-Radar Cookbook
-
-
This Project Pythia Cookbook covers the basics of working with weather radar data in Python.
-Motivation
-This cookbook provides the essential materials to learning how to work with weather radar data using Python.
-Most of the curriculum is focused around the Python ARM Toolkit, which is defined as:
-“a Python module containing a collection of weather radar algorithms and utilities. Py-ART is used by the Atmospheric Radiation Measurement (ARM) user facility for working with data from a number of its precipitation and cloud radars, but has been designed so that it can be used by others in the radar and atmospheric communities to examine, processes, and analyze data from many types of weather radars.”
-Once you go through this material, you will have the skills to read in radar data, apply data corrections, and visualize your data, building off of the core foundational Python material covered in the Foundations Book
-Structure
-This cookbook is broken up into two main sections - “Foundations” and “Example Workflows.”
-Foundations
-The foundational content includes the:
--
-
Py-ART Basics - an overview of Py-ART package, how to read in data, and basic plotting functionality
-Py-ART Corrections - how to correct your data, especially when dealing with raw radar data
-Py-ART Gridding - how to utilize the gridding tools in Py-ART
-
If you are new to Py-ART, starting with the basics is a good place to start, and is required to know before moving onto Py-ART Corrections and Py-ART Gridding.
-Example Workflows
-Here, we apply the lessons learned in the foundational material to various analysis workflows, including everything from reading in the data to plotting a beautiful visualization at the end. We include the additional dataset-specific details, focusing on building upon the foundational materials rather than duplicating previous content.
-Running the Notebooks
-You can either run the notebook using Binder or on your local machine.
-Running on Binder
-The simplest way to interact with a Jupyter Notebook is through
-Binder, which enables the execution of a
-Jupyter Book in the cloud. The details of how this works are not
-important for now. All you need to know is how to launch a Pythia
-Foundations book chapter via Binder. Simply navigate your mouse to
-the top right corner of the book chapter you are viewing and click
-on the rocket ship icon, (see figure below), and be sure to select
-“launch Binder”. After a moment you should be presented with a
-notebook that you can interact with. I.e. you’ll be able to execute
-and even change the example programs. You’ll see that the code cells
-have no output at first, until you execute them by pressing
-Shift Enter. Complete details on how to interact with
-a live Jupyter notebook are described in Getting Started with
-Jupyter.
Running on Your Own Machine
-If you are interested in running this material locally on your computer, you will need to follow this workflow:
--
-
Clone the “radar-cookbook” repository
---git clone https://github.com/ProjectPythiaCookbooks/radar-cookbook.git -
-Move into the
-radar-cookbookdirectory--cd radar-cookbook -
-Create and activate your conda environment from the
-environment.ymlfile--conda env create -f environment.yml -conda activate radar-cookbook-dev -
-Move into the
-notebooksdirectory and start up Jupyterlab--cd notebooks/ -jupyter lab -
-
At this point, you can interact with the notebooks! Make sure to check out the “Getting Started with Jupyter” content from the Pythia Foundations material if you are new to Jupyter or need a refresher.
-
-
-# Radar Cookbook
-
-[](https://github.com/ProjectPythiaCookbooks/radar-cookbook/actions/workflows/nightly-build.yaml)
-[](https://aws-uswest2-binder.pangeo.io/v2/gh/ProjectPythiaCookbooks/radar-cookbook/main?labpath=notebooks)
-
-This Project Pythia Cookbook covers the basics of working with weather radar data in Python.
-
-## Motivation
-This cookbook provides the essential materials to learning how to work with weather radar data using Python.
-
-Most of the curriculum is focused around the Python ARM Toolkit, which is defined as:
-
-"a Python module containing a collection of weather radar algorithms and utilities. Py-ART is used by the Atmospheric Radiation Measurement (ARM) user facility for working with data from a number of its precipitation and cloud radars, but has been designed so that it can be used by others in the radar and atmospheric communities to examine, processes, and analyze data from many types of weather radars."
