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.

“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 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°
<time step> = monthly

E.g., GPCP v2.3 (Adler et al. 2018) would be:

gpcp_tp_mm_global_197901_202205_025_monthly.nc

Gauge-Based Products

		Spatial Coverage
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	P. Xie, Chen, and Shi (2010)
CRU TS v4.06	0.5°		x		Monthly	1901/01-2021/12	Download	Harris et al. (2020)
EM-EARTH	0.1°		x		Daily	1950/01-2019/12	Download	Tang, Clark, and Papalexiou (2022)
GHCN v2	5°		x		Monthly	1900/01-2015/05	Download	Peterson and Vose (1997)
GPCC v2020	0.25°		x		Monthly	1891/01-2022/08	Download	Schneider et al. (2011)
PREC/L	0.5°		x		Monthly	1948/01-2022/08	Download	Chen et al. (2002)
UDel v5.01	0.5°		x		Monthly	1901/01-2017/12	Download	Willmott and Matsuura (2001)

Satellite-Based Products

		Spatial Coverage
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	Funk et al. (2015)
CMAP	2.5°	x	x	x	Monthly	1979/01-2022/07	Download	Pingping Xie and Arkin (1997)
CMORPH	0.25°	60°SN	60°SN	60°SN	Daily	1998/01-2021/12	Download	Joyce et al. (2004)
GPCP v2.3	0.5°	x	x	x	Monthly	1979/01-2022/05	Download	Adler et al. (2018)
GPM IMERGM v06	0.1°	x	x	x	Monthly	2000/06-2020/12	Download	G. J. Huffman et al. (2019)
MSWEP v2.8	0.1°	x	x	x	Monthly	1979/02-2022/06	Download	Beck et al. (2019)
PERSIANN-CDR	0.25°	60°SN	60°SN	60°SN	Monthly	1983/01-2022/06	Download	Ashouri et al. (2015)
TRMM 3B43 v7	0.25°	50°SN	50°SN	50°SN	Monthly	1998/01-2019/12	Download	George J. Huffman et al. (2010)

Reanalysis Products

		Spatial Coverage
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	Slivinski et al. (2019)
ERA-20C	1.125°	x	x	x	Monthly	1900/01-2010/12	Download	Poli et al. (2016)
ERA5	0.25°	x	x	x	Monthly	1959/01-2021/12	Download	Hersbach et al. (2020)
JRA-55	1.25°	x	x	x	Monthly	1958/01-2021/12	Download	Kobayashi et al. (2015)
MERRA-2	0.5° x 0.625°	x	x	x	Monthly	1980/01-2023/01	Download	Gelaro et al. (2017)
NCEP/NCAR R1	1.875°	x	x	x	Monthly	1948/01-2022/08	Download	Kalnay et al. (1996)
NCEP/DOE R2	1.875°	x	x	x	Monthly	1979/01-2022/08	Download	Kanamitsu et al. (2002)

Hydrological Model Forcing

		Spatial Coverage
Data Set	Spatial Resolution	Global	Land	Ocean	Temporal Resolution	Record Length	Get Data	Reference
FLDAS	0.1°		x		Monthly	1982/01-2021/12	Download	McNally et al. (2017)
GLDAS CLSM v2.0	0.25°		x		Daily	1948/01-2014/12	Download	Rodell et al. (2004)
GLDAS NOAH v2.0	0.25°		x		Monthly	1948/01-2014/12	Download	Rodell et al. (2004)
GLDAS VIC v2.0	1°		x		Monthly	1948/01-2014/12	Download	Rodell et al. (2004)
TerraClimate	4\(km\)		x		Monthly	1958/01-2021/12	Download	Abatzoglou et al. (2018)

Recipe

In this introductory recipe we will first download the GPM-IMERGM data set. We will then subset the downloaded data over Central Europe for the 2001-2010 period, and crop it to the national scale for Czechia. 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. It can handle most other precipitation data sets in “.nc” format, as well as any other “.nc” file generated by its functions.

