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NOAA Integrated Surface Data (ISD)

Weather ISD NOAA

Dati cronologici per le previsioni metereologiche orarie in tutto il mondo (ad esempio, temperatura, precipitazioni, vento) generati da National Oceanic and Atmospheric Administration (NOAA).

Il set di dati Integrated Surface Dataset (ISD) è costituito da osservazioni metereologiche in superficie in tutto il mondo provenienti da più di 35.000 stazioni, benché la copertura spaziale migliore sia evidente nelle aree America del Nord, Europa, Australia e in parte dell’Asia. I parametri inclusi sono: qualità dell’aria, pressione atmosferica, temperatura atmosferica/punto di rugiada, venti atmosferici, nuvole, precipitazioni, onde oceaniche, maree e altro ancora. ISD fa riferimento ai dati inclusi nel database digitale, oltre al formato in cui sono archiviate le osservazioni metereologiche orarie, sinottiche (3 ore) e giornaliere.

Volume e conservazione

Il set di dati è archiviato nel formato Parquet. Viene aggiornato ogni giorno e contiene circa 400 milioni di righe (20 GB) in totale a oggi (2019).

Questo set di dati include record cronologici accumulati dal 2008 a oggi. Puoi usare le impostazioni dei parametri nell’SDK per recuperare i dati entro un intervallo di tempo specifico.

Posizione di archiviazione

Questo set di dati è archiviato nell’area Stati Uniti orientali di Azure. L’allocazione delle risorse di calcolo nell’area Stati Uniti orientali è consigliata per motivi di affinità.

Informazioni aggiuntive

Il set di dati viene generato da NOAA Integrated Surface Database. Informazioni aggiuntive su questo set di dati sono disponibili qui e qui. Invia un messaggio di posta elettronica a se hai domande sull’origine dati.

Notifiche

MICROSOFT FORNISCE I SET DI DATI APERTI DI AZURE “COSÌ COME SONO”. MICROSOFT NON OFFRE ALCUNA GARANZIA O CONDIZIONE ESPLICITA O IMPLICITA RELATIVAMENTE ALL’USO DEI SET DI DATI DA PARTE DELL’UTENTE. NELLA MISURA MASSIMA CONSENTITA DALLE LEGGI LOCALI, MICROSOFT NON RICONOSCE ALCUNA RESPONSABILITÀ RELATIVAMENTE A DANNI O PERDITE COMMERCIALI, INCLUSI I DANNI DIRETTI, CONSEQUENZIALI, SPECIALI, INDIRETTI, INCIDENTALI O PUNITIVI DERIVANTI DALL’USO DEI SET DI DATI DA PARTE DELL’UTENTE.

Questo set di dati viene fornito in conformità con le condizioni originali in base alle quali Microsoft ha ricevuto i dati di origine. Il set di dati potrebbe includere dati provenienti da Microsoft.

Access

Available inWhen to use
Azure Notebooks

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

Azure Databricks

Use this when you need the scale of an Azure managed Spark cluster to process the dataset.

Azure Synapse

Use this when you need the scale of an Azure managed Spark cluster to process the dataset.

Preview

usaf wban datetime latitude longitude elevation cloudCoverage stationName countryOrRegion p_k year day version month
789700 11634 2/27/2021 7:59:00 PM 10.583 -61.35 12 null PIARCO INTL AP TD 789700-11634 2021 27 1 2
479710 42402 2/27/2021 2:59:00 PM 27.1 142.183 8 null CHICHIJIMA JA 479710-42402 2021 27 1 2
479310 42204 2/27/2021 2:59:00 PM 26.356 127.768 44 null KADENA AB JA 479310-42204 2021 27 1 2
912320 41418 2/27/2021 1:59:00 PM 15.117 145.717 64 null FRANCISCO C. ADA/SAIPAN INTERNATIONAL ARPT CQ 912320-41418 2021 27 1 2
912180 41414 2/27/2021 1:59:00 PM 13.583 144.917 187 null ANDERSEN AFB AIRPORT GQ 912180-41414 2021 27 1 2
912460 41606 2/27/2021 11:59:00 AM 19.283 166.65 4 null WAKE ISLAND AIRFIELD WQ 912460-41606 2021 27 1 2
934360 00488 2/27/2021 11:59:00 AM -41.327 174.805 12 null WELLINGTON INTL NZ 934360-00488 2021 27 1 2
704540 25704 2/27/2021 9:59:00 AM 51.883 -176.65 6 null ADAK NAS US 704540-25704 2021 27 1 2
912850 21504 2/27/2021 9:59:00 AM 19.721 -155.048 12 null HILO INTERNATIONAL AIRPORT US 912850-21504 2021 27 1 2
911700 22508 2/27/2021 9:59:00 AM 21.487 -158.028 255 null WHEELER ARMY AIRFIELD US 911700-22508 2021 27 1 2
Name Data type Unique Values (sample) Description
cloudCoverage string 8 CLR
OVC

