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.

This statistic shows a ranking of the estimated worldwide average temperature in 2020, differentiated by country. The figure refers to the projected annual average temperature for the period 2020-2039 as modelled by the GISS-E2-R model in the RCP 4.5 scenario (Medium-low emission).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

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.

The table Global Temperatures by Country is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://columbia.redivis.com/datasets/1e0a-f4931vvyg. It contains 577462 rows across 4 variables.

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

Copenhagen recorded the highest average temperature among Nordic capital cities in 2023, with 10.1 degrees Celsius. The Danish capital typically recorded the highest average annual temperature among Nordic capital cities over the past 100 years. The highest annual temperature recorded in Copenhagen was 10.7 degrees Celsius in both 2014 and 2020.

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.

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.

The average mean temperature in the Nordic countries has seen an upward trend in the past four decades. In 2022, the average mean temperature in Denmark reached 9.5 degrees Celsius, a 28-percent increase in comparison to 1983. All Nordic countries recorded the highest average temperatures in the displayed period in 2018 and 2019.

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.

In 2022, Bosnia and Herzegovina ranked first among the countries in Central and Eastern Europe (CEE) by mean temperature anomalies compared to the average from 1991 to 2021, which stood at 1.23 degrees Celsius. Serbia and Croatia followed with 1.21 and 1.19 degrees Celsius above the baseline, respectively. The lowest anomaly was recorded in Belarus, where the temperature departed from the average norm by about 0.5 degrees Celsius.

The table Global Temperatures by City is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://columbia.redivis.com/datasets/1e0a-f4931vvyg. It contains 8599212 rows across 7 variables.

The monthly average temperature in the United States between 2020 and 2025 shows distinct seasonal variation, following similar patterns. For instance, in April 2025, the average temperature across the North American country stood at 12.02 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.

(From http://www.worldclim.org/methods) - For a complete description, see:

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.

The data layers were generated through interpolation of average monthly climate data from weather stations on a 30 arc-second resolution grid (often referred to as 1 km2 resolution). Variables included are monthly total precipitation, and monthly mean, minimum and maximum temperature, and 19 derived bioclimatic variables.

The WorldClim interpolated climate layers were made using: * Major climate databases compiled by the Global Historical Climatology Network (GHCN), the FAO, the WMO, the International Center for Tropical Agriculture (CIAT), R-HYdronet, and a number of additional minor databases for Australia, New Zealand, the Nordic European Countries, Ecuador, Peru, Bolivia, among others. * The SRTM elevation database (aggregeated to 30 arc-seconds, 1 km) * The ANUSPLIN software. ANUSPLIN is a program for interpolating noisy multi-variate data using thin plate smoothing splines. We used latitude, longitude, and elevation as independent variables.

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.

Version 1:

I collected this version of the dataset to perform A/B Testing on data related to the Coronavirus. All of the data was obtained from The National Oceanic and Atmospheric Administration (NOAA), ensuring its authenticity. The dataset contains 1,392,575 instances and 23 features and uses metric units for measurement, named the weather of 187 countries in 2020.csv.

The weather observations were collected by stations located in 187 countries/regions, spanning from January 22, 2020 to July 27, 2020.

When weather data is not available for a country or region, such as Guatemala, I use the nearest country or region with a weather station, as advised by the NOAA support team. However, this approach can lead to duplicated instances in the dataset, totaling about 320,000 instances. These instances are not truly duplicated, as each belongs to a country or region without its own weather station. In these cases, neighboring countries or regions, such as Mexico, report the weather situation for the affected regions to NOAA. While the weather situations may not be exactly the same in different regions, we can consider them to be the same to avoid potential errors, which may arise due to NOAA's grouping of regions.

Version 2:

I've uploaded a new version of this dataset that has 1,026,119 instances and 23 features and uses metric units for measurement, named the weather of 155 countries in 2020.csv. This version has no duplicated instances. So, You can choose one of these to work with. And please feel free to give me feedback, or ask me questions in the discussion tab of the dataset.