Installation

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

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 data_name, destination, domain, and time_res.

data_name is set to “all” by default, but you can specify the names of your data sets of interest only.
destination 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).
time_res 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 show_info:

download_data(data_name = 'gpm-imerg')
gpm_global <- raster::brick('gpm-imerg_tp_mm_global_200006_202012_025_monthly.nc')
show_info(gpm_global)

[1] "class      : RasterBrick "                                         
[2] "dimensions : 720, 1440, 1036800, 247  (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 "
[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, 2020-12-01 (min, max)"
[9] "varname    : tp "

Processing

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

subset_spacetime to subset the data in time and space.
subset_space to subset the data to the region of interest.
subset_time to select the years of interest.
mon_to_year to aggregate the data from monthly into annual.
rescale_data to go from the native resolution (0.25°) to coarser ones (e.g., 0.5°, 1°, 1.5°, 2°, etc).
make_ts to generate a time series by taking the area weighted average over each time step.

Subset

To subset our data to a desired region and period of interest, we use the subset_spacetime function, which has four arguments data, years, bbox, and autosave.

data is the path to the data set of interest or a RasterBrick object.
years is the period of interest in the form (start_year, end_year)
bbox is the bounding box of the region of interest with the coordinates in degrees in the form (xmin, xmax, ymin, ymax).
autosave is set to FALSE by default. If TRUE data will be automatically stored in the same location of the input file.

Let’s subset the GPM-IMERGM data set over Central Europe (2,28,42,58) for the 1981-2020 period, and inspect its content with show_info:

gpm_subset <- subset_spacetime(gpm_global, years = c(2001, 2010), bbox = c(2,28,42,58))
show_info(gpm_subset)

[1] "class      : RasterBrick "
[2] "dimensions : 64, 104, 6656, 120  (nrow, ncol, ncell, nlayers)"
[3] "resolution : 0.25, 0.25  (x, y)"
[4] "extent     : 2, 28, 42, 58  (xmin, xmax, ymin, ymax)"
[5] "crs        : +proj=longlat +datum=WGS84 "
[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 : 1.272205e+01, 4.698483e+00, 5.927317e+00, 2.240815e+00, 1.315575e+01, 1.301118e+00, 3.831070e+00, 4.547474e-13, 2.739577e+01, 1.662540e+00, 2.002276e+01, 1.084265e+00, 4.843051e+00, 3.975639e+00, 5.638179e+00, ... "
[9] "max values :     443.4645,     158.5196,     374.7221,     229.5028,     163.2903,     251.5495,     330.9900,     336.4113,     456.0420,     454.0903,     452.1386,     236.0807,     277.7888,     255.8143,     195.8183, ... "
[10] "time       : 2001-01-01, 2010-12-01 (min, max)"

Crop

To further crop our data to a desired polygon other than a rectangle, we use the crop_data function, which has three arguments x, shp_path, autosave.

x is the path to a “.nc” data set file or a RasterBrick object.
shp_path is the path to a “.shp” file that we want to use to crop our data.
autosave is set to FALSE by default. If TRUE data will be automatically stored in the same location of the input file.

Let’s crop our GPM-IMERG subset to cover only Czechia with the respective shape file, and inspect its content with show_info:

gpm_cz <- crop_data(x = gpm_subset, shp_path = "CZE_adm0.shp")
show_info(gpm_cz)

[1] "class      : RasterBrick "
[2] "dimensions : 64, 104, 6656, 480  (nrow, ncol, ncell, nlayers)"
[3] "resolution : 0.25, 0.25  (x, y)"
[4] "extent     : 2, 28, 42, 58  (xmin, xmax, ymin, ymax)"
[5] "crs        : +proj=longlat +datum=WGS84 "
[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 :   43.226040,   30.070290,   65.995613,   46.767975,   44.382591,   52.406155,   83.416138,   51.177319,   88.692894,   14.673723,   49.876202,   55.442097,   21.179314,   57.682911,   33.612221, ... "
[9] "max values :    89.99401,    70.54952,   158.95328,   106.25800,    90.21087,   135.75002,   248.73044,   138.78595,   158.15816,    51.39417,   113.63100,   141.96646,    77.34425,   162.56750,   132.85863, ... "
[10] "time       : 2001-01-01, 2010-12-01 (min, max)"

Generate Time series

To make a time series out of our data, we use the make_ts function, which has two argument data and autosave.

data is the path to a “.nc” data set file or a RasterBrick object.
autosave is set to FALSE by default. If TRUE data will be automatically stored in the same location of the input file.

