pRecipe was conceived back in 2019 as part of MRVG’s doctoral dissertation at the Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Czech Republic. 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 24 different precipitation data sets; seven gauge-based, eight satellite-based, five reanalysis, and four 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\(^\circ\)
<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

Data Set	Spatial Resolution	Temporal Resolution	Record Length	Get Data	Reference
CPC-Global	0.5\(^\circ\)	Daily	1979-01 to 2022-08	Download	P. Xie, Chen, and Shi (2010)
CRU TS v4.06	0.5\(^\circ\)	Monthly	1901-01 to 2021-12	Download	Harris et al. (2020)
EM-EARTH	0.1\(^\circ\)	Daily	1950-01 to 2019-12	Download	Tang, Clark, and Papalexiou (2022)
GHCN v2	5\(^\circ\)	Monthly	1900-01 to 2015-05	Download	Peterson and Vose (1997)
GPCC v2020	0.25\(^\circ\)	Monthly	1891-01 to 2022-08	Download	Schneider et al. (2011)
PRECL/L	0.5\(^\circ\)	Monthly	1948-01 to 2022-08	Download	Chen et al. (2002)
UDel v5.01	0.5\(^\circ\)	Monthly	1901-01 to 2017-12	Download	Willmott and Matsuura (2001)

Satellite-Based Products

Data Set	Spatial Resolution	Temporal Resolution	Record Length	Get Data	Reference
CHIRPS v2.0	0.05\(^\circ\)	Monthly	1981-01 to 2022-07	Download	Funk et al. (2015)
CMAP	2.5\(^\circ\)	Monthly	1979-01 to 2022-07	Download	Pingping Xie and Arkin (1997)
CMORPH	0.25\(^\circ\)	Daily	1998-01 to 2021-12	Download	Joyce et al. (2004)
GPCP v2.3	0.5\(^\circ\)	Monthly	1979-01 to 2022-05	Download	Adler et al. (2018)
GPM IMERGM v06	0.1\(^\circ\)	Monthly	2000-06 to 2020-12	Download	G. J. Huffman et al. (2019)
MSWEP v2.8	0.1\(^\circ\)	Monthly	1979-02 to 2022-06	Download	Beck et al. (2019)
PERSIANN-CDR	0.25\(^\circ\)	Monthly	1983-01 to 2022-06	Download	Ashouri et al. (2015)
TRMM 3B43 v7	0.25\(^\circ\)	Monthly	1998-01 to 2019-12	Download	George J. Huffman et al. (2010)

Reanalysis Products

Data Set	Spatial Resolution	Temporal Resolution	Record Length	Get Data	Reference
20CR v3	1\(^\circ\)	Monthly	1836-01 to 2015-12	Download	Slivinski et al. (2019)
ERA-20C	1.125\(^\circ\)	Monthly	1900-01 to 2010-12	Download	Poli et al. (2016)
ERA5	0.25\(^\circ\)	Monthly	1959-01 to 2021-12	Download	Hersbach et al. (2020)
NCEP/NCAR R1	1.875\(^\circ\)	Monthly	1948-01 to 2022-08	Download	Kalnay et al. (1996)
NCEP/DOE R2	1.875\(^\circ\)	Monthly	1979-01 to 2022-08	Download	Kanamitsu et al. (2002)

Hydrological Model Forcing

Data Set	Spatial Resolution	Temporal Resolution	Record Length	Get Data	Reference
GLDAS CLSM v2.0	0.25\(^\circ\)	Daily	1948-01 to 2014-12	Download	Rodell et al. (2004)
GLDAS NOAH v2.0	0.25\(^\circ\)	Monthly	1948-01 to 2014-12	Download	Rodell et al. (2004)
GLDAS VIC v2.0	1\(^\circ\)	Monthly	1948-01 to 2014-12	Download	Rodell et al. (2004)
TerraClimate	4\(km\)	Monthly	1958-01 to 2021-12	Download	Abatzoglou et al. (2018)

Recipe

In this introductory recipe we will first download the ERA5 data set. We will then subset the downloaded data over Central Europe for the 1981-2020 period, and crop it to the national scale for Czech Republic. 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)
#Load additional packages
library(data.table)
library(raster)

Download

Downloading the entire data collection or only a few data sets is quite straightforward. You just call the download_data function, which has two arguments data_name and destination.

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.

