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NOAA NEXRAD Level II

Weather Radar NEXRAD AIforEarth NOAA

Données récentes de niveau II provenant du système NEXRAD.

NEXRAD (Next-Generation Radar, ou « Radar nouvelle génération ») est un réseau de 159 stations radar à travers les États-Unis exploité par la NOAA (National Oceanic and Atmospheric Administration, ou « Agence américaine d’observation océanique et atmosphérique »). Ce jeu de données est utilisé pour les prévisions météorologiques et la climatologie.

Ce jeu de données est disponible sur Azure grâce au programme NOAA Big Data.

Les données sont disponibles pour les 90 derniers jours ; les données plus anciennes sont disponibles au niveau de stockage archive et peuvent être mises à disposition à la demande (contactez aiforearthdatasets@microsoft.com).

Ressources de stockage

Les données sont stockées dans des blobs (un blob par scan) situés dans le centre de données USA Est 2, dans le conteneur de blob suivant :

https://nexradsa.blob.core.windows.net/nexrad-l2

Les scans sont placés selon la convention suivante :

https://nexradsa.blob.core.windows.net/nexrad-l2/year/month/day/station/filename

Les noms de fichiers individuels respectent la convention suivante :

[station][year][month][day][time]

Par exemple, le fichier suivant contient un scan de la station KHPX, le 7 juillet 1997, à 00:08.27 GMT :

https://nexradsa.blob.core.windows.net/nexrad-l2/1997/07/07/KHPX/KHPX19970707_000827.gz

Un exemple Python complet d’accès et de traçage d’un scan NEXRAD est disponible dans le notebook fourni sous “accès aux données”.

Nous fournissons également un jeton SAP (signature d’accès partagé) en lecture seule pour permettre l’accès aux données NEXRAD via, par exemple, BlobFuse, ce qui vous permet de monter des conteneurs de blob en tant que lecteurs :

https://nexradsa.blob.core.windows.net/nexrad-l2?st=2019-07-26T22%3A26%3A29Z&se=2034-07-27T22%3A26%3A00Z&sp=rl&sv=2018-03-28&sr=c&sig=oHaHPOVn3hs9Dm2WtAKAT64zmZkwwceam8XD8HSVrSg%3D

Les instructions de montage pour Linux sont disponibles ici.

Les données NEXRAD peuvent consommer des centaines de téraoctets. Par conséquent, le traitement à grande échelle est plus efficace dans le centre de données Azure USA Est où les scans sont stockés. Si vous utilisez des données NEXRAD pour des applications en sciences de l’environnement, notamment des prévisions météorologiques, nous vous recommandons de demander une subvention AI for Earth afin de répondre à vos besoins en calcul.

Index

Une liste de tous les fichiers NEXRAD est disponible ici, sous forme de fichier .txt compressé :

https://nexradsa.blob.core.windows.net/nexrad-index/nexrad-index.zip

Nous maintenons également une base de données SQLite pour faciliter l’interrogation des images par lieu et par heure. Consultez l’exemple de bloc-notes pour plus de détails.

Belle image


Radar météorologique près d’Oklahoma City le 5 juin 1991.

Contact

Pour toute question sur ce jeu de données, contactez aiforearthdatasets@microsoft.com.

Access

Available inWhen to use
Azure Notebooks

Quickly explore the dataset with Jupyter notebooks hosted on Azure or your local machine.

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Azure Notebooks

Azure Notebooks

Package: Language: Python

Demo notebook for accessing NEXRAD data on Azure

This notebook provides an example of accessing NEXRAD data from blob storage on Azure, then plotting it using the fantastic Py-ART library for working with radar data.

We will demonstrate how to access and plot a scan given a known scan filename, as well as how to access scans by lat/lon/time.

NEXRAD data are stored in the East US data center, so this notebook will run most efficiently on Azure compute located in East US. We recommend that substantial computation depending on NEXRAD data also be situated in East US: you don't want to download hundreds of terabytes to your laptop! If you are using NEXRAD data for environmental science applications (including weather forecasting), consider applying for an AI for Earth grant to support your compute requirements.

