Weather Source: OnPoint Climatology - Global Coverage

#	postal_code	country	doy_std	avg_of__daily_min_temperature_air_2m_f	std_of__daily_min_temperature_air_2m_f	avg_of__daily_avg_temperature_air_2m_f	std_of__daily_avg_temperature_air_2m_f	avg_of__daily_max_temperature_air_2m_f	std_of__daily_max_temperature_air_2m_f	avg_of__daily_min_temperature_wetbulb_2m_f	std_of__daily_min_temperature_wetbulb_2m_f	avg_of__daily_avg_temperature_wetbulb_2m_f	std_of__daily_avg_temperature_wetbulb_2m_f	avg_of__daily_max_temperature_wetbulb_2m_f	std_of__daily_max_temperature_wetbulb_2m_f	avg_of__daily_min_temperature_dewpoint_2m_f	std_of__daily_min_temperature_dewpoint_2m_f	avg_of__daily_avg_temperature_dewpoint_2m_f	std_of__daily_avg_temperature_dewpoint_2m_f	avg_of__daily_max_temperature_dewpoint_2m_f	std_of__daily_max_temperature_dewpoint_2m_f	avg_of__daily_min_temperature_feelslike_2m_f	std_of__daily_min_temperature_feelslike_2m_f	avg_of__daily_avg_temperature_feelslike_2m_f	std_of__daily_avg_temperature_feelslike_2m_f	avg_of__daily_max_temperature_feelslike_2m_f	std_of__daily_max_temperature_feelslike_2m_f	avg_of__daily_min_humidity_relative_2m_pct	std_of__daily_min_humidity_relative_2m_pct	avg_of__daily_avg_humidity_relative_2m_pct	std_of__daily_avg_humidity_relative_2m_pct	avg_of__daily_max_humidity_relative_2m_pct	std_of__daily_max_humidity_relative_2m_pct	avg_of__daily_min_humidity_specific_2m_gpkg	std_of__daily_min_humidity_specific_2m_gpkg	avg_of__daily_avg_humidity_specific_2m_gpkg	std_of__daily_avg_humidity_specific_2m_gpkg	avg_of__daily_max_humidity_specific_2m_gpkg	std_of__daily_max_humidity_specific_2m_gpkg	avg_of__daily_min_pressure_2m_mb	std_of__daily_min_pressure_2m_mb	avg_of__daily_avg_pressure_2m_mb	std_of__daily_avg_pressure_2m_mb	avg_of__daily_max_pressure_2m_mb	std_of__daily_max_pressure_2m_mb	avg_of__daily_min_pressure_mean_sea_level_mb	std_of__daily_min_pressure_mean_sea_level_mb	avg_of__daily_avg_pressure_mean_sea_level_mb	std_of__daily_avg_pressure_mean_sea_level_mb	avg_of__daily_max_pressure_mean_sea_level_mb	