The Feature Explanations:

• STATION: The station identifier
• Country/Region: The country or region where the station is located
• DATE: The date of the weather observation in YYYY-MM-DD format
• Year: The year of the weather observation
• Month: The month of the weather observation
• Day: The day of the weather observation
• PRCP: The amount of precipitation (rain or snow)
• SNWD: The depth of snow on the ground
• TAVG: The average temperature
• TMAX: The maximum temperature
• TMIN: The minimum temperature
• SNOW: The amount of snowfall
• LATITUDE: The latitude of the station's location
• LONGITUDE: The longitude of the station's location
• ELEVATION: The elevation of the station's location
• PRCP_ATTRIBUTES: Additional attributes for the precipitation data
• TAVG_ATTRIBUTES: Additional attributes for the average temperature data
• TMAX_ATTRIBUTES: Additional attributes for the maximum temperature data
• TMIN_ATTRIBUTES: Additional attributes for the minimum temperature data
• DAPR: The number of days since the last precipitation of any amount
• MDPR: The multiday precipitation total
• WESD: The water equivalent of the snow depth on the ground
• SNWD_ATTRIBUTES: Additional attributes for the snow depth data

This statistic presents the perceived changes in annual global temperatures in the last 18 years, in selected European Countries in 2018. According to data published by Ipsos, the average guess among respondents in these countries was between 7 to 13 years, compared to the actual figure of **.

Context

This dataset contains weather details of five most important countries including Germany and Italy which was affected greatly with Covid_19 spread.

Content

It is believed that climate conditions might be one of the major reasons for the spread of covid_19. This Dataset contains climate changes occured from 19th February to 17th April 2020. This contains the climate changes recorded for every 10 mins on the aforementioned countries.

File Description

The file contains below columns:

Temperature - Actual Temperature Recorded in degree celsius Wind_speed - Wind Speed Description - Description of the current weather Weather - Categorical value depicts the types of weather name - Depicts the country name temp_min - Minimum temperature recorded temp_max - Maximum temperature recorded

Other variables are pretty much self explanatory.

Acknowledgements

As part of my thesis project, this dataset was being prepared with a help of web scraper which will trigger an open source REST API end point for every 10 minutes. It was hosted in an EC2 instance which will update a CSV file periodically. Thought that this could contribute for the analysis of Covid_19 spread, hence shared the same.

Hope this could be useful!

Inspiration

As mentioned earlier, Climate could be one of the significant factors which spreads covid_19. Need to analyse further on the same. Italy could be considered for the research as we have the climate data for that country. Alongside, this country was affected largely.

This dataset contains the World Average Degree Days Database for the period 1964-2013. Follow datasource.kapsarc.org for timely data to advance energy economics research.*

Summary_64-13_freq=1D Average Degree Days of various indices for respective countries for the period 1964-2013, converted to a 1 day frequency

Summary_64-13_freq=6hrs Average Degree Days of various indices for respective countries for the period 1964-2013, calculated at 6 hrs frequency

T2m.hdd.18C Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=18°C and frequency of 6 hrs

T2m.cdd.18C Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=18°C and frequency of 6 hrs

t2m.hdd.15.6C Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=15.6°C and frequency of 6 hrs

t2m.hdd.18.3C Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=18.3°C and frequency of 6 hrs

t2m.hdd.21.1C Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=21.1°C and frequency of 6 hrs

t2m.cdd.15.6C Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=15.6°C and frequency of 6 hrs

t2m.cdd.18.3C Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=18.3°C and frequency of 6 hrs

t2m.cdd.21.1C Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=21.1°C and frequency of 6 hrs

t2m.hdd.60F Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=60°F and frequency of 6 hrs

t2m.hdd.65F Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=65°F and frequency of 6 hrs

t2m.hdd.70F Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=70°F and frequency of 6 hrs

t2m.cdd.60F Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=60°F and frequency of 6 hrs

t2m.cdd.65F Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=65°F and frequency of 6 hrs

t2m.cdd.70F Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=70°F and frequency of 6 hrs

HI.hdd.57.56F Calculation of Heating Degree Days using the Heat Index at Tref=57.56°F and frequency of 6 hrs

HI.hdd.63.08F Calculation of Heating Degree Days using the Heat Index at Tref=63.08°F and frequency of 6 hrs

HI.hdd.68.58F Calculation of Heating Degree Days using the Heat Index at Tref=68.58°F and frequency of 6 hrs

HI.cdd.57.56F Calculation of Cooling Degree Days using the Heat Index at Tref=57.56°F and frequency of 6 hrs

HI.cdd.63.08F Calculation of Cooling Degree Days using the Heat Index at Tref=63.08°F and frequency of 6 hrs

HI.cdd.68.58F Calculation of Cooling Degree Days using the Heat Index at Tref=68.58°F and frequency of 6 hrs

HUM.hdd.13.98C Calculation of Heating Degree Days using the Humidex at Tref=13.98°C and frequency of 6 hrs

HUM.hdd.17.4C Calculation of Heating Degree Days using the Humidex at Tref=17.40°C and frequency of 6 hrs

