diff --git "a/run_AIFS_v0_2_1.ipynb" "b/run_AIFS_v0_2_1.ipynb" new file mode 100644--- /dev/null +++ "b/run_AIFS_v0_2_1.ipynb" @@ -0,0 +1,2771 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "nf6PiMYmLEtZ" + }, + "source": [ + "This notebook runs ECMWF's aifs-single-0.2.1 data-driven model, using ECMWF's [open data](https://www.ecmwf.int/en/forecasts/datasets/open-data) dataset and the [anemoi-inference](https://anemoi-inference.readthedocs.io/en/latest/apis/level1.html) package." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YDZrZ8HVxLfU" + }, + "source": [ + "# 1. Install Required Packages and Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 44979, + "status": "ok", + "timestamp": 1733384436074, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "_sT8Re5jLRAH", + "outputId": "384c13e8-e739-4e31-90c5-11970da5808f" + }, + "outputs": [], + "source": [ + "# Uncomment the lines below to install the required packages\n", + "\n", + "# !pip install -q anemoi-inference[huggingface]==0.4.3 anemoi-models==0.2.1\n", + "# !pip install -q earthkit-regrid==0.3.4 ecmwf-opendata\n", + "# !pip install -q flash_attn" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "executionInfo": { + "elapsed": 3143, + "status": "ok", + "timestamp": 1733384445221, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "VBJmsrqGLEtb" + }, + "outputs": [], + "source": [ + "import datetime\n", + "from collections import defaultdict\n", + "\n", + "import numpy as np\n", + "import earthkit.data as ekd\n", + "import earthkit.regrid as ekr\n", + "\n", + "from anemoi.inference.runners.simple import SimpleRunner\n", + "from anemoi.inference.outputs.printer import print_state\n", + "\n", + "from ecmwf.opendata import Client as OpendataClient" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x-JJcbnhLEtc" + }, + "source": [ + "# 2. Retrieve Initial Conditions from ECMWF Open Data\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B8DkdKfUxaPr" + }, + "source": [ + "### List of parameters to retrieve form ECMWF open data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "executionInfo": { + "elapsed": 195, + "status": "ok", + "timestamp": 1733384595021, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "yZOyITo6LEtd" + }, + "outputs": [], + "source": [ + "PARAM_SFC = [\"10u\", \"10v\", \"2d\", \"2t\", \"msl\", \"skt\", \"sp\", \"tcw\", \"lsm\", \"z\", \"slor\", \"sdor\"]\n", + "PARAM_PL = [\"gh\", \"t\", \"u\", \"v\", \"w\", \"q\"]\n", + "LEVELS = [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-CdbpgHLxczB" + }, + "source": [ + "### Select a date" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "executionInfo": { + "elapsed": 2109, + "status": "ok", + "timestamp": 1733384601142, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "XwWLA0OcLEtd" + }, + "outputs": [], + "source": [ + "DATE = OpendataClient().latest()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 388, + "status": "ok", + "timestamp": 1733384601842, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "3_Fy3a0WLEte", + "outputId": "96799389-eb2d-4b1d-f5f3-60e8a309c9ef" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial date is 2024-12-20 06:00:00\n" + ] + } + ], + "source": [ + "print(\"Initial date is\", DATE)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UMmVhb5uxiB9" + }, + "source": [ + "### Get the data from the ECMWF Open Data API" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "executionInfo": { + "elapsed": 215, + "status": "ok", + "timestamp": 1733384619515, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "8X2ShHMeLEtf" + }, + "outputs": [], + "source": [ + "def get_open_data(param, levelist=[]):\n", + " fields = defaultdict(list)\n", + " # Get the data for the current date and the previous date\n", + " for date in [DATE - datetime.timedelta(hours=6), DATE]:\n", + " data = ekd.from_source(\"ecmwf-open-data\", date=date, param=param, levelist=levelist)\n", + " for