std_of__daily_max_pressure_mean_sea_level_mb	avg_of__daily_min_wind_speed_10m_mph	std_of__daily_min_wind_speed_10m_mph	avg_of__daily_avg_wind_speed_10m_mph	std_of__daily_avg_wind_speed_10m_mph	avg_of__daily_max_wind_speed_10m_mph	std_of__daily_max_wind_speed_10m_mph	avg_vec_of__daily_avg_vec_wind_direction_10m_deg	avg_vec_of__daily_avg_vec_wind_direction_80m_deg	avg_vec_of__daily_avg_vec_wind_direction_100m_deg	avg_of__pos_daily_tot_precipitation_in	frq_of__pos_daily_tot_precipitation_in_pct	std_of__pos_daily_tot_precipitation_in	frq_of__daily_tot_precipitation_zero_in_pct	frq_of__daily_tot_precipitation_001_010_in_pct	frq_of__daily_tot_precipitation_010_025_in_pct	frq_of__daily_tot_precipitation_025_050_in_pct	frq_of__daily_tot_precipitation_050_100_in_pct	frq_of__daily_tot_precipitation_100_250_in_pct	frq_of__daily_tot_precipitation_250_500_in_pct	frq_of__daily_tot_precipitation_500_gtr_in_pct	avg_of__pos_daily_tot_snowfall_in	frq_of__pos_daily_tot_snowfall_in_pct	std_of__pos_daily_tot_snowfall_in	frq_of__daily_tot_snowfall_zero_in_pct	frq_of__daily_tot_snowfall_001_010_in_pct	frq_of__daily_tot_snowfall_010_025_in_pct	frq_of__daily_tot_snowfall_025_050_in_pct	frq_of__daily_tot_snowfall_050_100_in_pct	frq_of__daily_tot_snowfall_100_150_in_pct	frq_of__daily_tot_snowfall_150_250_in_pct	frq_of__daily_tot_snowfall_250_gtr_in_pct	avg_of__pos_daily_snowdepth_in	std_of__pos_daily_snowdepth_in	avg_of__daily_min_cloud_cover_tot_pct	std_of__daily_min_cloud_cover_tot_pct	avg_of__daily_avg_cloud_cover_tot_pct	std_of__daily_avg_cloud_cover_tot_pct	avg_of__daily_max_cloud_cover_tot_pct	std_of__daily_max_cloud_cover_tot_pct	avg_of__daily_min_radiation_solar_total_wpm2	std_of__daily_min_radiation_solar_total_wpm2	avg_of__daily_avg_radiation_solar_total_wpm2	std_of__daily_avg_radiation_solar_total_wpm2	avg_of__daily_max_radiation_solar_total_wpm2	std_of__daily_max_radiation_solar_total_wpm2	avg_of__daily_tot_radiation_solar_total_wpm2	std_of__daily_tot_radiation_solar_total_wpm2
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Data Quality

