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What's New

.. ipython:: python
    :suppress:

    import climpred
    from climpred import HindcastEnsemble
    import matplotlib as mpl

    mpl.rcdefaults()
    mpl.use("Agg")
    # cut border when saving (for maps)
    mpl.rcParams["savefig.bbox"] = "tight"

climpred v2.5.0 (2024-07-05)

Internals/Minor Fixes

climpred v2.4.0 (2023-11-09)

Internals/Minor Fixes

climpred v2.3.0 (2022-11-25)

Note

As both maintainers moved out of academia into industry, this will be probably the last release for a while. If you are interested in maintaining climpred, please ping us.

Bug Fixes

New Features

Internals/Minor Fixes

Bug Fixes

Documentation

climpred v2.2.0 (2021-12-20)

Bug Fixes

New Features

>>> hind = climpred.tutorial.load_dataset("CESM-DP-SST")
>>> hind.lead.attrs["units"] = "years"
>>> climpred.HindcastEnsemble(hind).get_initialized()
<xarray.Dataset>
Dimensions:     (lead: 10, member: 10, init: 64)
Coordinates:
  * lead        (lead) int32 1 2 3 4 5 6 7 8 9 10
  * member      (member) int32 1 2 3 4 5 6 7 8 9 10
  * init        (init) object 1954-01-01 00:00:00 ... 2017-01-01 00:00:00
    valid_time  (lead, init) object 1955-01-01 00:00:00 ... 2027-01-01 00:00:00
Data variables:
    SST         (init, lead, member) float64 ...
>>> import climpred
>>> hind = climpred.tutorial.load_dataset("NMME_hindcast_Nino34_sst")
>>> obs = climpred.tutorial.load_dataset("NMME_OIv2_Nino34_sst")
>>> hindcast = climpred.HindcastEnsemble(hind).add_observations(obs)
>>> # skill for each init month separated
>>> skill = hindcast.verify(
...     metric="rmse",
...     dim="init",
...     comparison="e2o",
...     skipna=True,
...     alignment="maximize",
...     groupby="month",
... )
>>> skill
<xarray.Dataset>
Dimensions:  (month: 12, lead: 12, model: 12)
Coordinates:
  * lead     (lead) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0
  * model    (model) object 'NCEP-CFSv2' 'NCEP-CFSv1' ... 'GEM-NEMO'
    skill    <U11 'initialized'
  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12
Data variables:
    sst      (month, lead, model) float64 0.4127 0.3837 0.3915 ... 1.255 3.98
>>> skill.sst.plot(hue="model", col="month", col_wrap=3)

Internals/Minor Fixes

Documentation

climpred v2.1.6 (2021-08-31)

Adding on to v2.1.5, more bias reduction methods wrapped from xclim are implemented.

Bug Fixes

New Features

climpred v2.1.5 (2021-08-12)

While climpred has used in the ASP summer colloquium 2021, many new features in :py:meth:`.HindcastEnsemble.remove_bias` were implemented.

Breaking changes

Bug Fixes

New Features

Documentation

climpred v2.1.4 (2021-06-28)

New Features

Documentation

Internals/Minor Fixes

  • Add weekly upstream CI, which raises issues for failures. Adapted from xarray. Manually trigger by git commit -m '[test-upstream]'. Skip climpred_testing CI by git commit -m '[skip-ci]' (:issue:`518`, :pr:`596`) Aaron Spring.

climpred v2.1.3 (2021-03-23)

New Features

Bug fixes

Internals/Minor Fixes

climpred v2.1.2 (2021-01-22)

This release is the fixed version for our Journal of Open Source Software (JOSS) article about climpred, see review.

New Features

Bug fixes

climpred v2.1.1 (2020-10-13)

Breaking changes

This version introduces a lot of breaking changes. We are trying to overhaul climpred to have an intuitive API that also forces users to think about methodology choices when running functions. The main breaking changes we introduced are for :py:meth:`.HindcastEnsemble.verify` and :py:meth:`.PerfectModelEnsemble.verify`. Now, instead of assuming defaults for most keywords, we require the user to define metric, comparison, dim, and alignment (for hindcast systems). We also require users to designate the number of iterations for bootstrapping.