Frazione del cielo coperta da tutte le nuvole visibili. Valori della copertura nuvolosa:

CLR = Clear skies FEW = Few clouds SCT = Scattered clouds BKN = Broken cloud cover OVC = Overcast OBS = Sky is obscured/can't be estimated POBS = Sky is partially obscured
countryOrRegion string 245 US
CA

Codice del paese.

datetime timestamp 6,920,272 2018-01-18 12:00:00
2018-02-28 12:00:00

Data e ora in formato UTC di un’OSSERVAZIONE-PUNTO-GEOFISICO.

day int 31 1
6

Giorno della colonna datetime.

elevation double 2,369 5.0
3.0

Elevazione del valore PUNTO-OSSERVAZIONE-GEOFISICO rispetto al livello medio del mare.

latitude double 34,854 38.544
31.78

Latitudine di un’OSSERVAZIONE-PUNTO-GEOFISICO in cui l’emisfero meridionale è negativo.

longitude double 58,179 -86.0
-96.622

Longitudine di PUNTO-OSSERVAZIONE-GEOFISICO in cui i valori a occidente rispetto all’intervallo compreso tra 000000 e 179999 vengono contrassegnati come negativi.

month int 12 1
12

Mese della colonna datetime.

p_k string 17,415 999999-53131
999999-22016

usaf-wban

pastWeatherIndicator int 11 2
6

Recupera l’indicatore del meteo passato, che mostra il meteo nell’ora passata

0: Cloud covering 1/2 or less of the sky throughout the appropriate period 1: Cloud covering more than 1/2 of the sky during part of the appropriate period and covering 1/2 or less during part of the period 2: Cloud covering more than 1/2 of the sky throughout the appropriate period 3: Sandstorm, duststorm or blowing snow 4: Fog or ice fog or thick haze 5: Drizzle 6: Rain 7: Snow, or rain and snow mixed 8: Shower(s) 9: Thunderstorm(s) with or without precipitation
precipDepth double 5,671 9999.0
3.0

Profondità delle PRECIPITAZIONI LIQUIDE misurata al momento di un’osservazione. Unità: millimetri. MIN: 0000; MAX: 9998; 9999 = Mancante; FATTORE DI SCALA: 10.

precipTime double 44 1.0
24.0

Quantità di tempo rispetto a cui è stata calcolata la PRECIPITAZIONE-LIQUIDA. Unità: ore. MIN: 00; MAX: 98; 99 = mancante.

presentWeatherIndicator int 101 10
5

Recupera l’indicatore del meteo corrente, che mostra il meteo nell’ora corrente

00: Cloud development not observed or not observable 01: Clouds generally dissolving or becoming less developed 02: State of sky on the whole unchanged 03: Clouds generally forming or developing 04: Visibility reduced by smoke, e.g. veldt or forest fires, industrial smoke or volcanic ashes 05: Haze 06: Widespread dust in suspension in the air, not raised by wind at or near the station at the time of observation 07: Dust or sand raised by wind at or near the station at the time of observation, but no well-developed dust whirl(s) sand whirl(s), and no duststorm or sandstorm seen or, in the case of ships, blowing spray at the station 08: Well developed dust whirl(s) or sand whirl(s) seen at or near the station during the preceding hour or at the time of observation, but no duststorm or sandstorm 09: Duststorm or sandstorm within sight at the time of observation, or at the station during the preceding hour For more: The section 'MW1' in ftp://ftp.ncdc.noaa.gov/pub/data/noaa/isd-format-document.pdf
seaLvlPressure double 2,214 1015.0
1014.2

Pressione dell’aria rispetto al livello medio del mare.

