docs/source/examples/subseasonal/daily-S2S-ECMWF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "breathing-location",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!conda install intake fsspec intake-xarray -c conda-forge -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1be16c36-b5e4-4093-af6c-bf0813378d06",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install climetlab\n",
    "\n",
    "#!pip install climetlab_s2s_ai_challenge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "progressive-intent",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# linting\n",
    "%load_ext nb_black\n",
    "%load_ext lab_black"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "black-grove",
   "metadata": {},
   "source": [
    "## Calculate skill of S2S model ECMWF for daily global reforecasts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aquatic-treasure",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"import climpred\\nimport xarray as xr\";\n",
       "                var nbb_formatted_code = \"import climpred\\nimport xarray as xr\";\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import climpred\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "headed-shopping",
   "metadata": {},
   "source": [
    "### Get hindcast"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "loaded-jerusalem",
   "metadata": {},
   "source": [
    "S2S output is hosted on the ECMWF S3 cloud, see https://github.com/ecmwf-lab/climetlab-s2s-ai-challenge\n",
    "\n",
    "Two ways to access:\n",
    "\n",
    "- direct access via `intake_xarray` and integrated caching via `fsspec`\n",
    "- `climetlab` with integrated caching\n",
    "\n",
    "Resources:\n",
    "\n",
    "- https://intake.readthedocs.io/en/latest/\n",
    "- https://intake-xarray.readthedocs.io/en/latest/\n",
    "- https://intake-thredds.readthedocs.io/en/latest/\n",
    "- https://filesystem-spec.readthedocs.io/en/latest/\n",
    "- https://climetlab.readthedocs.io/en/latest/\n",
    "\n",
    "Hindcasts/Reforecasts are stored in `netcdf` or `grib` format.\n",
    "For each initialization `init` (CF convention: `standard_name=forecast_reference_time`) in the year 2020 (see `dates`), reforecasts are available started on the same `dayofyear` from 2000-2019.\n",
    "\n",
    "Here we get the reforecast skill for 2m temperature of forecasts initialized 2nd Jan 2000-2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "sunset-count",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"dates = xr.cftime_range(start=\\\"2020-01-02\\\", freq=\\\"7D\\\", end=\\\"2020-12-31\\\")\";\n",
       "                var nbb_formatted_code = \"dates = xr.cftime_range(start=\\\"2020-01-02\\\", freq=\\\"7D\\\", end=\\\"2020-12-31\\\")\";\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dates = xr.cftime_range(start=\"2020-01-02\", freq=\"7D\", end=\"2020-12-31\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eight-amendment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20200102'"
      ]
     },
     "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 = \"var = \\\"t2m\\\"\\ndate = dates[0].strftime(\\\"%Y%m%d\\\")\\ndate\";\n",
       "                var nbb_formatted_code = \"var = \\\"t2m\\\"\\ndate = dates[0].strftime(\\\"%Y%m%d\\\")\\ndate\";\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = \"t2m\"\n",
    "date = dates[0].strftime(\"%Y%m%d\")\n",
    "date"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adverse-standing",
   "metadata": {},
   "source": [
    "#### `intake_xarray`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "brief-village",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"import intake\\nimport fsspec\\nfrom aiohttp import ClientSession, ClientTimeout\\n\\ntimeout = ClientTimeout(total=600)\\nfsspec.config.conf[\\\"https\\\"] = dict(client_kwargs={\\\"timeout\\\": timeout})\\n\\nimport intake_xarray\\n\\ncache_path = \\\"my_caching_folder\\\"\\nfsspec.config.conf[\\\"simplecache\\\"] = {\\\"cache_storage\\\": cache_path, \\\"same_names\\\": True}\";\n",
       "                var nbb_formatted_code = \"import intake\\nimport fsspec\\nfrom aiohttp import ClientSession, ClientTimeout\\n\\ntimeout = ClientTimeout(total=600)\\nfsspec.config.conf[\\\"https\\\"] = dict(client_kwargs={\\\"timeout\\\": timeout})\\n\\nimport intake_xarray\\n\\ncache_path = \\\"my_caching_folder\\\"\\nfsspec.config.conf[\\\"simplecache\\\"] = {\\\"cache_storage\\\": cache_path, \\\"same_names\\\": 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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import intake\n",
    "import fsspec\n",
    "from aiohttp import ClientSession, ClientTimeout\n",
    "\n",
    "timeout = ClientTimeout(total=600)\n",
    "fsspec.config.conf[\"https\"] = dict(client_kwargs={\"timeout\": timeout})\n",
    "\n",
    "import intake_xarray\n",
    "\n",
    "cache_path = \"my_caching_folder\"\n",
    "fsspec.config.conf[\"simplecache\"] = {\"cache_storage\": cache_path, \"same_names\": True}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "latin-convention",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"forecast = (\\n    intake_xarray.NetCDFSource(\\n        f\\\"simplecache::https://storage.ecmwf.europeanweather.cloud/s2s-ai-challenge/data/test-input/0.3.0/netcdf/ecmwf-forecast-{var}-{date}.nc\\\"\\n    )\\n    .to_dask()\\n    .compute()\\n)\";\n",
       "                var nbb_formatted_code = \"forecast = (\\n    intake_xarray.NetCDFSource(\\n        f\\\"simplecache::https://storage.ecmwf.europeanweather.cloud/s2s-ai-challenge/data/test-input/0.3.0/netcdf/ecmwf-forecast-{var}-{date}.nc\\\"\\n    )\\n    .to_dask()\\n    .compute()\\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forecast = (\n",
    "    intake_xarray.NetCDFSource(\n",
    "        f\"simplecache::https://storage.ecmwf.europeanweather.cloud/s2s-ai-challenge/data/test-input/0.3.0/netcdf/ecmwf-forecast-{var}-{date}.nc\"\n",
    "    )\n",
    "    .to_dask()\n",
    "    .compute()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "systematic-dance",
   "metadata": {},
   "source": [
    "#### `climetlab`\n",
    "\n",
    "`climetlab` wraps `cdsapi` to download from the Copernicus Climate Data Store (CDS) and from plug-in sources:\n",
    "\n",
    "- https://climetlab.readthedocs.io/en/latest/\n",
    "- https://github.com/ecmwf-lab/climetlab-s2s-ai-challenge\n",
    "- https://cds.climate.copernicus.eu/cdsapp#!/home\n",
    "- https://github.com/ecmwf/cdsapi/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "acting-projector",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"import climetlab\";\n",
       "                var nbb_formatted_code = \"import climetlab\";\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import climetlab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "controlled-hostel",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "By downloading data from this dataset, you agree to the terms and conditions defined at https://apps.ecmwf.int/datasets/data/s2s/licence/. If you do not agree with such terms, do not download the data. \n"
     ]
    },
    {
     "data": {
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       "model_id": "229b6d2cc83e4e789a09c55663503add",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"forecast_climetlab = (\\n    climetlab.load_dataset(\\n        \\\"s2s-ai-challenge-test-input\\\",\\n        origin=\\\"ecmwf\\\",\\n        date=[20200102, 20200206],  # get to initializations/forecast_times\\n        parameter=var,\\n        format=\\\"netcdf\\\",\\n    )\\n    .to_xarray()\\n    .compute()\\n)\";\n",
       "                var nbb_formatted_code = \"forecast_climetlab = (\\n    climetlab.load_dataset(\\n        \\\"s2s-ai-challenge-test-input\\\",\\n        origin=\\\"ecmwf\\\",\\n        date=[20200102, 20200206],  # get to initializations/forecast_times\\n        parameter=var,\\n        format=\\\"netcdf\\\",\\n    )\\n    .to_xarray()\\n    .compute()\\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forecast_climetlab = (\n",
    "    climetlab.load_dataset(\n",
    "        \"s2s-ai-challenge-test-input\",\n",
    "        origin=\"ecmwf\",\n",
    "        date=[20200102, 20200206],  # get to initializations/forecast_times\n",
    "        parameter=var,\n",
    "        format=\"netcdf\",\n",
    "    )\n",
    "    .to_xarray()\n",
    "    .compute()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cf84407a-e53d-42d5-b65b-d60d7843e7bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"xr.testing.assert_equal(forecast_climetlab.sel(forecast_time=\\\"2020-01\\\"), forecast)\";\n",
       "                var nbb_formatted_code = \"xr.testing.assert_equal(forecast_climetlab.sel(forecast_time=\\\"2020-01\\\"), forecast)\";\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xr.testing.assert_equal(forecast_climetlab.sel(forecast_time=\"2020-01\"), forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "enhanced-optics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * realization    (realization) int64 0 1 2 3 4 5 6 7 ... 44 45 46 47 48 49 50\n",
       "  * forecast_time  (forecast_time) datetime64[ns] 2020-01-02 2020-02-06\n",
       "  * lead_time      (lead_time) timedelta64[ns] 1 days 2 days ... 45 days 46 days\n",
       "  * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
       "  * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
       "    valid_time     (forecast_time, lead_time) datetime64[ns] 2020-01-03 ... 2..."
      ]
     },
     "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 = \"forecast_climetlab.coords\";\n",
       "                var nbb_formatted_code = \"forecast_climetlab.coords\";\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forecast_climetlab.coords"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "horizontal-convenience",
   "metadata": {},
   "source": [
    "### Get observations\n",
    "\n",
    "Choose from:\n",
    "\n",
    "- CPC\n",
    "- ERA5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "verbal-saver",
   "metadata": {},
   "source": [
    "#### CPC as observations\n",
