{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "You can run this notebook in a [live session](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/main?urlpath=lab/tree/docs/source/prediction-ensemble-object.ipynb) [binder badge](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/main?urlpath=lab/tree/docs/source/prediction-ensemble-object.ipynb) or view it [on Github](https://github.com/pangeo-data/climpred/blob/main/docs/source/prediction-ensemble-object.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# PredictionEnsemble Objects\n", "\n", "One of the major features of `climpred` is our objects that are based upon the {py:class}`.PredictionEnsemble` class. We supply users with a {py:class}`.HindcastEnsemble` or {py:class}`.PerfectModelEnsemble` object. We encourage users to take advantage of these high-level objects, which wrap all of our core functions.\n", "\n", "Briefly, we consider a {py:class}`.HindcastEnsemble` to be one that is initialized from some observational-like product (e.g., assimilated data, reanalysis products, or a model reconstruction). Thus, this object is built around comparing the initialized ensemble to various observational products.\n", "In contrast, a {py:class}`.PerfectModelEnsemble` is one that is initialized off of a model control simulation. These forecasting systems are not meant to be compared directly to real-world observations. Instead, they provide a contained model environment with which to theoretically study the limits of predictability. You can read more about the terminology used in `climpred` [here](terminology.html#Terminology).\n", "\n", "Let's create a demo object to explore some of the functionality and why they are much smoother to use than direct function calls." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 1;\n", " var nbb_unformatted_code = \"# linting\\n%load_ext nb_black\\n%load_ext lab_black\";\n", " var nbb_formatted_code = \"# linting\\n%load_ext nb_black\\n%load_ext lab_black\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# linting\n", "%load_ext nb_black\n", "%load_ext lab_black" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:15:47.017072Z", "start_time": "2020-01-06T18:15:44.607885Z" } }, "outputs": [ { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 2;\n", " var nbb_unformatted_code = \"%matplotlib inline\\nimport matplotlib.pyplot as plt\\nimport xarray as xr\\nimport numpy as np\\n\\nfrom climpred import HindcastEnsemble, PerfectModelEnsemble\\nfrom climpred.tutorial import load_dataset\\nimport climpred\";\n", " var nbb_formatted_code = \"%matplotlib inline\\nimport matplotlib.pyplot as plt\\nimport xarray as xr\\nimport numpy as np\\n\\nfrom climpred import HindcastEnsemble, PerfectModelEnsemble\\nfrom climpred.tutorial import load_dataset\\nimport climpred\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import xarray as xr\n", "import numpy as np\n", "\n", "from climpred import HindcastEnsemble, PerfectModelEnsemble\n", "from climpred.tutorial import load_dataset\n", "import climpred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now pull in some sample data that is packaged with `climpred`. We'll start out with a {py:class}`.HindcastEnsemble` demo, followed by a {py:class}`.PerfectModelEnsemble` case." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## HindcastEnsemble" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:15:47.840210Z", "start_time": "2020-01-06T18:15:47.823392Z" } }, "outputs": [ { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 3;\n", " var nbb_unformatted_code = \"initialized = climpred.tutorial.load_dataset(\\n \\\"CESM-DP-SST\\\"\\n) # CESM-DPLE hindcast ensemble output.\\nobs = climpred.tutorial.load_dataset(\\\"ERSST\\\") # ERSST observations.\";\n", " var nbb_formatted_code = \"initialized = climpred.tutorial.load_dataset(\\n \\\"CESM-DP-SST\\\"\\n) # CESM-DPLE hindcast ensemble output.\\nobs = climpred.tutorial.load_dataset(\\\"ERSST\\\") # ERSST observations.\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "initialized = climpred.tutorial.load_dataset(\n", " \"CESM-DP-SST\"\n", ") # CESM-DPLE hindcast ensemble output.\n", "obs = climpred.tutorial.load_dataset(\"ERSST\") # ERSST observations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to add a ``units`` attribute to the hindcast ensemble so that `climpred` knows how to interpret the lead units." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:15:48.153858Z", "start_time": "2020-01-06T18:15:48.151726Z" } }, "outputs": [ { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 4;\n", " var nbb_unformatted_code = \"initialized[\\\"lead\\\"].attrs[\\\"units\\\"] = \\\"years\\\"\";\n", " var nbb_formatted_code = \"initialized[\\\"lead\\\"].attrs[\\\"units\\\"] = \\\"years\\\"\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "initialized[\"lead\"].attrs[\"units\"] = \"years\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we instantiate the {py:class}`.HindcastEnsemble` object and append all of our products to it." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:15:49.785084Z", "start_time": "2020-01-06T18:15:49.769174Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/aaron.spring/Coding/climpred/climpred/utils.py:191: UserWarning: Assuming annual resolution starting Jan 1st due to numeric inits. Please change ``init`` to a datetime if it is another resolution. We recommend using xr.CFTimeIndex as ``init``, see https://climpred.readthedocs.io/en/stable/setting-up-data.html.\n", " warnings.warn(\n" ] }, { "data": { "text/html": [ "

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<Initialized Ensemble>\n",
       "Dimensions:     (lead: 10, member: 10, init: 64)\n",
       "Coordinates:\n",
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       "    valid_time  (lead, init) object 1955-01-01 00:00:00 ... 2027-01-01 00:00:00\n",
       "Data variables:\n",
       "    SST         (init, lead, member) float64 -0.2404 -0.2085 ... 0.7442 0.7384
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<Initialized Ensemble>\n",
       "Dimensions:     (lead: 10, member: 10, init: 64)\n",
       "Coordinates:\n",
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       "    valid_time  (lead, init) object 1955-01-01 00:00:00 ... 2027-01-01 00:00:00\n",
       "Data variables:\n",
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       "Dimensions:  (time: 61)\n",
       "Coordinates:\n",
       "  * time     (time) object 1955-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
       "Data variables:\n",
       "    SST      (time) float32 17.79 17.84 18.0 18.03 ... 18.41 18.44 18.54 18.64
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 6;\n", " var nbb_unformatted_code = \"hindcast = hindcast.add_observations(obs)\\nhindcast\";\n", " var nbb_formatted_code = \"hindcast = hindcast.add_observations(obs)\\nhindcast\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hindcast = hindcast.add_observations(obs)\n", "hindcast" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can apply most standard `xarray` functions directly to our objects! `climpred` will loop through the objects and apply the function to all applicable {py:class}`xarray.Dataset` within the object. If you reference a dimension that doesn't exist for the given {py:class}`xarray.Dataset`, it will ignore it. This is useful, since the initialized ensemble is expected to have dimension `init`, while other products have dimension `time` (see more [here](setting-up-data.html#setting-up-your-dataset)).\n", "\n", "Let's start by taking the ensemble mean of the initialized ensemble. Just using deterministic [metrics](metrics.html#Metrics) here, so we don't need the individual ensemble members. Note that above our initialized ensemble had a `member` dimension, and now it is reduced. Those `xarray` functions do not raise errors such as `ValueError`, `KeyError`, `DimensionError`, but show respective warnings, which can be filtered away with `warnings.filterwarnings(\"ignore\")`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:15:50.873957Z", "start_time": "2020-01-06T18:15:50.866688Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/aaron.spring/Coding/climpred/climpred/classes.py:713: UserWarning: Error due to verification/control/uninitialized: xr.mean(('member',), {}) failed\n", "ValueError: Dataset does not contain the dimensions: ['member']\n", " warnings.warn(\n" ] }, { "data": { "text/html": [ "

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       "Dimensions:  (time: 61)\n",
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       "  * time     (time) object 1955-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
       "Data variables:\n",
       "    SST      (time) float32 17.79 17.84 18.0 18.03 ... 18.41 18.44 18.54 18.64
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 7;\n", " var nbb_unformatted_code = \"hindcast = hindcast.mean(\\\"member\\\")\\nhindcast\";\n", " var nbb_formatted_code = \"hindcast = hindcast.mean(\\\"member\\\")\\nhindcast\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hindcast = hindcast.mean(\"member\")\n", "hindcast" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Arithmetic Operations with PredictionEnsemble Objects\n", "\n", "{py:class}`.PredictionEnsemble` objects support arithmetic operations, i.e., `+`, `-`, `/`, `*`. You can perform these operations on a {py:class}`.HindcastEnsemble` or {py:class}`.PerfectModelEnsemble` by pairing the operation with an `int`, `float`, `np.ndarray`, {py:class}`xarray.DataArray`, {py:class}`xarray.Dataset`, or with another {py:class}`.PredictionEnsemble` object.