MIN: 08600 MAX: 10900 UNITÀ: hectopascal

snowDepth double 652 1.0
3.0

Profondità di neve e ghiaccio sul suolo. MIN: 0000 MAX: 1200 UNITÀ: centimetri

stationName string 16,677 TUCSON 11 W
PANTHER JUNCTION 2 N

Nome della stazione meteorologica.

temperature double 1,467 15.0
13.0

Temperatura dell’aria. MIN: -0932 MAX: +0618 UNITÀ: Gradi Celsius

usaf string 16,732 999999
062350

Numero di stazione di AIR FORCE CATALOG.

version double 1 1.0
wban string 2,556 99999
53131

Numero NCDC WBAN.

windAngle int 362 180
270

Angolo, misurato in senso orario, tra il nord geografico e la direzione da cui soffia il vento. MIN: 001 MAX: 360 UNITÀ: Gradi angolari

windSpeed double 621 2.1
1.5

Velocità dello spostamento orizzontale dell’aria oltre un punto fisso.

MIN: 0000 MAX: 0900 UNITÀ: metri al secondo

year int 14 2019
2020

Anno della colonna datetime.

Select your preferred service:

Azure Notebooks

Azure Databricks

Azure Synapse

Azure Notebooks

Package: Language: Python Python
In [1]:
# This is a package in preview.
from azureml.opendatasets import NoaaIsdWeather

from datetime import datetime
from dateutil.relativedelta import relativedelta


end_date = datetime.today()
start_date = datetime.today() - relativedelta(months=1)

# Get historical weather data in the past month.
isd = NoaaIsdWeather(start_date, end_date)
# Read into Pandas data frame.
isd_df = isd.to_pandas_dataframe()
ActivityStarted, to_pandas_dataframe ActivityStarted, to_pandas_dataframe_in_worker Target paths: ['/year=2019/month=6/'] Looking for parquet files... Reading them into Pandas dataframe... Reading ISDWeather/year=2019/month=6/part-00049-tid-7654660707407597606-ec55d6c6-0d34-4a97-b2c8-d201080c9a98-89240.c000.snappy.parquet under container isdweatherdatacontainer Done. ActivityCompleted: Activity=to_pandas_dataframe_in_worker, HowEnded=Success, Duration=116905.15 [ms] ActivityCompleted: Activity=to_pandas_dataframe, HowEnded=Success, Duration=116907.63 [ms]
In [2]:
isd_df.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 7790719 entries, 2709 to 11337856 Data columns (total 22 columns): usaf object wban object datetime datetime64[ns] latitude float64 longitude float64 elevation float64 windAngle float64 windSpeed float64 temperature float64 seaLvlPressure float64 cloudCoverage object presentWeatherIndicator float64 pastWeatherIndicator float64 precipTime float64 precipDepth float64 snowDepth float64 stationName object countryOrRegion object p_k object year int32 day int32 version float64 dtypes: datetime64[ns](1), float64(13), int32(2), object(6) memory usage: 1.3+ GB
In [1]:
# Pip install packages
import os, sys

!{sys.executable} -m pip install azure-storage-blob
!{sys.executable} -m pip install pyarrow
!{sys.executable} -m pip install pandas
In [2]:
# Azure storage access info
azure_storage_account_name = "azureopendatastorage"
azure_storage_sas_token = r""
container_name = "isdweatherdatacontainer"
folder_name = "ISDWeather/"
In [3]:
from azure.storage.blob import BlockBlobServicefrom azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient

if azure_storage_account_name is None or azure_storage_sas_token is None:
    raise Exception(
        "Provide your specific name and key for your Azure Storage account--see the Prerequisites section earlier.")

print('Looking for the first parquet under the folder ' +
      folder_name + ' in container "' + container_name + '"...')
container_url = f"https://{azure_storage_account_name}.blob.core.windows.net/"
blob_service_client = BlobServiceClient(
    container_url, azure_storage_sas_token if azure_storage_sas_token else None)

container_client = blob_service_client.get_container_client(container_name)
blobs = container_client.list_blobs(folder_name)
sorted_blobs = sorted(list(blobs), key=lambda e: e.name, reverse=True)
targetBlobName = ''
for blob in sorted_blobs:
    if blob.name.startswith(folder_name) and blob.name.endswith('.parquet'):
        targetBlobName = blob.name
        break

print('Target blob to download: ' + targetBlobName)
_, filename = os.path.split(targetBlobName)
blob_client = container_client.get_blob_client(targetBlobName)
with open(filename, 'wb') as local_file:
    blob_client.download_blob().download_to_stream(local_file)
In [4]:
# Read the parquet file into Pandas data frame
import pandas as pd

print('Reading the parquet file into Pandas data frame')
df = pd.read_parquet(filename)
In [5]:
# you can add your filter at below
print('Loaded as a Pandas data frame: ')
df
In [6]:
 