    "compare against CPC observations: http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.temperature/.daily/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "assumed-prompt",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"cache = True\\nif not cache:\\n    chunk_dim = \\\"T\\\"\\n    grid = 1.5\\n    tmin = xr.open_dataset(\\n        f\\\"http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.temperature/.daily/.tmin/T/(0000%201%20Jan%202020)/(0000%2001%20Apr%202020)/RANGEEDGES/X/0/{grid}/358.5/GRID/Y/90/{grid}/-90/GRID/dods\\\",\\n        chunks={chunk_dim: \\\"auto\\\"},\\n    ).rename({\\\"tmin\\\": \\\"t\\\"})\\n    tmax = xr.open_dataset(\\n        f\\\"http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.temperature/.daily/.tmax/T/(0000%201%20Jan%202020)/(0000%2001%20Apr%202020)/RANGEEDGES/X/0/{grid}/358.5/GRID/Y/90/{grid}/-90/GRID/dods\\\",\\n        chunks={chunk_dim: \\\"auto\\\"},\\n    ).rename({\\\"tmax\\\": \\\"t\\\"})\\n    t = (tmin + tmax) / 2\\n\\n    t[\\\"T\\\"] = xr.cftime_range(start=\\\"2020-01-01\\\", freq=\\\"1D\\\", periods=t.T.size)\\n\\n    t = t.rename({\\\"X\\\": \\\"longitude\\\", \\\"Y\\\": \\\"latitude\\\", \\\"T\\\": \\\"time\\\"})\\n\\n    t[\\\"t\\\"].attrs = tmin[\\\"t\\\"].attrs\\n    t[\\\"t\\\"].attrs[\\\"long_name\\\"] = \\\"Daily Temperature\\\"\\n    t = t.rename({\\\"t\\\": \\\"t2m\\\"}) + 273.15\\n    t[\\\"t2m\\\"].attrs[\\\"units\\\"] = \\\"K\\\"\\n    t.to_netcdf(\\\"my_caching_folder/obs_CPC.nc\\\")\\nelse:\\n    obs = xr.open_dataset(\\\"my_caching_folder/obs_CPC.nc\\\").compute()\";\n",
       "                var nbb_formatted_code = \"cache = True\\nif not cache:\\n    chunk_dim = \\\"T\\\"\\n    grid = 1.5\\n    tmin = xr.open_dataset(\\n        f\\\"http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.temperature/.daily/.tmin/T/(0000%201%20Jan%202020)/(0000%2001%20Apr%202020)/RANGEEDGES/X/0/{grid}/358.5/GRID/Y/90/{grid}/-90/GRID/dods\\\",\\n        chunks={chunk_dim: \\\"auto\\\"},\\n    ).rename({\\\"tmin\\\": \\\"t\\\"})\\n    tmax = xr.open_dataset(\\n        f\\\"http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.temperature/.daily/.tmax/T/(0000%201%20Jan%202020)/(0000%2001%20Apr%202020)/RANGEEDGES/X/0/{grid}/358.5/GRID/Y/90/{grid}/-90/GRID/dods\\\",\\n        chunks={chunk_dim: \\\"auto\\\"},\\n    ).rename({\\\"tmax\\\": \\\"t\\\"})\\n    t = (tmin + tmax) / 2\\n\\n    t[\\\"T\\\"] = xr.cftime_range(start=\\\"2020-01-01\\\", freq=\\\"1D\\\", periods=t.T.size)\\n\\n    t = t.rename({\\\"X\\\": \\\"longitude\\\", \\\"Y\\\": \\\"latitude\\\", \\\"T\\\": \\\"time\\\"})\\n\\n    t[\\\"t\\\"].attrs = tmin[\\\"t\\\"].attrs\\n    t[\\\"t\\\"].attrs[\\\"long_name\\\"] = \\\"Daily Temperature\\\"\\n    t = t.rename({\\\"t\\\": \\\"t2m\\\"}) + 273.15\\n    t[\\\"t2m\\\"].attrs[\\\"units\\\"] = \\\"K\\\"\\n    t.to_netcdf(\\\"my_caching_folder/obs_CPC.nc\\\")\\nelse:\\n    obs = xr.open_dataset(\\\"my_caching_folder/obs_CPC.nc\\\").compute()\";\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cache = True\n",
    "if not cache:\n",
    "    chunk_dim = \"T\"\n",
    "    grid = 1.5\n",
    "    tmin = xr.open_dataset(\n",
    "        f\"http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.temperature/.daily/.tmin/T/(0000%201%20Jan%202020)/(0000%2001%20Apr%202020)/RANGEEDGES/X/0/{grid}/358.5/GRID/Y/90/{grid}/-90/GRID/dods\",\n",
    "        chunks={chunk_dim: \"auto\"},\n",
    "    ).rename({\"tmin\": \"t\"})\n",
    "    tmax = xr.open_dataset(\n",
    "        f\"http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.temperature/.daily/.tmax/T/(0000%201%20Jan%202020)/(0000%2001%20Apr%202020)/RANGEEDGES/X/0/{grid}/358.5/GRID/Y/90/{grid}/-90/GRID/dods\",\n",
    "        chunks={chunk_dim: \"auto\"},\n",
    "    ).rename({\"tmax\": \"t\"})\n",
    "    t = (tmin + tmax) / 2\n",
    "\n",
    "    t[\"T\"] = xr.cftime_range(start=\"2020-01-01\", freq=\"1D\", periods=t.T.size)\n",
    "\n",
    "    t = t.rename({\"X\": \"longitude\", \"Y\": \"latitude\", \"T\": \"time\"})\n",
    "\n",
    "    t[\"t\"].attrs = tmin[\"t\"].attrs\n",
    "    t[\"t\"].attrs[\"long_name\"] = \"Daily Temperature\"\n",
    "    t = t.rename({\"t\": \"t2m\"}) + 273.15\n",
    "    t[\"t2m\"].attrs[\"units\"] = \"K\"\n",
    "    t.to_netcdf(\"my_caching_folder/obs_CPC.nc\")\n",
    "else:\n",
    "    obs = xr.open_dataset(\"my_caching_folder/obs_CPC.nc\").compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cellular-indonesia",
   "metadata": {},
   "source": [
    "#### ERA5 as observations\n",
    "\n",
    "compare against ERA5 reanalysis fetched via https://github.com/ecmwf/cdsapi/ from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f507218-27c6-4a7d-a949-f1d5e2d45b66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"forecast_times = forecast_climetlab.set_coords(\\\"valid_time\\\").valid_time.values.flatten()\\n\\n# takes a while, continue with obs from above\\nobs_era = (\\n    climetlab.load_source(\\n        \\\"cds\\\",\\n        \\\"reanalysis-era5-single-levels\\\",\\n        product_type=\\\"reanalysis\\\",\\n        time=[\\\"00:00\\\"],\\n        grid=[1.5, 1.5],\\n        param=\\\"2t\\\",\\n        date=forecast_times,\\n    )\\n    .to_xarray()\\n    .squeeze(drop=True)\\n)\";\n",
       "                var nbb_formatted_code = \"forecast_times = forecast_climetlab.set_coords(\\\"valid_time\\\").valid_time.values.flatten()\\n\\n# takes a while, continue with obs from above\\nobs_era = (\\n    climetlab.load_source(\\n        \\\"cds\\\",\\n        \\\"reanalysis-era5-single-levels\\\",\\n        product_type=\\\"reanalysis\\\",\\n        time=[\\\"00:00\\\"],\\n        grid=[1.5, 1.5],\\n        param=\\\"2t\\\",\\n        date=forecast_times,\\n    )\\n    .to_xarray()\\n    .squeeze(drop=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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forecast_times = forecast_climetlab.set_coords(\"valid_time\").valid_time.values.flatten()\n",