100%

gap-free and error corrected

Avail. Formats

.bin, .json, and .xml

File

Coverage

248

Countries

[Sample] climatology-postalcode-day.csv

Attribute	Type	Example	Mapping
postal_code	String	WC2N 5DN	Location ID
country	String	GB	Country Name
doy_std	Integer	1	Latitude-Longitude
avg_of__daily_min_temperature_air_2m_f	Float	36.2	Latitude-Longitude
std_of__daily_min_temperature_air_2m_f	Float	6.5	Latitude-Longitude
avg_of__daily_avg_temperature_air_2m_f	Float	40.6	Latitude-Longitude
std_of__daily_avg_temperature_air_2m_f	Float	6.5	Latitude-Longitude
avg_of__daily_max_temperature_air_2m_f	Float	44.8	Latitude-Longitude
std_of__daily_max_temperature_air_2m_f	Float	6.2	Latitude-Longitude
avg_of__daily_min_temperature_wetbulb_2m_f	Float	35.3	Latitude-Longitude
std_of__daily_min_temperature_wetbulb_2m_f	Float	6.5	Latitude-Longitude
avg_of__daily_avg_temperature_wetbulb_2m_f	Float	39.4	Latitude-Longitude
std_of__daily_avg_temperature_wetbulb_2m_f	Float	6.5	Latitude-Longitude
avg_of__daily_max_temperature_wetbulb_2m_f	Float	43.2	Latitude-Longitude
std_of__daily_max_temperature_wetbulb_2m_f	Float	6.5	Latitude-Longitude
avg_of__daily_min_temperature_dewpoint_2m_f	Float	33.7	Latitude-Longitude
std_of__daily_min_temperature_dewpoint_2m_f	Float	7.1	Latitude-Longitude
avg_of__daily_avg_temperature_dewpoint_2m_f	Float	37.8	Latitude-Longitude
std_of__daily_avg_temperature_dewpoint_2m_f	Float	6.9	Latitude-Longitude
avg_of__daily_max_temperature_dewpoint_2m_f	Float	41.8	Latitude-Longitude
std_of__daily_max_temperature_dewpoint_2m_f	Float	7.2	Latitude-Longitude
avg_of__daily_min_temperature_feelslike_2m_f	Float	29.4	Latitude-Longitude
std_of__daily_min_temperature_feelslike_2m_f	Float	7.5	Latitude-Longitude
avg_of__daily_avg_temperature_feelslike_2m_f	Float	34.3	Latitude-Longitude
std_of__daily_avg_temperature_feelslike_2m_f	Float	7.3	Latitude-Longitude
avg_of__daily_max_temperature_feelslike_2m_f	Float	39.2	Latitude-Longitude
std_of__daily_max_temperature_feelslike_2m_f	Float	6.9	Latitude-Longitude
avg_of__daily_min_humidity_relative_2m_pct	Float	82.0	Latitude-Longitude
std_of__daily_min_humidity_relative_2m_pct	Float	9.0	Latitude-Longitude
avg_of__daily_avg_humidity_relative_2m_pct	Float	90.0	Latitude-Longitude
std_of__daily_avg_humidity_relative_2m_pct	Float	5.0	Latitude-Longitude
avg_of__daily_max_humidity_relative_2m_pct	Float	95.2	Latitude-Longitude
std_of__daily_max_humidity_relative_2m_pct	Float	3.0	Latitude-Longitude
avg_of__daily_min_humidity_specific_2m_gpkg	Float	4.2	Latitude-Longitude
std_of__daily_min_humidity_specific_2m_gpkg	Float	1.1	Latitude-Longitude
avg_of__daily_avg_humidity_specific_2m_gpkg	Float	5.0	Latitude-Longitude
std_of__daily_avg_humidity_specific_2m_gpkg	Float	1.3	Latitude-Longitude
avg_of__daily_max_humidity_specific_2m_gpkg	Float	5.8	Latitude-Longitude
std_of__daily_max_humidity_specific_2m_gpkg	Float	1.5	Latitude-Longitude
avg_of__daily_min_pressure_2m_mb	Float	1003.5	Latitude-Longitude
std_of__daily_min_pressure_2m_mb	Float	14.1	Latitude-Longitude
avg_of__daily_avg_pressure_2m_mb	Float	1007.7	Latitude-Longitude
std_of__daily_avg_pressure_2m_mb	Float	12.9	Latitude-Longitude
avg_of__daily_max_pressure_2m_mb	Float	1011.8	Latitude-Longitude
std_of__daily_max_pressure_2m_mb	Float	11.9	Latitude-Longitude
avg_of__daily_min_pressure_mean_sea_level_mb	Float	1011.1	Latitude-Longitude
std_of__daily_min_pressure_mean_sea_level_mb	Float	14.2	Latitude-Longitude
avg_of__daily_avg_pressure_mean_sea_level_mb	Float	1015.2	Latitude-Longitude
