diff --git a/README.md b/README.md index ffb3f2c..cb7078e 100644 --- a/README.md +++ b/README.md @@ -3,8 +3,8 @@ ***ARM Summer School 2024: Open Science in the Department of Energy's Atmospheric Radiation Measurement (ARM) User Facility: Connecting State-of-the-Art Models with Diverse Field Campaign Observations*** -[![badge](https://img.shields.io/static/v1.svg?logo=Jupyter&label=ARM+JupyterHub&message=ACE+Environment&color=blue)](https://jupyterhub.arm.gov/hub/user-redirect/git-pull?repo=https%3A//github.com/ARM-Development/arm-ams-short-course-2024&urlpath=lab/tree/arm-ams-short-course-2024/notebooks&branch=main) -[![nightly-build](https://github.com/ARM-Development/arm-ams-short-course-2024/actions/workflows/nightly-build.yaml/badge.svg)](https://github.com/ARM-Development/arm-ams-short-course-2024/actions/workflows/nightly-build.yaml) +[![badge](https://img.shields.io/static/v1.svg?logo=Jupyter&label=ARM+JupyterHub&message=ACE+Environment&color=blue)](https://jupyterhub.arm.gov/hub/user-redirect/git-pull?repo=https%3A//github.com/ARM-Development/arm-summer-school-2024&urlpath=lab/tree/arm-summer-school-2024/notebooks&branch=main) +[![nightly-build](https://github.com/ARM-Development/arm-summer-school-2024/actions/workflows/nightly-build.yaml/badge.svg)](https://github.com/ARM-Development/arm-summer-school-2024/actions/workflows/nightly-build.yaml) ## Motivation @@ -27,7 +27,7 @@ ARM Summer School 2024 Instructors ### Contributors - + ## Structure diff --git a/_toc.yml b/_toc.yml index b7b6e59..7f3ab5a 100644 --- a/_toc.yml +++ b/_toc.yml @@ -36,4 +36,5 @@ parts: - file: tutorials/comble/comble-mip-tutorial - file: tutorials/emc2/InstrumentSimulatorsForModelEvaluation.ipynb - file: tutorials/microhh/analyze_microhh - - file: tutorials/machine_learning/ARM_DQO_Spike_Detection \ No newline at end of file + - file: tutorials/machine_learning/ARM_DQO_Spike_Detection + - file: tutorials/pyart/sail_qpe_grid diff --git a/images/github-token.png b/images/github-token.png new file mode 100644 index 0000000..a41f20c Binary files /dev/null and b/images/github-token.png differ diff --git a/preliminary/checklist/git.md b/preliminary/checklist/git.md index 70a9a13..b10aa6e 100644 --- a/preliminary/checklist/git.md +++ b/preliminary/checklist/git.md @@ -73,10 +73,10 @@ However, a better, more secure practice is to use a GitHub Personal Access Token more customizable permissions and can be revoked without affecting your main GitHub user account password. -Take a moment to work through the instructions on the [GitHub personal access tokens](https://docs.github.com/en/github/authenticating-to-github/keeping-your-account-and-data-secure/creating-a-personal-access-token) page. For this hackweek, you need to check the **repo**, **admin:org**, and **workflow** scope. +Take a moment to work through the instructions on the [GitHub personal access tokens](https://docs.github.com/en/github/authenticating-to-github/keeping-your-account-and-data-secure/creating-a-personal-access-token) page. Make sure you select the "classic" option. **Do not use the fine-grained personal access token option. Screenshot from github.com setting up the token: -![github-token](../../img/github-token.png) +![github-token](../../images/github-token.png) Once you have created