-
-Once you go through this material, you will have the skills to read in radar data, apply data corrections, and visualize your data, building off of the core foundational Python material covered in the [Foundations Book](https://foundations.projectpythia.org/landing-page.html)
-
-## Authors
-
-[Max Grover](https://github.com/mgrover1), [Zachary Sherman](https://github.com/zssherman)
-
-### Contributors
-
-![\"ARM](\"../images/sample-nexrad-moore.png\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9dc63b4a-ebb9-41be-bf43-ff4549f37cbd",
- "metadata": {},
- "source": [
- "# Looking at NEXRAD Data from Moore, Oklahoma\n",
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "79d482d1-ae86-44bc-ae65-7abbc8afe278",
- "metadata": {},
- "source": [
- "## Overview\n",
- " \n",
- "Within this notebook, we will cover:\n",
- "\n",
- "1. How to access NEXRAD data from AWS\n",
- "1. How to read this data into Py-ART\n",
- "1. How to customize your plots and maps\n",
- "\n",
- "## Prerequisites\n",
- "| Concepts | Importance | Notes |\n",
- "| --- | --- | --- |\n",
- "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Required | Projections and Features |\n",
- "| [Matplotlib Basics](https://foundations.projectpythia.org/core/matplotlib/matplotlib-basics.html) | Required | Basic plotting |\n",
- "| [Py-ART Basics](../foundations/pyart-basics) | Required | IO/Visualization |\n",
- "\n",
- "- **Time to learn**: 45 minutes\n",
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "44203db4-00ab-4fad-8b61-ead013282b80",
- "metadata": {
- "tags": []
- },
- "source": [
- "## Imports"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "77872ced-4f00-4520-8e4e-651c7aa2961b",
- "metadata": {},
- "outputs": [],
- "source": [
- "import pyart\n",
- "import fsspec\n",
- "from metpy.plots import USCOUNTIES\n",
- "import matplotlib.pyplot as plt\n",
- "import cartopy.crs as ccrs\n",
- "import cartopy.feature as cfeature\n",
- "import warnings\n",
- "warnings.filterwarnings(\"ignore\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "87af5f44-55e6-4d23-9523-6e49e2563f88",
- "metadata": {},
- "source": [
- "## How to Access NEXRAD Data from Amazon Web Services (AWS)\n",
- "\n",
- "Let’s start first with NEXRAD Level 2 data, which is ground-based radar data collected by the National Oceanic and Atmospheric Administration (NOAA), as a part of the National Weather Service (NWS) observing network."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "cd7a8f06-9580-4320-8e81-f13229f3a67b",
- "metadata": {
- "tags": []
- },
- "source": [
- "### Level 2 Data\n",
- "Level 2 data includes all of the fields in a single file - for example, a file may include:\n",
- "- Reflectivity\n",
- "- Velocity"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e53147e6-a08a-444b-a7df-9b2a958c4c3b",
- "metadata": {},
- "source": [
- "### Search for Data from the Moore, Oklahoma Tornado (May 20, 2013)\n",
- "\n",
- "Data We will access data from the **`noaa-nexrad-level2 bucket`**, with the data organized as:\n",
- "\n",
- "```\n",
- "s3://noaa-nexrad-level2/year/month/date/radarsite/{radarsite}{year}{month}{date}_{hour}{minute}{second}_V06\n",
- "```\n",
- "\n",
- "We can use [fsspec](https://filesystem-spec.readthedocs.io/en/latest/), a tool to work with filesystems in Python, to search through the bucket to find our files!\n",
- "\n",
- "We start first by setting up our [AWS S3 filesystem](https://aws.amazon.com/s3/)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "79521c33-842a-4eb5-b9f7-e57cf420ea96",
- "metadata": {},
- "outputs": [],
- "source": [
- "fs = fsspec.filesystem(\"s3\", anon=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "11ee73ad-32f0-49db-b896-adccceb8cfea",
- "metadata": {},
- "source": [
- "Once we setup our filesystem, we can list files from May 20, 2013 from the NWS Oklahoma City, Oklahoma (KTLX) site, around 2000 UTC."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "25aa5b04-818f-4d45-b175-d8e460ae8e0d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['noaa-nexrad-level2/2013/05/20/KTLX/KTLX20130520_200356_V06.gz',\n",
- " 'noaa-nexrad-level2/2013/05/20/KTLX/KTLX20130520_200811_V06.gz',\n",
- " 'noaa-nexrad-level2/2013/05/20/KTLX/KTLX20130520_201229_V06.gz',\n",
- " 'noaa-nexrad-level2/2013/05/20/KTLX/KTLX20130520_201643_V06.gz',\n",
- " 'noaa-nexrad-level2/2013/05/20/KTLX/KTLX20130520_202058_V06.gz',\n",
- " 'noaa-nexrad-level2/2013/05/20/KTLX/KTLX20130520_202511_V06.gz',\n",
- " 'noaa-nexrad-level2/2013/05/20/KTLX/KTLX20130520_202928_V06.gz',\n",
- " 'noaa-nexrad-level2/2013/05/20/KTLX/KTLX20130520_203346_V06.gz',\n",