\n", "\n", "An obvious application would be to area-weight an initialized ensemble and all of its associated datasets simultaneously." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 8;\n", " var nbb_unformatted_code = \"dple3d = climpred.tutorial.load_dataset(\\\"CESM-DP-SST-3D\\\")\\nverif3d = climpred.tutorial.load_dataset(\\\"FOSI-SST-3D\\\")\\narea = dple3d[\\\"TAREA\\\"]\";\n", " var nbb_formatted_code = \"dple3d = climpred.tutorial.load_dataset(\\\"CESM-DP-SST-3D\\\")\\nverif3d = climpred.tutorial.load_dataset(\\\"FOSI-SST-3D\\\")\\narea = dple3d[\\\"TAREA\\\"]\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dple3d = climpred.tutorial.load_dataset(\"CESM-DP-SST-3D\")\n", "verif3d = climpred.tutorial.load_dataset(\"FOSI-SST-3D\")\n", "area = dple3d[\"TAREA\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we load in a subset of ``CESM-DPLE`` over the eastern tropical Pacific. The file includes `TAREA`, which describes the area of each cell on the curvilinear mesh." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/aaron.spring/Coding/climpred/climpred/utils.py:191: UserWarning: Assuming annual resolution starting Jan 1st due to numeric inits. Please change ``init`` to a datetime if it is another resolution. We recommend using xr.CFTimeIndex as ``init``, see https://climpred.readthedocs.io/en/stable/setting-up-data.html.\n", " warnings.warn(\n", "/Users/aaron.spring/Coding/climpred/climpred/utils.py:191: UserWarning: Assuming annual resolution starting Jan 1st due to numeric inits. Please change ``init`` to a datetime if it is another resolution. We recommend using xr.CFTimeIndex as ``init``, see https://climpred.readthedocs.io/en/stable/setting-up-data.html.\n", " warnings.warn(\n" ] }, { "data": { "text/html": [ "

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       "Data variables:\n",
       "    SST      (time, nlat, nlon) float32 25.53 25.43 25.35 ... 27.03 27.1 27.05
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 9;\n", " var nbb_unformatted_code = \"hindcast3d = HindcastEnsemble(dple3d)\\nhindcast3d = hindcast3d.add_observations(verif3d)\\nhindcast3d\";\n", " var nbb_formatted_code = \"hindcast3d = HindcastEnsemble(dple3d)\\nhindcast3d = hindcast3d.add_observations(verif3d)\\nhindcast3d\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hindcast3d = HindcastEnsemble(dple3d)\n", "hindcast3d = hindcast3d.add_observations(verif3d)\n", "hindcast3d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can perform an area-weighting operation with the `HindcastEnsemble` object and the area {py:class}`xarray.DataArray`. `climpred` cycles through all of the datasets appended to the {py:class}`.HindcastEnsemble` and applies them. You can see below that the dimensionality is reduced to single time series without spatial information." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

climpred.HindcastEnsemble

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<Initialized Ensemble>\n",
       "Dimensions:     (init: 64, lead: 10)\n",
       "Coordinates:\n",
       "  * init        (init) object 1954-01-01 00:00:00 ... 2017-01-01 00:00:00\n",
       "  * lead        (lead) int32 1 2 3 4 5 6 7 8 9 10\n",
       "    valid_time  (lead, init) object 1955-01-01 00:00:00 ... 2027-01-01 00:00:00\n",
       "Data variables:\n",
       "    SST         (init, lead) float64 -0.3539 0.1947 0.3623 ... 0.662 1.016 1.249
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<Observations>\n",
       "Dimensions:  (time: 68)\n",
       "Coordinates:\n",
       "  * time     (time) object 1948-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
       "Data variables:\n",
       "    SST      (time) float64 24.76 24.48 23.73 24.68 ... 24.78 24.21 24.92 25.95
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 10;\n", " var nbb_unformatted_code = \"hindcast3d_aw = (hindcast3d * area).sum([\\\"nlat\\\", \\\"nlon\\\"]) / area.sum([\\\"nlat\\\", \\\"nlon\\\"])\\nhindcast3d_aw\";\n", " var nbb_formatted_code = \"hindcast3d_aw = (hindcast3d * area).sum([\\\"nlat\\\", \\\"nlon\\\"]) / area.sum([\\\"nlat\\\", \\\"nlon\\\"])\\nhindcast3d_aw\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hindcast3d_aw = (hindcast3d * area).sum([\"nlat\", \"nlon\"]) / area.sum([\"nlat\", \"nlon\"])\n", "hindcast3d_aw" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTE**: Be careful with the arithmetic operations. Some of the behavior can be unexpected in combination with the fact that generic `xarray` methods can be applied to `climpred` objects. For instance, one might be interested in removing a climatology from the verification data to move it to anomaly space. It's safest to do anything like climatology removal before constructing `climpred` objects.