std_of__daily_avg_pressure_mean_sea_level_mb	Float	13.1	Latitude-Longitude
avg_of__daily_max_pressure_mean_sea_level_mb	Float	1019.3	Latitude-Longitude
std_of__daily_max_pressure_mean_sea_level_mb	Float	12.0	Latitude-Longitude
avg_of__daily_min_wind_speed_10m_mph	Float	7.5	Latitude-Longitude
std_of__daily_min_wind_speed_10m_mph	Float	4.1	Latitude-Longitude
avg_of__daily_avg_wind_speed_10m_mph	Float	11.9	Latitude-Longitude
std_of__daily_avg_wind_speed_10m_mph	Float	4.8	Latitude-Longitude
avg_of__daily_max_wind_speed_10m_mph	Float	16.7	Latitude-Longitude
std_of__daily_max_wind_speed_10m_mph	Float	6.8	Latitude-Longitude
avg_vec_of__daily_avg_vec_wind_direction_10m_deg	Float	241	Latitude-Longitude
avg_vec_of__daily_avg_vec_wind_direction_80m_deg	Float	245	Latitude-Longitude
avg_vec_of__daily_avg_vec_wind_direction_100m_deg	Float	245	Latitude-Longitude
avg_of__pos_daily_tot_precipitation_in	Float	0.19	Latitude-Longitude
frq_of__pos_daily_tot_precipitation_in_pct	Float	39	Latitude-Longitude
std_of__pos_daily_tot_precipitation_in	Float	0.18	Latitude-Longitude
frq_of__daily_tot_precipitation_zero_in_pct	Float	61	Latitude-Longitude
frq_of__daily_tot_precipitation_001_010_in_pct	Float	18	Latitude-Longitude
frq_of__daily_tot_precipitation_010_025_in_pct	Float	9	Latitude-Longitude
frq_of__daily_tot_precipitation_025_050_in_pct	Float	7	Latitude-Longitude
frq_of__daily_tot_precipitation_050_100_in_pct	Float	4	Latitude-Longitude
frq_of__daily_tot_precipitation_100_250_in_pct	Float	0	Latitude-Longitude
frq_of__daily_tot_precipitation_250_500_in_pct	Float	0	Latitude-Longitude
frq_of__daily_tot_precipitation_500_gtr_in_pct	Float	0	Latitude-Longitude
avg_of__pos_daily_tot_snowfall_in	Float	0.68	Latitude-Longitude
frq_of__pos_daily_tot_snowfall_in_pct	Float	1	Latitude-Longitude
std_of__pos_daily_tot_snowfall_in	Float	0.16	Latitude-Longitude
frq_of__daily_tot_snowfall_zero_in_pct	Float	99	Latitude-Longitude
frq_of__daily_tot_snowfall_001_010_in_pct	Float	0	Latitude-Longitude
frq_of__daily_tot_snowfall_010_025_in_pct	Float	0	Latitude-Longitude
frq_of__daily_tot_snowfall_025_050_in_pct	Float	0	Latitude-Longitude
frq_of__daily_tot_snowfall_050_100_in_pct	Float	0	Latitude-Longitude
frq_of__daily_tot_snowfall_100_150_in_pct	Float	0	Latitude-Longitude
frq_of__daily_tot_snowfall_150_250_in_pct	Float	0	Latitude-Longitude
frq_of__daily_tot_snowfall_250_gtr_in_pct	Float	0	Latitude-Longitude
avg_of__pos_daily_snowdepth_in	Float	0.8	Latitude-Longitude
std_of__pos_daily_snowdepth_in	Float	0.3	Latitude-Longitude
avg_of__daily_min_cloud_cover_tot_pct	Float	45	Latitude-Longitude
std_of__daily_min_cloud_cover_tot_pct	Float	39	Latitude-Longitude
avg_of__daily_avg_cloud_cover_tot_pct	Float	76	Latitude-Longitude
std_of__daily_avg_cloud_cover_tot_pct	Float	26	Latitude-Longitude
avg_of__daily_max_cloud_cover_tot_pct	Float	96	Latitude-Longitude
std_of__daily_max_cloud_cover_tot_pct	Float	15	Latitude-Longitude
avg_of__daily_min_radiation_solar_total_wpm2	Float	0.0	Latitude-Longitude
std_of__daily_min_radiation_solar_total_wpm2	Float	0.0	Latitude-Longitude
avg_of__daily_avg_radiation_solar_total_wpm2	Float	23.4	Latitude-Longitude
std_of__daily_avg_radiation_solar_total_wpm2	Float	14.5	Latitude-Longitude
avg_of__daily_max_radiation_solar_total_wpm2	Float	132.5	Latitude-Longitude
std_of__daily_max_radiation_solar_total_wpm2	Float	76.8	Latitude-Longitude
avg_of__daily_tot_radiation_solar_total_wpm2	Float	561.0	Latitude-Longitude
std_of__daily_tot_radiation_solar_total_wpm2	Float	346.9	Latitude-Longitude

