The highest average temperature recorded in 2024 until November was in August, at 16.8 degrees Celsius. Since 2015, the highest average daily temperature in the UK was registered in July 2018, at 18.7 degrees Celsius. The summer of 2018 was the joint hottest since institutions began recording temperatures in 1910. One noticeable anomaly during this period was in December 2015, when the average daily temperature reached 9.5 degrees Celsius. This month also experienced the highest monthly rainfall in the UK since before 2014, with England, Wales, and Scotland suffering widespread flooding. Daily hours of sunshine Unsurprisingly, the heat wave that spread across the British Isles in 2018 was the result of particularly sunny weather. July 2018 saw an average of 8.7 daily sun hours in the United Kingdom. This was more hours of sun than was recorded in July 2024, which only saw 5.8 hours of sun. Temperatures are on the rise Since the 1960s, there has been an increase in regional temperatures across the UK. Between 1961 and 1990, temperatures in England averaged nine degrees Celsius, and from 2013 to 2022, average temperatures in the country had increased to 10.3 degrees Celsius. Due to its relatively southern location, England continues to rank as the warmest country in the UK.

The average temperature in December 2024 was 38.25 degrees Fahrenheit in the United States, the fourth-largest country in the world. The country has extremely diverse climates across its expansive landmass. Temperatures in the United States On the continental U.S., the southern regions face warm to extremely hot temperatures all year round, the Pacific Northwest tends to deal with rainy weather, the Mid-Atlantic sees all four seasons, and New England experiences the coldest winters in the country. The North American country has experienced an increase in the daily minimum temperatures since 1970. Consequently, the average annual temperature in the United States has seen a spike in recent years. Climate Change The entire world has seen changes in its average temperature as a result of climate change. Climate change occurs due to increased levels of greenhouse gases which act to trap heat in the atmosphere, preventing it from leaving the Earth. Greenhouse gases are emitted from various sectors but most prominently from burning fossil fuels. Climate change has significantly affected the average temperature across countries worldwide. In the United States, an increasing number of people have stated that they have personally experienced the effects of climate change. Not only are there environmental consequences due to climate change, but also economic ones. In 2022, for instance, extreme temperatures in the United States caused over 5.5 million U.S. dollars in economic damage. These economic ramifications occur for several reasons, which include higher temperatures, changes in regional precipitation, and rising sea levels.

The monthly mean temperature data presented in this dataset was obtained from the Climate Prediction Center (CPC) Global Land Surface Air Temperature Analysis, which was loaded into Python using xarray. The data was then filtered to include only the latitude and longitude coordinates corresponding to each city in the dataset. In order to select the nearest location to each city, the 'select' method with the nearest point was used, resulting in temperature data that may not be exactly at the city location. The data is presented on a 0.5x0.5 degree grid across the globe.

The temperature data provides a valuable resource for time series analysis, and if you are interested in obtaining temperature data for additional cities, please let me know. I will also be sharing the source code on GitHub for anyone who would like to reproduce the data or analysis.

The UK daily temperature data contain maximum and minimum temperatures (air, grass and concrete slab) measured over a period of up to 24 hours. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM, DLY3208 or AWSDLY messages. The data span from 1853 to 2023. For details on measurement techniques, including calibration information and changes in measurements, see section 5.2 of the MIDAS User Guide linked to from this record. Soil temperature data may be found in the UK soil temperature datasets linked from this record.

This version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2023.

This dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily temperature observations within the full MIDAS collection.

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.

Historical changes of annual temperature and precipitation indices at selected 210 U.S. cities

This dataset provide:

Annual average temperature, total precipitation, and temperature and precipitation extremes calculations for 210 U.S. cities.

Historical rates of changes in annual temperature, precipitation, and the selected temperature and precipitation extreme indices in the 210 U.S. cities.

Estimated thresholds (reference levels) for the calculations of annual extreme indices including warm and cold days, warm and cold nights, and precipitation amount from very wet days in the 210 cities.

Annual average of daily mean temperature, Tmax, and Tmin are included for annual average temperature calculations. Calculations were based on the compiled daily temperature and precipitation records at individual cities.

