add herbie notebook (#811)

.binder/environment.yml

@@ -13,7 +13,7 @@ dependencies:
   - pydap
   - matplotlib-base
   - nc-time-axis>=1.4.0
-  - netcdf4==1.5.1  # see https://github.com/pydata/xarray/issues/4925
+  - netcdf4  # ==1.5.1  # see https://github.com/pydata/xarray/issues/4925
   - toolz
   - xrft
   - esmtools>=1.1.3

docs/source/examples.rst

@@ -19,6 +19,7 @@ Numerical Weather Prediction
   :maxdepth: 1
 
   examples/NWP/NWP_GEFS_6h_forecasts.ipynb
+  examples/NWP/Herbie.ipynb
 
 
 Subseasonal

docs/source/examples/NWP/Herbie.ipynb

@@ -0,0 +1,174 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b39af99-1bb7-4fff-be94-85de8dcb7fb8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install herbie-data --quiet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "descending-disease",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# linting\n",
+    "%load_ext nb_black\n",
+    "%load_ext lab_black"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "smart-knock",
+   "metadata": {},
+   "source": [
+    "# Skill from ECMWF downloaded with `herbie`\n",
+    "\n",
+    "[`herbie`](https://herbie.readthedocs.io/en/latest/user_guide/_tutorial_notebooks/fast.html) downloads forecasts data easily. The resulting datasets is out-of-the-box compatible with `climpred`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "insured-feeding",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "\n",
+    "import climpred  # forecast verification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "288c9453-6daa-4ca7-80ba-ef6457e22740",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from herbie import Herbie\n",
+    "\n",
+    "H = Herbie(date=\"2022-01-27 00:00\", model=\"ecmwf\", product=\"enfo\", fxx=24 * 1)\n",
+    "ds = H.xarray(\":2t:\")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b10672b-5374-431d-9b3e-c08c0334aac2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# take the first with multiple members as forecast\n",
+    "init = ds[0][[\"t2m\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d537fac9-5a4e-4536-92a3-60ff52818ecf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "H = Herbie(date=\"2022-01-28 00:00\", model=\"ecmwf\", product=\"enfo\", fxx=0)\n",
+    "ds = H.xarray(\":2t:\")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b09be2e9-a7a2-4ab2-9222-49a06524976c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# take first and make ensemble member mean as observations\n",
+    "obs = ds[0].mean(\"number\").drop([\"step\", \"valid_time\"]).expand_dims(\"time\")[[\"t2m\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "molecular-chuck",
+   "metadata": {},
+   "source": [
+    "## Forecast skill verification\n",
+    "\n",
+    "Using using {py:class}`.HindcastEnsemble`.\n",
+    "\n",
+    "`climpred` expects `init`, `lead` and optional `member` as dimensions, see [setting-up-your-dataset](setting-up-data.html#setting-up-your-dataset). Existing dimensions are renamed automatically if CF `standard_names` match."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7aa2554a-a9c3-4a8f-a153-ada1d2d71d20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hindcast = climpred.HindcastEnsemble(\n",
+    "    init.expand_dims([\"time\", \"step\"])\n",
+    ").add_observations(obs)\n",
+    "\n",
+    "hindcast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "conceptual-richmond",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "skill = hindcast.verify(\n",
+    "    metric=\"crps\", comparison=\"m2o\", dim=[\"init\", \"member\"], alignment=\"same_init\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "colored-luther",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "skill.t2m.plot(robust=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cdc2e81e-6a09-4505-98b0-0ea59c827f8b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}