{ "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", " 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\"}" ] }, { "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": "huggingface-aifs0.2.1", "language": "python", "name": "huggingface-aifs0.2.1" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "00131dfdac6c4787b3c6aefb08f13beb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", 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