f in data:\n", + " # Open data is between -180 and 180, we need to shift it to 0-360\n", + " assert f.to_numpy().shape == (721,1440)\n", + " values = np.roll(f.to_numpy(), -f.shape[1] // 2, axis=1)\n", + " # Interpolate the data to from 0.25 to N320\n", + " values = ekr.interpolate(values, {\"grid\": (0.25, 0.25)}, {\"grid\": \"N320\"})\n", + " # Add the values to the list\n", + " name = f\"{f.metadata('param')}_{f.metadata('levelist')}\" if levelist else f.metadata(\"param\")\n", + " fields[name].append(values)\n", + "\n", + " # Create a single matrix for each parameter\n", + " for param, values in fields.items():\n", + " fields[param] = np.stack(values)\n", + "\n", + " return fields" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_yjso9wvxli0" + }, + "source": [ + "### Get Input Fields" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "executionInfo": { + "elapsed": 186, + "status": "ok", + "timestamp": 1733384638318, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "as23RAZiLEtf" + }, + "outputs": [], + "source": [ + "fields = {}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VJs1usVsxq5Q" + }, + "source": [ + "#### Add the single levels fields" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17, + "referenced_widgets": [ + "a7c36aeb00074c6c92735e169b765251", + "7ba380ae6b76488299d17aa1bffece67", + "b695ad246f574c3993d4ca3925be2853", + "6c51d6cbcf924425824f8c4390164834", + "ba596bf1fa9545eda887a2439af3c70c", + "d3f5816d005b48fa981d2e9e947fe020", + "7d9d217ab1bd4010be8eee17e335083a", + "ea41eba5fbed4832b97520086a567d67", + "f362732783d741cd982a21f73b647a18", + "ee9c57247f1d4e63b255784b4342b7b6", + "fa7fbe6608b1416a8b3ad52bea2a06eb", + "ee8fecacdf934cacb4c5966da66f68ba", + "b8f734cd22254a559cbf0d4270ceacb4", + "7487efa049164d9a92b01d41deac2a1d", + "00131dfdac6c4787b3c6aefb08f13beb", + "769c57208a284dd3a281d225ec9276f3", + "a53a407a6f6e451497336766244135b1", + "caf04b00d2ee45de9d5667ecac91cf1d", + "cbbd457e67034ce58f82a31228a85ff3", + "9db824f32e8c49f298aa110df69465bd", + "38181e60ddf14b2ab0b98a46d04c5892", + "a8d1c1ad0e40429a85bce2f7d2ba15be", + "7fc7795fd4b14f9f912629eaee028df3", + "305be4f74c6c42a6abd1e83649b5e635", + "9f5ed1c328ef49b783f8e3f62711fb5f", + "52b672a66d2a475394bdef455a4cebf8", + "5f91729034eb47fab123411dc3353e22", + "d6974595ebd043039970461f78f0195e", + "ebee72a8dae74add85d1e58c5a7ecc0b", + "46015c2b0e4d4788b07468f874345557", + "29607e1346af4a3188a33f7324bd2ff2", + "36ff6fe16f494b6e8d75fd46569724e7", + "3d271316983f41e0b2d0ef9548003f55" + ] + }, + "executionInfo": { + "elapsed": 32963, + "status": "ok", + "timestamp": 1733384688321, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "b8sjaN5SLEtf", + "outputId": "228d3a4d-ba64-4500-9b7e-64cfe1430401" + }, + "outputs": [], + "source": [ + "fields.update(get_open_data(param=PARAM_SFC))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m-6jpVe9xvxr" + }, + "source": [ + "#### Add the pressure levels fields" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17, + "referenced_widgets": [ + "e85831a585b4474284eeccc7403c6d6a", + "95909816a021463082be3fbd717b91f8", + "612a1882b3184d41ba337f0da2c513be", + "cf80c790c9994f168bd349fd93180fa8", + "85fa8cffc2a54c34a0a5661c309ccd95", + "fdb25a1d306e40539295ea995d59d011", + "4e3e32358ece4093af2e06615531ed77", + "83d83d3305d84c98a871fc8559e2e177", + "12ddda55fc884a48b8d4563843da20fe", + "7a27c1645f924655805568959f6256c1", + "0ffec54991a94c10b6e455696389f725", + "e172d23a055d4184bc00a7f7a5f59a8a", + "dbd3608f85634dcba7242d7e2672fa0f", + "611bc2338a1343d1b534eada92fd3545", + "68473001b7a746a5ae5a819071839a70", + "6d5a830229a049babef7b6fed8b3da2a", + "f197a28a70164daeb51f84247056f00d", + "16b342b766a840e7b2466add51d83e1a", + "fb25c8b6d79446dc94c6b977a1b99572", + "c5903fc789d54233a5250705b0267337", + "d3be2aaf4f27438e82ce7433a865b9ba", + "f8074f08f07c485c9de25abcc50c8456" + ] + }, + "executionInfo": { + "elapsed": 