your token, be sure to save it on your computer in case you need to re-authenticate again. The token will give you access to your @@ -111,7 +111,7 @@ of your mouse is accessible on the JupyterHub by using the 'Shift' key. ``` ```shell -Cloning into 'github_setup_check'... +Cloning into 'check_github_setup'... Username for 'https://github.com': Password for 'https://attendee@github.com': remote: Enumerating objects: 3, done. diff --git a/preliminary/checklist_index.md b/preliminary/checklist_index.md index 489d489..2f2c144 100644 --- a/preliminary/checklist_index.md +++ b/preliminary/checklist_index.md @@ -58,7 +58,7 @@ If this is your first time accessing the ARM Jupyterhub, then please follow the When filling out the form, be sure to complete the entries as follows: * **Visitor Role**: *Collaborator* -* **Resources**: *ARM Data Center Enhanced Jupyterhub Access* +* **Resources**: *ARM ADC JupyterHub Workshop* * **Justification**: *Participation in the ARM Summer School* * **Start Date**: May 19 * **End Date**: May 24 diff --git a/schedule.md b/schedule.md index ebf040c..1ea34ca 100644 --- a/schedule.md +++ b/schedule.md @@ -12,10 +12,10 @@ | :---: | :----: | :---: | | 08:30 AM - 09:30 AM | [Intro to ARM + Welcome](https://docs.google.com/presentation/d/1X1lc7fCF4jDaD_nUZ8l6X7-4O0ZR3eB7/edit?usp=sharing&ouid=104304750518137712212&rtpof=true&sd=true) | Sally McFarlane and Jim Mather | | 09:30 AM - 10:00 AM | Coffee Break | | -| 10:00 AM - 11:00 AM | [Intro to ARM Data Workbench](https://docs.google.com/presentation/d/1v8d_Fd6gqKap2MyonIx9YeTSakY_pUAL/edit?usp=sharing&ouid=104304750518137712212&rtpof=true&sd=true) | Sujata Goswami + Mike Giansiracusa | +| 10:00 AM - 11:00 AM | [Intro to ARM Data Workbench](https://docs.google.com/presentation/d/1iZYW8Ch-pC5R0qn1eDfTA6YCqK1qugX4/edit?usp=sharing&ouid=104304750518137712212&rtpof=true&sd=true) | Sujata Goswami + Mike Giansiracusa | | 11:00 AM - 12:00 PM | [Intro to ARM Open Source Software](https://docs.google.com/presentation/d/1e4IAEWNxw2ly8HTMcuz4fLhwpBNcrg2D/edit?usp=sharing&ouid=104304750518137712212&rtpof=true&sd=true) | Scott Collis + Joe O'Brien | | 12:00 PM - 01:00 PM | Working Lunch: Elevator Pitch Intros | Max Grover | -| 01:00 PM - 01:45 PM | [Intro to COMBLE-MIP](https://docs.google.com/presentation/d/1v1WGTEhguSBBQ_lAdj_9c85YpJwHDPqy/edit?usp=sharing&ouid=104304750518137712212&rtpof=true&sd=true) | Tim Juliano | +| 01:00 PM - 01:45 PM | [Intro to COMBLE-MIP](https://docs.google.com/presentation/d/1pXFU-2K4PDwpAY9vHqQ-3pIBn0uFg-8M/edit?usp=sharing&ouid=109458928369860454495&rtpof=true&sd=true) | Tim Juliano | | 01:45 PM - 02:30 PM | [Intro to LASSO](https://docs.google.com/presentation/d/1I4coa4yfnGot2Z_ksSxk0jIIu8X-WrGK/edit?usp=sharing&ouid=104304750518137712212&rtpof=true&sd=true) | William Gustafson | | 02:30 PM - 03:00 PM | Coffee Break | | | 03:00 PM - 03:45 PM | [Intro to SAIL and WRF Model Data](https://docs.google.com/presentation/d/1bnRNTT-cxB_tRSeWcEfbYGEbqqROMsve/edit?usp=sharing&ouid=104304750518137712212&rtpof=true&sd=true) | Dan Feldman | @@ -27,7 +27,8 @@ | :---: | :----: | :---: | | 08:30 AM - 09:30 AM | [AI/Machine Learning for Data Quality](tutorials/machine_learning/ARM_DQO_Spike_Detection.ipynb) | Mia Li | | 09:30 AM - 10:00 AM | Coffee Break | | -| 