Temperature and precipitation extreme indices include: warmest daily Tmax and Tmin, coldest daily Tmax and Tmin , warm days and nights, cold days and nights, maximum 1-day precipitation, maximum consecutive 5-day precipitation, precipitation amounts from very wet days.

Number of missing daily Tmax, Tmin, and precipitation values are included for each city.

Rates of change were calculated using linear regression, with some climate indices applied with the Box-Cox transformation prior to the linear regression.

The historical observations from ACIS belong to Global Historical Climatological Network - daily (GHCN-D) datasets. The included stations were based on NRCC’s “ThreadEx” project, which combined daily temperature and precipitation extremes at 255 NOAA Local Climatological Locations, representing all large and medium size cities in U.S. (See Owen et al. (2006) Accessing NOAA Daily Temperature and Precipitation Extremes Based on Combined/Threaded Station Records).

Resources:

See included README file for more information.

Additional technical details and analyses can be found in: Lai, Y., & Dzombak, D. A. (2019). Use of historical data to assess regional climate change. Journal of climate, 32(14), 4299-4320. https://doi.org/10.1175/JCLI-D-18-0630.1

Other datasets from the same project can be accessed at: https://kilthub.cmu.edu/projects/Use_of_historical_data_to_assess_regional_climate_change/61538

ACIS database for historical observations: http://scacis.rcc-acis.org/

GHCN-D datasets can also be accessed at: https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/

Station information for each city can be accessed at: http://threadex.rcc-acis.org/

• 2024 August updated -

  Annual calculations for 2022 and 2023 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2022 and 2023 data.

  Note that future updates may be infrequent.

• 2022 January updated -

  Annual calculations for 2021 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2021 data.

• 2021 January updated -

  Annual calculations for 2020 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2020 data.

• 2020 January updated -

  Annual calculations for 2019 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2019 data.

  Thresholds for all 210 cities were combined into one single file – Thresholds.csv.

• 2019 June updated -

  Baltimore was updated with the 2018 data (previously version shows NA for 2018) and new ID to reflect the GCHN ID of Baltimore-Washington International AP. city_info file was updated accordingly.

  README file was updated to reflect the use of "wet days" index in this study. The 95% thresholds for calculation of wet days utilized all daily precipitation data from the reference period and can be different from the same index from some other studies, where only days with at least 1 mm of precipitation were utilized to calculate the thresholds. Thus the thresholds in this study can be lower than the ones that would've be calculated from the 95% percentiles from wet days (i.e., with at least 1 mm of precipitation).

Data on daily maximum, mean and minimum temperatures (Please visit the reference link for other climate information). The multiple file formats are available for datasets download in API.

The monthly average temperature in the United States between 2020 and 2025 shows distinct seasonal variation, following similar patterns. For instance, in August 2025, the average temperature across the North American country stood at 22.98 degrees Celsius. Rising temperatures Globally, 2016, 2019, 2021 and 2024 were some of the warmest years ever recorded since 1880. Overall, there has been a dramatic increase in the annual temperature since 1895. Within the U.S. annual temperatures show a great deal of variation depending on region. For instance, Florida tends to record the highest maximum temperatures across the North American country, while Wyoming recorded the lowest minimum average temperature in recent years. Carbon dioxide emissions Carbon dioxide is a known driver of climate change, which impacts average temperatures. Global historical carbon dioxide emissions from fossil fuels have been on the rise since the industrial revolution. In recent years, carbon dioxide emissions from fossil fuel combustion and industrial processes reached over 37 billion metric tons. Among all countries globally, China was the largest emitter of carbon dioxide in 2023.

The average temperature in the contiguous United States reached 55.5 degrees Fahrenheit (13 degrees Celsius) in 2024, approximately 3.5 degrees Fahrenheit higher than the 20th-century average. These levels represented a record since measurements started in ****. Monthly average temperatures in the U.S. were also indicative of this trend. Temperatures and emissions are on the rise The rise in temperatures since 1975 is similar to the increase in carbon dioxide emissions in the U.S. Although CO₂ emissions in recent years were lower than when they peaked in 2007, they were still generally higher than levels recorded before 1990. Carbon dioxide is a greenhouse gas and is the main driver of climate change. Extreme weather Scientists worldwide have found links between the rise in temperatures and changing weather patterns. Extreme weather in the U.S. has resulted in natural disasters such as hurricanes and extreme heat waves becoming more likely. Economic damage caused by extreme temperatures in the U.S. has amounted to hundreds of billions of U.S. dollars over the past few decades.