Let’s generate the time series for our three different GPM-IMERGM data sets (Global, Central Europe, and Czechia), and inspect its first 12 rows:

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

          date     value           name            type
 1: 2000-06-01  93.60162 GPM IMERGM v06 Satellite-based
 2: 2000-07-01  96.01442 GPM IMERGM v06 Satellite-based
 3: 2000-08-01  94.16792 GPM IMERGM v06 Satellite-based
 4: 2000-09-01  90.38524 GPM IMERGM v06 Satellite-based
 5: 2000-10-01  93.90120 GPM IMERGM v06 Satellite-based
 6: 2000-11-01  93.55994 GPM IMERGM v06 Satellite-based
 7: 2000-12-01  96.68792 GPM IMERGM v06 Satellite-based
 8: 2001-01-01  94.71431 GPM IMERGM v06 Satellite-based
 9: 2001-02-01  85.94786 GPM IMERGM v06 Satellite-based
10: 2001-03-01  96.12793 GPM IMERGM v06 Satellite-based
11: 2001-04-01  96.99244 GPM IMERGM v06 Satellite-based
12: 2001-05-01 100.50446 GPM IMERGM v06 Satellite-based

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

          date     value           name            type
 1: 2001-01-01  96.67884 GPM IMERGM v06 Satellite-based
 2: 2001-02-01  58.80170 GPM IMERGM v06 Satellite-based
 3: 2001-03-01  96.04202 GPM IMERGM v06 Satellite-based
 4: 2001-04-01  80.09136 GPM IMERGM v06 Satellite-based
 5: 2001-05-01  55.94958 GPM IMERGM v06 Satellite-based
 6: 2001-06-01  92.74124 GPM IMERGM v06 Satellite-based
 7: 2001-07-01  95.06115 GPM IMERGM v06 Satellite-based
 8: 2001-08-01  76.70639 GPM IMERGM v06 Satellite-based
 9: 2001-09-01 141.68700 GPM IMERGM v06 Satellite-based
10: 2001-10-01  62.51384 GPM IMERGM v06 Satellite-based
11: 2001-11-01  97.12927 GPM IMERGM v06 Satellite-based
12: 2001-12-01  71.00100 GPM IMERGM v06 Satellite-based

gpm_cz_ts <- make_ts(gpm_cz)
head(gpm_cz_ts, 12)

          date     value           name            type
 1: 2001-01-01  59.36666 GPM IMERGM v06 Satellite-based
 2: 2001-02-01  50.59915 GPM IMERGM v06 Satellite-based
 3: 2001-03-01  96.69115 GPM IMERGM v06 Satellite-based
 4: 2001-04-01  73.23477 GPM IMERGM v06 Satellite-based
 5: 2001-05-01  64.74244 GPM IMERGM v06 Satellite-based
 6: 2001-06-01  86.48493 GPM IMERGM v06 Satellite-based
 7: 2001-07-01 127.52908 GPM IMERGM v06 Satellite-based
 8: 2001-08-01  94.31304 GPM IMERGM v06 Satellite-based
 9: 2001-09-01 119.28491 GPM IMERGM v06 Satellite-based
10: 2001-10-01  30.82040 GPM IMERGM v06 Satellite-based
11: 2001-11-01  72.33474 GPM IMERGM v06 Satellite-based
12: 2001-12-01  91.77480 GPM IMERGM v06 Satellite-based

NOTE: When working with files from memory (i.e., without saving .nc files) the make_ts functions may not generate the name and type columns appropriately.

Visualize

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

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

Let’s plot our three different GPM-IMERGM data sets (Global, Central Europe, and Czechia)

Maps

To see a map of any data set raw or processed, we use plot_map which takes only one layer of the RasterBrick as input.

plot_map(gpm_global[[1]])

plot_map(gpm_subset[[1]])

plot_map(gpm_cz[[1]])

Time Series Visuals

To draw a time series generated by make_ts, we use any of the options below, which takes only a make_ts “.csv” generated file.

Line

plot_line(gpm_global_ts)

plot_line(gpm_subset_ts)

plot_line(gpm_cz_ts)

Heatmap

plot_heatmap(gpm_global_ts)

plot_heatmap(gpm_subset_ts)

plot_heatmap(gpm_cz_ts)

Boxplot

plot_box(gpm_global_ts)

plot_box(gpm_subset_ts)

plot_box(gpm_cz_ts)

Density

plot_density(gpm_global_ts)

plot_density(gpm_subset_ts)

plot_density(gpm_cz_ts)

Summary

NOTE: For good aesthetics we recommend saving plot_summary with ggsave(<filename>, <plot>, width = 16.3, height = 15.03).