10:00 AM - 12:00 PM | [Aerosol + Profiling Measurements in ARM](https://docs.google.com/presentation/d/1cqg9WOEoMFUHuW10ajIFyrtJ45Q2mg-k/edit?usp=sharing&ouid=104304750518137712212&rtpof=true&sd=true)| Damao Zhang + Bobby Jackson | +| 10:00 AM - 11:00 AM | [Aerosol + Profiling Measurements in ARM](https://docs.google.com/presentation/d/1cqg9WOEoMFUHuW10ajIFyrtJ45Q2mg-k/edit?usp=sharing&ouid=104304750518137712212&rtpof=true&sd=true)| Damao Zhang + Bobby Jackson | +| 11:00 AM - 12:00 PM | [Intro to ARM Radar Data Products](https://docs.google.com/presentation/d/1D-x3ze1N0UK7elKoqb98MYj3rPr4PXGT/edit?usp=sharing&ouid=104304750518137712212&rtpof=true&sd=true) | Ya-Chien Feng and Joe O'Brien | | 12:00 PM - 01:00 PM | [Working Lunch: Intro to xwrf](tutorials/xarray/xwrf-xarray-intro) | Max Grover | | 01:00 PM - 05:00 PM | Break into Groups: Plot your Data! | | @@ -47,8 +48,7 @@ | Time | Topic | Presenter(s) | | :---: | :----: | :---: | -| 08:30 AM - 09:30 AM | [Intro to ARM Radar Data Products](https://docs.google.com/presentation/d/14O3OSaZadIvVmZPHa3f5cf52avmp_zxx/edit?usp=sharing&ouid=104304750518137712212&rtpof=true&sd=true) | Ya-Chien Feng and Joe O'Brien | -| 09:30 AM - 10:00 AM | Radar Data Retrievals | Joe O'Brien | +| 08:30 AM - 10:00 AM | Radar Data Retrievals | Joe O'Brien | | 10:00 AM - 10:30 AM | Coffee Break | | | 10:30 AM - 12:00 PM | Office Hours: How is it going? | | | 12:00 PM - 01:00 PM | Working Lunch: Installing Local Python | Max Grover | diff --git a/tutorials/comble/comble-mip-tutorial.ipynb b/tutorials/comble/comble-mip-tutorial.ipynb index 9880a4f..0000921 100644 --- a/tutorials/comble/comble-mip-tutorial.ipynb +++ b/tutorials/comble/comble-mip-tutorial.ipynb @@ -9,7 +9,9 @@ "source": [ "# COMBLE-MIP Tutorial\n", "\n", - "* Please direct any questions to Tim Juliano (NSF NCAR, tjuliano@ucar.edu); updated 5/16/24\n", + "* Please direct any questions to Tim Juliano (NSF NCAR, tjuliano@ucar.edu); updated 5/19/24\n", + "\n", + " * Credit to Florian Tornow (Columbia/NASA GISS) and Ann Fridlind (NASA GISS) for develping much of this code

\n", "\n", "* The below notebook focuses on the DOE-funded Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) that took place from 2019-2020. More information about COMBLE can be found in Geerts et al. (2022).
Reference
Geerts, B., et al., 2022: The COMBLE campaign: A study of marine boundary layer clouds in Arctic cold-air outbreaks, Bull. Amer. Meteor. Soc, 103, E1371–E1389. https://doi.org/10.1175/BAMS-D-21-0044.1\n", "\n", @@ -1453,7 +1455,7 @@ "source": [ "In addition to the raw model outputs, we have post-processed the WRF and DHARMA outputs for use with EMC^2. Those files may be found at the following paths:\n", "* WRF: ```/data/project/ARM_Summer_School_2024_Data/comble-mip/output_les/wrf/WRF_Lx25_dx100_FixN_raw/emc2_prep*```\n", - "* DHARMA: ```/data/project/ARM_Summer_School_2024_Data/comble-mip/output_les/dharma/emc2/emc2_prep*``` \\\n", + "* DHARMA: ```/data/project/ARM_Summer_School_2024_Data/comble-mip/output_les/dharma/emc2/emc2_prep*```\n", "\n", "EMC^2-ready files from WRF are available hourly while the three DHARMA files *11521.tgz, *20296.tgz, and *29549.tgz are from 06, 12, and 18 UTC, respectively." ] diff --git a/tutorials/index.md b/tutorials/index.md