Author: Mijael Rodrigo Vargas Godoy, Yannis Markonis
Date: 2025-04-02

# Introduction to pRecipe

#### Mijael Rodrigo Vargas Godoy, Yannis Markonis

#### 2025-04-02

`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:

- 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 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.

- 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)

    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!

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