Historical Weather Data
This dataset falls under the category Environmental Data Climate Data.
It contains the following data: historical weather data
This dataset was scouted on 2022-02-28 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://www.worldweatheronline.com/hosur-weather-history/tamil-nadu/in.aspx URL for data access and license information.

Context

The dataset was created by keeping in mind the necessity of such historical weather data in the community. The datasets for top 8 Indian cities as per the population.

Content

The dataset was used with the help of the worldweatheronline.com API and the wwo_hist package. The datasets contain hourly weather data from 01-01-2009 to 01-01-2020. The data of each city is for more than 10 years. This data can be used to visualize the change in data due to global warming or can be used to predict the weather for upcoming days, weeks, months, seasons, etc. Note : The data was extracted with the help of worldweatheronline.com API and I can't guarantee about the accuracy of the data.

Acknowledgements

The data is owned by worldweatheronline.com and is extracted with the help of their API.

Inspiration

The main target of this dataset can be used to predict weather for the next day or week with huge amounts of data provided in the dataset. Furthermore, this data can also be used to make visualization which would help to understand the impact of global warming over the various aspects of the weather like precipitation, humidity, temperature, etc.

These daily weather records were compiled from a subset of stations in the Global Historical Climatological Network (GHCN)-Daily dataset. A weather record is considered broken if the value exceeds the maximum (or minimum) value recorded for an eligible station. A weather record is considered tied if the value is the same as the maximum (or minimum) value recorded for an eligible station. Daily weather parameters include Highest Min/Max Temperature, Lowest Min/Max Temperature, Highest Precipitation, Highest Snowfall and Highest Snow Depth. All stations meet defined eligibility criteria. For this application, a station is defined as the complete daily weather records at a particular location, having a unique identifier in the GHCN-Daily dataset. For a station to be considered for any weather parameter, it must have a minimum of 30 years of data with more than 182 days complete in each year. This is effectively a 30-year record of service requirement, but allows for inclusion of some stations which routinely shut down during certain seasons. Small station moves, such as a move from one property to an adjacent property, may occur within a station history. However, larger moves, such as a station moving from downtown to the city airport, generally result in the commissioning of a new station identifier. This tool treats each of these histories as a different station. In this way, it does not thread the separate histories into one record for a city. Records Timescales are characterized in three ways. In order of increasing noteworthiness, they are Daily Records, Monthly Records and All Time Records. For a given station, Daily Records refers to the specific calendar day: (e.g., the value recorded on March 7th compared to every other March 7th). Monthly Records exceed all values observed within the specified month (e.g., the value recorded on March 7th compared to all values recorded in every March). All-Time Records exceed the record of all observations, for any date, in a station's period of record. The Date Range and Location features are used to define the time and location ranges which are of interest to the user. For example, selecting a date range of March 1, 2012 through March 15, 2012 will return a list of records broken or tied on those 15 days. The Location Category and Country menus allow the user to define the geographic extent of the records of interest. For example, selecting Oklahoma will narrow the returned list of records to those that occurred in the state of Oklahoma, USA. The number of records broken for several recent periods is summarized in the table and updated daily. Due to late-arriving data, the number of recent records is likely underrepresented in all categories, but the ratio of records (warm to cold, for example) should be a fairly strong estimate of a final outcome. There are many more precipitation stations than temperature stations, so the raw number of precipitation records will likely exceed the number of temperature records in most climatic situations.

This dataset contains historical weather data of 2 different places , the data features parameters like temperature, humidity, dew point, precipitation, pressure, cloud cover, vapor pressure deficit, wind speed, and wind direction.

Feel free to FORK THIS NOTEBOOK in order to correctly load the data for your project!

Overview: This dataset offers a comprehensive collection of Daily weather readings from major cities around the world. In the first release, it included only capitals, but now it also adds main cities worldwide and hourly data as well, making up to ~1250 cities. Some locations provide historical data tracing back to January 2, 1833, giving users a deep dive into long-term weather patterns and their evolution.