- " 'noaa-nexrad-level2/2013/05/20/KTLX/KTLX20130520_203800_V06.gz',\n",
- " 'noaa-nexrad-level2/2013/05/20/KTLX/KTLX20130520_204215_V06.gz',\n",
- " 'noaa-nexrad-level2/2013/05/20/KTLX/KTLX20130520_204630_V06.gz',\n",
- " 'noaa-nexrad-level2/2013/05/20/KTLX/KTLX20130520_205045_V06.gz',\n",
- " 'noaa-nexrad-level2/2013/05/20/KTLX/KTLX20130520_205459_V06.gz',\n",
- " 'noaa-nexrad-level2/2013/05/20/KTLX/KTLX20130520_205914_V06.gz']"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "files = sorted(fs.glob(\"s3://noaa-nexrad-level2/2013/05/20/KTLX/KTLX20130520_20*\"))\n",
- "files"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "180a2ed5-7716-4723-92c4-67bf573155d2",
- "metadata": {},
- "source": [
- "We now have a list of files we can read in! "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e12be782-9ca5-4d23-bc24-8b5fdeb37ef6",
- "metadata": {},
- "source": [
- "## Read the Data into PyART\n",
- "\n",
- "When reading into PyART, we can use the `pyart.io.read_nexrad_archive` or `pyart.io.read` module to read in our data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "7a8b7606-da44-486d-859c-007527f72b5a",
- "metadata": {},
- "outputs": [],
- "source": [
- "radar = pyart.io.read_nexrad_archive(f's3://{files[3]}')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "676cdf8a-e98c-4a80-9809-5ff384e65f3b",
- "metadata": {},
- "source": [
- "Notice how for the **NEXRAD Level 2** data, we have several fields available"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "74207d9d-16c8-4c6e-a41a-3e7b23a86811",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['differential_phase',\n",
- " 'differential_reflectivity',\n",
- " 'velocity',\n",
- " 'cross_correlation_ratio',\n",
- " 'reflectivity',\n",
- " 'spectrum_width']"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "list(radar.fields)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "17a9af05-e326-4f42-97d9-046a10dba6a7",
- "metadata": {},
- "source": [
- "### Plot a quick-look of the dataset\n",
- "Let's get a quicklook of the reflectivity and velocity fields"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "2775c3aa-424d-4559-946e-d18eec2ecd5e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "![\"TRACER](\"../images/storm-animation.gif\")
\n",
- "# Plotting Data from a Field Campaign (TRACER)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "84c6a08e-e1eb-4358-9991-4ecec6810482",
- "metadata": {},
- "source": [
- "## Overview\n",
- "\n",
- "Within this notebook, we will cover:\n",
- "\n",
- "1. How to access data from the Atmospheric Radiation Measurment (ARM) user facility\n",
- "1. How to setup a workflow to plot both cross sections (RHIs) and horizontal scans (PPIs)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b3770751-dc1f-404b-918b-dfe14e05e035",
- "metadata": {},
- "source": [
- "## Prerequisites\n",
- "| Concepts | Importance | Notes |\n",
- "| --- | --- | --- |\n",
- "| [Matplotlib Basics](https://foundations.projectpythia.org/core/matplotlib/matplotlib-basics.html) | Required | Basic plotting |\n",
- "| [Py-ART Basics](../foundations/pyart-basics) | Required | IO/Visualization |"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "75fd5b88-7e2f-4455-ab6d-27f3c2114d22",
- "metadata": {},
- "source": [
- "---\n",
- "## Imports"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 70,
- "id": "5c4490ba-188b-4700-84db-de827efd2734",
- "metadata": {},
- "outputs": [],
- "source": [
- "import glob\n",
- "import os\n",
- "from pathlib import Path\n",
- "\n",
- "import act\n",
- "import imageio.v2 as imageio\n",
- "import matplotlib\n",
- "import matplotlib.pyplot as plt\n",
- "import pyart"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4e4ddb9b-dd3d-4eba-ad32-814cdc7590cb",
- "metadata": {
- "tags": []
- },
- "source": [
- "## Grab Data from ARM\n",
- "\n",
- "One of the better cases was from June 2, 2022, where several cold pools and single-cell storms traversed through the domain."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d6dc342b-b5b3-443a-b7b4-e40388dbc5c1",
- "metadata": {},
- "source": [
- "### The Tracking Aerosol Convection Interactions ExpeRiment (TRACER) Field Campaign\n",
- "\n",
- "Data is available from the Atmospheric Radiation Measurment user facility, which helped to lead the TRACER field campaign in Houston, Texas.\n",
- "\n",
- "The data are available from the ARM data portal (https://adc.arm.gov/).\n",
- "\n",
- "We are interested in the C-band radar, which is utilizing a cell-tracking algorithm, with the datastream\n",
