# climpred > .. image:: https://i.imgur.com/HPOdOsR.png > > Verification of weather and climate forecasts > > .. > Table version of badges inspired by pySTEPS. > > .. list-table:: > :stub-columns: 1 > :widths: 10 90 > > * - docs > - |docs| |context7| |joss| |doi| > * - tests > - |ci| |upstream| |codecov| |precommit| > * - package > - |conda| |conda downloads| |pypi| |pypi downloads| > * - license > - |license| > * - community > - |gitter| |contributors| |forks| |stars| |issues| |PRs| > * - tutorials > - |gallery| |workshop| |cloud| > > .. |docs| image:: https://img.shields.io/readthedocs/climpred/latest.svg?style=flat > :target: https://climpred.readthedocs.io/en/stable/?badge=stable > :alt: Documentation Status > > .. |context7| image:: https://img.shields.io/badge/docs-LLM-008A61 > :target: https://context7.com/pangeo-data/climpred/llms.txt > :alt: context7 docs for LLMs > > .. |joss| image:: https://joss.theoj.org/papers/246d440e3fcb19025a3b0e56e1af54ef/status.svg > :target: https://joss.theoj.org/papers/246d440e3fcb19025a3b0e56e1af54ef > :alt: JOSS paper > > .. |doi| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4556085.svg > :target: https://doi.org/10.5281/zenodo.4556085 > :alt: DOI > > .. |ci| image:: https://github.com/pangeo-data/climpred/actions/workflows/climpred_testing.yml/badge.svg > :target: https://github.com/pangeo-data/climpred/actions/workflows/climpred_testing.yml > :alt: CI > > .. |upstream| image:: https://github.com/pangeo-data/climpred/actions/workflows/upstream-dev-ci.yml/badge.svg > :target: https://github.com/pangeo-data/climpred/actions/workflows/upstream-dev-ci.yml > :alt: CI upstream > > .. |codecov| image:: https://codecov.io/gh/pangeo-data/climpred/branch/main/graph/badge.svg > :target: https://codecov.io/gh/pangeo-data/climpred > :alt: coverage > > .. |precommit| image:: https://results.pre-commit.ci/badge/github/pangeo-data/climpred/main.svg > :target: https://results.pre-commit.ci/latest/github/pangeo-data/climpred/main > :alt: pre-commit.ci status > > .. |conda| image:: https://img.shields.io/conda/vn/conda-forge/climpred.svg > :target: https://anaconda.org/conda-forge/climpred > :alt: Conda Version > > .. |pypi| image:: https://img.shields.io/pypi/v/climpred.svg > :target: https://pypi.python.org/pypi/climpred/ > :alt: pypi Version > > .. |license| image:: https://img.shields.io/github/license/pangeo-data/climpred.svg > :alt: license > :target: LICENSE.txt > > .. |gitter| image:: https://badges.gitter.im/Join%20Chat.svg > :target: https://gitter.im/climpred > :alt: gitter chat > > .. |contributors| image:: https://img.shields.io/github/contributors/pangeo-data/climpred > :alt: GitHub contributors > :target: https://github.com/pangeo-data/climpred/graphs/contributors > > .. |conda downloads| image:: https://img.shields.io/conda/dn/conda-forge/climpred > :alt: Conda downloads > :target: https://anaconda.org/conda-forge/climpred > > .. |pypi downloads| image:: https://pepy.tech/badge/climpred > :alt: pypi downloads > :target: https://pepy.tech/project/climpred > > .. |gallery| image:: https://img.shields.io/badge/climpred-examples-ed7b0e.svg > :alt: climpred gallery > :target: https://mybinder.org/v2/gh/pangeo-data/climpred/main?urlpath=lab%2Ftree%2Fdocs%2Fsource%2Fquick-start.ipynb > > .. |workshop| image:: https://img.shields.io/badge/climpred-workshop-f5a252 > :alt: climpred workshop > :target: https://mybinder.org/v2/gh/bradyrx/climpred_workshop/master > > .. |cloud| image:: https://img.shields.io/badge/climpred-cloud_demo-f9c99a > :alt: climpred cloud demo > :target: https://github.com/aaronspring/climpred-cloud-demo > > .. |forks| image:: https://img.shields.io/github/forks/pangeo-data/climpred > :alt: GitHub forks > :target: https://github.com/pangeo-data/climpred/network/members > > .. |stars| image:: https://img.shields.io/github/stars/pangeo-data/climpred > :alt: GitHub stars > :target: https://github.com/pangeo-data/climpred/stargazers > > .. |issues| image:: https://img.shields.io/github/issues/pangeo-data/climpred > :alt: GitHub issues > :target: https://github.com/pangeo-data/climpred/issues > > .. |PRs| image:: https://img.shields.io/github/issues-pr/pangeo-data/climpred > :alt: GitHub PRs > :target: https://github.com/pangeo-data/climpred/pulls > > .. > > > We are actively looking for new contributors for climpred! > `Riley `_ moved to McKinsey's > Climate Analytics team as a climate software engineer. > `Aaron `_ moved to XING as a data scientist. > We especially hope for python enthusiasts from seasonal, subseasonal or weather > prediction community. In our past coding journey, collaborative coding, feedbacking > issues and pull requests advanced our code and thinking about forecast verification > more than we could have ever expected. > Feel free to implement your own new feature or take a look at the > `good first issue `_ > tag in the issues. If you are interested in maintaining climpred, please ping us. > > Installation > ============ > > You can install the latest release of ``climpred`` using ``pip`` or ``conda``: > > .. code-block:: bash > > python -m pip install climpred[complete] > > .. code-block:: bash > > conda install -c conda-forge climpred > > You can also install the bleeding edge (pre-release versions) by cloning this > repository or installing directly from GitHub: > > .. code-block:: bash > > git clone https://github.com/pangeo-data/climpred.git > cd climpred > python -m pip install . --upgrade > > .. code-block:: bash > > pip install git+https://github.com/pangeo-data/climpred.git > > > Documentation > ============= > > Documentation is in development and can be found on readthedocs_. > > .. _readthedocs: https://climpred.readthedocs.io/en/latest/ > > Star History > ============ > > .. image:: https://api.star-history.com/svg?repos=pangeo-data/climpred&type=date&legend=top-left > :alt: Star History Chart > :target: https://www.star-history.com/#pangeo-data/climpred&type=date&legend=top-left 2019-2026, Riley X. Brady, Aaron Spring, and contributors ## Pages - [climpred: verification of weather and climate forecasts](index.html.md): | docs | [![Documentation Status](https://img.shields.io/readthedocs/climpred/stable.svg?style=... - [linting](examples/NWP/Herbie.html.md): ```ipython3 - [!conda install intake fsspec intake-xarray intake-thredds -c conda-forge -y](examples/NWP/NWP_GEFS_6h_forecasts.html.md): ```ipython3 - [Significance Testing](examples/decadal/Significance.html.md): You can run this notebook in a [live session](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/ma... - [Verification Alignment](alignment.html.md): You can run this notebook in a [live session](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/ma... - [API Reference](api.html.md): This page provides an auto-generated summary of `climpred`’s API. - [Bias Removal](bias_removal.html.md): You can run this notebook in a [live session](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/ma... - [What’s New](changelog.html.md): - Documentation is now machine-readable for AI agents and LLM tools. The [sphinx-llm](https://github... - [climpred.bootstrap.bootstrap_uninit_pm_ensemble_from_control_cftime](api/climpred.bootstrap.bootstrap_uninit_pm_ensemble_from_control_cftime.html.md): Create a pseudo-ensemble from control run. - [climpred.bootstrap.bootstrap_uninitialized_ensemble](api/climpred.bootstrap.bootstrap_uninitialized_ensemble.html.md): Resample uninitialized hindcast from historical members. - [climpred.bootstrap.dpp_threshold](api/climpred.bootstrap.dpp_threshold.html.md): Calc DPP significance levels from re-sampled dataset. - [climpred.bootstrap.resample_skill_exclude_resample_dim_from_dim](api/climpred.bootstrap.resample_skill_exclude_resample_dim_from_dim.html.md): Page content - [climpred.bootstrap.resample_skill_loop](api/climpred.bootstrap.resample_skill_loop.html.md): Page content - [climpred.bootstrap.resample_skill_resample_before](api/climpred.bootstrap.resample_skill_resample_before.html.md): Page content - [climpred.bootstrap.varweighted_mean_period_threshold](api/climpred.bootstrap.varweighted_mean_period_threshold.html.md): Calc variance-weighted mean period significance levels from resampled dataset. - [climpred.classes.HindcastEnsemble.add_observations](api/climpred.classes.HindcastEnsemble.add_observations.html.md): Add verification data against which to verify the initialized ensemble. - [climpred.classes.HindcastEnsemble.add_uninitialized](api/climpred.classes.HindcastEnsemble.add_uninitialized.html.md): Add a companion uninitialized ensemble for comparison to verification data. - [climpred.classes.HindcastEnsemble.bootstrap](api/climpred.classes.HindcastEnsemble.bootstrap.html.md): Bootstrap with replacement according to Goddard *et al.* [[2013](../smoothing.md#id18)]. - [climpred.classes.HindcastEnsemble.generate_uninitialized](api/climpred.classes.HindcastEnsemble.generate_uninitialized.html.md): Generate `uninitialized` by resampling from `initialized`. - [climpred.classes.HindcastEnsemble.get_initialized](api/climpred.classes.HindcastEnsemble.get_initialized.html.md): Return the [`xarray.Dataset`](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray... - [climpred.classes.HindcastEnsemble.get_observations](api/climpred.classes.HindcastEnsemble.get_observations.html.md): Return the [`xarray.Dataset`](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray... - [climpred.classes.HindcastEnsemble.get_uninitialized](api/climpred.classes.HindcastEnsemble.get_uninitialized.html.md): Return the [`xarray.Dataset`](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray... - [climpred.classes.HindcastEnsemble](api/climpred.classes.HindcastEnsemble.html.md): An object for initialized prediction ensembles. - [climpred.classes.HindcastEnsemble.plot](api/climpred.classes.HindcastEnsemble.plot.html.md): Plot datasets from [`PredictionEnsemble`](climpred.classes.PredictionEnsemble.md#climpred.classes.Pr... - [climpred.classes.HindcastEnsemble.plot_alignment](api/climpred.classes.HindcastEnsemble.plot_alignment.html.md): Plot `initialized` `valid_time` where matching `verification` `time`. - [climpred.classes.HindcastEnsemble.remove_bias](api/climpred.classes.HindcastEnsemble.remove_bias.html.md): Remove bias from [`HindcastEnsemble`](climpred.classes.HindcastEnsemble.md#climpred.classes.Hindcast... - [climpred.classes.HindcastEnsemble.remove_seasonality](api/climpred.classes.HindcastEnsemble.remove_seasonality.html.md): Remove seasonal cycle from [`PredictionEnsemble`](climpred.classes.PredictionEnsemble.md#climpred.cl... - [climpred.classes.HindcastEnsemble.smooth](api/climpred.classes.HindcastEnsemble.smooth.html.md): Smooth in space and/or aggregate in time in `PredictionEnsemble`. - [climpred.classes.HindcastEnsemble.verify](api/climpred.classes.HindcastEnsemble.verify.html.md): Verify the initialized ensemble against observations. - [climpred.classes.PerfectModelEnsemble.add_control](api/climpred.classes.PerfectModelEnsemble.add_control.html.md): Add the control run that initialized the prediction ensemble. - [climpred.classes.PerfectModelEnsemble.bootstrap](api/climpred.classes.PerfectModelEnsemble.bootstrap.html.md): Bootstrap with replacement according to Goddard *et al.* [[2013](../smoothing.md#id18)]. - [climpred.classes.PerfectModelEnsemble.generate_uninitialized](api/climpred.classes.PerfectModelEnsemble.generate_uninitialized.html.md): Generate an uninitialized ensemble by resampling from the control simulation. - [climpred.classes.PerfectModelEnsemble.get_control](api/climpred.classes.PerfectModelEnsemble.get_control.html.md): Return the control as an [`xarray.Dataset`](https://docs.xarray.dev/en/stable/generated/xarray.Datas... - [climpred.classes.PerfectModelEnsemble.get_initialized](api/climpred.classes.PerfectModelEnsemble.get_initialized.html.md): Return the [`xarray.Dataset`](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray... - [climpred.classes.PerfectModelEnsemble.get_uninitialized](api/climpred.classes.PerfectModelEnsemble.get_uninitialized.html.md): Return the [`xarray.Dataset`](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html#xarray... - [climpred.classes.PerfectModelEnsemble](api/climpred.classes.PerfectModelEnsemble.html.md): An object for “perfect model” prediction ensembles. - [climpred.classes.PerfectModelEnsemble.plot](api/climpred.classes.PerfectModelEnsemble.plot.html.md): Plot datasets from [`PredictionEnsemble`](climpred.classes.PredictionEnsemble.md#climpred.classes.Pr... - [climpred.classes.PerfectModelEnsemble.remove_seasonality](api/climpred.classes.PerfectModelEnsemble.remove_seasonality.html.md): Remove seasonal cycle from [`PredictionEnsemble`](climpred.classes.PredictionEnsemble.md#climpred.cl... - [climpred.classes.PerfectModelEnsemble.smooth](api/climpred.classes.PerfectModelEnsemble.smooth.html.md): Smooth in space and/or aggregate in time in `PredictionEnsemble`. - [climpred.classes.PerfectModelEnsemble.verify](api/climpred.classes.PerfectModelEnsemble.verify.html.md): Verify initialized predictions against a configuration of its members. - [climpred.classes.PredictionEnsemble._\_add_\_](api/climpred.classes.PredictionEnsemble.__add__.html.md): Page content - [climpred.classes.PredictionEnsemble._\_contains_\_](api/climpred.classes.PredictionEnsemble.__contains__.html.md): Check variable in [`PredictionEnsemble`](climpred.classes.PredictionEnsemble.md#climpred.classes.Pre... - [climpred.classes.PredictionEnsemble._\_delitem_\_](api/climpred.classes.PredictionEnsemble.__delitem__.html.md): Remove a variable from [`PredictionEnsemble`](climpred.classes.PredictionEnsemble.md#climpred.classe... - [climpred.classes.PredictionEnsemble._\_getattr_\_](api/climpred.classes.PredictionEnsemble.__getattr__.html.md): Allow for `xarray` methods to be applied to our prediction objects. - [climpred.classes.PredictionEnsemble._\_getitem_\_](api/climpred.classes.PredictionEnsemble.__getitem__.html.md): Allow subsetting variable(s) from - [climpred.classes.PredictionEnsemble._\_iter_\_](api/climpred.classes.PredictionEnsemble.__iter__.html.md): Iterate over underlying [`xarray.Dataset`](https://docs.xarray.dev/en/stable/generated/xarray.Datase... - [climpred.classes.PredictionEnsemble._\_len_\_](api/climpred.classes.PredictionEnsemble.__len__.html.md): Return number of all variables [`PredictionEnsemble`](climpred.classes.PredictionEnsemble.md#climpre... - [climpred.classes.PredictionEnsemble._\_mul_\_](api/climpred.classes.PredictionEnsemble.__mul__.html.md): Page content - [climpred.classes.PredictionEnsemble._\_sub_\_](api/climpred.classes.PredictionEnsemble.__sub__.html.md): Page content - [climpred.classes.PredictionEnsemble._\_truediv_\_](api/climpred.classes.PredictionEnsemble.__truediv__.html.md): Page content - [climpred.classes.PredictionEnsemble.chunks](api/climpred.classes.PredictionEnsemble.chunks.html.md): Return chunks of [`PredictionEnsemble`](climpred.classes.PredictionEnsemble.md#climpred.classes.Pred... - [climpred.classes.PredictionEnsemble.chunksizes](api/climpred.classes.PredictionEnsemble.chunksizes.html.md): Return chunksizes of [`PredictionEnsemble`](climpred.classes.PredictionEnsemble.md#climpred.classes.... - [climpred.classes.PredictionEnsemble.coords](api/climpred.classes.PredictionEnsemble.coords.html.md): Return coordinates of [`PredictionEnsemble`](climpred.classes.PredictionEnsemble.md#climpred.classes... - [climpred.classes.PredictionEnsemble.data_vars](api/climpred.classes.PredictionEnsemble.data_vars.html.md): Return data variables of [`PredictionEnsemble`](climpred.classes.PredictionEnsemble.md#climpred.clas... - [climpred.classes.PredictionEnsemble.dims](api/climpred.classes.PredictionEnsemble.dims.html.md): Return dimension of [`PredictionEnsemble`](climpred.classes.PredictionEnsemble.md#climpred.classes.P... - [climpred.classes.PredictionEnsemble.equals](api/climpred.classes.PredictionEnsemble.equals.html.md): Check if [`PredictionEnsemble`](climpred.classes.PredictionEnsemble.md#climpred.classes.PredictionEn... - [climpred.classes.PredictionEnsemble](api/climpred.classes.PredictionEnsemble.html.md): The main object [`PredictionEnsemble`](#climpred.classes.PredictionEnsemble). - [climpred.classes.PredictionEnsemble.identical](api/climpred.classes.PredictionEnsemble.identical.html.md): Check if [`PredictionEnsemble`](climpred.classes.PredictionEnsemble.md#climpred.classes.PredictionEn... - [climpred.classes.PredictionEnsemble.nbytes](api/climpred.classes.PredictionEnsemble.nbytes.html.md): Bytes sizes of all PredictionEnsemble._datasets. - [climpred.classes.PredictionEnsemble.sizes](api/climpred.classes.PredictionEnsemble.sizes.html.md): Return sizes of [`PredictionEnsemble`](climpred.classes.PredictionEnsemble.md#climpred.classes.Predi... - [climpred.comparisons.Comparison](api/climpred.comparisons.Comparison.html.md): Master class for all comparisons. See [Comparisons](../comparisons.md#comparisons). - [climpred.comparisons._e2c](api/climpred.comparisons._e2c.html.md): Compare ensemble mean forecast to single member verification. - [climpred.comparisons._e2o](api/climpred.comparisons._e2o.html.md): Compare the ensemble mean forecast to the verification data. - [climpred.comparisons._m2c](api/climpred.comparisons._m2c.html.md): Compare all other member forecasts to a single member verification. - [climpred.comparisons._m2e](api/climpred.comparisons._m2e.html.md): Compare all members to ensemble mean while leaving out the verif in ensemble mean. - [climpred.comparisons._m2m](api/climpred.comparisons._m2m.html.md): Compare all members to all others in turn while leaving out verification member. - [climpred.comparisons._m2o](api/climpred.comparisons._m2o.html.md): Compare each ensemble member individually to the verification data. - [climpred.graphics.plot_bootstrapped_skill_over_leadyear](api/climpred.graphics.plot_bootstrapped_skill_over_leadyear.html.md): Plot Ensemble Prediction skill as in Li et al. 2016 Fig.3a-c. - [climpred.graphics.plot_ensemble_perfect_model](api/climpred.graphics.plot_ensemble_perfect_model.html.md): Plot datasets from PerfectModelEnsemble. - [climpred.graphics.plot_lead_timeseries_hindcast](api/climpred.graphics.plot_lead_timeseries_hindcast.html.md): Plot datasets from HindcastEnsemble. - [climpred.horizon.horizon](api/climpred.horizon.horizon.html.md): Calculate the predictability horizon based on a condition ``cond`. - [climpred.metrics.Metric](api/climpred.metrics.Metric.html.md): Master class for all metrics. See [Metrics](../metrics.md#metrics). - [climpred.metrics._bias_slope](api/climpred.metrics._bias_slope.html.md): Bias slope between verification data and forecast standard deviations. - [climpred.metrics._brier_score](api/climpred.metrics._brier_score.html.md): Brier Score for binary events. - [climpred.metrics._conditional_bias](api/climpred.metrics._conditional_bias.html.md): Conditional bias between forecast and verification data. - [climpred.metrics._contingency](api/climpred.metrics._contingency.html.md): Contingency table. - [climpred.metrics._crps](api/climpred.metrics._crps.html.md): Continuous Ranked Probability Score (CRPS). - [climpred.metrics._crpss](api/climpred.metrics._crpss.html.md): Continuous Ranked Probability Skill Score. - [climpred.metrics._crpss_es](api/climpred.metrics._crpss_es.html.md): Continuous Ranked Probability Skill Score Ensemble Spread. - [climpred.metrics._discrimination](api/climpred.metrics._discrimination.html.md): Discrimination. - [climpred.metrics._effective_sample_size](api/climpred.metrics._effective_sample_size.html.md): Effective sample size for temporally correlated data. - [climpred.metrics._get_norm_factor](api/climpred.metrics._get_norm_factor.html.md): Get normalization factor for