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

Time Series Analysis

Once we have generated our time series, we can start evaluating the data with:

pod to calculate the probability of detection.
far to calculate the false alarm rate.
csi to calculate the critical success index.
nse to calculate the Nash–Sutcliffe efficiency.

The above functions have three arguments x, ref, and th (except for nse).

x is the time series data.table generated by make_ts.
ref is a data.table time series with the referential data used for evaluation.
th is the number to be used as the detection threshold.

NOTE: Not demonstrated in the current demo because such metrics are not intended for monthly data but rather higher temporal resolution data, e.g., daily or subdaily (coming soon).

Coming Soon

More functions for data processing and analysis and expanding the database.

References

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Adler, Robert F., Mathew R. P. Sapiano, George J. Huffman, Jian-Jian Wang, Guojun Gu, David Bolvin, Long Chiu, et al. 2018. “The Global Precipitation Climatology Project (GPCP) Monthly Analysis (New Version 2.3) and a Review of 2017 Global Precipitation.” Atmosphere 9 (4): 138. https://doi.org/10.3390/atmos9040138.

Ashouri, Hamed, Kuo-Lin Hsu, Soroosh Sorooshian, Dan K. Braithwaite, Kenneth R. Knapp, L. Dewayne Cecil, Brian R. Nelson, and Olivier P. Prat. 2015. “PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies.” Bulletin of the American Meteorological Society 96 (1): 69–83.

Beck, Hylke E, Eric F Wood, Ming Pan, Colby K Fisher, Diego G Miralles, Albert IJM Van Dijk, Tim R McVicar, and Robert F Adler. 2019. “MSWEP V2 Global 3-Hourly 0.1 Precipitation: Methodology and Quantitative Assessment.” Bulletin of the American Meteorological Society 100 (3): 473–500.

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Funk, Chris, Pete Peterson, Martin Landsfeld, Diego Pedreros, James Verdin, Shraddhanand Shukla, Gregory Husak, et al. 2015. “The Climate Hazards Infrared Precipitation with Stations—a New Environmental Record for Monitoring Extremes.” Scientific Data 2 (1): 150066. https://doi.org/10.1038/sdata.2015.66.

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Huffman, G. J., E. F. Stocker, D. T. Bolvin, E. J. Nelkin, and Jackson Tan. 2019. GPM IMERG Final Precipitation L3 1 Month 0.1 Degree x 0.1 Degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC).

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Rodell, Matthew, P. R. Houser, U. E. A. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, K. Arsenault, B. Cosgrove, J. Radakovich, and M. Bosilovich. 2004. “The Global Land Data Assimilation System.” Bulletin of the American Meteorological Society 85 (3): 381–94.

Schneider, Udo, Andreas Becker, Peter Finger, Anja Meyer-Christoffer, Bruno Rudolf, and Markus Ziese. 2011. “GPCC Full Data Reanalysis Version 6.0 at 0.5: Monthly Land-Surface Precipitation from Rain-Gauges Built on GTS-Based and Historic Data.” GPCC Data Rep., Doi 10.

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Tang, Guoqiang, Martyn P. Clark, and Simon Michael Papalexiou. 2022. “EM-Earth: The Ensemble Meteorological Dataset for Planet Earth.” Bulletin of the American Meteorological Society 103 (4): E996–1018. https://doi.org/10.1175/BAMS-D-21-0106.1.

Vargas Godoy, Mijael Rodrigo, Yannis Markonis, Martin Hanel, Jan Kyselỳ, and Simon Michael Papalexiou. 2021. “The Global Water Cycle Budget: A Chronological Review.” Surveys in Geophysics 42 (5): 1075–1107.

Willmott, C. J., and K. Matsuura. 2001. Terrestrial Air Temperature and Precipitation: Monthly and Annual Time Series (1950 - 1999).

Xie, P., M. Chen, and W. Shi. 2010. “CPC Global Unified Gauge-Based Analysis of Daily Precipitation.” In Preprints, 24th Conf. On Hydrology, Atlanta, GA, Amer. Metero. Soc. Vol. 2.

Xie, Pingping, and Phillip A. Arkin. 1997. “Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite Estimates, and Numerical Model Outputs.” Bulletin of the American Meteorological Society 78 (11): 2539–58.

pRecipe| Precipitation R Recipes

Mijael Rodrigo Vargas Godoy, Yannis Markonis

2023-05-26