index df9c2ee..73d5a89 100644 --- a/tutorials/index.md +++ b/tutorials/index.md @@ -16,3 +16,10 @@ time you log in. | [...and LASSO-ShCu part 2](./lasso/lasso-cacti_part2.ipynb) | | [Introduction to LASSO-CACTI](./lasso/lasso-cacti.ipynb) | | [...and LASSO-CACTI part 2](./lasso/lasso-cacti_part2.ipynb) | +| [Introduction to xwrf](./xarray/xwrf-xarray-intro.ipynb) | +| [COMBLE-MIP](./comble/comble-mip-tutorial.ipynb) | +| [Harnessing Instrument Simulators for Model Evalution](./emc2/InstrumentSimulatorsForModelEvaluation.ipynb) | +| [Analyze microhh LES data](./microhh/manalyze_microhh.ipynb) | +| [ARM DQ Office ML Spike Detection](./machine_learning/ARM_DQO_Spike_Detection.ipynb) | +| [Snowfall Retrievals from SAIL X-Band Radar](./pyart/sail_qpe_grid.ipynb) | + diff --git a/tutorials/machine_learning/ARM_DQO_Spike_Detection.ipynb b/tutorials/machine_learning/ARM_DQO_Spike_Detection.ipynb index 06c3aee..058fabe 100644 --- a/tutorials/machine_learning/ARM_DQO_Spike_Detection.ipynb +++ b/tutorials/machine_learning/ARM_DQO_Spike_Detection.ipynb @@ -227,7 +227,8 @@ "source": [ "da_normal = ds_normal[target_variable].values\n", "time_steps=1\n", - "diff_normal = da_normal - np.roll(da_normal, shift = time_steps) # See Section 3 Feature Engineering" + "diff_normal = da_normal - np.roll(da_normal, shift = time_steps) # See Section 5 Feature Engineering\n", + "diff_normal[:time_steps] = [0]*time_steps" ] }, { diff --git a/tutorials/pyart/sail_qpe_grid.ipynb b/tutorials/pyart/sail_qpe_grid.ipynb new file mode 100644 index 0000000..70acad0 --- /dev/null +++ b/tutorials/pyart/sail_qpe_grid.ipynb @@ -0,0 +1,381 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fa933168-2433-421d-aa02-d65324c5ecd0", + "metadata": {}, + "source": [ + "\"ARM" + ] + }, + { + "cell_type": "markdown", + "id": "e52504ca", + "metadata": {}, + "source": [ + "# Snowfall Retrievals from SAIL X-Band Radar" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "496972cc", + "metadata": { + "scrolled": true + }, + "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 warnings\n", + "warnings.simplefilter(\"ignore\", UserWarning)\n", + "\n", + "import glob\n", + "import os\n", + "import datetime\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pyart\n", + "import xarray as xr\n", + "from matplotlib.dates import DateFormatter\n", + "\n", + "from metpy.plots import USCOUNTIES\n", + "\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature" + ] + }, + { + "cell_type": "markdown", + "id": "1dbbf233", + "metadata": { + "tags": [] + }, + "source": [ + "## Setup Helper Functions\n", + "We setup helper functions to calculate the snowfall retrieval, using following notation:\n", + "\n", + "$Z = A*S ^ {B}$\n", + "\n", + "Where:\n", + "- Z = Reflectivity in dBZ\n", + "- A = Coefficient applied to Z-S Relationship (not in the exponent)\n", + "- S = Liquid snowfall rate\n", + "- B = Coefficient applied to Z-S Relationship (in the exponent)\n", + "\n", + "We also need to apply a snow water equivalent ratio (`swe`) to convert from liquid to snow (ex. 8 inches of snow --> 1 inch of rain would be 8.0).