Azure Databricks

Package: Language: Python Python
In [1]:
# This is a package in preview.
# You need to pip install azureml-opendatasets in Databricks cluster. https://docs.microsoft.com/en-us/azure/data-explorer/connect-from-databricks#install-the-python-library-on-your-azure-databricks-cluster
from azureml.opendatasets import NoaaIsdWeather

from datetime import datetime
from dateutil.relativedelta import relativedelta


end_date = datetime.today()
start_date = datetime.today() - relativedelta(months=1)
isd = NoaaIsdWeather(start_date, end_date)
isd_df = isd.to_spark_dataframe()
ActivityStarted, to_spark_dataframe ActivityStarted, to_spark_dataframe_in_worker ActivityCompleted: Activity=to_spark_dataframe_in_worker, HowEnded=Success, Duration=87171.59 [ms] ActivityCompleted: Activity=to_spark_dataframe, HowEnded=Success, Duration=87176.63 [ms]
In [2]:
display(isd_df.limit(5))
usafwbandatetimelatitudelongitudeelevationwindAnglewindSpeedtemperatureseaLvlPressurecloudCoveragepresentWeatherIndicatorpastWeatherIndicatorprecipTimeprecipDepthsnowDepthstationNamecountryOrRegionp_kyeardayversionmonth
726163547702019-06-30T21:38:00.000+000042.805-72.004317.0null2.617.2nullnull61null1.043.0nullJAFFREY MINI-SLVR RNCH APTUS726163-547702019301.06
726163547702019-06-30T21:52:00.000+000042.805-72.004317.0null1.517.21008.6nullnullnull1.043.0nullJAFFREY MINI-SLVR RNCH APTUS726163-547702019301.06
726163547702019-06-30T22:52:00.000+000042.805-72.004317.0null2.118.91008.8CLRnullnull1.00.0nullJAFFREY MINI-SLVR RNCH APTUS726163-547702019301.06
726163547702019-06-30T23:52:00.000+000042.805-72.004317.0null1.518.31009.1FEWnullnull6.094.0nullJAFFREY MINI-SLVR RNCH APTUS726163-547702019301.06
703260255032019-06-15T07:54:00.000+000058.683-156.65615.0704.110.01005.6null61null1.00.0nullKING SALMON AIRPORTUS703260-255032019151.06
In [1]:
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "isdweatherdatacontainer"
blob_relative_path = "ISDWeather/"
blob_sas_token = r""
In [2]:
# Allow SPARK to read from Blob remotely
wasbs_path = 'wasbs://%s@%s.blob.core.windows.net/%s' % (blob_container_name, blob_account_name, blob_relative_path)
spark.conf.set(
  'fs.azure.sas.%s.%s.blob.core.windows.net' % (blob_container_name, blob_account_name),
  blob_sas_token)
print('Remote blob path: ' + wasbs_path)
In [3]:
# SPARK read parquet, note that it won't load any data yet by now
df = spark.read.parquet(wasbs_path)
print('Register the DataFrame as a SQL temporary view: source')
df.createOrReplaceTempView('source')
In [4]:
# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))

Azure Synapse

Package: Language: Python Python
In [25]:
# This is a package in preview.
from azureml.opendatasets import NoaaIsdWeather

from datetime import datetime
from dateutil.relativedelta import relativedelta


end_date = datetime.today()
start_date = datetime.today() - relativedelta(months=1)
isd = NoaaIsdWeather(start_date, end_date)
isd_df = isd.to_spark_dataframe()
In [26]:
# Display top 5 rows
display(isd_df.limit(5))
Out[26]:
In [1]:
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "isdweatherdatacontainer"
blob_relative_path = "ISDWeather/"
blob_sas_token = r""
In [2]:
# Allow SPARK to read from Blob remotely
wasbs_path = 'wasbs://%s@%s.blob.core.windows.net/%s' % (blob_container_name, blob_account_name, blob_relative_path)
spark.conf.set(
  'fs.azure.sas.%s.%s.blob.core.windows.net' % (blob_container_name, blob_account_name),
  blob_sas_token)
print('Remote blob path: ' + wasbs_path)
In [3]:
# SPARK read parquet, note that it won't load any data yet by now
df = spark.read.parquet(wasbs_path)
print('Register the DataFrame as a SQL temporary view: source')
df.createOrReplaceTempView('source')
In [4]:
# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))

Urban Heat Islands

From the Urban Innovation Initiative at Microsoft Research, data processing and analytics scripts for hourly NOAA weather station data that produce daily urban heat island indices for hundreds of U.S. cities, January 1, 2008 - present, including automated daily updating. Urban heat island effects are then examined over time and across cities, as well as aligned with population density.