131873, + "status": "ok", + "timestamp": 1733384822925, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "zDGmj1IELEtg", + "outputId": "b9ce620d-1688-43f5-e510-09623e20d888" + }, + "outputs": [], + "source": [ + "fields.update(get_open_data(param=PARAM_PL, levelist=LEVELS))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UihlVXsbx2q0" + }, + "source": [ + "#### Convert geopotential height into geopotential" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "executionInfo": { + "elapsed": 206, + "status": "ok", + "timestamp": 1733384828222, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "H8D93y_5LEtg" + }, + "outputs": [], + "source": [ + "# Transform GH to Z\n", + "for level in LEVELS:\n", + " gh = fields.pop(f\"gh_{level}\")\n", + " fields[f\"z_{level}\"] = gh * 9.80665" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "w570pN1gx6yS" + }, + "source": [ + "### Create Initial State" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "executionInfo": { + "elapsed": 187, + "status": "ok", + "timestamp": 1733384830787, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "PndCJ7JKLEtg" + }, + "outputs": [], + "source": [ + "input_state = dict(date=DATE, fields=fields)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ceToC7Kuyddk" + }, + "source": [ + "# 3. Load the Model and Run the Forecast" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9ozNl0kZy7wv" + }, + "source": [ + "### Download the Model's Checkpoint from Hugging Face & create a Runner" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 173, + "referenced_widgets": [ + "50215614639344198a7b297ba05b75a0", + "04d53ab404ea4571938fa1049296a322", + "b7eaa9417e2144a3a91a4defa4883a28", + "5d53b4f6d81c4d63a8f717cc4575d1f5", + "d4a1986815394711bf612145074cfa7e", + "bd4aa43801dd48c88a6354bbb0b80115", + "32341120752344898e2932b6ce5f4dfb", + "b0691e09b17944879def85aa0ed8397b", + "b9543d271edf446aa4d0b1c4c61b62e7", + "726b5cbb17504bbcbd8053b52208a72d", + "c250ee19694e4b08855f1a851f4b44d0" + ] + }, + "executionInfo": { + "elapsed": 25240, + "status": "ok", + "timestamp": 1733384899272, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "SlBKKvxxVVb0", + "outputId": "451e1aeb-5667-41dd-b986-eff9b07c761c" + }, + "outputs": [], + "source": [ + "checkpoint = {\"huggingface\":\"ecmwf/aifs-single-0.2.1\"}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "executionInfo": { + "elapsed": 193, + "status": "ok", + "timestamp": 1733384905394, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "FAw6hansLEth" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "62c500d276a94f14ba3f2bef80688f4a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Fetching 10 files: 0%| | 0/10 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def fix(lons):\n", + " # Shift the longitudes from 0-360 to -180-180\n", + " return np.where(lons > 180, lons - 360, lons)\n", + "\n", + "latitudes = state[\"latitudes\"]\n", + "longitudes = state[\"longitudes\"]\n", + "values = state[\"fields\"][\"2t\"]\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 6), subplot_kw={\"projection\": ccrs.PlateCarree()})\n", + "ax.coastlines()\n", + "ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n", + "\n", + "triangulation = tri.Triangulation(fix(longitudes), latitudes)\n", + "ax.tricontourf(triangulation, values, levels=20, transform=ccrs.PlateCarree(), cmap=\"turbo\")\n", + "plt.title(\"Temperature at 2m {}\".format(state[\"date\"]))\n", + "plt.show()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [ + { + "file_id": "https://huggingface.co/ecmwf/aifs-single/blob/main/run_AIFS_v0_2_1.ipynb", + "timestamp": 1733385064965 + } + ], + "toc_visible": true + }, + "kernelspec": { + "display_name": "hf_venv", + "language": "python", + "name": "hf_venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": 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