\n", "\n", "Naturally, they would remove some climatology time slice as we do here below. However, note that in the below example, the intialized ensemble returns all zeroes for `SST`. The reasoning here is that when `hindcast.sel(time=...)` is called, `climpred` only applies that slicing to datasets that include the `time` dimension. Thus, it skips the initialized ensemble and returns the original dataset untouched. This feature is advantageous for cases like `hindcast.mean('member')`, where it takes the ensemble mean in all cases that ensemble members exist. So when it performs `hindcast - hindcast.sel(time=...)`, it subtracts the identical initialized ensemble from itself returning all zeroes. \n", "\n", "In short, any sort of bias correcting or drift correction can also be done _prior_ to instantiating a {py:class}`.PredictionEnsemble` object. Alternatively, detrending or removing a mean state can also be done _after_ instantiating a {py:class}`.PredictionEnsemble` object. Also consider {py:meth}`.HindcastEnsemble.remove_bias` and {py:meth}`.HindcastEnsemble.remove_seasonality`. But beware of unintuitive behaviour. Removing a `time` anomaly in {py:class}`.PredictionEnsemble`, does not modify `initialized` and therefore returns all `0`s." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/aaron.spring/Coding/climpred/climpred/classes.py:707: UserWarning: Error due to initialized: xr.sel((), {'time': slice('1960', '2014', None)}) failed\n", "KeyError: 'time is not a valid dimension or coordinate'\n", " warnings.warn(f\"Error due to initialized: {msg}\")\n", "/Users/aaron.spring/Coding/climpred/climpred/classes.py:707: UserWarning: Error due to initialized: xr.mean(('time',), {}) failed\n", "ValueError: Dataset does not contain the dimensions: ['time']\n", " warnings.warn(f\"Error due to initialized: {msg}\")\n" ] }, { "data": { "text/html": [ "

climpred.HindcastEnsemble

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<Initialized Ensemble>\n",
       "Dimensions:     (nlat: 37, nlon: 26, init: 64, lead: 10)\n",
       "Coordinates:\n",
       "    TLAT        (nlat, nlon) float64 -9.75 -9.75 -9.75 ... -0.1336 -0.1336\n",
       "    TLONG       (nlat, nlon) float64 250.8 251.9 253.1 ... 276.7 277.8 278.9\n",
       "  * init        (init) object 1954-01-01 00:00:00 ... 2017-01-01 00:00:00\n",
       "  * lead        (lead) int32 1 2 3 4 5 6 7 8 9 10\n",
       "    TAREA       (nlat, nlon) float64 3.661e+13 3.661e+13 ... 3.714e+13 3.714e+13\n",
       "    valid_time  (lead, init) object 1955-01-01 00:00:00 ... 2027-01-01 00:00:00\n",
       "Dimensions without coordinates: nlat, nlon\n",
       "Data variables:\n",
       "    SST         (init, lead, nlat, nlon) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
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<Observations>\n",
       "Dimensions:  (nlat: 37, nlon: 26, time: 68)\n",
       "Coordinates:\n",
       "    TLAT     (nlat, nlon) float64 -9.75 -9.75 -9.75 ... -0.1336 -0.1336 -0.1336\n",
       "    TLONG    (nlat, nlon) float64 250.8 251.9 253.1 254.2 ... 276.7 277.8 278.9\n",
       "  * time     (time) object 1948-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
       "    TAREA    (nlat, nlon) float64 3.661e+13 3.661e+13 ... 3.714e+13 3.714e+13\n",
       "Dimensions without coordinates: nlat, nlon\n",
       "Data variables:\n",
       "    SST      (time, nlat, nlon) float32 0.01611 0.01459 0.0161 ... 1.543 1.49
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 11;\n", " var nbb_unformatted_code = \"hindcast3d - hindcast3d.sel(time=slice(\\\"1960\\\", \\\"2014\\\")).mean(\\\"time\\\")\";\n", " var nbb_formatted_code = \"hindcast3d - hindcast3d.sel(time=slice(\\\"1960\\\", \\\"2014\\\")).mean(\\\"time\\\")\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hindcast3d - hindcast3d.sel(time=slice(\"1960\", \"2014\")).mean(\"time\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To fix this always handle all {py:class}`.PredictionEnsemble` datasets `initialized` with dimensions `lead` or `init` and `observations`/`control` with dimension `time` at the same time to avoid these zeros." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/aaron.spring/Coding/climpred/climpred/classes.py:707: UserWarning: Error due to initialized: xr.sel((), {'time': slice('1960', '2014', None)}) failed\n", "KeyError: 'time is not a valid dimension or coordinate'\n", " warnings.warn(f\"Error due to initialized: {msg}\")\n", "/Users/aaron.spring/Coding/climpred/climpred/classes.py:707: UserWarning: Error due to initialized: xr.mean(('time',), {}) failed\n", "ValueError: Dataset does not contain the dimensions: ['time']\n", " warnings.warn(f\"Error due to initialized: {msg}\")\n", "/Users/aaron.spring/Coding/climpred/climpred/classes.py:718: UserWarning: xr.sel((), {'init': slice('1960', '2014', None)}) failed\n", "KeyError: 'init is not a valid dimension or coordinate'\n", " warnings.warn(msg)\n", "/Users/aaron.spring/Coding/climpred/climpred/classes.py:718: UserWarning: xr.mean(('init',), {}) failed\n", "ValueError: Dataset does not contain the dimensions: ['init']\n", " warnings.warn(msg)\n" ] }, { "data": { "text/html": [ "