- "* **`houcsapr2cfrS2.a1`**"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e9dab624-06a6-4f7a-af60-6a9ce2694552",
- "metadata": {},
- "source": [
- "### Use the ARM Live API to Download the Data, using ACT\n",
- "\n",
- "The Atmospheric Data Community Toolkit (ACT) has a helpful module to interface with the data server:\n",
- "* [Download Data API](https://arm-doe.github.io/ACT/API/generated/act.discovery.download_data.html#act.discovery.download_data)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "b36e5eb5-96bd-4dab-8523-540266006e13",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
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- "\u001b[0;34m\u001b[0m \u001b[0mstartdate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0menddate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mDocstring:\u001b[0m\n",
- "This tool will help users utilize the ARM Live Data Webservice to download\n",
- "ARM data.\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "username : str\n",
- " The username to use for logging into the ADC archive.\n",
- "token : str\n",
- " The access token for accessing the ADC archive.\n",
- "datastream : str\n",
- " The name of the datastream to acquire.\n",
- "startdate : str\n",
- " The start date of the data to acquire. Formats accepted are\n",
- " YYYY-MM-DD, DD.MM.YYYY, DD/MM/YYYY, YYYYMMDD, YYYY/MM/DD or\n",
- " any of the previous formats with THH:MM:SS added onto the end\n",
- " (ex. 2020-09-15T12:00:00).\n",
- "enddate : str\n",
- " The end date of the data to acquire. Formats accepted are\n",
- " YYYY-MM-DD, DD.MM.YYYY, DD/MM/YYYY, YYYYMMDD or YYYY/MM/DD, or\n",
- " any of the previous formats with THH:MM:SS added onto the end\n",
- " (ex. 2020-09-15T13:00:00).\n",
- "time: str or None\n",
- " The specific time. Format is HHMMSS. Set to None to download all files\n",
- " in the given date interval.\n",
- "output : str\n",
- " The output directory for the data. Set to None to make a folder in the\n",
- " current working directory with the same name as *datastream* to place\n",
- " the files in.\n",
- "\n",
- "Returns\n",
- "-------\n",
- "files : list\n",
- " Returns list of files retrieved\n",
- "\n",
- "Notes\n",
- "-----\n",
- "This programmatic interface allows users to query and automate\n",
- "machine-to-machine downloads of ARM data. This tool uses a REST URL and\n",
- "specific parameters (saveData, query), user ID and access token, a\n",
- "datastream name, a start date, and an end date, and data files matching\n",
- "the criteria will be returned to the user and downloaded.\n",
- "\n",
- "By using this web service, users can setup cron jobs and automatically\n",
- "download data from /data/archive into their workspace. This will also\n",
- "eliminate the manual step of following a link in an email to download data.\n",
- "All other data files, which are not on the spinning\n",
- "disk (on HPSS), will have to go through the regular ordering process.\n",
- "More information about this REST API and tools can be found on `ARM Live\n",
- "![](\"storm-animation.gif\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f0d1170d-2b89-463b-94b5-ea3229550272",
- "metadata": {},
- "source": [
- "---\n",
- "## Summary\n",
- "Within this example, we walked through how to access ARM data from a field campaign in Texas, plot a quick look of the data, and refine our plots to investigate a storm!\n",
- "\n",
- "### What's Next?\n",
- "We will showcase other data workflow examples, including field campaigns in other regions and data access methods from other data centers."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c70225df-ce45-4fb8-a2b9-1a2f70d030c0",
- "metadata": {},
- "source": [
- "## Resources and References\n",
- "* [ARM Data Discovery](https://adc.arm.gov/discovery)\n",
- "* [TRACER Field Campaign](https://www.arm.gov/research/campaigns/amf2021tracer)\n",
- "* CSAPR Radar Data:\n",
- " - Bharadwaj, N., Collis, S., Hardin, J., Isom, B., Lindenmaier, I., Matthews, A., & Nelson, D. C-Band Scanning ARM Precipitation Radar (CSAPR2CFR). Atmospheric Radiation Measurement (ARM) User Facility. https://doi.org/10.5439/1467901\n",
- "* Py-ART:\n",
- " - Helmus, J.J. & Collis, S.M., (2016). The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language. Journal of Open Research Software. 4(1), p.e25. DOI: http://doi.org/10.5334/jors.119\n",
- "* ACT:\n",
- " - Adam Theisen, Ken Kehoe, Zach Sherman, Bobby Jackson, Alyssa Sockol, Corey Godine, Max Grover, Jason Hemedinger, Jenni Kyrouac, Maxwell Levin, Michael Giansiracusa (2022). The Atmospheric Data Community Toolkit (ACT). Zenodo. DOI: https://doi.org/10.5281/zenodo.6712343"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b61cb262-26ba-47dc-8d52-3225f084aaef",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3.8.9 64-bit",
