Error analysis of the global pre-processing workflow

Here we reproduce the error analysis shown in Maussion et al. (2019) for the pre-processing part only, and for the new glacier directories (version 1.4). The error analysis of user runs needs a separate handling, see the deal_with_errors notebook for more information.

Get the files

We download the glacier_statistics files from the preprocessed directories folders, at the level 5. That is, we are going to count all errors that happened during the pre-processing chain.

from oggm import utils
import pandas as pd
import seaborn as sns
# This is CRU + centerlines. But you can try CRU+elev_bands, or ERA5+elev_bands, etc!
url = 'https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/L3-L5_files/CRU/centerlines/qc3/pcp2.5/no_match/RGI62/b_040/L5/summary/'
# this can take some time
df = []
for rgi_reg in range(1, 19):
    fpath = utils.file_downloader(url + f'glacier_statistics_{rgi_reg:02d}.csv')
    df.append(pd.read_csv(fpath, index_col=0, low_memory=False))
df = pd.concat(df, sort=False).sort_index()

Analyze the errors

sns.countplot(y="error_task", data=df);
../_images/preprocessing_errors_8_0.png
"% area errors all sources: {:.2f}%".format(df.loc[~df['error_task'].isnull()].rgi_area_km2.sum() / df.rgi_area_km2.sum() * 100)
'% area errors all sources: 3.24%'

Most errors occur because the interpolation of tstar did not work.

We now look at the errors that occur already before applying the climate tasks:

dfe = df.loc[~df['error_task'].isnull()]
dfe = dfe.loc[~dfe['error_task'].isin(['local_t_star', 'mu_star_calibration'])]
"% area errors before climate: {:.2f}%".format(dfe.rgi_area_km2.sum() / df.rgi_area_km2.sum() * 100)
'% area errors before climate: 0.41%'
sns.countplot(y="error_task", data=dfe);
../_images/preprocessing_errors_12_0.png
# 15 largest glaciers
df.loc[~df['error_task'].isnull()].sort_values(by='rgi_area_km2', ascending=False)[['rgi_area_km2', 'error_task', 'error_msg']].iloc[:15]
rgi_area_km2 error_task error_msg
rgi_id
RGI60-17.15804 790.282 mu_star_calibration MassBalanceCalibrationError: RGI60-17.15804 mu...
RGI60-17.15808 721.950 local_t_star MassBalanceCalibrationError: RGI60-17.15808: m...
RGI60-13.54431 663.729 flowline_model_run_historical RuntimeError: CFL error: required time step sm...
RGI60-17.15897 428.505 local_t_star MassBalanceCalibrationError: RGI60-17.15897: m...
RGI60-05.10735 392.607 local_t_star MassBalanceCalibrationError: RGI60-05.10735: m...
RGI60-05.10634 388.067 local_t_star MassBalanceCalibrationError: RGI60-05.10634: m...
RGI60-05.10617 373.290 local_t_star MassBalanceCalibrationError: RGI60-05.10617: m...
RGI60-05.10762 309.061 local_t_star MassBalanceCalibrationError: RGI60-05.10762: m...
RGI60-05.10748 289.188 local_t_star MassBalanceCalibrationError: RGI60-05.10748: m...
RGI60-13.43483 282.721 flowline_model_run_historical RuntimeError: CFL error: required time step sm...
RGI60-17.15898 268.749 local_t_star MassBalanceCalibrationError: RGI60-17.15898: m...
RGI60-01.16377 254.841 flowline_model_run_historical RuntimeError: CFL error: required time step sm...
RGI60-05.10988 207.766 local_t_star MassBalanceCalibrationError: RGI60-05.10988: m...
RGI60-14.04404 203.336 flowline_model_run_historical RuntimeError: CFL error: required time step sm...
RGI60-05.10959 183.850 local_t_star MassBalanceCalibrationError: RGI60-05.10959: m...

What’s next?