Data used by OGGM

Data used by OGGM#

In this notebook, we want to showcase what OGGM does best: preparing data for your modelling workflow.

We use preprocessed directories which contain most data available from the OGGM shop to illustrate how these could be used to inform data-based workflows.

Preprocessed directories with additional products#

We are going to use the South Glacier example taken from the ITMIX experiment. It is a small (5.6 km2) glacier in Alaska.

## Libs
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import xarray as xr
import salem

# OGGM
import oggm.cfg as cfg
from oggm import utils, workflow, tasks, graphics

# Initialize OGGM and set up the default run parameters
cfg.initialize(logging_level='WARNING')
cfg.PARAMS['use_multiprocessing'] = False
# Local working directory (where OGGM will write its output)
cfg.PATHS['working_dir'] = utils.gettempdir('OGGM_Toy_Thickness_Model')
# We use the directories with the shop data in it: "W5E5_w_data"
base_url = 'https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/L3-L5_files/2023.1/elev_bands/W5E5_w_data/'
gdirs = workflow.init_glacier_directories(['RGI60-14.05446'], from_prepro_level=3, prepro_base_url=base_url, prepro_border=10)

2023-03-23 08:17:43: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.

2023-03-23 08:17:43: oggm.cfg: Multiprocessing switched OFF according to the parameter file.

2023-03-23 08:17:43: oggm.cfg: Multiprocessing: using all available processors (N=8)

2023-03-23 08:17:43: oggm.workflow: init_glacier_directories from prepro level 3 on 1 glaciers.

2023-03-23 08:17:43: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 1 glaciers

# Pick our glacier
gdir = gdirs[0]
gdir

<oggm.GlacierDirectory>
  RGI id: RGI60-14.05446
  Region: 14: South Asia West
  Subregion: 14-02: Karakoram                       
  Name: Hassanabad Glacier I
  Glacier type: Glacier
  Terminus type: Land-terminating
  Status: Glacier or ice cap
  Area: 45.712 km2
  Lon, Lat: (74.619, 36.4371)
  Grid (nx, ny): (134, 174)
  Grid (dx, dy): (105.0, -105.0)

Hugonnet dh/dt data#

# get the axes ready
f, ax = plt.subplots(figsize=(9, 9))

smap.set_data(ds.hugonnet_dhdt)
smap.set_cmap('RdBu')
smap.set_plot_params(vmin=-3, vmax=3)
smap.plot(ax=ax)
smap.append_colorbar(ax=ax, label='dh/dt')

ax.set_title('Hugonnet dh/dt');

../_images/04093e22d5d6614a11f3de8378cb3dd7bb1abb9b61b8eefd73476f049b5b2125.png

ITSLive velocity data#

# get the velocity data
u = ds.itslive_vx.where(ds.glacier_mask)
v = ds.itslive_vy.where(ds.glacier_mask)
ws = ds.itslive_v.where(ds.glacier_mask)

The .where(ds.glacier_mask) command will remove the data outside of the glacier outline.

# get the axes ready
f, ax = plt.subplots(figsize=(9, 9))

# Quiver only every N grid point
us = u[1::3, 1::3]
vs = v[1::3, 1::3]

smap.set_data(ws)
smap.set_cmap('Blues')
smap.set_plot_params()
smap.plot(ax=ax)
smap.append_colorbar(ax=ax, label='ice velocity (m yr$^{-1}$)')

# transform their coordinates to the map reference system and plot the arrows
xx, yy = smap.grid.transform(us.x.values, us.y.values, crs=gdir.grid.proj)
xx, yy = np.meshgrid(xx, yy)
qu = ax.quiver(xx, yy, us.values, vs.values)
qk = ax.quiverkey(qu, 0.82, 0.97, 10, '10 m yr$^{-1}$',
                  labelpos='E', coordinates='axes')
ax.set_title('ITS-LIVE velocity');

../_images/f5eaf6edb83fadee24f2a62f32158ea9e8eb977ca291477e7e590c5a9c64341d.png

Millan 2022 velocity data#

# get the velocity data
u = ds.millan_vx.where(ds.glacier_mask)
v = ds.millan_vy.where(ds.glacier_mask)
ws = ds.millan_v.where(ds.glacier_mask)

# get the axes ready
f, ax = plt.subplots(figsize=(9, 9))

# Quiver only every N grid point
us = u[1::3, 1::3]
vs = v[1::3, 1::3]

smap.set_data(ws)
smap.set_cmap('Blues')
smap.plot(ax=ax)
smap.append_colorbar(ax=ax, label='ice velocity (m yr$^{-1}$)')

# transform their coordinates to the map reference system and plot the arrows
xx, yy = smap.grid.transform(us.x.values, us.y.values, crs=gdir.grid.proj)
xx, yy = np.meshgrid(xx, yy)
qu = ax.quiver(xx, yy, us.values, vs.values)
qk = ax.quiverkey(qu, 0.82, 0.97, 10, '10 m yr$^{-1}$',
                  labelpos='E', coordinates='axes')
ax.set_title('Millan 2022 velocity');

../_images/53791e221b7cfc599951a8aace46efb7eb087d6f3ced80a96b9bc82ba553c76a.png

Thickness data from Farinotti 2019 and Millan 2022#

# get the axes ready
f, ax = plt.subplots(figsize=(9, 9))
smap.set_cmap('viridis')
smap.set_data(ds.consensus_ice_thickness)
smap.plot(ax=ax)
smap.append_colorbar(ax=ax, label='ice thickness (m)')
ax.set_title('Farinotti 2019 thickness');

../_images/bd19075ce98665efd87410a617c5fb41c60d99bba9bc5b82b41da90df0376496.png

# get the axes ready
f, ax = plt.subplots(figsize=(9, 9))
smap.set_cmap('viridis')
smap.set_data(ds.millan_ice_thickness.where(ds.glacier_mask))
smap.plot(ax=ax)
smap.append_colorbar(ax=ax, label='ice thickness (m)')
ax.set_title('Millan 2022 thickness');