Description

This dataset contains daily historical weather data recorded at multiple weather stations from January 1, 2020, to December 30, 2020. The data includes temperature, precipitation, humidity, wind speed, and weather conditions, providing a comprehensive view of the weather patterns over the year. This dataset is ideal for climate analysis, weather prediction, and educational purposes.

Columns

• Date: The date of the observation.
• Station: The weather station identifier.
• Temperature: The recorded temperature (in Celsius).
• Precipitation: The recorded precipitation (in mm).
• Humidity: The recorded humidity (in %).
• WindSpeed: The recorded wind speed (in km/h).
• WeatherCondition: The recorded weather condition (e.g., sunny, rainy, snowy).

Source

Data generated synthetically for educational purposes.

Potential Uses

• Climate change analysis
• Weather pattern prediction
• Agricultural planning

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.

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)

Dataset from National Environment Agency. For more information, visit https://data.gov.sg/datasets/d_03bb2eb67ad645d0188342fa74ad7066/view

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.

Historical Weather Data
This dataset falls under the category Environmental Data.
It contains the following data: Historical climate datasets
This dataset was scouted on 2022-02-12 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/ho-chi-minh-city-weather-history/vn.aspxSee URL for data access and license information.

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.

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

Clean, reliable data – Weather and more!

We’ve spent millions of dollars over the last 25+ years working with global weather data space. Raw data, from a multitude of government sources, private weather networks and Earth Observation platforms, is assimilated into proprietary numerical weather prediction (NWP) models.

This Model output, analyzed and cleansed by the latest AI technologies gives Weather Data AI a clean, proprietary, high resolution global gridded weather database, available to you for whatever purpose you may need.

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 [#].

Historical weather data is essential for understanding environmental trends, assessing climate risk, and building predictive models for infrastructure, agriculture, and sustainability initiatives. Among all variables, temperature and humidity serve as core indicators of environmental change and operational risk.

Ambios offers high-resolution Historical Weather Data focused on temperature and humidity, sourced from over 3,000+ first-party sensors across 20 countries. This dataset provides hyperlocal, verified insights for data-driven decision-making across industries.

-Historical weather records for temperature and humidity -First-party sensor data from a decentralized network -Global coverage across 20 countries and diverse climate zones -Time-stamped, high-frequency measurements with environmental context -Designed to support ESG disclosures, research, risk modeling, and infrastructure planning

Use cases include:

-Long-term climate trend analysis and model validation -Historical baselining for ESG and sustainability frameworks -Resilience planning for heatwaves, humidity spikes, and changing climate conditions -Agricultural research and water management strategy -Infrastructure and energy load forecasting -Academic and scientific studies on regional weather patterns

Backed by Ambios’ decentralized physical infrastructure (DePIN), the data is reliable, traceable, and scalable—empowering organizations to make informed decisions grounded in historical environmental intelligence.

Whether you're building ESG models, planning smart infrastructure, or conducting climate research, Ambios Historical Weather Data offers the precision and credibility needed for long-term environmental insight.

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.

Washington DC Weather Data

1. File information: duration 2015/08~2024/07

• name: location
• datetime: date
• tempmax: maximum temperature (°C) at the location.
• tempmin: minimum temperature (°C) at the location.
• temp: temperature (°C) at the location. Daily values are average values (mean) for the day.
• feelslikemax: maximum feels like temperature (°C) at the location.
• feelslikemin: minimum feels like temperature (°C) at the location.
• feelslike: what the temperature feels like (°C) accounting for heat index or wind chill. Daily values are average values (mean) for the day.
• dew: dew point temperature (°C)
• humidity: relative humidity in %
• precip: the amount of liquid precipitation that fell or is predicted to fall in the period.
• precipprob: the likelihood of measurable precipitation ranging from 0% to 100%
• precipcover: the proportion of hours where there was non-zero precipitation
• preciptype: an array indicating the type(s) of precipitation expected or that occurred.
• snow: the amount of snow that fell or is predicted to fall
• snowdepth: the depth of snow on the ground
• windgust: instantaneous wind speed at a location
• windspeed: the sustained wind speed measured as the average windspeed that occurs during the preceding one to two minutes. Daily values are the maximum hourly value for the day.
• winddir: direction from which the wind is blowing
• sealevelpressure: the sea level atmospheric or barometric pressure in millibars
• cloudcover: the sea level atmospheric or barometric pressure in millibars
• visibility: distance at which distant objects are visible
• solarradiation: (W/m2) the solar radiation power at the instantaneous moment of the observation (or forecast prediction)
• solarenergy: (MJ /m2 ) indicates the total energy from the sun that builds up over a day.
• uvindex: a value between 0 and 10 indicating the level of ultra violet (UV) exposure for that day.
• severerisk: a value between 0 and 100 representing the risk of convective storms
• sunrise: the formatted time of the sunrise
• sunset: the formatted time of the sunset
• moonphase: represents the fractional portion through the current moon lunation cycle ranging from 0 (the new moon) to 0.5 (the full moon) and back to 1 (the next new moon)
• conditions: textual representation of the weather conditions.
• description: longer text descriptions suitable for displaying in weather displays
• icon: a fixed, machine readable summary that can be used to display an icon
• stations: the weather stations used when collecting a historical observation record Parameters information: https://www.visualcrossing.com/resources/documentation/weather-api/timeline-weather-api/

Data source : https://www.visualcrossing.com/

2. Recommended analysis

- EDA / Visualize the weather information

- Time-series prediction of the weather

- Dimension reduction(PCA, t-SNE, UMAP)

Feel free to leave comments on the discussion. I'd appreciate your upvote if you find my dataset useful! 😀

The backbone of CustomWeather's forecasting arm is our proven, high-resolution model, the CW100. The CW100 Model is based on physics, not statistics or airport observations. As a result, it can achieve significantly better accuracy than statistical models, especially for non-airport locations. While other forecast models are designed to forecast the entire atmosphere, the CW100 greatly reduces computational requirements by focusing entirely on conditions near the ground. This reduction of computations allows it to resolve additional physical processes near the ground that are not resolved by other models. It also allows the CW100 to operate at a much higher resolution, typically 100x finer than standard models and other gridded forecasts.

Detailed Forecasts:
Features a detailed 48-hour outlook broken into four segments per day: morning, afternoon, evening, and overnight. Each segment provides condition descriptions, high/low temperatures, wind speed and direction, humidity, comfort level, UV index, expected and probability of precipitation, 6-hr forecasted precip amounts, and miles of visibility. Available for over 85,000 forecast points globally. The information is updated four times per day.

Extended Forecasts Days 1-15:
Features condition descriptions, high/low temperatures, wind speed and direction, humidity, comfort level, UV index, expected and probability of precipitation, and miles of visibility. Available for over 85,000 forecast points globally. The information is updated four times per day.

Hour-by-Hour Forecasts: Features Hour-by-Hour forecasts. The product is available as 12 hour, 48 hour and 168 hour blocks. Each hourly forecast includes weather descriptions, wind conditions, temperature, dew point, humidity, visibility, rainfall totals, snowfall totals, and precipitation probability. Available for over 85,000 forecast points globally. Updated four times per day.

Historical Longer Term Forecasts: Includes historical hourly and/or daily forecast data from 2009 until present date. Data will include condition descriptions, high/low temperatures, wind speed and direction, dew point, humidity, comfort level, UV index, probability of precipitation, rainfall and snowfall amounts. Available for over 85,000 forecast points globally. The information is updated four times per day.

Below are available time periods per each type of forecast from the GFS model and from CustomWeather's proprietary CW100 model:

GFS: 7-day hourly forecasts from August 2nd 2009; 48-hour to 5-day detailed forecasts from August 4th 2009; 15-day forecasts from October 9th 2008.

CW100: 7-day hourly forecasts from November 27, 2012; 48-hour detailed forecasts from November 12, 2011; 7-day forecasts from December 6, 2010, 15-day forecasts from August 6, 2012. CW100 is CustomWeather's proprietary model.

MOS: (Model Output Statistics) for any global location using archive of model and observation data. 0.25 degree resolution. 15-day hourly forecasts from January 1, 2017; 15-day forecasts from April 19, 2017.

Hourly geographically aggregated weather data for Europe. This data package contains radiation and temperature data, at hourly resolution, for Europe, aggregated by Renewables.ninja from the NASA MERRA-2 reanalysis. It covers the European countries using a population-weighted mean across all MERRA-2 grid cells within the given country.

World Weather Records (WWR) is an archived publication and digital data set. WWR is meteorological data from locations around the world. Through most of its history, WWR has been a publication, first published in 1927. Data includes monthly mean values of pressure, temperature, precipitation, and where available, station metadata notes documenting observation practices and station configurations. In recent years, data were supplied by National Meteorological Services of various countries, many of which became members of the World Meteorological Organization (WMO). The First Issue included data from earliest records available at that time up to 1920. Data have been collected for periods 1921-30 (2nd Series), 1931-40 (3rd Series), 1941-50 (4th Series), 1951-60 (5th Series), 1961-70 (6th Series), 1971-80 (7th Series), 1981-90 (8th Series), 1991-2000 (9th Series), and 2001-2011 (10th Series). The most recent Series 11 continues, insofar as possible, the record of monthly mean values of station pressure, sea-level pressure, temperature, and monthly total precipitation for stations listed in previous volumes. In addition to these parameters, mean monthly maximum and minimum temperatures have been collected for many stations and are archived in digital files by NCEI. New stations have also been included. In contrast to previous series, the 11th Series is available for the partial decade, so as to limit waiting period for new records. It begins in 2010 and is updated yearly, extending into the entire decade.

The Alaska Climate Center (ACC) houses the historical weather records for the state of Alaska (mostly official U.S. Government data). These are in the form of raw handwritten data, summarized coded forms, surface and upper air weather map analyses, and selected digital data. They are stored in map sets, report sets, catalogs/indexes, individual maps, bibliographies, file folders, micrographics, and microfiche. It is not a single data base, but rather an extensive repository of historical records from communities and areas throughout the state. These individual data records and reports number in the thousands. The Center publishes a series of Alaska climate technical reports and Arctic climate atlases. In addition, it serves as a repository for publications on Arctic climate-related matters.

Information about weather-related conditions in Sweden during the period 1500-1870 has been extracted from various historical documents. The information is presented as cited text, together with the date and geographical region for which the information is relevant.

Since the database essentially consists of excerpts from different historical documentary sources of various kinds (Institutional chronicles, accountings, private weather diaries etc) the language is Swedish, though citations of original texts are occasionally given in other languages whenever relevant and when other languages were originally used.

See the Swedish description for more information.

The database contains a large number of contemporary descriptions for the period 1500–1870 from various types of documents — direct observations in diaries, administrative notes on activities that have been affected by weather conditions, letter collections, newspaper articles, etc. — of weather conditions in Sweden within current borders.

** Database file structure and content:

The database is collected in a spreadsheet (xlsx). The same information is also presented in a semicolon-separated text file (csv) (character set: Western Europe, ISO-8859-15 / EURO). File size: 1.6 MB (xlsx) and 4.1 MB (csv). The number of file rows, including the title row, is 20896.

In addition to the data file itself, the dataset also contains a source list in xlsx format. The file has two pages: "Otryckta källor" (unprinted sources) and "Bibliografi" (bibliography). The same information is also presented in two comma-separated csv files (character set: Western Europe, ISO-8859-15 / EURO).

The main database file contains information in eight columns with the following headings (here also translated to English):

• År (year)
• Månad (month)
• Dag (day)
• Annan tidsangivelse (other time indication)
• Område (area)
• Väder (weather)
• Källa (source)
• Ytterligare hänvisning/information (additional reference / information)

The database main language is Swedish. Quotations of writings in old language are generally preserved as in their original spellings.

For a more detailed description of the database content, please see the Swedish data description.

** Main sources and collection method:

The data collection was performed by systematically reading through available archive material and literature relevant to the subject. Information that was considered to be of value for climate history was entered into the database, either as a quotation or in the form of comments, together with an indication of the source material for each individual item (row) in the database. Each such item refers to a more or less specified geographical location and either a specific date or an approximate time period.

Data have been collected along three main channels: unprinted archive material, printed sources and literature.

Unprinted archive material has been retrieved mainly from the National Archives. For the period 1500–1540, data comes mainly from the database of the "Svenskt diplomatariums huvudkartotek" (Sdhk) (Swedish diplomatarium's main file). For the time thereafter, data from, among others, the "Riksregistraturet" (national registry), a collection of copies of letters issued by the "kungliga kansliet" (royal chancellery), has been used. Several unprinted letters, diaries, accounts and reports have also been searched.

A particularly extensive individual source is Märta Helena Reenstierna's (known as the Årsta lady) diaries from Årsta Gård in Stockholm, written during the period 1793–1839 and kept in the Nordic Museum's archives. These diaries contain a large number of notes on local weather conditions. More than half of all individual entries in the database originate from the Årsta diaries.

Printed sources include editions of source publications such as Gustav Vasa's (King Gustav I of Sweden) letters in 29 volumes. There are also the Royal Swedish Academy of Sciences' Transactions which, among other things, contain meteorological observations.

The category literature contains a number of local historical presentations. There are also early attempts at climate historical overviews and interpretations. Corporate history and military history literature have also been used.

** The roles of primary researchers during the construction of the database

The main part of the work with building up the database was done during the period 2006–2010 by Johan Söderberg, Lotta Leijonhufvud, Dag Retsö and Ulrica Söderlind at the Department of Economic History, Stockholm University, under the leadership of Johan Söderberg. Curation of the database prior to publication in SND was carried out during 2019–2020 by Lotta Leijonhufvud in collaboration with Anders Moberg, Department of Physical Geography, Stockholm University.

Previous, unpublished, versions of the database have been used in the following studies (see list of publications):

• Edvinsson et al. (2009). Väder, skördar och priser i Sverige.
• Leijonhufuvud et al. (2010). Five centuries of Stockholm winter/spring temperatures reconstructed from documentary evidence and instrumental observations.
• Wetter et al. (2014). The year-long unprecedented European heat and drought of 1540 – a worst case.
• Retsö (2015). Documentary evidence of historical floods and extreme rainfall events in Sweden 1400–1800.

Context

The dataset featured below was created by reconciling measurements from requests of individual weather attributes provided by the European Climate Assessment (ECA). The measurements of this particular dataset were recorded by a weather station near Heathrow airport in London, UK.

-> This weather dataset is a great addition to this London Energy Dataset. You can join both datasets on the 'date' attribute, after some preprocessing, and perform some interesting data analytics regarding how energy consumption was impacted by the weather in London.

Content

The size for the file featured within this Kaggle dataset is shown below — along with a list of attributes and their description summaries: - london_weather.csv - 15341 observations x 10 attributes

1. date - recorded date of measurement - (int)
2. cloud_cover - cloud cover measurement in oktas - (float)
3. sunshine - sunshine measurement in hours (hrs) - (float)
4. global_radiation - irradiance measurement in Watt per square meter (W/m2) - (float)
5. max_temp - maximum temperature recorded in degrees Celsius (°C) - (float)
6. mean_temp - mean temperature in degrees Celsius (°C) - (float)
7. min_temp - minimum temperature recorded in degrees Celsius (°C) - (float)
8. precipitation - precipitation measurement in millimeters (mm) - (float)
9. pressure - pressure measurement in Pascals (Pa) - (float)
10. snow_depth - snow depth measurement in centimeters (cm) - (float)

Source

Weather Data - https://www.ecad.eu/dailydata/index.php

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.