This statistic shows cities in the United States with the highest average annual temperatures. Data is based on recordings from 1981 to 2010. In San Antonio, Texas the average temperature is 80.7 degrees Fahrenheit. Some cities that have the hottest maximum summer temperatures will not be included in this list due to their extreme temperature variance.

The hottest average annual temperature recorded at a single location was **** degrees Celsius in Makkah, Saudi Arabia in 2010 and again in 2016. Makkah, also spelled Mecca, sees millions of Muslim enter the city every year. Although 2010 set the record for the hottest year globally on record, the record was then broken several times throughout the following decade. As of 2019, the hottest year on record globally was 2016, followed by 2019.

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).

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.

Locations with the hottest average annual temperatures in 2019 saw an average temperature above ** degrees Celsius over the course of the year. 2019 was a record-breaking hot year for many regions of the world. Matam, Senegal and Yelimane, Mali are both found in the Sahel zone of West Africa.

Semi-arid urban environments are undergoing an increase in air temperatures, both in average temperatures and in the frequency and intensity of extreme heat events. Within cities, different varieties of urban landcovers (ULC) and their densities influence local air temperatures, either mitigating or increasing heat. Currently, understanding how various combinations of ULCs influence air temperature at the block to neighborhood scale is limited due to the complexities of urban energy balances at small scales. We quantified how ULC influences air temperature at 60 m resolution for day and nighttime climate normals and heatwaves, by integrating data from microclimate temperature sensor networks and high-resolution (1 m2) ULC for Denver Colorado’s urban core. We derived ULC drivers of air temperature using a structural equation model, and projected urban heat scenarios of climate normals and heatwaves throughout the extent of urban Denver. We found that, in conjunction with other ULCs, urban tree canopy reduced daytime air temperatures (-0.026° C per % cover), and the combination of impervious surfaces and buildings increased daytime air temperature (0.021° C per % cover). During night hours, irrigated turf and tree canopy reduced temperatures more than the daytime (-0.038° C per % cover). Overall, ULC drove ~17% and 25% of local air temperature during the day and night respectively, with regional factors making up the rest. During heatwaves, ULC influence on daytime air temperatures was strengthened. The results provided evidence showing daytime urban heat mitigation is due to increases in surface shading and transpiration from tree canopies and nighttime heat mitigation in a semi-arid environment is primarily due to transpiration-derived cooling. Our findings inform urban planners seeking to identify potential hot and cool spots within a semi-arid city and mitigate high urban air temperatures through using ULC withing larger urban climate mitigation strategies.

It is expected that the highest temperature in Summer on average will be approximately *** degrees Fahrenheit hotter in New York City by 2050 compared to the year 2000. The Winter lowest temperature will be *** degrees hotter by 2050. The city of Chicago, Illinois expects an even higher increase of *** degrees Fahrenheit in Summer's highest temperature and an increase of *** degrees in Winter.

Extreme heat in the U.S. – additional information

Projected changes in global average temperature are associated with widespread changes in weather patterns. Scientific studies indicate that extreme weather events, such as heat waves, are likely to become more frequent or more intense within the next few years. These changes may lead to an increase in heat-related deaths in the United States. Outdoor temperatures can affect daily life in many ways. Extreme heat and the combination of high heat and humidity can pose a serious risk for human health. Exposure to extreme heat can lead to heat stroke and dehydration, as well as cardiovascular, respiratory and cerebrovascular disease. When the weather becomes excessively hot, it can be deadly. According to the National Weather Service, heat waves caused ** fatalities in the United States in 2015.

The average temperatures in the U.S. have risen significantly since 1895. Long-term changes in climate can directly or indirectly affect many aspects of a person’s life. For example, warmer days could increase air conditioning or water supply costs. One way to measure the influence of temperature change on energy demand is by using heating and cooling degree days. Cooling degree days measure the difference between outdoor temperature and a temperature that people generally find comfortable indoors. Cooling degree days have not increased significantly over the past decades. However, a slight increase is evident for this period. In 2014, there were around ***** cooling degree days in the U.S., compared to ***** in 2009. More cooling degree days indicate an increase in temperature, leading to a greater likeliness of using air conditioning.

Context

Global warming is the ongoing rise of the average temperature of the Earth's climate system and has been demonstrated by direct temperature measurements and by measurements of various effects of the warming - Wikipedia

So a dataset on the temperature of major cities of the world will help analyze the same. Also weather information is helpful for a lot of data science tasks like sales forecasting, logistics etc.

Thanks to University of Dayton, the dataset is available as separate txt files for each city here. The data is available for research and non-commercial purposes only.. Please refer to this page for license.

Content

Daily level average temperature values is present in city_temperature.csv file

Acknowledgements

University of Dayton for making this dataset available in the first place!

Photo credits: James Day on Unsplash

Inspiration

Some ideas are: 1. How is the average temperature of the world changing over time? 2. Is the temperature information helpful for other forecasting tasks?

