{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Quick Start\n", "\n", "The easiest way to get up and running is to load in one of our example datasets (or load in some data of your own) and to convert them to either a [HindcastEnsemble](api/climpred.classes.HindcastEnsemble.html#climpred.classes.HindcastEnsemble) or [PerfectModelEnsemble](api/climpred.classes.PerfectModelEnsemble.html#climpred.classes.PerfectModelEnsemble) object.\n", "\n", "`climpred` provides example datasets from the MPI-ESM-LR decadal prediction ensemble and the CESM decadal prediction ensemble. See our [examples](examples.html) to see some analysis cases." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:16:20.185284Z", "start_time": "2020-01-06T18:16:18.011271Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import xarray as xr\n", "\n", "from climpred import HindcastEnsemble\n", "from climpred.tutorial import load_dataset\n", "import climpred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can view the datasets available to be loaded with the [load_datasets()](api/climpred.tutorial.load_dataset.html#climpred.tutorial.load_dataset) command without passing any arguments:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:16:20.722510Z", "start_time": "2020-01-06T18:16:20.718506Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'MPI-control-1D': area averages for the MPI control run of SST/SSS.\n", "'MPI-control-3D': lat/lon/time for the MPI control run of SST/SSS.\n", "'MPI-PM-DP-1D': perfect model decadal prediction ensemble area averages of SST/SSS/AMO.\n", "'MPI-PM-DP-3D': perfect model decadal prediction ensemble lat/lon/time of SST/SSS/AMO.\n", "'CESM-DP-SST': hindcast decadal prediction ensemble of global mean SSTs.\n", "'CESM-DP-SSS': hindcast decadal prediction ensemble of global mean SSS.\n", "'CESM-DP-SST-3D': hindcast decadal prediction ensemble of eastern Pacific SSTs.\n", "'CESM-LE': uninitialized ensemble of global mean SSTs.\n", "'MPIESM_miklip_baseline1-hind-SST-global': hindcast initialized ensemble of global mean SSTs\n", "'MPIESM_miklip_baseline1-hist-SST-global': uninitialized ensemble of global mean SSTs\n", "'MPIESM_miklip_baseline1-assim-SST-global': assimilation in MPI-ESM of global mean SSTs\n", "'ERSST': observations of global mean SSTs.\n", "'FOSI-SST': reconstruction of global mean SSTs.\n", "'FOSI-SSS': reconstruction of global mean SSS.\n", "'FOSI-SST-3D': reconstruction of eastern Pacific SSTs\n", "'GMAO-GEOS-RMM1': daily RMM1 from the GMAO-GEOS-V2p1 model for SubX\n", "'RMM-INTERANN-OBS': observed RMM with interannual variablity included\n" ] } ], "source": [ "load_dataset()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From here, loading a dataset is easy. Note that you need to be connected to the internet for this to work -- the datasets are being pulled from the [climpred-data](https://github.com/bradyrx/climpred-data) repository. Once loaded, it is cached on your computer so you can reload extremely quickly. These datasets are very small (< 1MB each) so they won't take up much space." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:16:21.157630Z", "start_time": "2020-01-06T18:16:21.142034Z" } }, "outputs": [], "source": [ "hind = climpred.tutorial.load_dataset('CESM-DP-SST')\n", "# Add lead attribute units.\n", "hind[\"lead\"].attrs[\"units\"] = \"years\"\n", "obs = climpred.tutorial.load_dataset('ERSST')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure your prediction ensemble's dimension labeling conforms to `climpred`'s [standards](setting-up-data.html). In other words, you need an `init`, `lead`, and (optional) `member` dimension. Make sure that your `init` and `lead` dimensions align. *E.g.*, a November 1st, 1954 initialization should be labeled as `init=1954` so that the lead=1 forecast is 1955." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:16:21.611974Z", "start_time": "2020-01-06T18:16:21.606643Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Dimensions: (init: 64, lead: 10, member: 10)\n", "Coordinates:\n", " * lead (lead) int32 1 2 3 4 5 6 7 8 9 10\n", " * member (member) int32 1 2 3 4 5 6 7 8 9 10\n", " * init (init) float32 1954.0 1955.0 1956.0 1957.0 ... 2015.0 2016.0 2017.0\n", "Data variables:\n", " SST (init, lead, member) float64 ...