normalizing distance metrics. - [climpred.metrics._less](api/climpred.metrics._less.html.md): Logarithmic Ensemble Spread Score. - [climpred.metrics._mae](api/climpred.metrics._mae.html.md): Mean Absolute Error (MAE). - [climpred.metrics._mape](api/climpred.metrics._mape.html.md): Mean Absolute Percentage Error (MAPE). - [climpred.metrics._median_absolute_error](api/climpred.metrics._median_absolute_error.html.md): Median Absolute Error. - [climpred.metrics._mse](api/climpred.metrics._mse.html.md): Mean Sqaure Error (MSE). - [climpred.metrics._msess](api/climpred.metrics._msess.html.md): Mean Squared Error Skill Score (MSESS). - [climpred.metrics._msess_murphy](api/climpred.metrics._msess_murphy.html.md): Murphy’s Mean Square Error Skill Score (MSESS). - [climpred.metrics._mul_bias](api/climpred.metrics._mul_bias.html.md): Multiplicative bias. - [climpred.metrics._nmae](api/climpred.metrics._nmae.html.md): Compute Normalized Mean Absolute Error (NMAE). - [climpred.metrics._nmse](api/climpred.metrics._nmse.html.md): Compte Normalized MSE (NMSE), also known as Normalized Ensemble Variance (NEV). - [climpred.metrics._nrmse](api/climpred.metrics._nrmse.html.md): Compute Normalized Root Mean Square Error (NRMSE). - [climpred.metrics._pearson_r](api/climpred.metrics._pearson_r.html.md): Pearson product-moment correlation coefficient. - [climpred.metrics._pearson_r_eff_p_value](api/climpred.metrics._pearson_r_eff_p_value.html.md): pearson_r_p_value accounting for autocorrelation. - [climpred.metrics._pearson_r_p_value](api/climpred.metrics._pearson_r_p_value.html.md): Probability that forecast and verification data are linearly uncorrelated. - [climpred.metrics._rank_histogram](api/climpred.metrics._rank_histogram.html.md): Rank histogram or Talagrand diagram. - [climpred.metrics._reliability](api/climpred.metrics._reliability.html.md): Reliability. - [climpred.metrics._rmse](api/climpred.metrics._rmse.html.md): Root Mean Sqaure Error (RMSE). - [climpred.metrics._roc](api/climpred.metrics._roc.html.md): Receiver Operating Characteristic. - [climpred.metrics._rps](api/climpred.metrics._rps.html.md): Ranked Probability Score. - [climpred.metrics._smape](api/climpred.metrics._smape.html.md): Symmetric Mean Absolute Percentage Error (sMAPE). - [climpred.metrics._spearman_r](api/climpred.metrics._spearman_r.html.md): Spearman’s rank correlation coefficient. - [climpred.metrics._spearman_r_eff_p_value](api/climpred.metrics._spearman_r_eff_p_value.html.md): \_spearman_r_p_value accounting for autocorrelation. - [climpred.metrics._spearman_r_p_value](api/climpred.metrics._spearman_r_p_value.html.md): Probability that forecast and verification data are monotonically uncorrelated. - [climpred.metrics._spread](api/climpred.metrics._spread.html.md): Ensemble spread taking the standard deviation over the member dimension. - [climpred.metrics._std_ratio](api/climpred.metrics._std_ratio.html.md): Ratio of standard deviations of the forecast over the verification data. - [climpred.metrics._threshold_brier_score](api/climpred.metrics._threshold_brier_score.html.md): Brier score of an ensemble for exceeding given thresholds. - [climpred.metrics._uacc](api/climpred.metrics._uacc.html.md): Bushuk’s unbiased Anomaly Correlation Coefficient (uACC). - [climpred.metrics._unconditional_bias](api/climpred.metrics._unconditional_bias.html.md): Unconditional additive bias. - [climpred.options.set_options](api/climpred.options.set_options.html.md): Set options for `climpred` in a controlled context. - [climpred.prediction.compute_perfect_model](api/climpred.prediction.compute_perfect_model.html.md): Compute a predictability skill score in a perfect-model framework. - [climpred.preprocessing.mpi.get_path](api/climpred.preprocessing.mpi.get_path.html.md): Get the path of a file for MPI-ESM standard output file names and directory. - [climpred.preprocessing.shared.load_hindcast](api/climpred.preprocessing.shared.load_hindcast.html.md): Concat