- "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": "3.8.9"
- },
- "vscode": {
- "interpreter": {
- "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/_preview/67/_sources/notebooks/foundations/pyart-basics.ipynb b/_preview/67/_sources/notebooks/foundations/pyart-basics.ipynb
deleted file mode 100644
index b77cd41..0000000
--- a/_preview/67/_sources/notebooks/foundations/pyart-basics.ipynb
+++ /dev/null
@@ -1,963 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "![\"ARM](\"../images/logos/arm_logo.png\"){kind=logo}
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "tags": []
- },
- "source": [
- "# Py-ART Basics\n",
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Overview\n",
- " \n",
- "Within this notebook, we will cover:\n",
- "\n",
- "1. General overview of Py-ART and its functionality\n",
- "1. Reading data using Py-ART\n",
- "1. An overview of the `pyart.Radar` object\n",
- "1. Create a Plot of our Radar Data\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "tags": []
- },
- "source": [
- "## Prerequisites\n",
- "| Concepts | Importance | Notes |\n",
- "| --- | --- | --- |\n",
- "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Helpful | Basic features |\n",
- "| [Matplotlib Basics](https://foundations.projectpythia.org/core/matplotlib/matplotlib-basics.html) | Helpful | Basic plotting |\n",
- "| [NumPy Basics](https://foundations.projectpythia.org/core/numpy/numpy-basics.html) | Helpful | Basic arrays |\n",
- "\n",
- "- **Time to learn**: 45 minutes\n",
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Imports"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
- "## library for working with weather radar data. Py-ART is partly\n",
- "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
- "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
- "## Science user facility.\n",
- "##\n",
- "## If you use this software to prepare a publication, please cite:\n",
- "##\n",
- "## JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
- "\n"
- ]
- }
- ],
- "source": [
- "import os\n",
- "import warnings\n",
- "\n",
- "import cartopy.crs as ccrs\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "\n",
- "import pyart\n",
- "from pyart.testing import get_test_data\n",
- "\n",
- "warnings.filterwarnings('ignore')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## An Overview of Py-ART"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "tags": []
- },
- "source": [
- "### History of the Py-ART\n",
- "\n",
- " * Development began to address the needs of ARM with the acquisition of a number of\n",
- " new scanning cloud and precipitation radar as part of the American Recovery Act.\n",
- " * The project has since expanded to work with a variety of weather radars and a wider user\n",
- " base including radar researchers and climate modelers.\n",
- " * The software has been released on GitHub as open source software under a BSD license.\n",
- " Runs on Linux, OS X. It also runs on Windows with more limited functionality.\n",
- "\n",
- "### What can PyART Do?\n",
- "\n",
- "Py-ART can be used for a variety of tasks from basic plotting to more complex\n",
- "processing pipelines. Specific uses for Py-ART include:\n",
- "\n",
- " * Reading radar data in a variety of file formats.\n",
- " * Creating plots and visualization of radar data.\n",
- " * Correcting radar moments while in antenna coordinates, such as:\n",
- " * Doppler unfolding/de-aliasing.\n",
- " * Attenuation correction.\n",
- " * Phase processing using a Linear Programming method.\n",
- " * Mapping data from one or multiple radars onto a Cartesian grid.\n",
- " * Performing retrievals.\n",
- " * Writing radial and Cartesian data to NetCDF files."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Reading in Data Using Py-ART"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Reading data in using `pyart.io.read`"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "When reading in a radar file, we use the `pyart.io.read` module.\n",
- "\n",
- "`pyart.io.read` can read a variety of different radar formats, such as Cf/Radial, LASSEN, and more. \n",
- "The documentation on what formats can be read by Py-ART can be found here:\n",
- "\n",
- "* [Py-ART IO Documentation](https://arm-doe.github.io/pyart/API/generated/pyart.io.html)\n",
- "\n",