Product Attributes

Attribute	Type	Example	Mapping
lat/long	String	42.774869, -71.249257	Latitude-Longitude
Designated Market Areas (DMAs)		Detroit, Michigan, 505
ZIP Codes	String	03079	ZIP Code
OnPoint IDs		6057677 - Madeira Island, Portugal

OnPoint Climatology is the statistical representation of weather over time and Weather Source possesses the most accurate and robust Climatology Data on the market.

OnPoint Climatology goes beyond average daily weather predictions to anticipate what the weather at your point of interest will be on a particular day and at a particular point in time. OnPoint Climatology creates a baseline for how your customers respond during average weather, then performs regression analysis using historical data to predict how they react during extreme weather events or anomalies. When coupled with forecast data, your organization will be equipped with tailored predictive models and empowered to take the appropriate business response when deviations from the baseline are anticipated. Discover how customers/assets respond during average (mean) conditions and how they respond during departures from normal (standard deviations), which have the biggest influence on behavior. May also be used as a long-range forecasting tool.

Africa (58)

Algeria

Angola

Benin

Botswana

Burkina Faso

Burundi

Cabo Verde

Cameroon

Central African Republic

Chad

Comoros

Congo

Congo (Democratic Republic of the)

Côte d'Ivoire

Djibouti

Egypt

Equatorial Guinea

Eritrea

Ethiopia

Gabon

Gambia

Ghana

Guinea

Guinea-Bissau

Kenya

Lesotho

Liberia

Libya

Madagascar

Malawi

Mali

Mauritania

Mauritius

Mayotte

Morocco

Mozambique

Namibia

Niger

Nigeria

Rwanda

Réunion

Saint Helena, Ascension and Tristan da Cunha

Sao Tome and Principe

Senegal

Seychelles

Sierra Leone

Somalia

South Africa

South Sudan

Sudan

Swaziland

Tanzania, United Republic of

Togo

Tunisia

Uganda

Western Sahara

Zambia

Zimbabwe

Asia (51)

Afghanistan

Armenia

Azerbaijan

Bahrain

Bangladesh

Bhutan

Brunei Darussalam

Cambodia

China

Cyprus

Georgia

Hong Kong

India

Indonesia

Iran (Islamic Republic of)

Iraq

Israel

Japan

Jordan

Kazakhstan

Korea (Democratic People's Republic of)

Korea (Republic of)

Kuwait

Kyrgyzstan

Lao People's Democratic Republic

Lebanon

Macao

Malaysia

Maldives

Mongolia

Myanmar

Nepal

Oman

Pakistan

Palestine, State of

Philippines

Qatar

Saudi Arabia

Singapore

Sri Lanka

Syrian Arab Republic

Taiwan

Tajikistan

Thailand

Timor-Leste

Turkey

Turkmenistan

United Arab Emirates

Uzbekistan

Vietnam

Yemen

Europe (51)

Albania

Andorra

Austria

Belarus

Belgium

Bosnia and Herzegovina

Bulgaria

Croatia

Czech Republic

Denmark

Estonia

Faroe Islands

Finland

France

Germany

Gibraltar

Greece

Guernsey

Holy See

Hungary

Iceland

Ireland

Isle of Man

Italy

Jersey

Latvia

Liechtenstein

Lithuania

Luxembourg

Macedonia (the former Yugoslav Republic of)

Malta

Moldova (Republic of)

Monaco

Montenegro

Netherlands

Norway

Poland

Portugal

Romania

Russian Federation

San Marino

Serbia

Slovakia

Slovenia

Spain

Svalbard and Jan Mayen

Sweden

Switzerland

Ukraine

United Kingdom

Åland Islands

North America (13)

Belize

Bermuda

Canada

Costa Rica

El Salvador

Greenland

Guatemala

Honduras

Mexico

Nicaragua

Panama

Saint Pierre and Miquelon

United States of America

Oceania (25)

American Samoa

Australia

Cook Islands

Fiji

French Polynesia

Guam

Kiribati

Marshall Islands

Micronesia (Federated States of)

Nauru

New Caledonia

New Zealand

Niue

Norfolk Island

Northern Mariana Islands

Palau

Papua New Guinea

Pitcairn

Samoa

Solomon Islands

Tokelau

Tonga

Tuvalu

Vanuatu

Wallis and Futuna

Other (8)

Bouvet Island

British Indian Ocean Territory

Christmas Island

Cocos (Keeling) Islands

French Southern Territories

Heard Island and McDonald Islands

South Georgia and the South Sandwich Islands

United States Minor Outlying Islands

South America (42)

Anguilla

Antigua and Barbuda

Argentina

Aruba

Bahamas

Barbados

Bolivia (Plurinational State of)

Bonaire, Sint Eustatius and Saba

Brazil

Cayman Islands

Chile

Colombia

Cuba

Curaçao

Dominica

Dominican Republic

Ecuador

Falkland Islands (Malvinas)

French Guiana

Grenada

Guadeloupe

Guyana

Haiti

Jamaica

Martinique

Montserrat

Paraguay

Peru

Puerto Rico

Saint Barthélemy

Saint Kitts and Nevis

Saint Lucia

Saint Martin (French part)

Saint Vincent and the Grenadines

Sint Maarten (Dutch part)

Suriname

Trinidad and Tobago

Turks and Caicos Islands

Uruguay

Venezuela (Bolivarian Republic of)

Virgin Islands (British)

Virgin Islands (U.S.)

