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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:
Data
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
Gauge-Based Products
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()
Satellite-Based Products
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()
Reanalysis Products
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()
Hydrological Model Forcing
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()
Introduction to pRecipe
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.
Installation
install.packages('pRecipe')
library(pRecipe)
Data Download
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.
- dataset is set to "all" by default, but you can specify the names of your data sets 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 data sets (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 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 "
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 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)"
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 : 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)"
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 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
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 data set).
Let's plot our three different GPM-IMERGM data sets (Global, South America, and Bolivia)
Maps
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%}
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
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%}
Coming Soon
More functions for data processing and analysis and expanding the database.
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
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pRecipe documentation built on Nov. 23, 2023, 1:08 a.m.
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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:
Data
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
Gauge-Based Products
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()
Satellite-Based Products
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()
Reanalysis Products
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()
Hydrological Model Forcing
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()
Introduction to pRecipe
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.
Installation
install.packages('pRecipe') library(pRecipe)
Data Download
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.
- dataset is set to "all" by default, but you can specify the names of your data sets 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 data sets (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 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 "
Processing
Once we have downloaded our database, we can start processing the data with:
crop_datato crop the data using a shapefile.fldmeanto generate a time series by taking the area weighted average over each timestep.remapto go from the native resolution (0.25°) to coarser ones (e.g., 0.5°, 1°, 1.5°, ...).subset_datato subset the data in time and/or space.yearstatto 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 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)"
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 : 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)"
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 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
Visualize
Either after we have processed our data as required or right after downloaded, we have different options to visualize our data:
plot_boxto see a seasonal boxplot.plot_densityto see the empirical density of monthly precipitation.plot_heatmapto see a heatmap of all monthly values.plot_lineto see the average time series.plot_mapto see the Cartesian lon-lat map of the first raster layer.plot_summaryto see line, heatmap, box, and density plot together in a single plot.plot_taylorto see a Taylor Diagram (requires a referential data set).
Let's plot our three different GPM-IMERGM data sets (Global, South America, and Bolivia)
Maps
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%}
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
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%}
Coming Soon
More functions for data processing and analysis and expanding the database.
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!
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