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.

• Parenthesis values are Celsius.

The dataset provides a comprehensive overview of the weather conditions across all cities of the world for a period of 12 months. It contains information on the average temperature in Celsius and Fahrenheit. This dataset is a valuable resource for researchers, meteorologists, and climate scientists who seek to understand the impact of climate change on different parts of the world. The data can be used to analyze trends in temperature, to develop predictive models for weather forecasting, and to evaluate the effectiveness of climate policies. The information in this dataset is updated regularly, ensuring that users have access to the most recent and accurate weather data available. With this dataset, users can gain valuable insights into the complex relationship between climate and the environment, and make informed decisions about climate change mitigation and adaptation strategies.

Description: ChatGPT

Abstract

Compilation of Earth Surface temperatures historical. Source: https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data

Documentation

Data compiled by the Berkeley Earth project, 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

%3C!-- --%3E

• LandAverageTemperature: global average land temperature in celsius

%3C!-- --%3E

• LandAverageTemperatureUncertainty: the 95% confidence interval around the average

%3C!-- --%3E

• LandMaxTemperature: global average maximum land temperature in celsius

%3C!-- --%3E

• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature

%3C!-- --%3E

• LandMinTemperature: global average minimum land temperature in celsius

%3C!-- --%3E

• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature

%3C!-- --%3E

• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius

%3C!-- --%3E

• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

%3C!-- --%3E

**Other files include: **

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)

%3C!-- --%3E

• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)

%3C!-- --%3E

• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)

%3C!-- --%3E

• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

%3C!-- --%3E

The raw data comes from the Berkeley Earth data page.

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.

Temperature in Iran decreased to 19.18 celsius in 2024 from 19.61 celsius in 2023. This dataset includes a chart with historical data for Iran Average Temperature.

Temperature in China increased to 8.52 celsius in 2024 from 8.41 celsius in 2023. This dataset includes a chart with historical data for China Average Temperature.

The North American Dataset contains sets of Maximum, Minimum and Average Temperature data and Precipitation data that are either (1) raw (non-adjusted though flagged for possible quality issues), (2) adjusted due to time of observation bias (TOB) or (3) put through the Pairwise Homogenization Algorithm (PHA). These files contain North American stations and its data are measured in hundredths of degrees Celsius (without decimal place) for temperature and tenths of millimeters (without decimal place) for Precipitation. Each file includes the entire available Period of Record.

Temperature in the United States increased to 10.73 celsius in 2024 from 10.25 celsius in 2023. This dataset includes a chart with historical data for the United States Average Temperature.

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 file contains daily land-surface average temperature results produced using anomalies and average temperature by the Berkeley Earth averaging method.

I claim no ownership of this dataset, everything was done by the Berkeley Earth team, I have merely converted the text dataset into a CSV file.

Here's the description of Berkeley dataset: Temperatures are in Celsius and reported as anomalies relative to the Jan 1951-Dec 1980 average.

The current dataset presented here is described as: Berkeley Earth daily TAVG full dataset This current analysis product is preliminary and may be subject to significant future revisions.This analysis was run on 06-Sep-2022 15:50:29

Results are based on a 50461 monthly time series with 21047039 observations and 48263 daily time series with 512331899 observations Estimated Jan 1951-Dec 1980 land-average temperature (C): 8.59 +/- 0.05

The CRU Time Series 4.05 dataset was developed and has been subsequently updated, improved and maintained with support from a number of funders, principally the UK's Natural Environment Research Council (NERC) and the US Department of Energy. Long-term support is currently provided by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre. Current gridded products (CRU TS) are presented either as ASCII grids, or in NetCDF format. The gridding process used in Brohan et al.. (2006) and earlier publications assigns each station to the 5 degree latitude/longitude box within which it is located. The gridding then simply averages all available station temperatures (as anomalies from 1961-90) within each grid box for each month from 1851. No account is taken of the station's elevation or location within the grid box (anomalies show little consistent dependence on altitude). A more up-to-date location for a station is not important for the gridding, unless a site change were to move the station to an adjacent grid box. In this instance, the data was derived as a subset of the original dataset. CRU publishes the data in NetCDF file format, however for data visualisation purposes the datasets was tranformed into tidy tables, represented in the South African Risk and Vulnerability Atlas (SARVA) by the South African Environmental Observation Network's uLwazi Node. Citation: University of East Anglia Climatic Research Unit; Harris, I.C.; Jones, P.D.; Osborn, T. (2021): CRU TS4.05: Climatic Research Unit (CRU) Time-Series (TS) version 4.05 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2020). NERC EDS Centre for Environmental Data Analysis, 2021. https://catalogue.ceda.ac.uk/uuid/c26a65020a5e4b80b20018f148556681

These tabular data sets represent mean monthly temperature (degrees Celsius) data from 800 meter resolution PRISM for the years 2016 and 2017 compiled for two spatial components of the NHDPlus version 2.1 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data for mean monthly temperature (degrees Celsius) from 800 meter resolution resolution PRISM data was produced by the PRISM Group at Oregon State University. Units are degrees degrees Celsius. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).

Context

Who among us doesn't talk a little about the weather now and then? Will it rain tomorrow and get so cold to shake your chin or will it make that cracking sun? Does global warming exist?

With this dataset, you can apply machine learning tools to predict the average temperature of Detroit city based on historical data collected over 5 years.