- "For most file formats listed on the page, using `pyart.io.read` should suffice since Py-ART has the ability to automatically detect the file format.\n",
- "\n",
- "Let's check out what arguments arguments `pyart.io.read()` takes in!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "\u001b[0;31mSignature:\u001b[0m \u001b[0mpyart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_rsl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mDocstring:\u001b[0m\n",
- "Read a radar file and return a radar object.\n",
- "\n",
- "Additional parameters are passed to the underlying read_* function.\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "filename : str\n",
- " Name of radar file to read.\n",
- "use_rsl : bool\n",
- " True will use the TRMM RSL library to read files which are supported\n",
- " both natively and by RSL. False will choose the native read function.\n",
- " RSL will always be used to read a file if it is not supported\n",
- " natively.\n",
- "\n",
- "Other Parameters\n",
- "-------------------\n",
- "field_names : dict, optional\n",
- " Dictionary mapping file data type names to radar field names. If a\n",
- " data type found in the file does not appear in this dictionary or has\n",
- " a value of None it will not be placed in the radar.fields dictionary.\n",
- " A value of None, the default, will use the mapping defined in the\n",
- " metadata configuration file.\n",
- "additional_metadata : dict of dicts, optional\n",
- " Dictionary of dictionaries to retrieve metadata from during this read.\n",
- " This metadata is not used during any successive file reads unless\n",
- " explicitly included. A value of None, the default, will not\n",
- " introduct any addition metadata and the file specific or default\n",
- " metadata as specified by the metadata configuration file will be used.\n",
- "file_field_names : bool, optional\n",
- " True to use the file data type names for the field names. If this\n",
- " case the field_names parameter is ignored. The field dictionary will\n",
- " likely only have a 'data' key, unless the fields are defined in\n",
- " `additional_metadata`.\n",
- "exclude_fields : list or None, optional\n",
- " List of fields to exclude from the radar object. This is applied\n",
- " after the `file_field_names` and `field_names` parameters.\n",
- "delay_field_loading : bool\n",
- " True to delay loading of field data from the file until the 'data'\n",
- " key in a particular field dictionary is accessed. In this case\n",
- " the field attribute of the returned Radar object will contain\n",
- " LazyLoadDict objects not dict objects. Not all file types support this\n",
- " parameter.\n",
- "\n",
- "Returns\n",
- "-------\n",
- "radar : Radar\n",
- " Radar object. A TypeError is raised if the format cannot be\n",
- " determined.\n",
- "\u001b[0;31mFile:\u001b[0m ~/opt/anaconda3/envs/radar-cookbook-dev/lib/python3.9/site-packages/pyart/io/auto_read.py\n",
- "\u001b[0;31mType:\u001b[0m function\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pyart.io.read?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's use a sample data file from `pyart` - which is [**cfradial** format](https://github.com/NCAR/CfRadial).\n",
- "\n",
- "When we read this in, we get a [`pyart.Radar` object](https://arm-doe.github.io/pyart/API/generated/pyart.core.Radar.html#pyart.core.Radar)!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "![\"Project](\"images/ProjectPythia_Logo_Final-01-Blue.svg\"){kind=logo}
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Project Pythia Notebook Template\n",
- "\n",
- "Next, title your notebook appropriately with a top-level Markdown header, `#`. Do not use this level header anywhere else in the notebook. Our book build process will use this title in the navbar, table of contents, etc. Keep it short, keep it descriptive. Follow this with a `---` cell to visually distinguish the transition to the prerequisites section."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Overview\n",
- "If you have an introductory paragraph, lead with it here! Keep it short and tied to your material, then be sure to continue into the required list of topics below,\n",
- "\n",
- "1. This is a numbered list of the specific topics\n",
- "1. These should map approximately to your main sections of content\n",
- "1. Or each second-level, `##`, header in your notebook\n",
- "1. Keep the size and scope of your notebook in check\n",
- "1. And be sure to let the reader know up front the important concepts they'll be leaving with"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Prerequisites\n",