    "\n",
    "# takes a while, continue with obs from above\n",
    "obs_era = (\n",
    "    climetlab.load_source(\n",
    "        \"cds\",\n",
    "        \"reanalysis-era5-single-levels\",\n",
    "        product_type=\"reanalysis\",\n",
    "        time=[\"00:00\"],\n",
    "        grid=[1.5, 1.5],\n",
    "        param=\"2t\",\n",
    "        date=forecast_times,\n",
    "    )\n",
    "    .to_xarray()\n",
    "    .squeeze(drop=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "practical-authority",
   "metadata": {},
   "source": [
    "### Forecast verification with `climpred.HindcastEnsemble`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5a0dcf01-b94c-4fa7-afae-fd19814ded66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * realization    (realization) int64 0 1 2 3 4 5 6 7 ... 44 45 46 47 48 49 50\n",
       "  * forecast_time  (forecast_time) datetime64[ns] 2020-01-02 2020-02-06\n",
       "  * lead_time      (lead_time) timedelta64[ns] 1 days 2 days ... 45 days 46 days\n",
       "  * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
       "  * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
       "    valid_time     (forecast_time, lead_time) datetime64[ns] 2020-01-03 ... 2..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"# PredictionEnsemble converts `lead_time` to `lead` due to standard_name 'forecast_period' and\\n#                             pd.Timedelta to `int` and set lead.attrs['units'] accordingly\\nforecast_climetlab.coords\";\n",
       "                var nbb_formatted_code = \"# PredictionEnsemble converts `lead_time` to `lead` due to standard_name 'forecast_period' and\\n#                             pd.Timedelta to `int` and set lead.attrs['units'] accordingly\\nforecast_climetlab.coords\";\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": [
       "<IPython.core.display.Javascript object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PredictionEnsemble converts `lead_time` to `lead` due to standard_name 'forecast_period' and\n",
    "#                             pd.Timedelta to `int` and set lead.attrs['units'] accordingly\n",
    "forecast_climetlab.coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "sexual-blues",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aaron.spring/Coding/climpred/climpred/checks.py:234: UserWarning: Did not find dimension \"init\", but renamed dimension forecast_time with CF-complying standard_name \"forecast_reference_time\" to init.\n",
      "  warnings.warn(\n",
      "/Users/aaron.spring/Coding/climpred/climpred/checks.py:234: UserWarning: Did not find dimension \"member\", but renamed dimension realization with CF-complying standard_name \"realization\" to member.\n",
      "  warnings.warn(\n",
      "/Users/aaron.spring/Coding/climpred/climpred/checks.py:234: UserWarning: Did not find dimension \"lead\", but renamed dimension lead_time with CF-complying standard_name \"forecast_period\" to lead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"fct = (\\n    climpred.HindcastEnsemble(\\n        forecast_climetlab.drop(\\\"valid_time\\\").isel(lead_time=range(4))\\n    )\\n    .add_observations(obs)\\n    .compute()\\n)\\n\\nmetric_kwargs = dict(\\n    metric=\\\"rmse\\\", comparison=\\\"e2o\\\", alignment=\\\"same_inits\\\", dim=\\\"init\\\"\\n)\";\n",
       "                var nbb_formatted_code = \"fct = (\\n    climpred.HindcastEnsemble(\\n        forecast_climetlab.drop(\\\"valid_time\\\").isel(lead_time=range(4))\\n    )\\n    .add_observations(obs)\\n    .compute()\\n)\\n\\nmetric_kwargs = dict(\\n    metric=\\\"rmse\\\", comparison=\\\"e2o\\\", alignment=\\\"same_inits\\\", dim=\\\"init\\\"\\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fct = (\n",
    "    climpred.HindcastEnsemble(\n",
    "        forecast_climetlab.drop(\"valid_time\").isel(lead_time=range(4))\n",
    "    )\n",
    "    .add_observations(obs)\n",
    "    .compute()\n",
    ")\n",
    "\n",
    "metric_kwargs = dict(\n",
    "    metric=\"rmse\", comparison=\"e2o\", alignment=\"same_inits\", dim=\"init\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "furnished-margin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x153344730>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"skill = fct.verify(**metric_kwargs)\\nskill[var].plot(col=\\\"lead\\\", robust=True)\";\n",
       "                var nbb_formatted_code = \"skill = fct.verify(**metric_kwargs)\\nskill[var].plot(col=\\\"lead\\\", robust=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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skill = fct.verify(**metric_kwargs)\n",
    "skill[var].plot(col=\"lead\", robust=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "damaged-meter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 18;\n",
       "                var nbb_unformatted_code = \"# still experimental, help appreciated https://github.com/pangeo-data/climpred/issues/605\\n# with climpred.set_options(seasonality=\\\"month\\\"):\\n#    fct = fct.remove_bias(metric_kwargs[\\\"alignment\\\"], cross_validate=False)\";\n",