Content

The given data set was produced from the Historical Hourly Weather Data [https://www.kaggle.com/selfishgene/historical-hourly-weather-data], which consists of about 5 years of hourly measurements of various weather attributes (eg. temperature, humidity, air pressure) from 30 US and Canadian cities.

From this rich database, a cutout was made by selecting only the city of Detroit (USA), highlighting only the temperature, converting it to Celsius degrees and keeping only one value for each date (corresponding to the average daytime temperature - from 9am to 5pm).

In addition, temperature values ​​were artificially and gradually increased by a few Celsius degrees over the available period. This will simulate a small global warming (or is it local?)...

In summary, the available dataset contains the average daily temperatures (collected during the day), artificially increased by a certain value, for the city of Detroit from October 2012 to November 2017.

The purpose of this dataset is to apply forecasting models in order to predict the value of the artificially warmed average daily temperature of Detroit.

See graph in the following image: black dots refer to the actual data and the blue line represents the predictive model (including a confidence area).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3089313%2Faf9614514242dfb6164a08c013bf6e35%2Fplot-ts2.png?generation=1567827710930876&alt=media" alt="">

Acknowledgements

This dataset wouldn't be possible without the previous work in Historical Hourly Weather Data.

Inspiration

What are the best forecasting models to address this particular problem? TBATS, ARIMA, Prophet? You tell me!

Temperature in Indonesia increased to 26.38 celsius in 2024 from 26.16 celsius in 2023. This dataset includes a chart with historical data for Indonesia Average Temperature.

The recent activities and impacts of global warming are numerous and have been extensively studied by scientists. Some of the most recent events such as rising temperatures, sea-level rise and human health impacts including heat-related illnesses. More awareness needs to be created regarding climate change and rising global temperatures.

The dataset contains a detailed summary of the land-surface average results produced by the Berkeley Averaging method. Temperatures are in Celsius and reported as anomalies relative to the Jan 1951-Dec 1980 average. Uncertainties represent the 95% confidence interval for statistical and spatial under sampling effects.

This dataset contains monthly temperature records for all states in Mexico from January 1985 to February 2025. The data includes temperatures in both Celsius and Fahrenheit, with three key metrics:Minimum average temperature for the monthMaximum average temperature for the monthOverall mean temperature for the monthAdditionally, this project includes:A visualization script that generates temperature trend charts efficientlyA sample chart illustrating temperature evolution in Mexico CityA requirements.txt file specifying dependencies for the scriptThe temperature data was sourced from the Mexican National Meteorological Service (SMN): SMN - Monthly Temperature Summaries.This dataset is useful for climate analysis, trend studies, and data visualization projects related to temperature variations across Mexico.

The Weather Generator Gridded Data consists of two products:

[1] statistically perturbed gridded 100-year historic daily weather data including precipitation [in mm], and detrended maximum and minimum temperature in degrees Celsius, and

[2] stochastically generated and statistically perturbed gridded 1000-year daily weather data including precipitation [in mm], maximum temperature [in degrees Celsius], and minimum temperature in degrees Celsius.

The base climate of this dataset is a combination of historically observed gridded data including Livneh Unsplit 1915-2018 (Pierce et. al. 2021), Livneh 1915-2015 (Livneh et. al. 2013) and PRISM 2016-2018 (PRISM Climate Group, 2014). Daily precipitation is from Livneh Unsplit 1915-2018, daily temperature is from Livneh 2013 spanning 1915-2015 and was extended to 2018 with daily 4km PRISM that was rescaled to the Livneh grid resolution (1/16 deg). The Livneh temperature was bias corrected by month to the corresponding monthly PRISM climate over the same period. Baseline temperature was then detrended by month over the entire time series based on the average monthly temperature from 1991-2020. Statistical perturbations and stochastic generation of the time series were performed by the Weather Generator (Najibi et al. 2024a and Najibi et al. 2024b).

The repository consists of 30 climate perturbation scenarios that range from -25 to +25 % change in mean precipitation, and from 0 to +5 degrees Celsius change in mean temperature. Changes in thermodynamics represent scaling of precipitation during extreme events by a scaling factor per degree Celsius increase in mean temperature and consists primarily of 7%/degree-Celsius with 14%/degree-Celsius as sensitivity perturbations. Further insight for thermodynamic scaling can be found in full report linked below or in Najibi et al. 2024a and Najibi et al. 2024b.

The data presented here was created by the Weather Generator which was developed by Dr. Scott Steinschneider and Dr. Nasser Najibi (Cornell University). If a separate weather generator product is desired apart from this gridded climate dataset, the weather generator code can be adopted to suit the specific needs of the user. The weather generator code and supporting information can be found here: https://github.com/nassernajibi/WGEN-v2.0/tree/main. The full report for the model and performance can be found here: https://water.ca.gov/-/media/DWR-Website/Web-Pages/Programs/All-Programs/Climate-Change-Program/Resources-for-Water-Managers/Files/WGENCalifornia_Final_Report_final_20230808.pdf

Mean January temperatures for land areas of the world in degrees Celsius. Fahrenheit and Celsius values are included in pop-ups. Data derived from WorldClim Global Climate Data daily January average temperatures from 1950-2000.

High Frequency Indicator: The dataset contains year- and month-wise historically compiled data from the year 1901 to till date on the maximum, minimum and mean temperatures recorded in India