Let’s download the ERA5 data set and inspect its content with show_info:

download_data(data_name = "era5", destination = ".")
show_info("era5_tp_mm_global_195901_202112_025_monthly.nc")

[1] "class      : RasterBrick "                                         
[2] "dimensions : 720, 1440, 1036800, 756  (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     : era5_tp_mm_global_195901_202112_025_monthly.nc "
[7] "names      : X1959.01.01, X1959.02.01, X1959.03.01, X1959.04.01, X1959.05.01, X1959.06.01, X1959.07.01, X1959.08.01, X1959.09.01, X1959.10.01, X1959.11.01, X1959.12.01, X1960.01.01, X1960.02.01, X1960.03.01, ... "
[8] "Date/time  : 1959-01-01, 2021-12-01 (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\(^\circ\)) to coarser ones (e.g., 0.5\(^\circ\), 1\(^\circ\), 1.5\(^\circ\), 2\(^\circ\), 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 three arguments data_file, years, and bbox.

data_file is the path to the data set of interest. The output file will be stored in the same location of the input file.
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).

Let’s subset the ERA5 data set over Central Europe (2,28,42,58) for the 1981-2020 period, and inspect its content with show_info:

subset_spacetime("era5_tp_mm_global_195901_202112_025_monthly.nc", 
                 years = c(1981, 2020), bbox = c(2,28,42,58))
show_info("era5_tp_mm_subset_198101_202012_025_monthly.nc")

[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 +no_defs "
[6] "source     : era5_tp_mm_subset_198101_202012_025_monthly.nc "
[7] "names      : X1981.01.01, X1981.02.01, X1981.03.01, X1981.04.01, X1981.05.01, X1981.06.01, X1981.07.01, X1981.08.01, X1981.09.01, X1981.10.01, X1981.11.01, X1981.12.01, X1982.01.01, X1982.02.01, X1982.03.01, ... "
[8] "Date/time  : 1981-01-01, 2020-12-01 (min, max)"
[9] "varname    : tp "

Crop

To further crop our data to a desired polygon other than a rectangle, we use the crop_data function, which has two arguments data_file, and shp_path.

data_file is the path to a “.nc” data set file.
shp_path is the path to a “.shp” file that we want to use to crop our data.

Let’s crop our ERA5 subset to cover only the Czech Republic with the respective shape file, and inspect its content with show_info:

crop_data(data_file = "era5_tp_mm_subset_198101_202012_025_monthly.nc", 
          shp_path = "CZE_adm0.shp")
show_info("era5_tp_mm_cropped_198101_202012_025_monthly.nc")

[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 +no_defs "
[6] "source     : era5_tp_mm_subset_1981_2020_025_monthly_cropped.nc "
[7] "names      : X1981.01.01, X1981.02.01, X1981.03.01, X1981.04.01, X1981.05.01, X1981.06.01, X1981.07.01, X1981.08.01, X1981.09.01, X1981.10.01, X1981.11.01, X1981.12.01, X1982.01.01, X1982.02.01, X1982.03.01, ... "
[8] "Date/time  : 1981-01-01, 2020-12-01 (min, max)"
[9] "varname    : tp "

Generate Time series

To make a time series out of our data, we use the make_ts function, which has one argument data_file.

data_file is the path to a “.nc” data set file.

Let’s generate the time series for our three different ERA5 data sets (Global, Central Europe, and Czech Republic), and inspect its first 12 rows:

make_ts("era5_tp_mm_global_195901_202112_025_monthly.nc")
era5_global_ts <- fread("era5_tp_mm_global_195901_202112_025_monthly_ts.csv")
head(era5_global_ts, 12)

          date    value
        <IDat>    <num>
 1: 1959-01-01 90.64116
 2: 1959-02-01 78.91318
 3: 1959-03-01 87.50946
 4: 1959-04-01 86.93461
 5: 1959-05-01 87.25465
 6: 1959-06-01 89.14776
 7: 1959-07-01 91.84578
 8: 1959-08-01 90.96841
 9: 1959-09-01 85.30629
10: 1959-10-01 88.53759
11: 1959-11-01 85.49954
12: 1959-12-01 90.00676

make_ts("era5_tp_mm_subset_198101_202012_025_monthly.nc")
era5_ce_ts <- fread("era5_tp_mm_subset_198101_202012_025_monthly_ts.csv")
head(era5_ce_ts, 12)

          date     value
        <IDat>     <num>
 1: 1981-01-01  73.83279
 2: 1981-02-01  46.39281
 3: 1981-03-01  89.47381
 4: 1981-04-01  47.78148
 5: 1981-05-01  88.37288
 6: 1981-06-01  98.71906
 7: 1981-07-01 100.87810
 8: 1981-08-01  69.74586
 9: 1981-09-01  89.17575
10: 1981-10-01 111.56850
11: 1981-11-01  74.93134
12: 1981-12-01 112.66740

make_ts("era5_tp_mm_cropped_198101_202012_025_monthly.nc")
era5_cze_ts <- fread("era5_tp_mm_cropped_198101_202012_025_monthly_ts.csv")
head(era5_cze_ts, 12)

          date     value
        <IDat>     <num>
 1: 1981-01-01  65.02662
 2: 1981-02-01  38.92457
 3: 1981-03-01  68.44784
 4: 1981-04-01  49.73646
 5: 1981-05-01  77.29006
 6: 1981-06-01  67.25784
 7: 1981-07-01 188.89990
 8: 1981-08-01  80.37363
 9: 1981-09-01  90.08721
10: 1981-10-01 115.19530
11: 1981-11-01  75.15187
12: 1981-12-01  89.48265