       "                var nbb_formatted_code = \"# still experimental, help appreciated https://github.com/pangeo-data/climpred/issues/605\\n# with climpred.set_options(seasonality=\\\"month\\\"):\\n#    fct = fct.remove_bias(metric_kwargs[\\\"alignment\\\"], cross_validate=False)\";\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# still experimental, help appreciated https://github.com/pangeo-data/climpred/issues/605\n",
    "# with climpred.set_options(seasonality=\"month\"):\n",
    "#    fct = fct.remove_bias(metric_kwargs[\"alignment\"], cross_validate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc9b2303-b711-4dfc-8062-2fb24f83fbdd",
   "metadata": {},
   "source": [
    "### biweekly aggregates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd7ef785-45d7-42c6-904f-9e25825b6e68",
   "metadata": {},
   "source": [
    "The `s2s-ai-challenge` requires biweekly target aggregates: https://s2s-ai-challenge.github.io/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ab4454c-e1c2-4f48-84eb-7bcc74c626a3",
   "metadata": {},
   "source": [
    "There are two approaches possible for biweekly aggregates with `climpred`:\n",
    "\n",
    "- create biweekly aggregate timeseries for observations and forecasts and then use `climpred`\n",
    "- use daily leads and daily observations and `HindcastEnsemble.smooth()` to aggregate biweekly"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76354e72-8d81-445c-baae-168d07abaf3a",
   "metadata": {},
   "source": [
    "#### biweekly aggregates before using `climpred`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66462c9c-e139-4f12-b834-2d7f3351f99d",
   "metadata": {},
   "source": [
    "##### forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2a4e2aff-6c23-4e16-aa1f-654bfdeb6d84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"from climpred.utils import convert_Timedelta_to_lead_units\\n\\nforecast = convert_Timedelta_to_lead_units(\\n    forecast_climetlab.rename({\\\"lead_time\\\": \\\"lead\\\"})\\n)\";\n",
       "                var nbb_formatted_code = \"from climpred.utils import convert_Timedelta_to_lead_units\\n\\nforecast = convert_Timedelta_to_lead_units(\\n    forecast_climetlab.rename({\\\"lead_time\\\": \\\"lead\\\"})\\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from climpred.utils import convert_Timedelta_to_lead_units\n",
    "\n",
    "forecast = convert_Timedelta_to_lead_units(\n",
    "    forecast_climetlab.rename({\"lead_time\": \"lead\"})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "44baedc1-0ad1-4a95-ba17-35151cb2590d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * realization    (realization) int64 0 1 2 3 4 5 6 7 ... 44 45 46 47 48 49 50\n",
       "  * forecast_time  (forecast_time) datetime64[ns] 2020-01-02 2020-02-06\n",
       "  * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
       "  * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
       "  * lead           (lead) int64 1 14 28"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 20;\n",
       "                var nbb_unformatted_code = \"# create 14D averages\\nforecast_w12 = forecast.sel(lead=range(1, 14)).mean(dim=\\\"lead\\\")\\nforecast_w34 = forecast.sel(lead=range(14, 28)).mean(dim=\\\"lead\\\")\\nforecast_w56 = forecast.sel(lead=range(28, 42)).mean(dim=\\\"lead\\\")\\nforecast_biweekly = xr.concat([forecast_w12, forecast_w34, forecast_w56], dim=\\\"lead\\\")\\nforecast_biweekly[\\\"lead\\\"] = [\\n    1,\\n    14,\\n    28,\\n]  # lead represents first day of biweekly aggregate\\nforecast_biweekly[\\\"lead\\\"].attrs[\\\"units\\\"] = \\\"days\\\"\\nforecast_biweekly.coords\";\n",
       "                var nbb_formatted_code = \"# create 14D averages\\nforecast_w12 = forecast.sel(lead=range(1, 14)).mean(dim=\\\"lead\\\")\\nforecast_w34 = forecast.sel(lead=range(14, 28)).mean(dim=\\\"lead\\\")\\nforecast_w56 = forecast.sel(lead=range(28, 42)).mean(dim=\\\"lead\\\")\\nforecast_biweekly = xr.concat([forecast_w12, forecast_w34, forecast_w56], dim=\\\"lead\\\")\\nforecast_biweekly[\\\"lead\\\"] = [\\n    1,\\n    14,\\n    28,\\n]  # lead represents first day of biweekly aggregate\\nforecast_biweekly[\\\"lead\\\"].attrs[\\\"units\\\"] = \\\"days\\\"\\nforecast_biweekly.coords\";\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create 14D averages\n",
    "forecast_w12 = forecast.sel(lead=range(1, 14)).mean(dim=\"lead\")\n",
    "forecast_w34 = forecast.sel(lead=range(14, 28)).mean(dim=\"lead\")\n",
    "forecast_w56 = forecast.sel(lead=range(28, 42)).mean(dim=\"lead\")\n",
    "forecast_biweekly = xr.concat([forecast_w12, forecast_w34, forecast_w56], dim=\"lead\")\n",
    "forecast_biweekly[\"lead\"] = [\n",
    "    1,\n",
    "    14,\n",
    "    28,\n",
    "]  # lead represents first day of biweekly aggregate\n",
    "forecast_biweekly[\"lead\"].attrs[\"units\"] = \"days\"\n",
    "forecast_biweekly.coords"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c71b8fbc-528e-4d69-9001-961b2016a2c3",
   "metadata": {},
   "source": [
    "##### observations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7221a139-8b33-4bac-b7dd-0252467fdf41",
   "metadata": {},
   "source": [
    "> here I use rolling 14D mean, and set the time index to the starting date, i.e. I get 14D averages for every previous time date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "399f14a0-fe34-4da3-b545-c569492e1629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 21;\n",
