{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Weather forecasting tutorial notebook\n", "## 1 Read and visualize .nc data\n", ".nc format is common when you deal with **LARGE** weather data, for example [ERA5](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5) data. They are in general an **xarray.Dataset**\n", "\n", "In this notebook we will show how to read and visualize this kind of data.\n", "\n", "A more detailed guide of .nc data/xarray dataset can be found [here]((https://docs.xarray.dev/en/stable/gallery.html))\n", "\n", "### 1.0 Load packages" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import xarray as xr # a library for working with netCDF files, see https://docs.xarray.dev/en/latest/getting-started-guide/installing.html, wee need (at least) to install \"xarray[io]\"\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.basemap import Basemap # an additional library for plotting in an earth coordinate, see https://matplotlib.org/basemap/stable/users/installation.html\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.1 Load dataset\n", "In general, it includes **Coordinates** and **Data variables**.\n", "\n", "We usually convert it to np array for further use. Note: if dataset is large, this may take a long time." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
<xarray.Dataset> Size: 330MB\n", "Dimensions: (latitude: 161, longitude: 281, time: 1826)\n", "Coordinates:\n", " * latitude (latitude) float32 644B 30.0 29.75 ... -9.75 -10.0\n", " * longitude (longitude) float32 1kB 70.0 70.25 ... 139.8 140.0\n", " * time (time) datetime64[ns] 15kB 2018-01-01 ... 2022-1...\n", "Data variables:\n", " mean_sea_level_pressure (time, latitude, longitude) float32 330MB ...