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NOAA NEXRAD Level II

Weather Radar NEXRAD AIforEarth NOAA

Senaste nivå-II-data från NEXRAD-systemet.

NEXRAD (Next-Generation Radar) är ett nätverk med 159 radarstationer i USA som drivs av National Oceanic and Atmospheric Administration (NOAA). Datamängden används till väderleksprognoser och klimatvetenskap.

Den här datamängden är tillgänglig i Azure tack vare NOAA:s stordataprogram.

Data finns tillgängliga för de senaste 90 dagarna. Äldre data finns tillgängliga i arkivlagring och kan göras tillgängliga på begäran (kontakta aiforearthdatasets@microsoft.com).

Lagringsresurser

Data lagras i blobbar (en blob per genomsökning) i datacentret Östra USA, i följande blobcontainer:

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

Genomsökningar placeras enligt följande konvention:

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

Enskilda filnamn följer följande konvention:

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

Exempelvis innehåller följande fil en genomsökning från KHPX-stationen den 7 juli 1997 klockan 00:08:27 GMT:

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

Ett komplett Python-exempel som visar hur du kommer åt och ritar ut en NEXRAD-bild finns i anteckningsboken under “Data Access”.

Vi tillhandahåller också en skrivskyddad SAS-token (signatur för delad åtkomst) som ger åtkomst till NEXRAD-data via exempelvis BlobFuse, vilket innebär att du kan montera blobcontainrar som enheter:

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

Monteringsanvisningarna för Linux finns här.

NEXRAD-data kan förbruka hundratals terabyte, så bearbetning i stor skala utförs lämpligast i Azures datacenter Östra USA där genomsökningarna lagras. Om du använder NEXRAD-data i miljövetenskapsprogram, inklusive väderleksprognoser, kan du ansöka om AI for Earth Grant som kan vara till hjälp vid dina beräkningar.

Index

En lista med alla NEXRAD-filer finns som en komprimerad .txt-fil här:

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

Vi har också en SQLite-databas som gör det lättare att fråga efter bilder baserat på plats och tid. Se anteckningsboksexemplet för mer information.

Fin bild


En väderbild nära Oklahoma City (USA) från den 5 juni 1991.

Kontakt

Om du har frågor om den här datamängden är du välkommen att kontakta aiforearthdatasets@microsoft.com.

Access

Available inWhen to use
Azure Notebooks

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

Select your preferred service:

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)