       "                var nbb_unformatted_code = \"# 14D rolling mean\\nobs_biweekly = obs.rolling(time=14, center=False).mean()\\nobs_biweekly = obs_biweekly.isel(time=slice(13, None)).assign_coords(\\n    time=obs.time.isel(time=slice(None, -13))\\n)  # time represents first day of the biweekly aggregate\";\n",
       "                var nbb_formatted_code = \"# 14D rolling mean\\nobs_biweekly = obs.rolling(time=14, center=False).mean()\\nobs_biweekly = obs_biweekly.isel(time=slice(13, None)).assign_coords(\\n    time=obs.time.isel(time=slice(None, -13))\\n)  # time represents first day of the biweekly aggregate\";\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 14D rolling mean\n",
    "obs_biweekly = obs.rolling(time=14, center=False).mean()\n",
    "obs_biweekly = obs_biweekly.isel(time=slice(13, None)).assign_coords(\n",
    "    time=obs.time.isel(time=slice(None, -13))\n",
    ")  # time represents first day of the biweekly aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cc2f74a0-44af-4c0e-9767-30b5bb54de5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aaron.spring/Coding/climpred/climpred/checks.py:234: UserWarning: Did not find dimension \"init\", but renamed dimension forecast_time with CF-complying standard_name \"forecast_reference_time\" to init.\n",
      "  warnings.warn(\n",
      "/Users/aaron.spring/Coding/climpred/climpred/checks.py:234: UserWarning: Did not find dimension \"member\", but renamed dimension realization with CF-complying standard_name \"realization\" to member.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 22;\n",
       "                var nbb_unformatted_code = \"fct_biweekly = climpred.HindcastEnsemble(forecast_biweekly).add_observations(\\n    obs_biweekly\\n)\";\n",
       "                var nbb_formatted_code = \"fct_biweekly = climpred.HindcastEnsemble(forecast_biweekly).add_observations(\\n    obs_biweekly\\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fct_biweekly = climpred.HindcastEnsemble(forecast_biweekly).add_observations(\n",
    "    obs_biweekly\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d6d35da1-65f7-440c-8861-41391632c4e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 23;\n",
       "                var nbb_unformatted_code = \"obs_edges = (\\n    obs_biweekly.groupby(\\\"time.month\\\")\\n    .quantile(q=[1 / 3, 2 / 3], dim=\\\"time\\\", skipna=False)\\n    .rename({\\\"quantile\\\": \\\"category_edge\\\"})\\n)\";\n",
       "                var nbb_formatted_code = \"obs_edges = (\\n    obs_biweekly.groupby(\\\"time.month\\\")\\n    .quantile(q=[1 / 3, 2 / 3], dim=\\\"time\\\", skipna=False)\\n    .rename({\\\"quantile\\\": \\\"category_edge\\\"})\\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "obs_edges = (\n",
    "    obs_biweekly.groupby(\"time.month\")\n",
    "    .quantile(q=[1 / 3, 2 / 3], dim=\"time\", skipna=False)\n",
    "    .rename({\"quantile\": \"category_edge\"})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4ea190a0-3efe-473e-84f9-1e3e3105f0f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 24;\n",
       "                var nbb_unformatted_code = \"model_edges = (\\n    forecast_biweekly.rename({\\\"forecast_time\\\": \\\"init\\\", \\\"realization\\\": \\\"member\\\"})\\n    .groupby(\\\"init.month\\\")\\n    .quantile(q=[1 / 3, 2 / 3], dim=[\\\"init\\\", \\\"member\\\"], skipna=False)\\n    .rename({\\\"quantile\\\": \\\"category_edge\\\"})\\n)\";\n",
       "                var nbb_formatted_code = \"model_edges = (\\n    forecast_biweekly.rename({\\\"forecast_time\\\": \\\"init\\\", \\\"realization\\\": \\\"member\\\"})\\n    .groupby(\\\"init.month\\\")\\n    .quantile(q=[1 / 3, 2 / 3], dim=[\\\"init\\\", \\\"member\\\"], skipna=False)\\n    .rename({\\\"quantile\\\": \\\"category_edge\\\"})\\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_edges = (\n",
    "    forecast_biweekly.rename({\"forecast_time\": \"init\", \"realization\": \"member\"})\n",
    "    .groupby(\"init.month\")\n",
    "    .quantile(q=[1 / 3, 2 / 3], dim=[\"init\", \"member\"], skipna=False)\n",
    "    .rename({\"quantile\": \"category_edge\"})\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26cfb272-dddd-4f98-81c8-93aabe5a6edb",
   "metadata": {},
   "source": [
    "##### `rps`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "271b2f96-128a-4821-b325-6b6c935c0a59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x15364c520>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 25;\n",
       "                var nbb_unformatted_code = \"# use the same observational category_edges for observations and forecasts\\nrps_same_edges = fct_biweekly.verify(\\n    metric=\\\"rps\\\",\\n    comparison=\\\"m2o\\\",\\n    alignment=\\\"same_inits\\\",\\n    dim=[\\\"member\\\", \\\"init\\\"],\\n    category_edges=obs_edges,\\n)\\n\\nrps_same_edges.t2m.plot(col=\\\"lead\\\", robust=True)\";\n",
       "                var nbb_formatted_code = \"# use the same observational category_edges for observations and forecasts\\nrps_same_edges = fct_biweekly.verify(\\n    metric=\\\"rps\\\",\\n    comparison=\\\"m2o\\\",\\n    alignment=\\\"same_inits\\\",\\n    dim=[\\\"member\\\", \\\"init\\\"],\\n    category_edges=obs_edges,\\n)\\n\\nrps_same_edges.t2m.plot(col=\\\"lead\\\", robust=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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use the same observational category_edges for observations and forecasts\n",
