Tutorial 2: Aggregation to Precipitation Sums¶

This tutorial will show you, how to calculate precipitation sums from the data stored in HDF5 and how to export the results to ArcGIS.

To import your RADOLAN data into the necessary HDF5 file, please follow the tutorial on raw data processing.

Example 1: Annual Precipitation Sums¶

In this example, the annual precipitation sums for the time period from 2001 to 2016 are calculated and exported to ArcGIS.

1. Import radproc¶

In [1]:

import radproc as rp

2. Load Data from HDF5 and Aggregate to Annual Precipitation Sums¶

The following function loads precipitation data of the specified time period (2001-2016) from an HDF5 file and generates a DataFrame with annual precipitation sums for every raster cell.

In [2]:

HDF = r"O:\Data\RW_2001_2016.h5"

annualSum = rp.hdf5_to_years(HDFFile=HDF, year_start=2001, year_end=2016)
# Display the first five rows of the new DataFrame
annualSum.head()

Out[2]:

Rasterzellen-ID	427005	427006	427903	427904	427905	427906	428803	428804	428805	428806	...	661855	661856	661857	662752	662753	662754	662755	662756	663652	663655
Datum (UTC)
2001-12-31 00:00:00+00:00	783.400024	809.500000	763.299988	773.500000	779.700012	788.500000	769.500000	780.400024	778.700012	783.000000	...	1066.599976	1098.699951	1125.400024	994.000000	998.700012	1020.599976	1056.099976	1075.200073	989.200012	1039.500000
2002-12-31 00:00:00+00:00	967.700012	980.799988	951.000000	935.500000	947.200012	950.700012	956.000000	960.700012	938.700012	955.400024	...	1491.599976	1529.500000	1539.199951	1383.099976	1387.500000	1407.400024	1478.099976	1488.800049	1380.400024	1467.099976
2003-12-31 00:00:00+00:00	597.200012	614.200012	578.000000	583.000000	592.200012	592.299988	573.400024	587.700012	587.299988	589.600037	...	697.600037	696.099976	683.700012	670.400024	652.599976	638.400024	663.599976	668.599976	663.099976	648.000000
2004-12-31 00:00:00+00:00	795.000000	801.000000	772.000000	768.099976	776.600037	792.799988	746.500000	767.099976	779.000000	792.900024	...	851.599976	872.200012	869.799988	803.000000	802.000000	804.099976	825.099976	841.599976	806.000000	813.700012
2005-12-31 00:00:00+00:00	728.099976	710.799988	678.500000	676.799988	730.200012	681.399963	671.400024	664.099976	658.799988	666.299988	...	914.799988	922.900024	911.099976	847.799988	861.500000	868.099976	893.200012	903.299988	850.799988	872.500000

5 rows × 23320 columns

Note: All of radproc’s aggregation functions are intended for analysis of longer time periods and currently only work for entire years starting in January! To resample smaller time periods, you can e.g. import and resample months with

May2016 = rp.load_months_from_hdf5(HDFFile=HDF, year=2016,  months=[5])
freq = 'M' # 'M' for monthly sums, 'D' for daily sums, 'H' for hourly sums
monthSum = May2016.resample(frequency=freq, closed = 'right', label = 'right').sum()

3. Export Results into ArcGIS Geodatabase¶

The following function exports all rows from the DataFrame calculated above into raster datasets in an ArcGIS File Geodatabase. Optionally, different statistics rasters can be created, e.g. the mean or the maximum value of each cell.

In [3]:

idRaster = r"O:\Data\idras"
outGDBPath = r"O:\Data"
GDBName = "Years_01_16.gdb"
statistics = ["mean", "max"]

rp.export_dfrows_to_gdb(dataDF=annualSum, idRaster=idRaster, outGDBPath=outGDBPath, GDBName=GDBName, statistics=statistics)

The resulting geodatabase will look like this in ArcGIS:

YearGDB

Example 2: Monthly Precipitation Sums¶

In this example, the monthly precipitation sums for the year 2016 are calculated and exported to ArcGIS.

1. Import radproc¶

In [4]:

import radproc as rp

2. Load Data from HDF5 and Aggregate to Monthly Precipitation Sums¶

The following function loads precipitation data of the year 2016 from your HDF5 file and generates a DataFrame with monthly precipitation sums for every raster cell.

In [5]:

HDF = r"O:\Data\RW_2001_2016.h5"

monthlySum = rp.hdf5_to_months(HDFFile=HDF, year_start=2016, year_end=2016)
# Display the first five rows of the new DataFrame
monthlySum.head()

Out[5]:

Rasterzellen-ID	427005	427006	427903	427904	427905	427906	428803	428804	428805	428806	...	661855	661856	661857	662752	662753	662754	662755	662756	663652	663655
Datum (UTC)
2016-01-31 00:00:00+00:00	71.400002	71.900002	68.000000	69.099998	70.099998	71.699997	65.500000	67.300003	66.199997	69.800003	...	128.199997	129.500000	129.600006	120.199997	121.300003	123.500000	125.699997	122.900002	118.099998	120.400002
2016-02-29 00:00:00+00:00	68.699997	71.199997	61.500000	64.300003	67.300003	68.400002	58.200001	61.700001	65.000000	64.300003	...	105.900002	106.199997	110.099998	96.800003	96.699997	100.099998	102.599998	101.400002	92.800003	97.400002
2016-03-31 00:00:00+00:00	41.799999	41.299999	40.299999	39.900002	40.099998	39.299999	37.700001	38.200001	38.799999	37.500000	...	65.500000	64.400002	65.199997	70.000000	67.500000	64.400002	65.099998	65.099998	69.800003	65.500000
2016-04-30 00:00:00+00:00	55.700001	56.600002	51.799999	52.400002	54.000000	55.299999	50.799999	51.200001	52.799999	54.400002	...	108.500000	112.199997	115.400002	106.700005	106.500000	107.699997	112.599998	114.300003	105.199997	108.099998
2016-05-31 00:00:00+00:00	38.200001	40.600002	47.299999	44.799999	39.400002	34.299999	46.799999	45.400002	43.200001	38.500000	...	121.299995	119.300003	115.099998	128.100006	138.199997	135.500000	124.300003	128.500000	130.199997	134.399994

5 rows × 23320 columns

3. Export Results into ArcGIS Geodatabase¶

The following function exports all rows from the DataFrame calculated above into raster datasets in an ArcGIS File Geodatabase. Optionally, different statistics rasters can be created, in this case the mean, maximum and minimum value of each cell as well as the range per cell.

In [6]:

idRaster = r"O:\Data\idras"
outGDBPath = r"O:\Data"
GDBName = "Months_16.gdb"
statistics = ["mean", "max", "min", "range"]

rp.export_dfrows_to_gdb(dataDF=monthlySum, idRaster=idRaster, outGDBPath=outGDBPath, GDBName=GDBName, statistics=statistics)

The resulting geodatabase will look like this in ArcGIS:

MonthGDB