HUM.hdd.21.09C Calculation of Heating Degree Days using the Humidex at Tref=21.09°C and frequency of 6 hrs

HUM.cdd.13.98C Calculation of Cooling Degree Days using the Humidex at Tref=13.98°C and frequency of 6 hrs

HUM.cdd.17.4C Calculation of Cooling Degree Days using the Humidex at Tref=17.40°C and frequency of 6 hrs

HUM.cdd.21.09C Calculation of Cooling Degree Days using the Humidex at Tref=21.09°C and frequency of 6 hrs

ESI.hdd.12.6C Calculation of Heating Degree Days using the Environmental Stress Index at Tref=12.6°C and frequency of 6 hrs

ESI.hdd.14.9C Calculation of Heating Degree Days using the Environmental Stress Index at Tref=14.9°C and frequency of 6 hrs

ESI.hdd.17.2C Calculation of Heating Degree Days using the Environmental Stress Index at Tref=17.2°C and frequency of 6 hrs

ESI.cdd.12.6C Calculation of Cooling Degree Days using the Environmental Stress Index at Tref=12.6°C and frequency of 6 hrs

ESI.cdd.14.9C Calculation of Cooling Degree Days using the Environmental Stress Index at Tref=14.9°C and frequency of 6 hrs

ESI.cdd.17.2C Calculation of Cooling Degree Days using the Environmental Stress Index at Tref=17.2°C and frequency of 6 hrs

Note:

Divide Degree Days by 4 to convert from 6 hrs to daily frequency

Context

Data description

The FAOSTAT Temperature Change domain disseminates statistics of mean surface temperature change by country, with annual updates. The current dissemination covers the period 1961–2023. Statistics are available for monthly, seasonal and annual mean temperature anomalies, i.e., temperature change with respect to a baseline climatology, corresponding to the period 1951–1980. The standard deviation of the temperature change of the baseline methodology is also available. Data are based on the publicly available GISTEMP data, the Global Surface Temperature Change data distributed by the National Aeronautics and Space Administration Goddard Institute for Space Studies (NASA-GISS).

Content

Statistical concepts and definitions

Statistical standards: Data in the Temperature Change domain are not an explicit SEEA variable. Nonetheless, country and regional calculations employ a definition of “Land area” consistent with SEEA Land Use definitions, specifically SEEA CF Table 5.11 “Land Use Classification” and SEEA AFF Table 4.8, “Physical asset account for land use.” The Temperature Change domain of the FAOSTAT Agri-Environmental Indicators section is compliant with the Framework for the Development of Environmental Statistics (FDES 2013), contributing to FDES Component 1: Environmental Conditions and Quality, Sub-component 1.1: Physical Conditions, Topic 1.1.1: Atmosphere, climate and weather, Core set/ Tier 1 statistics a.1.

Statistical unit: Countries and Territories.

Statistical population: Countries and Territories.

Reference area: Area of all the Countries and Territories of the world. In 2019: 190 countries and 37 other territorial entities.

Code - reference area: FAOSTAT, M49, ISO2 and ISO3 (http://www.fao.org/faostat/en/#definitions). FAO Global Administrative Unit Layer (GAUL National level – reference year 2014. FAO Geospatial data repository GeoNetwork. Permanent address: http://www.fao.org:80/geonetwork?uuid=f7e7adb0-88fd-11da-a88f-000d939bc5d8.

Code - Number of countries/areas covered: In 2019: 190 countries and 37 other territorial entities.

Time coverage: 1961-2023

Periodicity: Monthly, Seasonal, Yearly

Base period: 1951-1980

Unit of Measure: Celsius degrees °C

Reference period: Months, Seasons, Meteorological year

Acknowledgements

Documentation on methodology: Details on the methodology can be accessed at the Related Documents section of the Temperature Change (ET) domain in the Agri-Environmental Indicators section of FAOSTAT.

Quality documentation: For more information on the methods, coverage, accuracy and limitations of the Temperature Change dataset please refer to the NASA GISTEMP website: https://data.giss.nasa.gov/gistemp/

                                                                          Source: http://www.fao.org/faostat/en/#data/ET/metadata

Inspiration

Climate change is one of the important issues that face the world in this technological era. The best proof of this situation is the historical temperature change. You can investigate if any hope there is for stopping global warming :)

• Can you find any correlation between temperature change and any other variable? (Using ISO3 codes for merging any other countries' data sets possible.)

• Prediction of temperature change: there is also an overall world temperature change in the country list as 'World'.