climpred.HindcastEnsemble

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<Initialized Ensemble>\n",
       "Dimensions:     (lead: 10, init: 64)\n",
       "Coordinates:\n",
       "  * lead        (lead) int32 1 2 3 4 5 6 7 8 9 10\n",
       "  * init        (init) object 1954-01-01 00:00:00 ... 2017-01-01 00:00:00\n",
       "    valid_time  (lead, init) object 1955-01-01 00:00:00 ... 2027-01-01 00:00:00\n",
       "Data variables:\n",
       "    SST         (init, lead) float64 -0.2046 -0.1688 -0.1335 ... 0.6326 0.6463
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<Observations>\n",
       "Dimensions:  (time: 61)\n",
       "Coordinates:\n",
       "  * time     (time) object 1955-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
       "Data variables:\n",
       "    SST      (time) float32 -0.3864 -0.3373 -0.17 ... 0.2632 0.3611 0.4653
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 12;\n", " var nbb_unformatted_code = \"hindcast - hindcast.sel(time=slice(\\\"1960\\\", \\\"2014\\\")).mean(\\\"time\\\").sel(\\n init=slice(\\\"1960\\\", \\\"2014\\\")\\n).mean(\\\"init\\\")\";\n", " var nbb_formatted_code = \"hindcast - hindcast.sel(time=slice(\\\"1960\\\", \\\"2014\\\")).mean(\\\"time\\\").sel(\\n init=slice(\\\"1960\\\", \\\"2014\\\")\\n).mean(\\\"init\\\")\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hindcast - hindcast.sel(time=slice(\"1960\", \"2014\")).mean(\"time\").sel(\n", " init=slice(\"1960\", \"2014\")\n", ").mean(\"init\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: Thinking in initialization space is not very intuitive and such combined `init` and `time` operations can lead to unanticipated changes in the {py:class}`.PredictionEnsemble`. The safest way is subtracting means before instantiating {py:class}`.PredictionEnsemble` or use {py:meth}`.HindcastEnsemble.remove_bias`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualizing PredictionEnsemble" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "{py:meth}`.PredictionEnsemble.plot()` showings all datasets associated if only `member`, `init` and `lead` dimensions are present." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 13;\n", " var nbb_unformatted_code = \"hindcast.plot()\";\n", " var nbb_formatted_code = \"hindcast.plot()\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hindcast.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have a huge bias because the initialized data is already converted to an anomaly, but `uninitialized` and observations is not. We also have a trend in all of our products, so we could also detrend them as well." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Detrend\n", "\n", "We can leverage `xarray`'s `.map()` function to apply/map a function to all variables in our datasets. We use {py:func}`.climpred.stats.rm_poly` to remove the trend." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 14;\n", " var nbb_unformatted_code = \"from climpred.stats import rm_poly\\n\\nhindcast_detrended = hindcast.map(rm_poly, deg=2, dim=\\\"init_or_time\\\")\\nhindcast_detrended.plot()\";\n", " var nbb_formatted_code = \"from climpred.stats import rm_poly\\n\\nhindcast_detrended = hindcast.map(rm_poly, deg=2, dim=\\\"init_or_time\\\")\\nhindcast_detrended.plot()\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from climpred.stats import rm_poly\n", "\n", "hindcast_detrended = hindcast.map(rm_poly, deg=2, dim=\"init_or_time\")\n", "hindcast_detrended.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And it looks like everything got detrended by a quadratic fit! That wasn't too hard." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Verify" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we've done our pre-processing, let's quickly compute some metrics. Check the [metrics](metrics.html#Metrics) for all the keywords you can use. The [API](api.html#analysis-functions) is currently pretty simple for the {py:class}`.HindcastEnsemble`. You can essentially compute standard skill metrics and [reference forecasts](reference_forecast.html#reference-forecasts), here ``persistence``." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:15:55.096705Z", "start_time": "2020-01-06T18:15:54.630606Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:  (skill: 2, lead: 10)\n",