multi-member, multi-initialization hindcast experiment. - [climpred.preprocessing.shared.rename_SLM_to_climpred_dims](api/climpred.preprocessing.shared.rename_SLM_to_climpred_dims.html.md): Rename ensemble dimensions common to SubX or CESM output. - [climpred.preprocessing.shared.rename_to_climpred_dims](api/climpred.preprocessing.shared.rename_to_climpred_dims.html.md): Rename existing dimension to CLIMPRED_ENSEMBLE_DIMS. - [climpred.preprocessing.shared.set_integer_time_axis](api/climpred.preprocessing.shared.set_integer_time_axis.html.md): Set time axis to integers starting from offset. - [climpred.reference.compute_climatology](api/climpred.reference.compute_climatology.html.md): Compute the skill of a climatology forecast. - [climpred.reference.compute_persistence](api/climpred.reference.compute_persistence.html.md): Compute the skill of a persistence forecast from a simulation. - [climpred.reference.compute_persistence_from_first_lead](api/climpred.reference.compute_persistence_from_first_lead.html.md): Compute persistence skill based on first `lead` in `initialized`. - [climpred.reference.compute_uninitialized](api/climpred.reference.compute_uninitialized.html.md): Verify an uninitialized ensemble against verification data. - [climpred.smoothing.spatial_smoothing_xesmf](api/climpred.smoothing.spatial_smoothing_xesmf.html.md): Quick regridding function. - [climpred.smoothing.temporal_smoothing](api/climpred.smoothing.temporal_smoothing.html.md): Apply temporal smoothing by creating rolling smooth-timestep means. - [climpred.stats.decorrelation_time](api/climpred.stats.decorrelation_time.html.md): Calculate the decorrelaton time of a time series. - [climpred.stats.dpp](api/climpred.stats.dpp.html.md): Calculate the Diagnostic Potential Predictability (DPP). - [climpred.stats.rm_poly](api/climpred.stats.rm_poly.html.md): Remove degree polynomial of degree `deg` along dimension `dim`. - [climpred.stats.rm_trend](api/climpred.stats.rm_trend.html.md): Remove degree polynomial along dimension `dim` - [climpred.stats.varweighted_mean_period](api/climpred.stats.varweighted_mean_period.html.md): Calculate the variance weighted mean period of time series. - [climpred.tutorial.load_dataset](api/climpred.tutorial.load_dataset.html.md): Load example data or a mask from an online repository. - [climpred.utils.convert_init_lead_to_valid_time_lead](api/climpred.utils.convert_init_lead_to_valid_time_lead.html.md): Convert `data(init,lead)` to `data(valid_time,lead)` visualizing predict barrier. - [climpred.utils.convert_valid_time_lead_to_init_lead](api/climpred.utils.convert_valid_time_lead_to_init_lead.html.md): Convert `data(valid_time,lead)` to `data(init,lead)`. - [!conda create -n ML_gpu tensorflow-gpu pytorch-gpu xarray dask matplotlib nb_conda_kernels jupyterlab cudatoolkit cupy esmtools climpred -y](examples/misc/climpred_gpu.html.md): ```ipython3 - [Code of Conduct](code_of_conduct.html.md): In the interest of fostering an open and welcoming environment, we as - [Comparisons](comparisons.html.md): Forecasts have to be verified against some product to evaluate their performance. - [Contribution Guide](contributing.html.md): Contributions are highly welcomed and appreciated. Every little help counts, - [Contributors](contributors.html.md): We are actively looking for new contributors for climpred! Riley moved to McKinsey’s - [!conda install intake fsspec intake-xarray -c conda-forge -y](examples/subseasonal/daily-S2S-ECMWF.html.md): ```ipython3 - [Calculate skill of a MJO Index of S2S models as function of daily lead time](examples/subseasonal/daily-S2S-IRIDL.html.md): ```ipython3 - [Calculate skill of a MJO Index of SubX model GEOS_V2p1 as function of daily lead time](examples/subseasonal/daily-subx-example.html.md): You can run this notebook in a [live session](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/ma... - [Diagnosing Potential