Global surface temperatures in 2010 tied 2005 as the warmest on record. The International Satellite Cloud Climatology Project (ISCCP) was established in 1982 as part of the World Climate Research Programme (WCRP) to collect and analyze the global distribution of clouds, their properties, and their diurnal, seasonal, and interannual variations. The LAS provides data for Monthly Near-Surface Air Temperature Averages from 1994 to 2008.

The Daily Air Temperature and Heat Index data available on CDC WONDER are county-level daily average air temperatures and heat index measures spanning the years 1979-2010. Temperature data are available in Fahrenheit or Celsius scales. Reported measures are the average temperature, number of observations, and range for the daily maximum and minimum air temperatures, and also percent coverage for the daily maximum heat index. Data are available by place (combined 48 contiguous states, region, division, state, county), time (year, month, day) and specified maximum and minimum air temperature, and heat index value. The data are derived from the North America Land Data Assimilation System (NLDAS) through NLDAS Phase 2, a collaboration project among several groups: the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC), the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Princeton University, the National Weather Service (NWS) Office of Hydrological Development (OHD), the University of Washington, and the NCEP Climate Prediction Center (CPC). In a study funded by the NASA Applied Sciences Program/Public Health Program, scientists at NASA Marshall Space Flight Center/ Universities Space Research Association developed the analysis to produce the data available on CDC WONDER.

This dataset provides values for TEMPERATURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Temperature Over Time by State (Starts: 1895)

State and County Temperature Changes

By Environmental Data [source]

About this dataset

Do you want to know how rising temperatures are changing the contiguous United States? The Washington Post has used National Oceanic and Atmospheric Administration's Climate Divisional Database (nClimDiv) and Gridded 5km GHCN-Daily Temperature and Precipitation Dataset (nClimGrid) data sets to help analyze warming temperatures in all of the Lower 48 states from 1895-2019. To provide this analysis, we calculated annual mean temperature trends in each state and county in the Lower 48 states. Our results can be found within several datasets now available on this repository.

We are offering: Annual average temperatures for counties and states, temperature change estimates for each of the Lower 48-states, temperature change estimates for counties in the contiguous U.S., county temperature change data joined to a shapefile in GeoJSON format, gridded temperature change data for the contiguous U.S. in GeoTiff format - all contained with our dataset! We invite those curious about climate change to explore these data sets based on our analysis over multiple stories published by The Washington Post such as Extreme climate change has arrived in America, Fires, floods and free parking: California’s unending fight against climate change, In fast-warming Minnesota, scientists are trying to plant the forests of the future, This giant climate hot spot is robbing West of its water ,and more!

By accessing our dataset containing columns such as fips code, year range from 1895-2019, three season temperatures (Fall/Spring/Summer/Winter), max warming season temps plus temp recorded total yearly - you can become an active citizen scientist! If publishing a story or graphic work based off this data set please credit The Washington Post with a link back to this repository while sending us an email so that we can track its usage as well - 2cdatawashpost.com.

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Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The main files provided by this dataset are climdiv_state_year, climdiv_county_year, model_state, model_county , climdiv_national_year ,and model county .geojson . Each file contains different information capturing climate change across different geographies of the United States over time spans from 1895.

Research Ideas

• Investigating and mapping the temperatures for all US states over the past 120 years, to observe long-term changes in temperature patterns.
• Examining regional biases in warming trends across different US counties and states to help inform resource allocation decisions for climate change mitigation and adaption initiatives.
• Utilizing the ClimDiv National Dataset to understand continental-level average annual temperature changes, allowing comparison of global average temperatures with US averages over a long period of time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: climdiv_state_year.csv | Column name | Description | |:--------------|:------------------------------------------------------------------------| | fips | Federal Information Processing Standard code for each county. (Integer) | | year | Year of the temperature data. (Integer) | | tempc | Temperature change from the previous year. (Float) |

File: climdiv_county_year.csv | Column name | Description | |:--------------|:------------------------------------------------------------------------| | fips | Federal Information Processing Standard code for each county. (Integer) | | year | Year of the temperature data. (Integer) | | tempc | Temperature change from the previous year. (Float) |

File: model_state.csv | Column name | Description | |:------------------...

