) |> kbl(align = 'lcccccccr') |> kable_styling("striped") |> add_header_above(c(" " = 1, " " = 1, "Spatial Coverage" = 3, " " = 1, " " = 1, " " = 1, " " = 1)) |> unclass() |> cat()
In this introductory demo we will first download the GPM-IMERGM data set. 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 data sets 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 data sets in ".nc" format, as well as any other ".nc" file generated by its functions.
install.packages('pRecipe') library(pRecipe)
Downloading the entire data collection or only a few data sets is quite straightforward. You just call the download_data
function, which has four arguments dataset, path, domain, and timestep.
Let's download the GPM-IMERGM data set and inspect its content with infoNC
:
download_data(dataset = 'gpm-imerg') gpm_global <- raster::brick('gpm-imerg_tp_mm_global_200006_202012_025_monthly.nc') infoNC(gpm_global)
[1] "class : RasterBrick " [2] "dimensions : 720, 1440, 1036800, 256 (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 : X2000.06.01, X2000.07.01, X2000.08.01, X2000.09.01, X2000.10.01, X2000.11.01, X2000.12.01, X2001.01.01, X2001.02.01, X2001.03.01, X2001.04.01, X2001.05.01, X2001.06.01, X2001.07.01, X2001.08.01, ... " [8] "Date/time : 2000-06-01, 2021-09-01 (min, max)" [9] "varname : tp "
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.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.
Let's subset the GPM-IMERGM data set over South America (-96, -30, -56, 24) for the 2001-2015 period, and inspect its content with infoNC
:
gpm_subset <- subset_data(gpm_global, box = c(-96, -30, -56, 24), yrs = c(2001, 2015)) infoNC(gpm_subset)
[1] "class : RasterBrick " [2] "dimensions : 320, 264, 84480, 180 (nrow, ncol, ncell, nlayers)" [3] "resolution : 0.25, 0.25 (x, y)" [4] "extent : -96, -30, -56, 24 (xmin, xmax, ymin, ymax)" [5] "crs : memory" [6] "source : r_tmp_2023-07-07_185756.586009_13478_99533.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 : 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.393141e-04, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, ... " [9] "max values : 1069.6940, 917.3006, 1102.5068, 915.1335, 1549.5077, 1169.6469, 1483.0360, 1453.4453, 1624.6550, 1233.0613, 1715.0280, 1285.7706, 881.6859, 874.7393, 883.4313, ... " [10] "time : 2001-01-01, 2015-12-01 (min, max)"
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.
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 : 4.359420e+01, 5.965404e+01, 9.195424e+00, 2.650523e+00, 4.473422e-01, 4.649702e-03, 3.581941e-04, 1.511060e-02, 3.136731e-01, 5.168897e-01, 3.443884e-01, 1.019173e+01, 3.299495e+00, 2.491986e+01, 1.160967e+01, ... " [9] "max values : 613.21777, 530.11438, 497.65503, 371.26581, 216.29959, 136.70122, 209.37540, 124.56583, 166.48785, 313.69836, 472.29553, 487.41364, 613.77014, 673.70099, 549.12671, ... " [10] "time : 2001-01-01, 2015-12-01 (min, max)"
To make a time series out of our data, we use the fldmean
function, which has one argument x.
Let's generate the time series for our three different GPM-IMERGM data sets (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: 2000-06-01 93.64844 2: 2000-07-01 96.05852 3: 2000-08-01 94.18216 4: 2000-09-01 90.43190 5: 2000-10-01 93.91238 6: 2000-11-01 93.61439 7: 2000-12-01 96.70333 8: 2001-01-01 94.67989 9: 2001-02-01 86.00950 10: 2001-03-01 96.15177 11: 2001-04-01 97.05069 12: 2001-05-01 100.53676
gpm_subset_ts <- fldmean(gpm_subset) head(gpm_subset_ts, 12)
date value <Date> <num> 1: 2001-01-01 106.52438 2: 2001-02-01 89.98158 3: 2001-03-01 113.35350 4: 2001-04-01 107.26019 5: 2001-05-01 123.50707 6: 2001-06-01 94.20347 7: 2001-07-01 102.07352 8: 2001-08-01 94.62878 9: 2001-09-01 96.31932 10: 2001-10-01 112.90529 11: 2001-11-01 102.68565 12: 2001-12-01 113.49551
gpm_bol_ts <- fldmean(gpm_bol) head(gpm_bol_ts, 12)
date value <Date> <num> 1: 2001-01-01 233.87604 2: 2001-02-01 183.34294 3: 2001-03-01 165.97789 4: 2001-04-01 85.02165 5: 2001-05-01 63.68961 6: 2001-06-01 24.68989 7: 2001-07-01 31.89638 8: 2001-08-01 17.94735 9: 2001-09-01 55.87102 10: 2001-10-01 103.33750 11: 2001-11-01 163.95180 12: 2001-12-01 156.72036
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 data set).Let's plot our three different GPM-IMERGM data sets (Global, South America, and Bolivia)
To see a map of any data set raw or processed, we use plot_map
.
plot_map(gpm_global)
{width=90%}
plot_map(gpm_subset)
{width=45%}
plot_map(gpm_bol)
{width=45%}
plot_line(gpm_global_ts)
{width=90%}
plot_line(gpm_subset_ts)
{width=90%}
plot_line(gpm_bol_ts)
{width=90%}
plot_heatmap(gpm_global_ts)
{width=90%}
plot_heatmap(gpm_subset_ts)
{width=90%}
plot_heatmap(gpm_bol_ts)
{width=90%}
plot_box(gpm_global_ts)
{width=90%}
plot_box(gpm_subset_ts)
{width=90%}
plot_box(gpm_bol_ts)
{width=90%}
plot_density(gpm_global_ts)
{width=90%}
plot_density(gpm_subset_ts)
{width=90%}
plot_density(gpm_bol_ts)
{width=90%}
NOTE: For good aesthetics we recommend saving plot_summary
with ggsave(<filename>, <plot>, width = 16, height = 13.5)
.
plot_summary(gpm_global_ts) #plot_summary(gpm_subset_ts) #plot_summary(gpm_cz_ts)
{width=90%}
More functions for data processing and analysis and expanding the database.
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!
Any scripts or data that you put into this service are public.
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