       "Coordinates:\n",
       "  * lead     (lead) int32 1 2 3 4 5 6 7 8 9 10\n",
       "  * skill    (skill) <U11 'initialized' 'persistence'\n",
       "Data variables:\n",
       "    SST      (skill, lead) float64 0.003274 0.004149 ... 0.01109 0.008786\n",
       "Attributes:\n",
       "    prediction_skill_software:     climpred https://climpred.readthedocs.io/\n",
       "    skill_calculated_by_function:  HindcastEnsemble.verify()\n",
       "    number_of_initializations:     64\n",
       "    alignment:                     same_verif\n",
       "    metric:                        mse\n",
       "    comparison:                    e2o\n",
       "    dim:                           init\n",
       "    reference:                     ['persistence']
" ], "text/plain": [ "\n", "Dimensions: (skill: 2, lead: 10)\n", "Coordinates:\n", " * lead (lead) int32 1 2 3 4 5 6 7 8 9 10\n", " * skill (skill) " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hindcast_detrended.verify(\n", " metric=\"mse\",\n", " comparison=\"e2o\",\n", " dim=\"init\",\n", " alignment=\"same_verif\",\n", " reference=\"persistence\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we leverage `xarray`'s plotting method to compute Mean Absolute Error and the Anomaly Correlation Coefficient against the ERSST observations, as well as the equivalent metrics computed for persistence forecasts for each of those metrics." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:15:57.438157Z", "start_time": "2020-01-06T18:15:56.024458Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 33;\n", " var nbb_unformatted_code = \"metrics = [\\\"mae\\\", \\\"acc\\\"]\\nfor metric in metrics:\\n hindcast_detrended.verify(\\n metric=metric,\\n comparison=\\\"e2o\\\",\\n dim=\\\"init\\\",\\n alignment=\\\"same_verif\\\",\\n reference=\\\"persistence\\\",\\n ).assign_coords(metric=metric.upper()).SST.plot(hue=\\\"skill\\\")\\n plt.suptitle(\\\"CESM Decadal Prediction Large Ensemble Global SSTs\\\", fontsize=16)\\n plt.show()\";\n", " var nbb_formatted_code = \"metrics = [\\\"mae\\\", \\\"acc\\\"]\\nfor metric in metrics:\\n hindcast_detrended.verify(\\n metric=metric,\\n comparison=\\\"e2o\\\",\\n dim=\\\"init\\\",\\n alignment=\\\"same_verif\\\",\\n reference=\\\"persistence\\\",\\n ).assign_coords(metric=metric.upper()).SST.plot(hue=\\\"skill\\\")\\n plt.suptitle(\\\"CESM Decadal Prediction Large Ensemble Global SSTs\\\", fontsize=16)\\n plt.show()\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "metrics = [\"mae\", \"acc\"]\n", "for metric in metrics:\n", " hindcast_detrended.verify(\n", " metric=metric,\n", " comparison=\"e2o\",\n", " dim=\"init\",\n", " alignment=\"same_verif\",\n", " reference=\"persistence\",\n", " ).assign_coords(metric=metric.upper()).SST.plot(hue=\"skill\")\n", " plt.suptitle(\"CESM Decadal Prediction Large Ensemble Global SSTs\", fontsize=16)\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## PerfectModelEnsemble\n", "\n", "We'll now play around a bit with the {py:class}`.PerfectModelEnsemble` object, using sample data from the `MPI-ESM` perfect model configuration." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:15:58.493102Z", "start_time": "2020-01-06T18:15:58.487299Z" } }, "outputs": [ { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 17;\n", " var nbb_unformatted_code = \"initialized = load_dataset(\\\"MPI-PM-DP-1D\\\") # initialized ensemble from MPI\\ncontrol = load_dataset(\\\"MPI-control-1D\\\") # base control run that initialized it\\n\\ninitialized[\\\"lead\\\"].attrs[\\\"units\\\"] = \\\"years\\\"\";\n", " var nbb_formatted_code = \"initialized = load_dataset(\\\"MPI-PM-DP-1D\\\") # initialized ensemble from MPI\\ncontrol = load_dataset(\\\"MPI-control-1D\\\") # base control run that initialized it\\n\\ninitialized[\\\"lead\\\"].attrs[\\\"units\\\"] = \\\"years\\\"\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "initialized = load_dataset(\"MPI-PM-DP-1D\") # initialized ensemble from MPI\n", "control = load_dataset(\"MPI-control-1D\") # base control run that initialized it\n", "\n", "initialized[\"lead\"].attrs[\"units\"] = \"years\"" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:15:59.750652Z", "start_time": "2020-01-06T18:15:59.695144Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/aaron.spring/Coding/climpred/climpred/utils.py:191: UserWarning: Assuming annual resolution starting Jan 1st due to numeric inits. Please change ``init`` to a datetime if it is another resolution. We recommend using xr.CFTimeIndex as ``init``, see https://climpred.readthedocs.io/en/stable/setting-up-data.html.\n", " warnings.warn(\n", "/Users/aaron.spring/Coding/climpred/climpred/utils.py:191: UserWarning: Assuming annual resolution starting Jan 1st due to numeric inits. Please change ``init`` to a datetime if it is another resolution. We recommend using xr.CFTimeIndex as ``init``, see https://climpred.readthedocs.io/en/stable/setting-up-data.html.\n", " warnings.warn(\n" ] }, { "data": { "text/html": [ "