Free sample available

5% discount if you buy via Datarade

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Monthly License	Available
Yearly License	Available
Usage-based	Available

Request detailed pricing

Self-reported by the provider

100%

gap-free and error corrected

Methods

Frequency

Format

Climate Analytics

Climate Modeling

Business Intelligence (BI) Analytics

Weather Data Climate Data Historical Weather Data Global Weather Data

20K Observation Locations

100% quality checked

249 countries covered

AWIS Weather Services houses, maintains, and constantly updates an extensive database full of Climate and Weather Data. We can offer Worldwide Historical Wea...

10 years data science experience

249 countries covered

Customised solutions for validation, scoring, post-processing, calibration and data cleansing of weather data products.

248 countries covered

OnPoint Weather Historical data provides hourly and daily weather values from the year 2000 to present.

249 countries covered

32 years of historical data

Ambee’s Weather Data API provides weather forecasting, historical data and real-time weather data. Get accurate & actionable proprietary data insights.

What is Weather Source: OnPoint Climatology - Global Coverage?

OnPoint Climatology is the statistical representation of weather over time and Weather Source possesses the most accurate and robust Climatology Data on the market.

What is Weather Source: OnPoint Climatology - Global Coverage used for?

This product has 4 key use cases. Weather Source recommends using the data for Climate Analytics, Climate Modeling, Business Intelligence (BI), and Analytics. Global businesses and organizations buy Weather Data from Weather Source to fuel their analytics and enrichment.

Who can use Weather Source: OnPoint Climatology - Global Coverage?

This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Weather Data. Get in touch with Weather Source to see what their data can do for your business and find out which integrations they provide.

Which countries does Weather Source: OnPoint Climatology - Global Coverage cover?

This product includes data covering 248 countries like USA, China, Japan, Germany, and India. Weather Source is headquartered in United States of America.

How much does Weather Source: OnPoint Climatology - Global Coverage cost?

Pricing information for Weather Source: OnPoint Climatology - Global Coverage is available by getting in contact with Weather Source. Connect with Weather Source to get a quote and arrange custom pricing models based on your data requirements.

How can I get Weather Source: OnPoint Climatology - Global Coverage?

Businesses can buy Weather Data from Weather Source and get the data via S3 Bucket, SFTP, Email, UI Export, and REST API. Depending on your data requirements and subscription budget, Weather Source can deliver this product in .bin, .json, .xml, .csv, .xls, .sql, and .txt format.

What is the data quality of Weather Source: OnPoint Climatology - Global Coverage?

Weather Source has reported that this product has the following quality and accuracy assurances: 100% gap-free and error corrected. You can compare and assess the data quality of Weather Source using Datarade’s data marketplace. Weather Source appears on selected Datarade top lists ranking the best data providers, including 10 Best Data Providers for Weather-Triggered Advertising, Top 7 Historical Weather Data Providers & APIs, and Top 10 Weather Data Providers.

What are similar products to Weather Source: OnPoint Climatology - Global Coverage?

This Tabular Data has 3 related products. These alternatives include Worldwide Hourly Historical Weather Data Climate Data Human Checked Weather Data starting in the mid 1900s, Customised Weather Data Validation, and Weather Source: OnPoint Weather Historical - Back to 2000. You can compare the best Weather Data providers and products via Datarade’s data marketplace and get the right data for your use case.

Pricing available upon request

License	Starts at
One-off purchase	Not available
Monthly License	Available
Yearly License	Available
Usage-based	Available

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Weather Source: OnPoint Climatology - Global Coverage

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Related Products

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Weather Source
Weather & Climate Technologies for Business

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Weather Source: OnPoint Climatology - Global Coverage

Data Dictionary

Description

Geography

Pricing

Suitable Company Sizes

Quality

Delivery

Use Cases

Categories

Related Products

Frequently asked questions

Weather Source Weather & Climate Technologies for Business

Trusted by

Weather Source
Weather & Climate Technologies for Business