US Average, Maximum, and Minimum Temperatures

Analyzing Daily Temperatures Across the USA

By Matthew Winter [source]

About this dataset

This dataset features the daily temperature summaries from various weather stations across the United States. It includes information such as location, average temperature, maximum temperature, minimum temperature, state name, state code, and zip code. All the data contained in this dataset has been filtered so that any values equaling -999 were removed. With this powerful set of data you to explore how climate conditions changed throughout the year and how they varied across different regions of the country. Dive into your own research today to uncover fascinating climate trends or use it to further narrow your studies specific to a region or city

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Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset offers a detailed look at daily average, minimum, and maximum temperatures across the United States. It contains information from 1120 weather stations throughout the year to provide a comprehensive look at temperature trends for the year.

The data contains a variety of columns including station, station name, location (latitude and longitude), state name zip code and date. The primary focus of this dataset is on the AvgTemp, MaxTemp and MinTemp columns which provide daily average, maximum and minimum temperature records respectively in degrees Fahrenheit.

To use this dataset effectively it is useful to consider multiple views before undertaking any analysis or making conclusions:
- Plot each individual record versus time by creating a line graph with stations as labels on different lines indicating changes over time. Doing so can help identify outliers that may need further examination; much like viewing data on a scatterplot looking for confidence bands or examining variance between points that are otherwise hard to see when all points are plotted on one graph only.
- A comparison of states can be made through creating grouped bar charts where states are grouped together with Avg/Max/Min temperatures included within each chart - thereby showing any variance that may exist between states during a specific period about which it's possible to make observations about themselves (rather than comparing them). For example - you could observe if there was an abnormally high temperature increase in California during July compared with other US states since all measurements would be represented visually providing opportunity for insights quickly compared with having to manually calculate figures from raw data sets only.

With these two initial approaches there will also be further visualizations possible regarding correlations between particular geographical areas versus different climatic conditions or through population analysis such as correlating areas warmer/colder than median observances verses relative population densities etc.. providing additional opportunities for investigation particularly when combined with key metrics collected over multiple years versus one single year's results exclusively allowing wider inferences to be made depending upon what is being requested in terms of outcomes desired from those who may explore this data set further down the line beyond its original compilation starter point here today!

Research Ideas

• Using the Latitude and Longitude values, this dataset can be used to create a map of average temperatures across the USA. This would be useful for seeing which areas were consistently hotter or colder than others throughout the year.
• Using the AvgTemp and StateName columns, predictors could use regression modeling to predict what temperature an area will have in a given month based on it's average temperature.
• By using the Date column and plotting it alongside MaxTemp or MinTemp values, visualization methods such as timelines could be utilized to show how temperatures changed during different times of year across various states in the US

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: 2015 USA Weather Data FINAL.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Matthew Winter.

The climate data are related to Albany and they cover a period that goes from 01/01/2015 to 05/31/2022. They include wind, temperature, pressure, humidity and precipitation data. Four datasets are included:

• daily_data.csv contains all the daily data.
• hourly_data.csv with the hourly data.
• monthly_data.csv includes data for each month.
• three_hour_data.csv where data were collected every three hours.

** **

For more information, check out here: https://www.ncei.noaa.gov/pub/data/cdo/documentation/LCD_documentation.pdf.

The following values can be encountered: s = suspect value (appears together with value). T = trace precipitation amount or snow depth (an amount too small to measure, usually < 0.005 inches water equivalent) (appears instead of numeric value). M = missing value (appears instead of value). VRB = variable wind direction. Remember to upvote if you found the dataset useful :).

Inspiration

The dataset can be used to perform supervised learning to predict one of the numerical features in the dataset, given a set of selected input features. You can perform an exploratory data analysis of the data, working with Pandas or Numpy(if you use Python).