Data License and Updates: This dataset is updated every Sunday using data from Meteostat API, ensuring access to the latest week's data without overburdening the data source.

Cities DataFrame (cities.csv)

This dataframe offers details about individual cities and weather stations. - Columns: - station_id: Unique ID for the weather station. - city_name: Name of the city. - country: The country where the city is located. - state: The state or province within the country. - iso2: The two-letter country code. - iso3: The three-letter country code. - latitude: Latitude coordinate of the city. - longitude: Longitude coordinate of the city.

Countries DataFrame (countires.csv)

This dataframe contains information about different countries, providing insights into their geographic and demographic characteristics. - Columns: - iso3: The three-letter code representing the country. - country: The English name of the country. - native_name: The native name of the country. - iso2: The two-letter code representing the country. - population: The population of the country. - area: The total land area of the country in square kilometers. - capital: The name of the capital city. - capital_lat: The latitude coordinate of the capital city. - capital_lng: The longitude coordinate of the capital city. - region: The specific region within the continent where the country is located. - continent: The continent to which the country belongs. - hemisphere: The hemisphere in which the country is located (e.g., Northern, Southern).

Daily Weather DataFrame (daily_weather.parquet)

This dataframe provides weather data on a daily basis. - Columns: - station_id: Unique ID for the weather station. - city_name: Name of the city where the station is located. - date: Date of the weather record. - season: Season corresponding to the date (e.g., summer, winter). - avg_temp_c: Average temperature in Celsius. - min_temp_c: Minimum temperature in Celsius. - max_temp_c: Maximum temperature in Celsius. - precipitation_mm: Precipitation in millimeters. - snow_depth_mm: Snow depth in millimeters. - avg_wind_dir_deg: Average wind direction in degrees. - avg_wind_speed_kmh: Average wind speed in kilometers per hour. - peak_wind_gust_kmh: Peak wind gust in kilometers per hour. - avg_sea_level_pres_hpa: Average sea-level pressure in hectopascals. - sunshine_total_min: Total sunshine duration in minutes.

These dataframes can be utilized for various analyses such as weather trend prediction, climate studies, geographic analysis, demographic insights, and more.

Dataset Image Source: Photo credits to 越过山丘. View the original image here.

Year: year MeanLayDate: mean Julian date when first egg of the each clutch was laid ENSOWinter: mean ENSO score from December to March. Hurricanes: total number of hurricanes in the North Atlantic basin DaysBelow18_max: number of days with maximum daily temperature below 18.5 degrees Celsius or with rain during the 28 days after the mean fledging date. A crude measurement of weather conditions post fledging. TimePeriod: population trajectory at the time (growing, declining, post-decline)

Original dataset can be found here: https://www.ncei.noaa.gov/metadata/geoportal/rest/metadata/item/gov.noaa.ncdc:C00861/html Menne, Matthew J., Imke Durre, Bryant Korzeniewski, Shelley McNeill, Kristy Thomas, Xungang Yin, Steven Anthony, Ron Ray, Russell S. Vose, Byron E.Gleason, and Tamara G. Houston (2012): Global Historical Climatology Network - Daily (GHCN-Daily), Version 3. 1988-2008. NOAA National Climatic Data Center. doi:10.7289/V5D21VHZ 2023. Matthew J. Menne, Imke Durre, Russell S. Vose, Byron E. Gleason, and Tamara G. Houston, 2012: An Overview of the Global Historical Climatology Network-Daily Database. J. Atmos. Oceanic Technol., 29, 897-910. doi:10.1175/JTECH-D-11-00103.1.

Weather Data for Locations Across the UK

This dataset contains historical weather data from various locations across the UK, spanning from 2009 to 2024. Each entry records the weather conditions for a specific day, providing insights into temperature, rain, humidity, cloud cover, wind speed, and wind direction. The data is useful for analyzing weather patterns and trends over time.

Columns:

• location: The name of the location (e.g., Holywood, Ardkeen).
• date: The date of the weather record (format: YYYY-MM-DD).
• min_temp (°C): The minimum temperature recorded on that day (in degrees Celsius).
• max_temp (°C): The maximum temperature recorded on that day (in degrees Celsius).
• rain (mm): The amount of rainfall recorded (in millimeters).
• humidity (%): The percentage of humidity.
• cloud_cover (%): The percentage of cloud cover.
• wind_speed (km/h): The wind speed recorded (in kilometers per hour).
• wind_direction: The direction of the wind (e.g., N, SSE, WSW).
• wind_direction_numerical: The numerical representation of the wind direction (e.g., 90.0 for east).

Example Data:

Use Cases:

• Weather pattern analysis for specific regions: By analyzing temperature, humidity, and wind patterns across different locations, users can study how weather behaves seasonally and regionally, identifying patterns or anomalies.
• Long-term climate studies: This dataset spans over 15 years, making it useful for examining long-term climate trends such as temperature fluctuations, increased rainfall, or shifts in wind direction.
• Building predictive models for weather forecasting: The data can be used to build machine learning models that predict future weather conditions based on historical patterns. This is helpful for industries such as agriculture, transportation, and event planning.
• Climate change research: Researchers can use this dataset to study the effects of climate change on temperature, precipitation, and wind patterns over time.
• Energy sector applications: The wind speed and temperature data can be used to optimize energy production, especially for renewable energy sources like wind and solar power.
• Tourism and event planning: By analyzing weather trends, businesses in the tourism and event industries can better plan for weather conditions, helping with decisions on the best times to host outdoor events or market destinations.
• Agriculture and crop planning: Farmers and agronomists can use this data to analyze seasonal weather conditions, helping them plan for crop planting, growth, and harvesting by understanding local climate conditions.
• Urban planning and infrastructure design: City planners can use this data to assess how weather conditions impact infrastructure, from drainage and flood risk management to energy usage patterns.

Data Range:

• Start Date: 2009-01-01
• End Date: 2024-11-12

This dataset contains Raleigh Durham International Airport weather data pulled from the NOAA web service described at Climate Data Online: Web Services Documentation. We have pulled this data and converted it to commonly used units. This dataset is an archive - it is not being updated.

Saudi Arabia hourly climate integrated surface data with the below data observations, WindSky conditionVisibilityAir temperatureDewSea level pressureNote: The dataset will contain the last 5 years hourly data, however, check the attachments section in this dataset if you need historical data.

Ambee Weather API gives access to real-time & forecasted local weather updates for temperature, pressure, humidity, wind, cloud coverage, visibility, and dew point of any location in the world by latitude and longitude

Provided here are daily historical weather pattern classifications covering the period from 1950 to 2020, where the observed weather patterns are valid at 1200 UTC daily. The observed weather pattern on each day is given as a number from 1 to 30, which matches up to the weather pattern numbers described in Neal et al. (2016). The method used to generate this updated classification is the same as used in Neal et al. (2016), with the exception of using ERA5 for both the daily pressure fields and daily climatology. The daily climatology is used to calculate the pressure anomalies before they are matched up to weather pattern definitions and uses ERA5 between 1951 and 2019. This daily climatology has also been filtered by applying a 3-, 15- and 31-day rolling mean. Column 1 of this dataset gives the date [YYYY-MM-DD] and column 2 of this dataset gives the observed weather pattern classification [#].

A complete list of live websites using the Wp Historical Weather technology, compiled through global website indexing conducted by WebTechSurvey.

Any Data is as good as its Description, so here's a brief explanation:

The following data set contains Temperature data (Minimum, Average, Maximum) in degrees Centigrade and Precipitation data in mm.

This data set contains daily Temperature and Precipitation data from 01/01/1990 to 20/07/2022. Data for the following cities is present : * Delhi * Bangalore * Chennai * Lucknow * Rajasthan * Mumbai * Bhubaneswar * Rourkela

The station Geolocation file will give you the approximate location from where these measurements are taken.

What Can you do with this Data Set ? * Can you Find the hottest/coldest years for each city? * Can you Find precipitation averages and tell when rainfall was abnormally less or abnormally more? * Can you Prove that temperature is increasing and if so at what rate (degree increase/ year)? * Can you create Effective Visualization to convey the same?

Note: This Data set is ideal for Beginners and college students to hone their data science and Visualization skills.

The dataset was sourced from the Historical Weather API, available at https://open-meteo.com. This API offers historical weather information, enabling users to access data related to past weather conditions across different locations. The primary aim of utilizing the Historical Weather API was to gather data for analyzing and comprehending historical weather patterns within a specified timeframe and ge- ographical region. This collected information served as the training dataset for our models. The API provides various parameters and variables, including time, weather- code (wmo code), temperature 2m max (°C), temperature 2m min (°C), temperature 2m mean (°C), sunrise (iso8601), sunset (iso8601), precipitation sum (mm), rain sum (mm), and snowfall sum (cm). The data collection process involved querying the His- torical Weather API with specific parameters and geographical coordinates to retrieve historical weather data for the desired time periods. Subsequently, the obtained data was stored in CSV format and further processed for analysis.

Comprehensive historical weather dataset including temperature, humidity, and precipitation measurements across multiple locations and time periods

This dataset contains historical weather observations from various global locations, with its data provided at two temporal resolutions: daily and hourly. It includes core meteorological variables such as temperature, precipitation, wind, humidity, and atmospheric pressure, along with geospatial and temporal metadata for each observation. The dataset covers diverse geographic regions, including cities all arround the world, and supports both short-term event analysis and long-term climate trend exploration.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Historical. The data include parameters of historical with a geographic location of Switzerland, Western Europe. The time period coverage is from 425 to -39 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Instead of relying on a single dataset, we integrate multiple authoritative sources, validate them, and tailor the outputs to match each client’s unique needs. From detailed weather statistics and event reconstructions to custom analytics for risk, engineering, insurance, or research, our process ensures accuracy, completeness, and context.

Whether you need... - a one-time dataset - ongoing access - specialized analysis ...we provide information in the format you need - from raw data to GIS-ready layers and summary PDF reports and graphics.

This product is built for anyone seeking high-quality historical weather intelligence: insurers quantifying past risks, engineers assessing infrastructure exposure, researchers analyzing climate trends, or businesses making data-driven decisions.

With our CCM-certified expertise and flexible delivery, you get not just data, but clarity and confidence in how weather impacts your world.

Pricing: Custom quotes available based on coverage, data volume, and deliverables. Typical engagements start at $1,000 (one-time) or $500/month for ongoing access or analytics.

Global Historical Climatology Network-hourly (GHCNh) is a multisource collection of weather station (meteorological) observations from the late 18th Century to the present from fixed weather stations over land across the globe. It is replacing the Integrated Surface Dataset (ISD) and will be used to generate the Local Climatological Data and Global Summary of the Day datasets. It is constructed to align with GHCN daily. Version 1 contains approximately 110 separate data sources and will be updated daily using the United States Air Force and NOAA Surface Weather Observations data streams. GHCNh v1 contains the following variables: altimeter; dew_point_temperature; precipitation; pressure_3hr_change; pres_wx_AU1; pres_wx_AU2; pres_wx_AU3; pres_wx_AW1; pres_wx_AW2; pres_wx_AW3; pres_wx_MW1; pres_wx_MW2; pres_wx_MW3; relative_humidity; Remarks; sea_level_pressure; sky_cov_baseht_1; sky_cov_baseht_2; sky_cov_baseht_3; sky_cover_1; sky_cover_2; sky_cover_3; station_level_pressure; dry bulb temperature; visibility; wet_bulb_temperature; wind_direction; wind_gust; wind_speed.