Dual threshold

Two threshold values are set to discriminate precipitation type between rain (\(th_{rain}\)) and snow (\(th_{snow}\)). It can be either used with any meteorological field, but with thresholds properly defined. If a value of the meteorological field is above \(th_{rain}\), precipitation is classified as rain. On the other hand, if the value is below \(th_{snow}\), precipitation is classified as snow. Finally, if values are between \(th_{snow}\) and \(th_{rain}\), precipitation is classified as a mixed type.

If the meteorological field chosen to discriminate precipitation is air temperature:

\(\begin{equation*} T_{a} <= T_{snow} \longrightarrow Snow \\ T_{snow} < T_{a} < T_{rain} \longrightarrow Mixed \\ T_{a} >= T_{rain} \longrightarrow Rain \end{equation*}\)

In the following example we’ll show how PyPROS classifies precipitation considering the dual threshold scheme.

First of all, we’ll import the required libraries.

from pypros.pros import PyPros

As an example, we’ll get the precipitation type classification from different methodologies for Catalonia on 2017-03-25 00.30 UTC. For this purpose we’ll use an air temperature, dew point temperature, digital elevation model (DEM) and reflectivity fields.

Those fields can be found in notebooks/data directory and we’ll keep the path for all of them:

tair_file = '../sample-data/INT_TAIR_20170325_0030.tif'
tdew_file = '../sample-data/INT_TDEW_20170325_0030.tif'
dem_file = '../sample-data/DEM_CAT.tif'

Now, we’ll define those parameters that PyPros class uses and are the same whether the methodology changes or not. These parameters are: variables_files and data_format. For more information on this class, see PyPros Class notebook.

variables_files = [tair_file,
                   tdew_file,
                   dem_file]
data_format = {'vars_files':['tair', 'tdew', 'dem']}

Air temperature thresholds

Since we want to apply an air temperature dual threshold, first we’ll define method PyPros parameter as 'dual_ta' and then we’ll set the threshold parameter to [0, 3] (\(^{\circ}\)C).

method = 'dual_ta'
threshold = [0, 3]

Now, we’re ready to call PyPros class!

dual_ta = PyPros(variables_files, method, threshold, data_format)

We can get a quicklook of the obtained field using plot_pros function:

import matplotlib.pyplot as plt
plt.imshow(dual_ta.result)
plt.show()

In addition, we can save the precipitation type field in a raster file using save_file function:

dual_ta.save_file(dual_ta.result, '../sample-data/output/dual_ta.tif')

If we have a reflectivity field, we can also apply it as a mask by using refl_mask function and save it as a raster file. However, we’ll have to read first the reflectivity field. For this purpose we need to import gdal.

from osgeo import gdal

refl_file = '../sample-data/CAPPI_XRAD_20170325_0030.tif'
refl_array = gdal.Open(refl_file).ReadAsArray()

Once we’ve read the refl_field we can call the refl_mask function.

dual_ta_masked = dual_ta.refl_mask(refl_array)

dual_ta.save_file(dual_ta_masked, '../sample-data/output/dual_ta_masked.tif')

Wet bulb temperature thresholds

Since we want to apply a wet bulb temperature dual threshold, first we’ll define method PyPros parameter as 'dual_tw' and then we’ll set the threshold parameter to [0, 2] (\(^{\circ}\)C).

method = 'dual_tw'
threshold = [0, 2]

Now, we’re ready to call PyPros class!

dual_tw = PyPros(variables_files, method, threshold, data_format)

We can get a quicklook of the obtained field using plot_pros function:

import matplotlib.pyplot as plt
plt.imshow(dual_tw.result)
plt.show()

In addition, we can save the precipitation type field in a raster file using save_file function:

dual_tw.save_file(dual_tw.result, '../sample-data/output/dual_tw.tif')

If we have a reflectivity field, we can also apply it as a mask by using refl_mask function and save it as a raster file. However, we’ll have to read first the reflectivity field. For this purpose we need to import gdal.

from osgeo import gdal

refl_file = '../sample-data/CAPPI_XRAD_20170325_0030.tif'
refl_array = gdal.Open(refl_file).ReadAsArray()

Once we’ve read the refl_field we can call the refl_mask function.

dual_tw_masked = dual_tw.refl_mask(refl_array)

dual_tw.save_file(dual_tw_masked, '../sample-data/output/dual_tw_masked.tif')