Visualize

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

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 ERA5 data sets (Global, Central Europe, and Czech Republic)

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.

global <- brick("era5_tp_mm_global_195901_202112_025_monthly.nc")
plot_map(global[[1]])

central_europe <- brick("era5_tp_mm_subset_198101_202012_025_monthly.nc")
plot_map(central_europe[[1]])

czechia <- brick("era5_tp_mm_cropped_198101_202012_025_monthly.nc")
plot_map(czechia[[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(era5_global_ts)

plot_line(era5_ce_ts)

plot_line(era5_cze_ts)

Heatmap

plot_heatmap(era5_global_ts)

plot_heatmap(era5_ce_ts)

plot_heatmap(era5_cze_ts)

Boxplot

plot_box(era5_global_ts)

plot_box(era5_ce_ts)

plot_box(era5_cze_ts)

Density

plot_density(era5_global_ts)

plot_density(era5_ce_ts)

plot_density(era5_cze_ts)

Summary

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

plot_summary(era5_global_ts)
#plot_summary(era5_ce_ts)
#plot_summary(era5_cze_ts)

Coming Soon

More functions for data processing and expanding the database.

References

Abatzoglou, John T, Solomon Z Dobrowski, Sean A Parks, and Katherine C Hegewisch. 2018. “TerraClimate, a High-Resolution Global Dataset of Monthly Climate and Climatic Water Balance from 1958–2015.” Scientific Data 5 (1): 1–12.

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.

Chen, Mingyue, Pingping Xie, John E. Janowiak, and Phillip A. Arkin. 2002. “Global Land Precipitation: A 50-Yr Monthly Analysis Based on Gauge Observations.” Journal of Hydrometeorology 3 (3): 249–66.

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.

Harris, Ian, Timothy J Osborn, Phil Jones, and David Lister. 2020. “Version 4 of the CRU TS Monthly High-Resolution Gridded Multivariate Climate Dataset.” Scientific Data 7 (1): 1–18.

Hersbach, Hans, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater, Julien Nicolas, et al. 2020. “The ERA5 Global Reanalysis.” Quarterly Journal of the Royal Meteorological Society 146 (730): 1999–2049.

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).

Huffman, George J, Robert F Adler, David T Bolvin, and Eric J Nelkin. 2010. “The TRMM Multi-Satellite Precipitation Analysis (TMPA).” In Satellite Rainfall Applications for Surface Hydrology, 3–22. Springer.

Joyce, Robert J, John E Janowiak, Phillip A Arkin, and Pingping Xie. 2004. “CMORPH: A Method That Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution.” Journal of Hydrometeorology 5 (3): 487–503.

Kalnay, Eugenia, Masao Kanamitsu, Robert Kistler, William Collins, Dennis Deaven, Lev Gandin, Mark Iredell, Suranjana Saha, Glenn White, and John Woollen. 1996. “The NCEP/NCAR 40-Year Reanalysis Project.” Bulletin of the American Meteorological Society 77 (3): 437–72.

Kanamitsu, Masao, Wesley Ebisuzaki, Jack Woollen, Shi-Keng Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter. 2002. “Ncep–Doe Amip-Ii Reanalysis (r-2).” Bulletin of the American Meteorological Society 83 (11): 1631–44.

Peterson, Thomas C., and Russell S. Vose. 1997. “An Overview of the Global Historical Climatology Network Temperature Database.” Bulletin of the American Meteorological Society 78 (12): 2837–50.

Poli, Paul, Hans Hersbach, Dick P Dee, Paul Berrisford, Adrian J Simmons, Frédéric Vitart, Patrick Laloyaux, et al. 2016. “ERA-20C: An Atmospheric Reanalysis of the Twentieth Century.” Journal of Climate 29 (11): 4083–97.

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.

Slivinski, Laura C, Gilbert P Compo, Jeffrey S Whitaker, Prashant D Sardeshmukh, Benjamin S Giese, Chesley McColl, Rob Allan, et al. 2019. “Towards a More Reliable Historical Reanalysis: Improvements for Version 3 of the Twentieth Century Reanalysis System.” Quarterly Journal of the Royal Meteorological Society 145 (724): 2876–2908.

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

2022-12-27