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NOAA NEXRAD Level II

Weather Radar NEXRAD AIforEarth NOAA

Datos actuales y de archivo de nivel II del sistema NEXRAD.

NEXRAD (Next-Generation Radar) es una red de 159 estaciones de radar en Estados Unidos que opera la Administración Nacional Oceánica y Atmosférica (NOAA). Este conjunto de datos se utiliza para la previsión meteorológica y la ciencia del clima.

Este conjunto de datos está disponible en Azure gracias al programa NOAA Big Data Program.

Recursos de almacenamiento

Los datos se almacenan en blobs (un blob por exploración) en el centro de datos Este de EE. UU., en el siguiente contenedor de blobs:

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

Las exploraciones se colocan conforme a la siguiente convención:

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

La nomenclatura de los archivos individuales se rige por la siguiente convención:

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

Por ejemplo, el archivo siguiente contiene una exploración de la estación KHPX realizada el 7 de julio de 1997 a las 00:08:27 horas GMT:

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

Hay disponible un ejemplo completo de Python del acceso a una exploración de NEXRAD y su trazado en el cuaderno que se proporciona en “Acceso a datos”.

También se proporciona un token SAS (firma de acceso compartido) de solo lectura para permitir el acceso a los datos de NEXRAD a través de, por ejemplo, BlobFuse, que permite montar contenedores de blobs como unidades:

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

Encontrará las instrucciones de montaje para Linux aquí.

Los datos de NEXRAD pueden consumir cientos de terabytes, por lo que el procesamiento a gran escala es óptimo en el centro de datos Este de EE. UU. de Azure, donde están almacenadas las exploraciones. Si utiliza datos de NEXRAD para actividades relacionadas con las ciencias ambientales, como la previsión meteorológica, plantéese solicitar una subvención AI for Earth para sustentar sus necesidades de proceso.

Índice

En la siguiente dirección encontrará una lista de todos los archivos de NEXRAD, con el formato de un archivo .txt comprimido dentro de un archivo ZIP:

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

También mantenemos una base de datos de SQLite para facilitar la consulta de imágenes por ubicación y hora. Consulte el cuaderno de ejemplo para obtener más información.

Bonita imagen


Exploración meteorológica cerca de Oklahoma City del 5 de junio de 1991.

Contacto

Si tiene preguntas relacionadas con este conjunto de datos, póngase en contacto con 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 [ ]:
# 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')

Functions

In [ ]:
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 [ ]:
year = '1991'; month = '06'; day = '05'; station = 'KTLX'; time = '162126';
filename = station + year + month + day + '_' + time + '.gz'
url = 'https://nexradsa.blob.core.windows.net/nexrad-l2/' + year + '/' + month + '/' + day + \
   '/' + station + '/' + filename
filename = download_url(url)
display_scan(filename)

Access and plot a scan by querying location and time

In [ ]:
start_date = '2017-07-05'; end_date = '2017-07-12'
    
# 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)
print('Found {} files near station: {}'.format(len(scan_files),scan_files[0][1]))

# Download only the first of those files
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)

Clean up temporary files (including the index)

In [ ]:
shutil.rmtree(temp_dir)