\n", + "\n", + "This equation now becomes:\n", + "\n", + "$Z = swe*A*S ^ {B}$\n", + "\n", + "Solving for S, we get:\n", + "\n", + "$S = swe * (\\frac{z}{a})^{1/B}$\n", + "\n", + "Where z is reflectivity in units of dB ($z =10^{Z/10}$)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ddb1d50e", + "metadata": {}, + "outputs": [], + "source": [ + "def snow_rate(radar, swe_ratio, A, B, key=\"snow_z\"):\n", + " \"\"\"\n", + " Snow rate applied to a pyart.Radar object\n", + " \n", + " Takes a given Snow Water Equivilent ratio (swe_ratio), A and B value\n", + " for the Z-S relationship and creates a radar field similar to DBZ\n", + " showing the radar estimated snowfall rate in mm/hr. Then the given\n", + " SWE_ratio, A and B are stored in the radar metadata for later \n", + " reference.\n", + "\n", + " \"\"\"\n", + " # Setting up for Z-S relationship:\n", + " snow_z = radar.fields['corrected_reflectivity']['data'].copy()\n", + " # Convert it from dB to linear units\n", + " z_lin = 10.0**(radar.fields['corrected_reflectivity']['data']/10.)\n", + " # Apply the Z-S relation.\n", + " snow_z = swe_ratio * (z_lin/A)**(1./B)\n", + " # Add the field back to the radar. Use reflectivity as a template\n", + " radar.add_field_like('corrected_reflectivity', key, snow_z,\n", + " replace_existing=True)\n", + " # Update units and metadata\n", + " radar.fields[key]['units'] = 'mm/h'\n", + " radar.fields[key]['standard_name'] = 'snowfall_rate'\n", + " radar.fields[key]['long_name'] = 'snowfall_rate_from_z'\n", + " radar.fields[key]['valid_min'] = 0\n", + " radar.fields[key]['valid_max'] = 500\n", + " radar.fields[key]['swe_ratio'] = swe_ratio\n", + " radar.fields[key]['A'] = A\n", + " radar.fields[key]['B'] = B\n", + " return radar" + ] + }, + { + "cell_type": "markdown", + "id": "a3f43284-59c1-4e80-bdfe-3a4b373b5c58", + "metadata": {}, + "source": [ + "## List the Available Files\n", + "We will use files on the Oak Ridge Laboratory Computing Facility (ORLCF), within the shared SAIL directory `/gpfs/wolf/atm124/proj-shared/sail`.\n", + "\n", + "These radar files have been merged from a single sweep in each file, to whole volume scans in each file." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d17b6a9b-4a2e-431e-b4b5-0729c19a7934", + "metadata": {}, + "outputs": [], + "source": [ + "file_list = sorted(glob.glob(\"/data/project/ARM_Summer_School_2024_Data/sail/radar/*\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "14f86016-9679-4253-8943-b1c86fc2b8f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['../data/sail/radar/gucxprecipradarcmacS2.c1.20220310.011126.nc']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "file_list" + ] + }, + { + "cell_type": "markdown", + "id": "44fd7db6", + "metadata": {}, + "source": [ + "## Read Data + Apply Snowfall Retrieval" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6e842671-fd63-4443-b6a6-c2cb4fdb60ee", + "metadata": {}, + "outputs": [], + "source": [ + "radar = radar = pyart.io.read(file_list[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f32eae14-4dfc-4273-9ec3-410f12533027", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jrobrien/micromamba/envs/arm-summer-school-2024-dev/lib/python3.11/site-packages/numpy/ma/core.py:6980: RuntimeWarning: overflow encountered in power\n", + " result = np.where(m, fa, umath.power(fa, fb)).view(basetype)\n" + ] + } + ], + "source": [ + "radar = snow_rate(radar, 8.5, 67, 