    "rps_same_edges = fct_biweekly.verify(\n",
    "    metric=\"rps\",\n",
    "    comparison=\"m2o\",\n",
    "    alignment=\"same_inits\",\n",
    "    dim=[\"member\", \"init\"],\n",
    "    category_edges=obs_edges,\n",
    ")\n",
    "\n",
    "rps_same_edges.t2m.plot(col=\"lead\", robust=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "23ac419c-b1e8-4768-a9ad-9070f3565a19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x153619c70>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 26;\n",
       "                var nbb_unformatted_code = \"# use different observational category_edges for observations and forecast category_edges for forecasts\\nrps_different_edges = fct_biweekly.verify(\\n    metric=\\\"rps\\\",\\n    comparison=\\\"m2o\\\",\\n    alignment=\\\"same_inits\\\",\\n    dim=[\\\"member\\\", \\\"init\\\"],\\n    category_edges=(obs_edges, model_edges),\\n)\\n\\nrps_different_edges.t2m.plot(col=\\\"lead\\\", robust=True)\";\n",
       "                var nbb_formatted_code = \"# use different observational category_edges for observations and forecast category_edges for forecasts\\nrps_different_edges = fct_biweekly.verify(\\n    metric=\\\"rps\\\",\\n    comparison=\\\"m2o\\\",\\n    alignment=\\\"same_inits\\\",\\n    dim=[\\\"member\\\", \\\"init\\\"],\\n    category_edges=(obs_edges, model_edges),\\n)\\n\\nrps_different_edges.t2m.plot(col=\\\"lead\\\", robust=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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use different observational category_edges for observations and forecast category_edges for forecasts\n",
    "rps_different_edges = fct_biweekly.verify(\n",
    "    metric=\"rps\",\n",
    "    comparison=\"m2o\",\n",
    "    alignment=\"same_inits\",\n",
    "    dim=[\"member\", \"init\"],\n",
    "    category_edges=(obs_edges, model_edges),\n",
    ")\n",
    "\n",
    "rps_different_edges.t2m.plot(col=\"lead\", robust=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99404476-792a-46aa-bfd8-34b7e46861a5",
   "metadata": {},
   "source": [
    "> Using the `model_edges` to categorize the forecasts and `obs_edges` to categorize the observations separately is an inherent bias correction and improves `rps`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab9c64c7-28f7-4daa-8cc9-99ad0188e94a",
   "metadata": {},
   "source": [
    "### biweekly aggregates with `smooth`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d029b286-7d3e-4c06-bad9-24ada8e2ea78",
   "metadata": {},
   "source": [
    "https://climpred.readthedocs.io/en/stable/api/climpred.classes.HindcastEnsemble.smooth.html#climpred.classes.HindcastEnsemble.smooth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "55ce72a2-692c-4e75-ac5c-0e258fec8d0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aaron.spring/Coding/climpred/climpred/checks.py:234: UserWarning: Did not find dimension \"init\", but renamed dimension forecast_time with CF-complying standard_name \"forecast_reference_time\" to init.\n",
      "  warnings.warn(\n",
      "/Users/aaron.spring/Coding/climpred/climpred/checks.py:234: UserWarning: Did not find dimension \"member\", but renamed dimension realization with CF-complying standard_name \"realization\" to member.\n",
      "  warnings.warn(\n",
      "/Users/aaron.spring/Coding/climpred/climpred/checks.py:234: UserWarning: Did not find dimension \"lead\", but renamed dimension lead_time with CF-complying standard_name \"forecast_period\" to lead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 27;\n",
       "                var nbb_unformatted_code = \"fct = (\\n    climpred.HindcastEnsemble(forecast_climetlab.drop(\\\"valid_time\\\"))  # daily lead\\n    .add_observations(obs)\\n    .compute()\\n)\";\n",
       "                var nbb_formatted_code = \"fct = (\\n    climpred.HindcastEnsemble(forecast_climetlab.drop(\\\"valid_time\\\"))  # daily lead\\n    .add_observations(obs)\\n    .compute()\\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fct = (\n",
    "    climpred.HindcastEnsemble(forecast_climetlab.drop(\"valid_time\"))  # daily lead\n",
    "    .add_observations(obs)\n",
    "    .compute()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9247f41a-cf53-4745-9d30-34605276aa97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x10f721550>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 28;\n",
       "                var nbb_unformatted_code = \"# first smooth and then subselect leads\\n# use the same observational category_edges for observations and forecasts\\nfct.smooth(dict(lead=14), how=\\\"mean\\\").verify(\\n    metric=\\\"rps\\\",\\n    comparison=\\\"m2o\\\",\\n    alignment=\\\"same_inits\\\",\\n    dim=[\\\"member\\\", \\\"init\\\"],\\n    category_edges=obs_edges,\\n).sel(lead=[\\\"1.0-14.0\\\", \\\"14.0-27.0\\\", \\\"28.0-41.0\\\"]).t2m.plot(col=\\\"lead\\\", robust=True)\";\n",
       "                var nbb_formatted_code = \"# first smooth and then subselect leads\\n# use the same observational category_edges for observations and forecasts\\nfct.smooth(dict(lead=14), how=\\\"mean\\\").verify(\\n    metric=\\\"rps\\\",\\n    comparison=\\\"m2o\\\",\\n    alignment=\\\"same_inits\\\",\\n    dim=[\\\"member\\\", \\\"init\\\"],\\n    category_edges=obs_edges,\\n).sel(lead=[\\\"1.0-14.0\\\", \\\"14.0-27.0\\\", \\\"28.0-41.0\\\"]).t2m.plot(col=\\\"lead\\\", robust=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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first smooth and then subselect leads\n",