Imports and environment

In [1]:
# Standard-ish imports
import matplotlib.pyplot as plt
import warnings
import urllib.request
import tempfile
import os
import requests
import shutil

# Less standard, but still pip- or conda-installable
import sqlite3
import geopy.distance

# pip install progressbar2, not progressbar
import progressbar

# Suppress some warnings generated within pyart
warnings.filterwarnings('ignore',category=DeprecationWarning)
warnings.filterwarnings('ignore',category=FutureWarning)
warnings.filterwarnings('ignore',category=UserWarning)
import pyart

%matplotlib inline

# URL of our index file
index_db_url = 'https://nexradsa.blob.core.windows.net/nexrad-index/NEXRAD_sqllite.db'

# Temporary folder for data we need during execution of this notebook (we'll clean up
# at the end, we promise)
temp_dir = os.path.join(tempfile.gettempdir(),'nexrad')
os.makedirs(temp_dir,exist_ok=True)

# Local copy of the index file
index_db_file_name = os.path.join(temp_dir,'NEXRAD_sqllite.db')
## You are using the Python ARM Radar Toolkit (Py-ART), an open source
## library for working with weather radar data. Py-ART is partly
## supported by the U.S. Department of Energy as part of the Atmospheric
## Radiation Measurement (ARM) Climate Research Facility, an Office of
## Science user facility.
##
## If you use this software to prepare a publication, please cite:
##
##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119

Functions

In [2]:
class DownloadProgressBar():
    """
    https://stackoverflow.com/questions/37748105/how-to-use-progressbar-module-with-urlretrieve
    """
    
    def __init__(self):
        self.pbar = None

    def __call__(self, block_num, block_size, total_size):
        if not self.pbar:
            self.pbar = progressbar.ProgressBar(max_value=total_size)
            self.pbar.start()
            
        downloaded = block_num * block_size
        if downloaded < total_size:
            self.pbar.update(downloaded)
        else:
            self.pbar.finish()
            

def download_url(url, destination_filename=None, progress_updater=None, force_download=False):
    """
    Download a URL to a temporary file
    """
    
    # This is not intended to guarantee uniqueness, we just know it happens to guarantee
    # uniqueness for this application.
    if destination_filename is None:
        url_as_filename = url.replace('://', '_').replace('.', '_').replace('/', '_')
        destination_filename = \
            os.path.join(temp_dir,url_as_filename)
    if (not force_download) and (os.path.isfile(destination_filename)):
        print('Bypassing download of already-downloaded file {}'.format(os.path.basename(url)))
        return destination_filename
    print('Downloading file {}'.format(os.path.basename(url)),end='')
    urllib.request.urlretrieve(url, destination_filename, progress_updater)  
    assert(os.path.isfile(destination_filename))
    nBytes = os.path.getsize(destination_filename)
    print('...done, {} bytes.'.format(nBytes))
    return destination_filename
    

def download_index_db():
    """
    We have created an index (as SQLite db file) that tracks all records added to our NEXRAD 
    archive; this function will download that index (~40GB) to a local temporary file, if it
    hasn't already been downloaded.  This is a much more sensible thing to do inside the East
    US data center than outside!
    """
    
    if os.path.isfile(index_db_file_name):
        print('Index file {} exists, bypassing download'.format(os.path.basename(index_db_file_name)))
        return        
    else:
        download_url(index_db_url, index_db_file_name, DownloadProgressBar())


def distance(lat1, lon1, lat2, lon2):
    """
    Compute the distance in meters between two lat/lon coordinate pairs
    """
    
    return geopy.distance.distance((lat1, lon1), (lat2, lon2)).m


def get_closest_coordinate(coordinate_list, lat, lon):
    """
    Find the closest point in a list of lat/lon pairs, used here to find the closest radar
    station to a given lat/lon pair.
    """
    
    return min(coordinate_list, key=lambda p: distance(lat, lon, p['lat'], p['lon']))


def get_records(sql):
    """
    Execute a SQL query on the index database; returns matching rows.
    """
    
    download_index_db()
    conn = sqlite3.connect(index_db_file_name)
    
    with conn:
        cursor = conn.execute(sql)
        rows = cursor.fetchall()
        return rows
    

def get_scans_for_nearest_station(lat, lon, start_date, end_date):
    """
    Find all records in a given date range from the station closest to the 
    specified lat/lon pair.
    """
    
    # ICAO is the for-letter code for the station, e.g. "KTLX"
    sql = 'SELECT lat, lon, ICAO, name FROM station_latlon'
    records = get_records(sql)

    coordinate_list = []
    for row in records:
        coordinate_list.append({'lat': row[0], 'lon': row[1],
                                'icao': row[2], 'name': row[3]})
    
    # Find the coordinates of the station closest to the given latitude and longitude
    print('Searching for the nearest station to {},{}'.format(lat,lon))
    closest_coordinate  = get_closest_coordinate(coordinate_list, lat, lon)
    
    print('Nearest station ({}, {}) found at {},{}'.format(
        closest_coordinate['icao'], closest_coordinate['name'],
        closest_coordinate['lat'], closest_coordinate['lon']))
    
    # Get scans for the nearest station for a given date range
    sql = '''SELECT * FROM station_index a INNER JOIN \
            station_latlon b ON a.name = b.ICAO \
            and (b.lat = {} and b.lon = {} and \
            date(a.date_time) >= '{}' \
            and date(a.date_time) <= '{}')'''.format(closest_coordinate['lat'], 
                                                     closest_coordinate['lon'], 
                                                     start_date, end_date)

    files_info = get_records(sql)
    return files_info


def display_scan(filename):
    """
    Use PyART to plot a NEXRAD scan stored in [filename].
    """
    
    radar = pyart.io.read_nexrad_archive(filename)
    display = pyart.graph.RadarDisplay(radar)
    fig = plt.figure()
    ax = fig.add_subplot()
    display.plot('reflectivity', 0, title='Reflectivity', ax=ax)
    plt.show()

Access and plot a scan by constructing a filename

In [6]:
year = '2020'; month = '09'; day = '02'; station = 'KMXX'; time = '011116';
filename = station + year + month + day + '_' + time + '_V06.ar2v'
url = 'https://nexradsa.blob.core.windows.net/nexrad-l2/' + year + '/' + month + '/' + day + \
   '/' + station + '/' + filename
filename = download_url(url)
display_scan(filename)
Downloading file KMXX20200902_011116_V06.ar2v...done, 7438474 bytes.

Access and plot a scan by querying location and time

In [10]:
start_date = '2020-08-02'; end_date = '2020-08-15'
    
# Coordinates near Redmond, WA
lat = 47.6740; lon = -122.1215

# Find all files from the nearest station in the given date range
#
# The first time you call this function, it will download the ~40GB index file.
scan_files = get_scans_for_nearest_station(lat, lon, start_date, end_date)

# MDM files are not actually scans
scan_files = [s for s in scan_files if 'MDM' not in s[6]]

print('Found {} files near station: {}'.format(len(scan_files),scan_files[0][1]))

# Download the first scan
year = str(scan_files[0][2]); month = str(scan_files[0][3]); day = str(scan_files[0][4]); 
station = scan_files[0][1]; filename = scan_files[0][6]
url = 'https://nexradsa.blob.core.windows.net/nexrad-l2/' + year.zfill(2) + '/' + \
month.zfill(2) + '/' + day.zfill(2) + \
   '/' + station + '/' + filename
filename = download_url(url)
display_scan(filename)
Index file NEXRAD_sqllite.db exists, bypassing download
Searching for the nearest station to 47.674,-122.1215
Nearest station (KATX, SEATTLE) found at 48.19472,-122.49444
Index file NEXRAD_sqllite.db exists, bypassing download
Found 2909 files near station: KATX
Found 2575 non-MDM files near station: KATX
Downloading file KATX20200802_005713_V06.ar2v...done, 3454725 bytes.

Clean up temporary files (including the index)

In [ ]:
shutil.rmtree(temp_dir)