1.28, key=\"snow_z_new\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2dfc6f73-6c9f-4738-bc07-25b6396e3504", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['DBZ',\n", + " 'VEL',\n", + " 'WIDTH',\n", + " 'ZDR',\n", + " 'PHIDP',\n", + " 'RHOHV',\n", + " 'NCP',\n", + " 'DBZhv',\n", + " 'cbb_flag',\n", + " 'sounding_temperature',\n", + " 'height',\n", + " 'signal_to_noise_ratio',\n", + " 'velocity_texture',\n", + " 'gate_id',\n", + " 'simulated_velocity',\n", + " 'corrected_velocity',\n", + " 'unfolded_differential_phase',\n", + " 'corrected_differential_phase',\n", + " 'filtered_corrected_differential_phase',\n", + " 'corrected_specific_diff_phase',\n", + " 'filtered_corrected_specific_diff_phase',\n", + " 'corrected_differential_reflectivity',\n", + " 'corrected_reflectivity',\n", + " 'height_over_iso0',\n", + " 'specific_attenuation',\n", + " 'path_integrated_attenuation',\n", + " 'specific_differential_attenuation',\n", + " 'path_integrated_differential_attenuation',\n", + " 'rain_rate_A',\n", + " 'snow_rate_ws2012',\n", + " 'snow_rate_ws88diw',\n", + " 'snow_rate_m2009_1',\n", + " 'snow_rate_m2009_2',\n", + " 'snow_z_new']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check to see if the snowfall retrieval was applied\n", + "list(radar.fields.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9536b167-c6da-4539-a996-e30c4a9d95b0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jrobrien/micromamba/envs/arm-summer-school-2024-dev/lib/python3.11/site-packages/shapely/set_operations.py:131: RuntimeWarning: invalid value encountered in intersection\n", + " return lib.intersection(a, b, **kwargs)\n", + "/Users/jrobrien/micromamba/envs/arm-summer-school-2024-dev/lib/python3.11/site-packages/shapely/set_operations.py:131: RuntimeWarning: invalid value encountered in intersection\n", + " return lib.intersection(a, b, **kwargs)\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display = pyart.graph.RadarMapDisplay(radar)\n", + "display.plot_ppi_map(\"corrected_reflectivity\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b1b54679-163a-4ca3-bbd4-8bab955d4598", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Check to see if the snowfall retrieval was applied\n", + "# Now display the snowfall rates\n", + "#-------------------\n", + "# Plot the Radar PPI\n", + "#-------------------\n", + "bounds = [38.4, 39.4, -107.6, -106.5]\n", + "\n", + "fig = plt.figure(figsize=[12, 12])\n", + "ax1 = fig.add_subplot(111, projection=ccrs.PlateCarree())\n", + "#ax1 = fig.add_subplot(111)\n", + "\n", + "ax1.add_feature(cfeature.STATES, linewidth=3)\n", + "ax1.add_feature(USCOUNTIES, alpha=0.4)\n", + "\n", + "# Create the Radar Display Object\n", + "display = pyart.graph.RadarMapDisplay(radar)\n", + "\n", + "# Plot the reflectivty\n", + "# Note - I did not add the sweep parameter since there is only one sweep per file!\n", + "display.plot_ppi_map('snow_z_new', \n", + " ax=ax1,\n", + " vmin=0,\n", + " vmax=50,\n", + " min_lat=bounds[0],\n", + " max_lat=bounds[1],\n", + " min_lon=bounds[2],\n", + " max_lon=bounds[3],\n", + " embellish=False,\n", + " norm=None,\n", + " cmap='Blues')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0aacb3c8-baa7-40ae-b269-2862fafd1f1b", + "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": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}