New Features

This release is accompanied by a bunch of new features. Math operations can now be used with our :py:class:`.PredictionEnsemble` objects and their variables can be sub-selected. Users can now quick plot time series forecasts with these objects. Bootstrapping is available for :py:class:`.HindcastEnsemble`. Spatial dimensions can be passed to metrics to do things like pattern correlation. New metrics have been implemented based on Contingency tables. We now include an early version of bias removal for :py:class:`.HindcastEnsemble`.

Depreciated

Bug Fixes

Documentation

Internals/Minor Fixes

climpred v2.1.0 (2020-06-08)

Breaking Changes

  • Keyword bootstrap has been replaced with iterations. We feel that this more accurately describes the argument, since "bootstrap" is really the process as a whole. (:pr:`354`) Aaron Spring.

New Features

Performance

The major change for this release is a dramatic speedup in bootstrapping functions, led by Aaron Spring. We focused on scalability with dask and found many places we could compute skill simultaneously over all bootstrapped ensemble members rather than at each iteration.

  • Bootstrapping uninitialized skill in the perfect model framework is now sped up significantly for annual lead resolution. (:pr:`332`) Aaron Spring.

  • General speedup in ~climpred.bootstrap.bootstrap_hindcast and ~climpred.bootstrap.bootstrap_perfect_model: (:pr:`285`) Aaron Spring.

    • Properly implemented handling for lazy results when inputs are chunked.
    • User gets warned when chunking potentially unnecessarily and/or inefficiently.

Bug Fixes

  • Alignment options now account for differences in the historical time series if reference='historical'. (:pr:`341`) Riley X. Brady.

Internals/Minor Fixes

Documentation

climpred v2.0.0 (2020-01-22)

New Features

  • Add support for days, pentads, weeks, months, seasons for lead time resolution. climpred now requires a lead attribute "units" to decipher what resolution the predictions are at. (:pr:`294`) Kathy Pegion and Riley X. Brady.

Documentation

  • New example pages for subseasonal-to-seasonal prediction using climpred. (:pr:`294`) Kathy Pegion

    • Calculate the skill of the MJO index as a function of lead time (link).
    • Calculate the skill of the MJO index as a function of lead time for weekly data (link).
    • Calculate ENSO skill as a function of initial month vs. lead time (link).
    • Calculate Seasonal ENSO skill (link).

  • Comparisons page rewritten for more clarity. (:pr:`310`) Riley X. Brady.

Bug Fixes

Internals/Minor Fixes

  • Updates to xskillscore v0.0.12 to get a 30-50% speedup in compute functions that rely on metrics from there. (:pr:`309`) Riley X. Brady.
  • Stacking dims is handled by comparisons, no need for internal keyword stack_dims. Therefore comparison now takes metric as argument instead. (:pr:`290`) Aaron Spring.
  • assign_attrs now carries dim (:pr:`290`) Aaron Spring.
  • reference changed to verif throughout hindcast compute functions. This is more clear, since reference usually refers to a type of forecast, such as persistence. (:pr:`310`) Riley X. Brady.
  • Comparison objects can now have aliases. (:pr:`310`) Riley X. Brady.

climpred v1.2.1 (2020-01-07)

Depreciated

  • mad no longer a keyword for the median absolute error metric. Users should now use median_absolute_error, which is identical to changes in xskillscore version 0.0.10. (:pr:`283`) Riley X. Brady
  • pacc no longer a keyword for the p value associated with the Pearson product-moment correlation, since it is used by the correlation coefficient. (:pr:`283`) Riley X. Brady
  • msss no longer a keyword for the Murphy's MSSS, since it is reserved for the standard MSSS. (:pr:`283`) Riley X. Brady

New Features

  • Metrics pearson_r_eff_p_value and spearman_r_eff_p_value account for autocorrelation in computing p values. (:pr:`283`) Riley X. Brady

  • Metric effective_sample_size computes number of independent samples between two time series being correlated. (:pr:`283`) Riley X. Brady

  • Added keywords for metrics: (:pr:`283`) Riley X. Brady

    • 'pval' for pearson_r_p_value
    • ['n_eff', 'eff_n'] for effective_sample_size
    • ['p_pval_eff', 'pvalue_eff', 'pval_eff'] for pearson_r_eff_p_value
    • ['spvalue', 'spval'] for spearman_r_p_value
    • ['s_pval_eff', 'spvalue_eff', 'spval_eff'] for spearman_r_eff_p_value
    • 'nev' for nmse

Internals/Minor Fixes

Documentation

climpred v1.2.0 (2019-12-17)

Depreciated

New Features

>>> hind = climpred.tutorial.load_dataset("CESM-DP-SST")
>>> ref = climpred.tutorial.load_dataset("ERSST")
>>> hindcast = climpred.HindcastEnsemble(hind)
>>> hindcast = hindcast.add_reference(ref, "ERSST")
>>> print(hindcast)
<climpred.HindcastEnsemble>
Initialized Ensemble:
    SST      (init, lead, member) float64 ...
ERSST:
    SST      (time) float32 ...
Uninitialized:
    None
>>> print(hindcast.get_initialized())
<xarray.Dataset>
Dimensions:  (init: 64, lead: 10, member: 10)
Coordinates:
* lead     (lead) int32 1 2 3 4 5 6 7 8 9 10
* member   (member) int32 1 2 3 4 5 6 7 8 9 10
* init     (init) float32 1954.0 1955.0 1956.0 1957.0 ... 2015.0 2016.0 2017.0
Data variables:
    SST      (init, lead, member) float64 ...
>>> print(hindcast.get_reference("ERSST"))
<xarray.Dataset>
Dimensions:  (time: 61)
Coordinates:
* time     (time) int64 1955 1956 1957 1958 1959 ... 2011 2012 2013 2014 2015
Data variables:
    SST      (time) float32 ...

Bug Fixes

Internals/Minor Fixes

Documentation

climpred v1.1.0 (2019-09-23)

Features

Bug Fixes

  • Correct implementation of probabilistic metrics from xskillscore in compute_perfect_model, bootstrap_perfect_model, compute_hindcast and bootstrap_hindcast, now requires xskillscore>=0.05 (:pr:`232`) Aaron Spring

Internals/Minor Fixes

Documentation

climpred v1.0.1 (2019-07-04)

Bug Fixes

Internals/Minor Fixes

climpred v1.0.0 (2019-07-03)

climpred v1.0.0 represents the first stable release of the package. It includes :py:class:`.HindcastEnsemble` and PerfectModelEnsemble objects to perform analysis with. It offers a suite of deterministic and probabilistic metrics that are optimized to be run on single time series or grids of data (e.g., lat, lon, and depth). Currently, climpred only supports annual forecasts.

Features

  • Bootstrap prediction skill based on resampling with replacement consistently in ReferenceEnsemble and PerfectModelEnsemble. (:pr:`128`) Aaron Spring
  • Consistent bootstrap function for climpred.stats functions via bootstrap_func wrapper. (:pr:`167`) Aaron Spring
  • many more metrics: _msss_murphy, _less and probabilistic _crps, _crpss (:pr:`128`) Aaron Spring

Bug Fixes

Internals/Minor Fixes

Documentation

  • Documentation built extensively in multiple PRs.

climpred v0.3 (2019-04-27)

climpred v0.3 really represents the entire development phase leading up to the version 1 release. This was done in collaboration between Riley X. Brady, Aaron Spring, and Andrew Huang. Future releases will have less additions.

Features

Bug Fixes

Internals/Minor Fixes

climpred v0.2 (2019-01-11)

Name changed to climpred, developed enough for basic decadal prediction tasks on a perfect-model ensemble and reference-based ensemble.

climpred v0.1 (2018-12-20)

Collaboration between Riley Brady and Aaron Spring begins.