climpred.PerfectModelEnsemble

" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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<Initialized Ensemble>\n",
       "Dimensions:     (period: 5, lead: 20, area: 3, init: 12, member: 10)\n",
       "Coordinates:\n",
       "  * lead        (lead) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20\n",
       "  * period      (period) object 'DJF' 'JJA' 'MAM' 'SON' 'ym'\n",
       "  * area        (area) object 'global' 'North_Atlantic' 'North_Atlantic_SPG'\n",
       "  * init        (init) object 3014-01-01 00:00:00 ... 3257-01-01 00:00:00\n",
       "  * member      (member) int64 0 1 2 3 4 5 6 7 8 9\n",
       "    valid_time  (lead, init) object 3015-01-01 00:00:00 ... 3277-01-01 00:00:00\n",
       "Data variables:\n",
       "    tos         (period, lead, area, init, member) float32 17.38 17.58 ... 8.955\n",
       "    sos         (period, lead, area, init, member) float32 34.38 34.37 ... 34.59\n",
       "    AMO         (period, lead, area, init, member) float32 0.1675 ... 0.06075
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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<Control Simulation>\n",
       "Dimensions:  (period: 5, time: 300, area: 3)\n",
       "Coordinates:\n",
       "  * time     (time) object 3000-01-01 00:00:00 ... 3299-01-01 00:00:00\n",
       "  * period   (period) object 'DJF' 'JJA' 'MAM' 'SON' 'ym'\n",
       "  * area     (area) object 'global' 'North_Atlantic' 'North_Atlantic_SPG'\n",
       "Data variables:\n",
       "    tos      (period, time, area) float32 17.38 8.76 7.321 ... 17.23 10.84 8.346\n",
       "    sos      (period, time, area) float32 34.37 33.5 34.72 ... 34.35 33.38 34.45\n",
       "    AMO      (period, time, area) float32 0.17 0.17 0.17 ... 0.07905 0.07905
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 18;\n", " var nbb_unformatted_code = \"pm = climpred.PerfectModelEnsemble(initialized).add_control(control)\\npm\";\n", " var nbb_formatted_code = \"pm = climpred.PerfectModelEnsemble(initialized).add_control(control)\\npm\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pm = climpred.PerfectModelEnsemble(initialized).add_control(control)\n", "pm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our objects are carrying sea surface temperature (`tos`), sea surface salinity (`sos`), and the Atlantic Multidecadal Oscillation index (`AMO`). Say we just want to look at skill metrics for temperature and salinity over the North Atlantic in JJA. We can just call a few easy `xarray` commands to filter down our object." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 19;\n", " var nbb_unformatted_code = \"pm = pm[[\\\"tos\\\", \\\"sos\\\"]].sel(area=\\\"North_Atlantic\\\", period=\\\"JJA\\\", drop=True)\";\n", " var nbb_formatted_code = \"pm = pm[[\\\"tos\\\", \\\"sos\\\"]].sel(area=\\\"North_Atlantic\\\", period=\\\"JJA\\\", drop=True)\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pm = pm[[\"tos\", \"sos\"]].sel(area=\"North_Atlantic\", period=\"JJA\", drop=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can easily compute for a host of metrics. Here I just show a number of deterministic skill metrics comparing all individual members to the initialized ensemble mean. See [comparisons](comparisons.html#comparisons) for more information on the `comparison` keyword." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:16:03.306736Z", "start_time": "2020-01-06T18:16:01.502565Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 20;\n", " var nbb_unformatted_code = \"METRICS = [\\\"mse\\\", \\\"rmse\\\", \\\"mae\\\", \\\"acc\\\", \\\"nmse\\\", \\\"nrmse\\\", \\\"nmae\\\", \\\"msss\\\"]\\n\\nresult = []\\nfor metric in METRICS:\\n result.append(pm.verify(metric=metric, comparison=\\\"m2e\\\", dim=[\\\"init\\\", \\\"member\\\"]))\\n\\nresult = xr.concat(result, \\\"metric\\\")\\nresult[\\\"metric\\\"] = METRICS\\n\\n# Leverage the `xarray` plotting wrapper to plot all results at once.\\nresult.to_array().plot(\\n col=\\\"metric\\\", hue=\\\"variable\\\", col_wrap=4, sharey=False, sharex=True\\n)\";\n", " var nbb_formatted_code = \"METRICS = [\\\"mse\\\", \\\"rmse\\\", \\\"mae\\\", \\\"acc\\\", \\\"nmse\\\", \\\"nrmse\\\", \\\"nmae\\\", \\\"msss\\\"]\\n\\nresult = []\\nfor metric in METRICS:\\n result.append(pm.verify(metric=metric, comparison=\\\"m2e\\\", dim=[\\\"init\\\", \\\"member\\\"]))\\n\\nresult = xr.concat(result, \\\"metric\\\")\\nresult[\\\"metric\\\"] = METRICS\\n\\n# Leverage the `xarray` plotting wrapper to plot all results at once.