Introduction to pRecipe

Mijael Rodrigo Vargas Godoy, Yannis Markonis

2024-12-07

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 and Markonis 2023).


~The Global Water Cycle Budget | Vargas Godoy et al. (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 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:

E.g., Daily GPCP v3.2 (Adler et al. 2018) would be:

gpcp-v3-2_tp_mm_global_197901_202109_025_daily.nc

Gauge-Based Products

Dataset Spatial Coverage Highest Temporal Resolution Available Record Length Get Data Reference
CPC-Global Land Daily 1979/01-2023/09 Download P. Xie, Chen, and Shi (2010)
CRU TS v4.08 Land Monthly 1901/01-2023/12 Download Harris et al. (2020)
EM-Earth Land Daily 1950/01-2019/12 Download Tang, Clark, and Papalexiou (2022)
GHCN v2 Land Monthly 1900/01-2015/05 Download Peterson and Vose (1997)
GPCC v2022 Land Daily 1891/01-2020/10 Download Schneider et al. (2011)
PREC/L Land Monthly 1948/01-2024/10 Download Chen et al. (2002)

Satellite-Based Products

Dataset Spatial Coverage Highest Temporal Resolution Available Record Length Get Data Reference
CHIRPS v2.0 Land 50°SN Daily 1981/01-2023/08 Download Funk et al. (2015)
CMAP Global Monthly 1979/01-2024/10 Download Pingping Xie and Arkin (1997)
CMORPH-CDR Global 60°SN Daily 1998/01-2023/04 Download Joyce et al. (2004)
GPCP v3.2 Global Daily 1979/01-2021/09 Download Adler et al. (2018)
GPM IMERGM Final v07 Global Daily 1998/01-2024/06 Download Huffman et al. (2019)
GSMaP v8 Global Daily 1998/01-2023/06 Download Kubota et al. (2020)
MSWEP v2.8 Global Daily 1979/01-2024/11 Download Beck et al. (2019)
PERSIANN-CDR Global 60°SN Daily 1983/01-2023/12 Download Ashouri et al. (2015)

Reanalysis Products

Dataset Spatial Coverage Highest Temporal Resolution Available Record Length Get Data Reference
20CR v3 Global Daily 1836/01-2015/12 Download Slivinski et al. (2019)
ERA-20C Global Daily 1900/01-2010/12 Download Poli et al. (2016)
ERA5 Global Monthly 1959/01-2021/12 Download Hersbach et al. (2020)
ERA5-Land Land Monthly 1959/01-2021/12 Download Muñoz-Sabater et al. (2021)
JRA-55 Global Daily 1958/01-2023/09 Download Kobayashi et al. (2015)
MERRA-2 Global Daily 1980/01-2024/10 Download Gelaro et al. (2017)
NCEP/NCAR R1 Global Daily 1948/01-2023/12 Download Kalnay et al. (1996)
NCEP/DOE R2 Global Daily 1979/01-2023/12 Download Kanamitsu et al. (2002)

Hydrological Model Forcing

Dataset Spatial Coverage Highest Temporal Resolution Available Record Length Get Data Reference
FLDAS Land Monthly 1982/01-2024/10 Download McNally et al. (2017)
GLDAS CLSM v2.0 Land Daily 1948/01-2014/12 Download Rodell et al. (2004)
GLDAS NOAH v2.0 Land Monthly 1948/01-2014/12 Download Rodell et al. (2004)
GLDAS VIC v2.0 Land Monthly 1948/01-2014/12 Download Rodell et al. (2004)
TerraClimate Land Monthly 1958/01-2023/12 Download Abatzoglou et al. (2018)

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.

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:

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:

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)

plot_map(gpm_subset)

plot_map(gpm_bol)

Time Series Visuals

Line

plot_line(gpm_global_ts)

plot_line(gpm_subset_ts)

plot_line(gpm_bol_ts)

Heatmap

plot_heatmap(gpm_global_ts)

plot_heatmap(gpm_subset_ts)

plot_heatmap(gpm_bol_ts)

Boxplot

plot_box(gpm_global_ts)

plot_box(gpm_subset_ts)

plot_box(gpm_bol_ts)

Density

plot_density(gpm_global_ts)

plot_density(gpm_subset_ts)

plot_density(gpm_bol_ts)

Summary

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

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

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