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.
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:
[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:
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 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 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: 2001-12-01 156.72036Visualize
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.