\n" ] } ], "source": [ "print(hind)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll quickly process the data to create anomalies. CESM-DPLE's drift-correction occurs over 1964-2014, so we'll remove that from the observations." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:16:22.963660Z", "start_time": "2020-01-06T18:16:22.958095Z" } }, "outputs": [], "source": [ "# subtract climatology\n", "obs = obs - obs.sel(time=slice(1964, 2014)).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll also remove a linear trend so that it doesn't artificially boost our predictability." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:16:23.267567Z", "start_time": "2020-01-06T18:16:23.248047Z" } }, "outputs": [], "source": [ "hind = climpred.stats.rm_trend(hind, dim='init')\n", "obs = climpred.stats.rm_trend(obs, dim='time')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have to add the lead attributes back on, because `xarray` sometimes drops attributes. This is a bug we're aware of that we are working on fixing for `climpred`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:16:23.551696Z", "start_time": "2020-01-06T18:16:23.549727Z" } }, "outputs": [], "source": [ "# Add lead attribute units.\n", "hind[\"lead\"].attrs[\"units\"] = \"years\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now create a [HindcastEnsemble](api/climpred.classes.HindcastEnsemble.html#climpred.classes.HindcastEnsemble) object and add our observations and name them `'Obs'`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:16:23.879979Z", "start_time": "2020-01-06T18:16:23.853991Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Initialized Ensemble:\n", " SST (init, lead, member) float64 0.005165 0.03014 ... 0.1842 0.1812\n", "Obs:\n", " SST (time) float32 -0.061960407 -0.023283795 ... 0.072058104 0.165859\n", "Uninitialized:\n", " None\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ribr5703/miniconda3/envs/climpred-dev/lib/python3.6/site-packages/climpred/utils.py:141: UserWarning: Assuming annual resolution due to numeric inits. Change init to a datetime if it is another resolution.\n", " 'Assuming annual resolution due to numeric inits. '\n" ] } ], "source": [ "hindcast = HindcastEnsemble(hind)\n", "hindcast = hindcast.add_observations(obs, 'Obs')\n", "print(hindcast)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll quickly calculate skill and persistence. We have a variety of possible [metrics](https://climpred.readthedocs.io/en/latest/metrics.html) to use. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:16:24.835164Z", "start_time": "2020-01-06T18:16:24.405766Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Dimensions: (lead: 10)\n", "Coordinates:\n", " * lead (lead) int64 1 2 3 4 5 6 7 8 9 10\n", " skill " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.style.use('fivethirtyeight')\n", "f, ax = plt.subplots(figsize=(8, 3))\n", "skill.SST.plot(marker='o', markersize=10, label='skill')\n", "persistence.SST.plot(marker='o', markersize=10, label='persistence',\n", " color='#a9a9a9')\n", "plt.legend()\n", "ax.set(title='Global Mean SST Predictability',\n", " ylabel='Anomaly \\n Correlation Coefficient',\n", " xlabel='Lead Year')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also check error in our forecasts." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:16:26.745102Z", "start_time": "2020-01-06T18:16:26.477602Z" } }, "outputs": [], "source": [ "result = hindcast.verify(metric='rmse', reference='persistence')\n", "skill = result.sel(skill='init')\n", "persistence = result.sel(skill='persistence')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:16:26.996690Z", "start_time": "2020-01-06T18:16:26.871707Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.style.use('fivethirtyeight')\n", "f, ax = plt.subplots(figsize=(8, 3))\n", "skill.SST.plot(marker='o', markersize=10, label='initialized forecast')\n", "persistence.SST.plot(marker='o', markersize=10, label='persistence',\n", " color='#a9a9a9')\n", "plt.legend()\n", "ax.set(title='Global Mean SST Forecast Error',\n", " ylabel='RMSE',\n", " xlabel='Lead Year')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.10" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }