Rank Histogram#

This example demonstrates how to plot a rank histogram.

Import the obs_sequence module and the matplots module for plotting.

import pydartdiags.obs_sequence.obs_sequence as obsq
from pydartdiags.matplots import matplots as mp

Chose an obs_seq file to read. In this example, we are using a small obs_seq file “obs_seq.final.1000” that comes with the pyDARTdiags package in the data directory, so we import os to get the path to the file.

import os
data_dir = os.path.join(os.getcwd(), "../..", "data")
data_file = os.path.join(data_dir, "obs_seq.final.1000")

Read the obs_seq file into an obs_seq object.

obs_seq = obsq.obs_sequence(data_file)

Chose an observation type. The observation types are stored in the ‘type’ column. To see which observation types are in the dataframe, use the unique method:

obs_seq.df['type'].unique()

array(['ACARS_TEMPERATURE', 'ACARS_U_WIND_COMPONENT',
       'ACARS_V_WIND_COMPONENT', 'AIRCRAFT_TEMPERATURE',
       'AIRCRAFT_U_WIND_COMPONENT', 'AIRCRAFT_V_WIND_COMPONENT'],
      dtype=object)

For this example, we are going to look at the rank histogram for ACARS_TEMPERATURE observations.

obs_type = 'ACARS_TEMPERATURE'

Chose the levels to plot. The vertical coordinate of the observations are stored in the vertical column. The units for the vertical coordinate are stored in the vert_unit column In this case the vertical coordinate of the observations are in pressure (Pa). Typically in atmospheric science we use hPa as the unit for pressure.

hPalevels = [0.0, 100.0,  150.0, 200.0, 250.0, 300.0, 400.0, 500.0, 700, 850, 925, 1000]
levels = [i * 100 for i in hPalevels] # convert hPa to Pa

Plot the rank histogram. The dataframe has prior and posterior information so both the prior and posterior rank histograms are plotted.

ens_size = 80
fig = mp.plot_rank_histogram(obs_seq, levels, obs_type, ens_size)

Total running time of the script: (0 minutes 2.518 seconds)

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