OGGM Mass-Balance sandbox

Next generation of OGGM's mass-balance models. Work in process!

At the moment these options are available:

• to compute degree days:
  • using monthly temperatures ('mb_monthly'), default option in OGGM
  • using monthly temperatures and daily temp std to generate daily temp. assuming normal distributed data ('mb_pseudo_daily')
  • using daily temperatures ('mb_real_daily')
• temperature lapse rates:
  • using a constant calibrated value independent of location and season (-6.5 K/km, grad_type: cte), default option in OGGM
  • using lapse rates from ERA5 that vary throughout the year and inbetween glacier locations, but that are constant inbetween the years (grad_type: 'var_an_cycle')
  • using lapse rates from ERA5 that vary throughout the year and inbetween glacier locations, different for each year (grad_type: 'var_an_cycle')

All options have been tested with the elevation flowline from Huss.

How to install/use !

the newest OGGM developer version has to be installed in order that MBsandbox works: e.g. do:

$ conda create --name env_mb
$ source activate env_mb
$ git clone  https://github.com/OGGM/oggm.git
$ cd oggm 
$ pip install -e .
$ git clone https://github.com/OGGM/massbalance-sandbox
$ cd massbalance-sandbox
$ pip install -e .

Test the installation via pytest while being in the massbalance-sandbox folder:

$ pytest .

The MBsandbox package can be imported in python by

>>> import MBsandbox

A simple use case is explained in docs/how_to_use.ipynb.

inside of MBsandbox

• mbmod_daily_oneflowline.py: process climate data, new mass-balance model TIModel_Parent with children, ...
• flowline_TIModel.py: copies of run_from_climate, run_random_climate that are compatible with TIModel
• help_func.py: helper functions to minimize the bias, optimise std_quot, to calibrate the melt factor given precipitation factor and geodetic observations, and to compute performance statistics
• tests: tests for different functions
• wip/...: work in process folder without documentation

docs/*:

simple use case: how_to_use.ipynb

how to get daily mb output and calibrate with geodetic data: how:to_daily_input_daily_output.ipynb

how to use MBsandbox with run_with_hydro hydro_compatility/hydro_compatibility_workflow.ipynb

mass balance intercomparison with figures: intercomparison_w_figures/*

• HEF_mb_type_intercomparison_oneflowline.ipynb: shows mb type intercomparison for Hintereisferner
  • plots in figures_hef
• refglaciersAlps_mb_type_intercomparison_oneflowline.ipynb : shows intercomparison for Alpine reference glaciers plots in:
  • figures_alps (performance measures for Alpine reference glaciers)
  • figures_alps_indiv (observed and modelled mb time series with performance measures for each Alpine reference glacier
  • stats_Alps_6mb_models_N_5000_with_mean_an_cycle.csv: dataset of optimal DDFs and performance measures for all Alpine glaciers and all mb types

preprocess_ERA5_daily: this is just a side product

• preprocess_ERA5_daily/cluster_aggregate_dailyERA5.ipynb: how to preprocess the ERA5_daily files (end product is here: https://cluster.klima.uni-bremen.de/~oggm/climate/era5/daily/v1.0/)

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