\\nresult.to_array().plot(\\n col=\\\"metric\\\", hue=\\\"variable\\\", col_wrap=4, sharey=False, sharex=True\\n)\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "METRICS = [\"mse\", \"rmse\", \"mae\", \"acc\", \"nmse\", \"nrmse\", \"nmae\", \"msss\"]\n", "\n", "result = []\n", "for metric in METRICS:\n", " result.append(pm.verify(metric=metric, comparison=\"m2e\", dim=[\"init\", \"member\"]))\n", "\n", "result = xr.concat(result, \"metric\")\n", "result[\"metric\"] = METRICS\n", "\n", "# Leverage the `xarray` plotting wrapper to plot all results at once.\n", "result.to_array().plot(\n", " col=\"metric\", hue=\"variable\", col_wrap=4, sharey=False, sharex=True\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is useful to compare the initialized ensemble to an uninitialized run. See [terminology](terminology.html#terminology) for a description on `uninitialized` simulations. This gives us information about how initialization leads to enhanced predictability over knowledge of external forcing, whereas a comparison to persistence just tells us how well a dynamical forecast simulation does in comparison to a naive method. We can use the {py:meth}`.PerfectModelEnsemble.generate_uninitialized` to resample the control run and create a pseudo-ensemble that approximates what an uninitialized ensemble would look like." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:16:03.557852Z", "start_time": "2020-01-06T18:16:03.308311Z" } }, "outputs": [ { "data": { "text/html": [ "

climpred.PerfectModelEnsemble

" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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<Initialized Ensemble>\n",
       "Dimensions:     (lead: 20, init: 12, member: 10)\n",
       "Coordinates:\n",
       "  * lead        (lead) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20\n",
       "  * init        (init) object 3014-01-01 00:00:00 ... 3257-01-01 00:00:00\n",
       "  * member      (member) int64 0 1 2 3 4 5 6 7 8 9\n",
       "    valid_time  (lead, init) object 3015-01-01 00:00:00 ... 3277-01-01 00:00:00\n",
       "Data variables:\n",
       "    tos         (lead, init, member) float32 13.46 13.64 13.72 ... 13.55 13.57\n",
       "    sos         (lead, init, member) float32 33.18 33.15 33.05 ... 33.18 33.26
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<Control Simulation>\n",
       "Dimensions:  (time: 300)\n",
       "Coordinates:\n",
       "  * time     (time) object 3000-01-01 00:00:00 ... 3299-01-01 00:00:00\n",
       "Data variables:\n",
       "    tos      (time) float32 13.5 13.74 13.78 13.86 ... 13.12 12.92 13.08 13.47\n",
       "    sos      (time) float32 33.23 33.19 33.2 33.21 ... 33.15 33.22 33.16 33.18
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<Uninitialized>\n",
       "Dimensions:     (lead: 20, init: 12, member: 10)\n",
       "Coordinates:\n",
       "    valid_time  (lead, init) object 3015-01-01 00:00:00 ... 3277-01-01 00:00:00\n",
       "  * lead        (lead) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20\n",
       "  * init        (init) object 3014-01-01 00:00:00 ... 3257-01-01 00:00:00\n",
       "  * member      (member) int64 0 1 2 3 4 5 6 7 8 9\n",
       "Data variables:\n",
       "    tos         (lead, init, member) float32 13.01 12.84 12.84 ... 13.44 13.57\n",
       "    sos         (lead, init, member) float32 33.08 33.08 33.0 ... 33.16 33.32
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 21;\n", " var nbb_unformatted_code = \"pm = pm.generate_uninitialized()\\npm\";\n", " var nbb_formatted_code = \"pm = pm.generate_uninitialized()\\npm\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pm = pm.generate_uninitialized()\n", "pm" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2020-01-06T18:16:03.562894Z", "start_time": "2020-01-06T18:16:03.560057Z" } }, "outputs": [ { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 22;\n", " var nbb_unformatted_code = \"pm = pm[[\\\"sos\\\"]] # Just assess for salinity.\";\n", " var nbb_formatted_code = \"pm = pm[[\\\"sos\\\"]] # Just assess for salinity.\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", " nbb_cells[i].set_text(nbb_formatted_code);\n", " }\n", " break;\n", " }\n", " }\n", " }, 500);\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pm = pm[[\"sos\"]] # Just assess for salinity." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we plot the ACC for the ``initialized``, ``uninitialized``, ``climatology`` and ``persistence`` forecasts for North Atlantic sea surface salinity in the JJA summer season." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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