    "# use the same observational category_edges for observations and forecasts\n",
    "fct.smooth(dict(lead=14), how=\"mean\").verify(\n",
    "    metric=\"rps\",\n",
    "    comparison=\"m2o\",\n",
    "    alignment=\"same_inits\",\n",
    "    dim=[\"member\", \"init\"],\n",
    "    category_edges=obs_edges,\n",
    ").sel(lead=[\"1.0-14.0\", \"14.0-27.0\", \"28.0-41.0\"]).t2m.plot(col=\"lead\", robust=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7800f7c0-4470-4bd5-9f8e-349c97377fd3",
   "metadata": {},
   "source": [
    "> category_edges have to be smoothed before manually by `climpred.smoothing.temporal_smoothing`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d43ea406-e3af-4897-ac9a-bc811bb85667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 29;\n",
       "                var nbb_unformatted_code = \"obs_edges = (\\n    climpred.smoothing.temporal_smoothing(fct.get_observations(), dict(lead=14))\\n    .groupby(\\\"time.month\\\")\\n    .quantile(q=[1 / 3, 2 / 3], dim=\\\"time\\\", skipna=False)\\n    .rename({\\\"quantile\\\": \\\"category_edge\\\"})\\n)\";\n",
       "                var nbb_formatted_code = \"obs_edges = (\\n    climpred.smoothing.temporal_smoothing(fct.get_observations(), dict(lead=14))\\n    .groupby(\\\"time.month\\\")\\n    .quantile(q=[1 / 3, 2 / 3], dim=\\\"time\\\", skipna=False)\\n    .rename({\\\"quantile\\\": \\\"category_edge\\\"})\\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "obs_edges = (\n",
    "    climpred.smoothing.temporal_smoothing(fct.get_observations(), dict(lead=14))\n",
    "    .groupby(\"time.month\")\n",
    "    .quantile(q=[1 / 3, 2 / 3], dim=\"time\", skipna=False)\n",
    "    .rename({\"quantile\": \"category_edge\"})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "567703d3-f668-4246-93e4-0910f2797aae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 30;\n",
       "                var nbb_unformatted_code = \"model_edges = (\\n    climpred.smoothing.temporal_smoothing(fct.get_initialized(), dict(lead=14))\\n    .groupby(\\\"init.month\\\")\\n    .quantile(q=[1 / 3, 2 / 3], dim=[\\\"init\\\", \\\"member\\\"], skipna=False)\\n    .rename({\\\"quantile\\\": \\\"category_edge\\\"})\\n)\";\n",
       "                var nbb_formatted_code = \"model_edges = (\\n    climpred.smoothing.temporal_smoothing(fct.get_initialized(), dict(lead=14))\\n    .groupby(\\\"init.month\\\")\\n    .quantile(q=[1 / 3, 2 / 3], dim=[\\\"init\\\", \\\"member\\\"], skipna=False)\\n    .rename({\\\"quantile\\\": \\\"category_edge\\\"})\\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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_edges = (\n",
    "    climpred.smoothing.temporal_smoothing(fct.get_initialized(), dict(lead=14))\n",
    "    .groupby(\"init.month\")\n",
    "    .quantile(q=[1 / 3, 2 / 3], dim=[\"init\", \"member\"], skipna=False)\n",
    "    .rename({\"quantile\": \"category_edge\"})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "749ffed2-46b1-4d96-9ac8-914f898f8f70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x14cc6f580>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 31;\n",
       "                var nbb_unformatted_code = \"# first smooth and then subselect leads\\n# use different observational category_edges for observations and forecast category_edges for forecasts\\nfct.smooth(dict(lead=14), how=\\\"mean\\\").verify(\\n    metric=\\\"rps\\\",\\n    comparison=\\\"m2o\\\",\\n    alignment=\\\"same_inits\\\",\\n    dim=[\\\"member\\\", \\\"init\\\"],\\n    category_edges=(obs_edges, model_edges),\\n).sel(lead=[\\\"1.0-14.0\\\", \\\"14.0-27.0\\\", \\\"28.0-41.0\\\"]).t2m.plot(col=\\\"lead\\\", robust=True)\";\n",
       "                var nbb_formatted_code = \"# first smooth and then subselect leads\\n# use different observational category_edges for observations and forecast category_edges for forecasts\\nfct.smooth(dict(lead=14), how=\\\"mean\\\").verify(\\n    metric=\\\"rps\\\",\\n    comparison=\\\"m2o\\\",\\n    alignment=\\\"same_inits\\\",\\n    dim=[\\\"member\\\", \\\"init\\\"],\\n    category_edges=(obs_edges, model_edges),\\n).sel(lead=[\\\"1.0-14.0\\\", \\\"14.0-27.0\\\", \\\"28.0-41.0\\\"]).t2m.plot(col=\\\"lead\\\", robust=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": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "# first smooth and then subselect leads\n",
    "# use different observational category_edges for observations and forecast category_edges for forecasts\n",
    "fct.smooth(dict(lead=14), how=\"mean\").verify(\n",
    "    metric=\"rps\",\n",
    "    comparison=\"m2o\",\n",
    "    alignment=\"same_inits\",\n",
    "    dim=[\"member\", \"init\"],\n",
    "    category_edges=(obs_edges, model_edges),\n",
    ").sel(lead=[\"1.0-14.0\", \"14.0-27.0\", \"28.0-41.0\"]).t2m.plot(col=\"lead\", robust=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a09ec278-105c-4653-81eb-27bc90786c5b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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