Interesting visualizations can be performed using, for instance, Python libraries like Matplotlib. A time series analysis and forecasting can be performed too. Moreover, this dataset is very good to practice queries using SQL or Pandas.

Collection methodology

The data were fetched from NCOI website. The data were split in 4 columns according to the REPORT_TYPE. Rows containing null values were dropped and empty or partially empty columns were not considered.

Acknowledgment

DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA, U.S. Department of Commerce DOC/NOAA/NWS > National Weather Service, NOAA, U.S. Department of Commerce DOD/USAF > U.S. Air Force, U.S. Department of Defense DOT/FAA > Federal Aviation Agency, U.S. Department of Transportation

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.

This dataset provides daily summaries of weather conditions at Norfolk International Airport, sourced from the National Oceanic and Atmospheric Administration (NOAA). NOAA publishes this data as part of their Global Historical Climatology Network – Daily dataset. It includes essential metrics such as maximum temperature, minimum temperature, average temperature, precipitation, snowfall, and average wind speed. The dataset is updated daily.

Annual dataset covering the conterminous U.S., from 1981 to now. Contains spatially gridded annual average daily mean temperature at 4km grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University.

Daily Weather Data (2000–2023)

Dataset Summary

This document provides a detailed summary of the country_weather_data.csv dataset, which contains daily weather observations from different countries spanning over two decades. The dataset is ideal for climate analytics, environmental modeling, and time series forecasting.

Dataset Structure

• Rows: Over 8,000 daily records
• Columns: 9
• Each row represents a unique daily weather record for different countries.

Key Columns

• Country: Country name
• Date: Date of observation (DD-MM-YYYY)
• Temp_Max: Maximum temperature (°C)
• Temp_Min: Minimum temperature (°C)
• Temp_Mean: Mean temperature (°C)
• Precipitation_Sum: Total daily precipitation (mm)
• Windspeed_Max: Maximum wind speed (km/h)
• Windgusts_Max: Maximum wind gusts (km/h)
• Sunshine_Duration: Total sunshine duration (seconds)

Types of Data

• Categorical: Country, Date
• Numerical: Temp_Max, Temp_Min, Temp_Mean, Precipitation_Sum, Windspeed_Max, Windgusts_Max, Sunshine_Duration

Potential Use Cases

• Climate Trend Analysis: Study long-term temperature and precipitation patterns.
• Environmental Research: Assess weather impacts on agriculture, biodiversity, and urban planning.
• Time Series Forecasting: Build predictive models for future weather conditions.
• Data Visualization Projects: Create dashboards and visual stories using real-world weather data.
• Educational Use: Teach data science, meteorology, and statistical modeling with practical examples.

The U.S. Daily Climate Normals for 1981 to 2010 are 30-year averages of meteorological parameters for thousands of U.S. stations located across the 50 states, as well as U.S. territories, commonwealths, the Compact of Free Association nations, and one station in Canada. NOAA Climate Normals are a large suite of data products that provide users with many tools to understand typical climate conditions for thousands of locations across the United States. As many NWS stations as possible are used, including those from the NWS Cooperative Observer Program (COOP) Network as well as some additional stations that have a Weather Bureau Army-Navy (WBAN) station identification number, including stations from the Climate Reference Network (CRN). The comprehensive U.S. Climate Normals dataset includes various derived products including daily air temperature normals (including maximum and minimum temperature normal, heating and cooling degree day normal, and others), precipitation normals (including snowfall and snow depth, percentiles, frequencies and other), and hourly normals (all normal derived from hourly data including temperature, dew point, heat index, wind chill, wind, cloudiness, heating and cooling degree hours, pressure normals). Users can access the data either by product or by station. Included in the dataset is extensive documentation to describe station metadata, filename descriptions, and methodology of producing the data. All data utilized in the computation of the 1981-2010 Climate Normals were taken from the ISD Lite (a subset of derived Integrated Surface Data), the Global Historical Climatology Network-Daily dataset, and standardized monthly temperature data (COOP). These source datasets (including intermediate datasets used in the computation of products) are also archived at the NOAA NCDC.