Predictability](examples/decadal/diagnose-potential-predictability.html.md): You can run this notebook in a [live session](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/ma... - [Examples](examples.html.md): Please use the `climpred-dev` environment - [Helpful Links](helpful-links.html.md): We hope to curate in the `climpred` documentation a comprehensive report of - [Initialized Datasets](initialized-datasets.html.md): Probably the hardest part in working with `climpred` is getting the `initialized` - [Literature](literature.html.md): References used in the documentation and used for studying predictability. - [Metrics](metrics.html.md): All high-level functions like [`HindcastEnsemble.verify()`](api/climpred.classes.HindcastEnsemble.ve... - [Calculate ENSO Skill of NMME model NCEP-CFSv2 as Function of Initial Month vs. Lead Time](examples/monseas/monthly-enso-subx-example.html.md): You can run this notebook in a [live session](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/ma... - [Demo of Perfect Model Predictability Functions](examples/decadal/perfect-model-predictability-demo.html.md): You can run this notebook in a [live session](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/ma... - [PredictionEnsemble Objects](prediction-ensemble-object.html.md): You can run this notebook in a [live session](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/ma... - [Publications Using `climpred`](publications.html.md): Below is a list of publications that have made use of `climpred` in their analysis. - [Quick Start](quick-start.html.md): You can run this notebook in a [live session](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/ma... - [Reference Forecasts](reference_forecast.html.md): To quantify the quality of an initialized forecast, it is useful to judge it against - [Related Packages](related-packages.html.md): We’re big fans of open-source software at `climpred` and want to support and - [Release Procedure](release_procedure.html.md): We follow semantic versioning, e.g., `v1.0.0`. A major version causes incompatible API - [Scope of `climpred`](scope.html.md): `climpred` aims to be the primary package used to analyze output from initialized - [Calculate Seasonal ENSO Skill of the NMME model NCEP-CFSv2](examples/monseas/seasonal-enso-subx-example.html.md): You can run this notebook in a [live session](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/ma... - [Setting Up Your Dataset](setting-up-data.html.md): `climpred` relies on a consistent naming system for - [Setting up your own output](examples/misc/setup_your_own_data.html.md): This demo demonstrates how you can setup your raw model output with `climpred.preprocessing` to matc... - [Significance Testing](significance.html.md): Significance testing is important for assessing whether a given initialized prediction - [Temporal and Spatial Smoothing](smoothing.html.md): You can run this notebook in a [live session](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/ma... - [Prediction Terminology](terminology.html.md): Terminology is often confusing and highly variable amongst those that make predictions - [Hindcast Predictions of Equatorial Pacific SSTs](examples/decadal/tropical-pacific-ssts.html.md): You can run this notebook in a [live session](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/ma... - [Implications of `verify(dim)`](examples/decadal/verify_dim_implications.html.md): You can run this notebook in a [live session](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/ma... - [Calculate skill of a MJO Index of SubX model GEOS_V2p1 as function of weekly lead time](examples/subseasonal/weekly-subx-example.html.md): You can run this notebook in a [live session](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/ma... - [Overview: Why climpred?](why-climpred.html.md): There are many packages out there related to computing metrics on initialized --- For more comprehensive documentation, see [llms-full.txt](llms-full.txt)