- "This section was inspired by [this template](https://github.com/alan-turing-institute/the-turing-way/blob/master/book/templates/chapter-template/chapter-landing-page.md) of the wonderful [The Turing Way](https://the-turing-way.netlify.app/welcome.html) Jupyter Book.\n",
- "\n",
- "Following your overview, tell your reader what concepts, packages, or other background information they'll **need** before learning your material. Tie this explicitly with links to other pages here in Foundations or to relevant external resources. Remove this body text, then populate the Markdown table, denoted in this cell with `|` vertical brackets, below, and fill out the information following. In this table, lay out prerequisite concepts by explicitly linking to other Foundations material or external resources, or describe generally helpful concepts.\n",
- "\n",
- "Label the importance of each concept explicitly as **helpful/necessary**.\n",
- "\n",
- "| Concepts | Importance | Notes |\n",
- "| --- | --- | --- |\n",
- "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Necessary | |\n",
- "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n",
- "| Project management | Helpful | |\n",
- "\n",
- "- **Time to learn**: estimate in minutes. For a rough idea, use 5 mins per subsection, 10 if longer; add these up for a total. Safer to round up and overestimate.\n",
- "- **System requirements**:\n",
- " - Populate with any system, version, or non-Python software requirements if necessary\n",
- " - Otherwise use the concepts table above and the Imports section below to describe required packages as necessary\n",
- " - If no extra requirements, remove the **System requirements** point altogether"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Imports\n",
- "Begin your body of content with another `---` divider before continuing into this section, then remove this body text and populate the following code cell with all necessary Python imports **up-front**:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import sys"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Your first content section"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This is where you begin your first section of material, loosely tied to your objectives stated up front. Tie together your notebook as a narrative, with interspersed Markdown text, images, and more as necessary,"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# as well as any and all of your code cells\n",
- "print(\"Hello world!\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### A content subsection\n",
- "Divide and conquer your objectives with Markdown subsections, which will populate the helpful navbar in Jupyter Lab and here on the Jupyter Book!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# some subsection code\n",
- "new = \"helpful information\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Another content subsection\n",
- "Keep up the good work! A note, *try to avoid using code comments as narrative*, and instead let them only exist as brief clarifications where necessary."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Your second content section\n",
- "Here we can move on to our second objective, and we can demonstrate"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Subsection to the second section\n",
- "\n",
- "#### a quick demonstration\n",
- "\n",
- "##### of further and further\n",
- "\n",
- "###### header levels"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "as well $m = a * t / h$ text! Similarly, you have access to other $\\LaTeX$ equation [**functionality**](https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Typesetting%20Equations.html) via MathJax (demo below from link),\n",
- "\n",
- "\\begin{align}\n",
- "\\dot{x} & = \\sigma(y-x) \\\\\n",
- "\\dot{y} & = \\rho x - y - xz \\\\\n",
- "\\dot{z} & = -\\beta z + xy\n",
- "\\end{align}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Check out [**any number of helpful Markdown resources**](https://www.markdownguide.org/basic-syntax/) for further customizing your notebooks and the [**Jupyter docs**](https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Working%20With%20Markdown%20Cells.html) for Jupyter-specific formatting information. Don't hesitate to ask questions if you have problems getting it to look *just right*."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Last Section\n",
- "\n",
- "If you're comfortable, and as we briefly used for our embedded logo up top, you can embed raw html into Jupyter Markdown cells (edit to see):"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "