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.

Dataset

This dataset was created by Dylan

Released under GPL 2

Contents

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

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.

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

This data has been sourced from the Met Office's historical station data available here: https://www.metoffice.gov.uk/research/climate/maps-and-data/historic-station-data

I have included the python script used to generate the dataset.

The data is held at a monthly level and contains: - max_temp: Mean daily maximum temperature - min_temp: Mean daily minimum temperature - air_frost_days: Days of air frost - rain_mm: Total rainfall - sun: Total sunshine duration (hours) - station: the station of the observation - lat: latitude of the station - long: longitude of the station - month_year: month date of the observation

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.

This product consists of meteorological data from 105 Arctic weather stations and 137 Antarctic stations, extracted from the National Climatic Data Center (NCDC)'s Integrated Surface Hourly (ISH) database. Variables include wind direction, wind speed, visibility, air temperature, dew point temperature, and sea level pressure. Temporal coverage varies by station, with the earliest record in 1913 and the latest in 2002. Data are in tab-delimited ASCII text format, with one file per station and year. Graphs of meteorological variables throughout the time series accompany the ASCII data.

This dataset includes the monthly and daily data used for the analysis of historical and future trends in precipitation and temperature at five Long-Term Agroecosystem Research (LTAR) sites: Kellogg Biological Station (KBS) in Michigan, Upper Mississippi River Basin (UMRB) in Iowa, Central Mississippi River Basin (CMRB) in Missouri, Southern Plains (SP) in Oklahoma, and Lower Mississippi River Basin (LMRB) in Mississippi. Historical data include the longest available record of daily precipitation, minimum temperature, and maximum temperature at weather stations from KBS, UMRB, CMRB, and LMRB, and the monthly 1895-2020 data from the National Ocean and Atmospheric Administration for the climate divisions that represent the five LTAR sites. Future data include 2020-2100 monthly predictions for the five sites from 26 Earth System Models and two Shared Socio-economic Pathways (SSP): the middle of the road SSP245 (a continuation of current emission rates and geo-political conditions), and the fossil fueled development scenario SSP 585 (intensification of fossil fuel energy sources and corresponding emissions). In addition, the data includes the trends calculated from historical and future data, snippets of R code used to calculate these trends, and README files that detail the content of each file.Trends in records of 50 years or more showed that temperatures have changed from 1900-2020, more for minimum (0.1 - 0.3 ℃ decade-1) than maximum (-0.1 - 0.2 ℃ decade-1), more for winter (-0.1 - 0.3 ℃ decade-1) than summer (-0.1 - 0.1 ℃ decade-1), and more often in the north than in the south. Except in Mississippi, annual precipitation has increased at rates of 25 mm decade-1 or greater over 1950-2020, but monthly trends were inconsistent. Projected trends suggest continued temperature increases, highlighting the need for research on management systems that are resilient to such increases.

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 [#].

This dataset contains Russian Historical Soil Temperature Data. This data set is a collection of monthly and annual average soil temperatures measured at Russian meteorological stations. Data were recovered from many sources and compiled by staff at the University of Colorado, USA, and the Russian Academy of Sciences in Puschino, Russia. Soil temperatures were measured at depths of 0.02 to 3.2 m using bent stem thermometers, extraction thermometers, and electrical resistance thermistors. Data coverage extends from the 1800s through 1990, but is not continuous. Data are not available for all stations for the entire period of coverage. For example, data collection began at many stations in the 1930s and 1950s, and not all stations continued taking measurements through 1990. This research was supported by the National Science Foundation (NSF) Office of Polar Programs (OPP) awards OPP-9614557, OPP-9907541, and OPP-0229766. Data are available as tar.gz files.

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.

Instrumental observations of the weather have been regularly performed in the Netherlands since the end of the 17th century. An inventory was made of this period before 1854 at KNMI. The observations of the series available here appeared to have been carried out sufficiently regularly and long enough in succession.

Dataset

This dataset was created by Eddie Sharick

Contents

Copyright Notice & Acknowledgements

All information regarding MET Office copyright policy can be found at: https://www.metoffice.gov.uk/about-us/legal#licences All data was sourced from: https://www.metoffice.gov.uk/research/climate/maps-and-data/historic-station-data

Context

The MET Office has been responsible for monitoring UK Weather since it's inception in 1854. 36 stations in the UK (often located in RAF bases) gather information that is used to predict future weather patterns and issue public advice. More recently, these large datasets have become useful to investigate how the UK climate has changed over the past 150+ years.

Content

Columns: - year: Year in which the measurements were taken - month: Month in which the measurements were taken - tmax: Mean daily maximum temperature (°C) - tmin: Mean daily minimum temperature (°C) - af: Days of air frost recorded that month (days) - rain: Total rainfall (mm) - sun: Total sunshine duration (hours) - station: Station location where measurement was recorded

Data was collected from the MET Office website as separate station csv files and combined to one data frame with a station label assigned. All characters (*,#,---) that denoted things such as the equipment used were removed from the set. Some sections include significant amounts of NA values. Note that a 0 entry does not denote an NA value but a score of 0 in that measured field.

Inspiration

Has the UK climate changed since the Victorian era? How does any climate change impact the UK in terms of weather risks? Are some regions more affected than others?

A good starting point: The monthly mean temperature is calculated from the average of the mean daily maximum and mean daily minimum temperature i.e. (tmax+tmin)/2.

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

Adiabat's Historical Weather Data & Analytics product delivers trusted, client-ready insights through an all-source approach that combines the best available weather and climate data into one seamless solution.

Weather Data collected by CIMIS automatic weather stations. The data is available in CSV format. Station data include measured parameters such as solar radiation, air temperature, soil temperature, relative humidity, precipitation, wind speed and wind direction as well as derived parameters such as vapor pressure, dew point temperature, and grass reference evapotranspiration (ETo).

Weather is among the most critical environmental variables influencing infrastructure, agriculture, energy, health, and climate strategy. Among all metrics, temperature and humidity form the baseline of accurate environmental modeling, building performance, and ESG reporting. Ambios provides high-quality Global Weather Data on real-time and historical temperature and humidity measurements. Sourced from over 3,000+ first-party sensors operating across 20 countries, our decentralized network ensures transparent, tamper-proof data with hyperlocal accuracy and high update frequency.

-Real-time temperature and humidity data updated every 15 minutes -Historical datasets with global coverage -100% first-party sensor data from a decentralized infrastructure -Compatible with ESG systems, climate models, and IoT platforms

Use cases include: -Climate risk and environmental impact assessments -Smart building energy efficiency and HVAC performance -Agricultural planning and weather-responsive irrigation -Supply chain risk modeling and operational forecasting -Urban microclimate monitoring and resilience planning -Scientific research, academic studies, and digital twins

Built on DePIN (Decentralized Physical Infrastructure Network) architecture, Ambios ensures complete traceability, verifiability, and scale. Our weather data delivers the transparency and precision that enterprises, governments, and researchers need for real-world decisions in real-time. Whether powering climate dashboards, optimizing building systems, or modeling regional weather impacts, Ambios Global Weather Data gives you trusted temperature and humidity insights globally and on demand.

This is an hourly future weather dataset for energy modeling applications. The dataset is primarily based on the output of a regional climate model (RCM), i.e., the Weather Research and Forecasting (WRF) model version 3.3.1. The WRF simulations are driven by the output of a general circulation model (GCM), i.e., the Community Climate System Model version 4 (CCSM4).

This dataset is in the EPW format, which can be read or translated by more than 25 building energy modeling programs (e.g., EnergyPlus, ESP-r, and IESVE), energy system modeling programs (e.g., System Advisor Model (SAM)), indoor air quality analysis programs (e.g., CONTAM), and hygrothermal analysis programs (e.g., WUFI). It contains 13 weather variables, which are the Dry-Bulb Temperature, Dew Point Temperature, Relative Humidity, Atmospheric Pressure, Horizontal Infrared Radiation Intensity from Sky, Global Horizontal Irradiation, Direct Normal Irradiation, Diffuse Horizontal Irradiation, Wind Speed, Wind Direction, Sky Cover, Albedo, and Liquid Precipitation Depth.

This dataset provides future weather data under two emissions scenarios - RCP4.5 and RCP8.5 - across two 10-year periods (2045-2054 and 2085-2094). It also includes simulated historical weather data for 1995-2004 to serve as the baseline for climate impact assessments. We strongly recommend using this built-in baseline rather than external sources (e.g., TMY3) for two key reasons: (1) it shares the same model grid as the future projections, thereby minimizing geographic-averaging bias, and (2) both historical and future datasets were generated by the same RCM, so their differences yield anomalies largely free of residual model bias.

This dataset offers a spatial resolution of 12 km by 12 km with extensive coverage across most of North America. Due to the enormous size of the entire dataset, in the first stage of its distribution, we provide weather data for the centroid of each Public Use Microdata Area (PUMA), excluding Hawaii. PUMAs are non-overlapping, statistical geographic areas that partition each state or equivalent entity into geographic areas containing no fewer than 100,000 people each. The 2,378 PUMAs as a whole cover the entirety of the U.S. The weather data can be utilized alongside the large-scale energy analysis tools, ResStock and ComStock, developed by National Renewable Energy Laboratory, whose smallest resolution is at the PUMA scale.

The authors observed an anomalous warming signal over the Great Plains in the end-of-century (2085 - 2094) RCP4.5 time slice. This anomaly is absent in the mid-century slice (2045 - 2054) under RCP4.5 and in both the mid- (2045 - 2054) and end-of-century (2085 - 2094) slices under RCP8.5. Consequently, we recommend that users exercise particular caution when using the RCP4.5 2085-2094 data, especially for analyses involving the Great Plains region.