• 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

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: air temperatureUnits: degrees Fahrenheit Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Related layers include Afternoon Air Temperature and Evening Air Temperature. Cities IncludedBoulder, CO Brooklyn, NY Greenwich Village, NY Columbia, SC Columbia, MO Columbus, OH Knoxville, TN Jacksonville, FL Las Vegas, NV Milwaukee, WI Nashville, TN Omaha, NE Philadelphia, PA Rockville, MD Gaithersburg, MD Takoma Park, MD San Francisco, CA Spokane, WA Abingdon, VA Albuquerque, NM Arlington, MA Woburn, MA Arlington, VA Atlanta, GA Charleston, SC Charlottesville, VA Clarksville, IN Farmville, VA Gresham, OR Harrisonburg, VA Kansas City, MO Lynchburg, VA Manhattan, NY Bronx, NY Newark, NJ Jersey City, NJ Elizabeth, NJ Petersburg, VA Raleigh, NC Durham, NC Richmond, VA Richmond, IN Salem, VA San Diego, CA Virginia Beach, VA Winchester, VA Austin, TX Burlington, VT Cincinnati, OH Detroit, MI El Paso, TX Houston, TX Jackson, MS Las Cruces, NM Miami, FL New Orleans, LA Providence, RI Roanoke, VA San Jose, CA Seattle, WA Vancouver, BC Canada Boston, MA Fort Lauderdale, FL Honolulu, HI Boise, ID Nampa, ID Los Angeles, CA Yonkers, NY Oakland, CA Berkeley, CA San Juan, PR Sacramento, CA San Bernardino, CA Victorville, CA West Palm Beach, FL Worcester, MA Washington, D.C. Baltimore, MD Portland, ORCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Morning Air Temperature Observations in Floating-Point (°F)

Temperature and precipitation projections for NYC reported by the New York City Panel on Climate Change (NPCC).

The New York City Panel on Climate Change (NPCC) started in 2009 and was codified in Local Law 42 of 2012 with a mandate to provide an authoritative and actionable source of scientific information on future climate change and its potential impacts.

The Intergovernmental Panel on Climate Change (IPCC) is the United Nations body for assessing the science related to climate change.

This statistic depicts China's major cities in 2023, by annual average temperature. The average temperature in Beijing was **** degrees Celsius in 2023.

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!

Analysis of ‘Temperature Time-Series for some Brazilian cities’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/volpatto/temperature-timeseries-for-some-brazilian-cities on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Temperature Time-Series for some Brazilian cities

Do you ever wonder how are temperatures in Brazilian cities? Too hot? Cold weather sometimes? And what about climate changes? Is Brazil getting hotter?

This is your chance to check it out!

Context

This datasets are collected in order to provide some answers for the above question through Data Analysis. Maybe you want to try some Machine Learning model in order to practice and predict the evolution of temperature in some Brazilian cities.

Content

The content is provided by NOAA GHCN v4 and post-processed by NASA's GISTEMP v4.

In summary, each data file contains a temperature time series for a station named according to the city. The time series provides temperature records by month for each year. Some mean measurement is calculated, like metANN and D-J-F. I can't give details about these quantities, nor how they are calculated. Please refer for NASA GISTEMP website in this regard. The most important seems to be metANN, which is an annual temperature mean.

Acknowledgements

These datasets are provided through NASA's GISTEMP v4 and recorded by NOAA GHCN v4. Thanks for researchers and staffs for the really nice work!

--- Original source retains full ownership of the source dataset ---

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: air temperatureUnits: degrees Fahrenheit Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Related layers include Morning Air Temperature and Afternoon Air Temperature. Cities IncludedBoulder, CO Brooklyn, NY Greenwich Village, NY Columbia, SC Columbia, MO Columbus, OH Knoxville, TN Jacksonville, FL Las Vegas, NV Milwaukee, WI Nashville, TN Omaha, NE Philadelphia, PA Rockville, MD Gaithersburg, MD Takoma Park, MD San Francisco, CA Spokane, WA Abingdon, VA Albuquerque, NM Arlington, MA Woburn, MA Arlington, VA Atlanta, GA Charleston, SC Charlottesville, VA Clarksville, IN Farmville, VA Gresham, OR Harrisonburg, VA Kansas City, MO Lynchburg, VA Manhattan, NY Bronx, NY Newark, NJ Jersey City, NJ Elizabeth, NJ Petersburg, VA Raleigh, NC Durham, NC Richmond, VA Richmond, IN Salem, VA San Diego, CA Virginia Beach, VA Winchester, VA Austin, TX Burlington, VT Cincinnati, OH Detroit, MI El Paso, TX Houston, TX Jackson, MS Las Cruces, NM Miami, FL New Orleans, LA Providence, RI Roanoke, VA San Jose, CA Seattle, WA Vancouver, BC Canada Boston, MA Fort Lauderdale, FL Honolulu, HI Boise, ID Nampa, ID Los Angeles, CA Yonkers, NY Oakland, CA Berkeley, CA San Juan, PR Sacramento, CA San Bernardino, CA Victorville, CA West Palm Beach, FL Worcester, MA Washington, D.C. Baltimore, MD Portland, ORCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Evening Air Temperature Observations in Floating-Point (°F)

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: heat indexUnits: degrees Fahrenheit Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Related layers include Afternoon Heat Index and Evening Heat Index. Cities IncludedBoulder, CO Brooklyn, NY Greenwich Village, NY Columbia, SC Columbia, MO Columbus, OH Knoxville, TN Jacksonville, FL Las Vegas, NV Milwaukee, WI Nashville, TN Omaha, NE Philadelphia, PA Rockville, MD Gaithersburg, MD Takoma Park, MD San Francisco, CA Spokane, WA Abingdon, VA Albuquerque, NM Arlington, MA Woburn, MA Arlington, VA Atlanta, GA Charleston, SC Charlottesville, VA Clarksville, IN Farmville, VA Gresham, OR Harrisonburg, VA Kansas City, MO Lynchburg, VA Manhattan, NY Bronx, NY Newark, NJ Jersey City, NJ Elizabeth, NJ Petersburg, VA Raleigh, NC Durham, NC Richmond, VA Richmond, IN Salem, VA San Diego, CA Virginia Beach, VA Winchester, VA Austin, TX Burlington, VT Cincinnati, OH Detroit, MI El Paso, TX Houston, TX Jackson, MS Las Cruces, NM Miami, FL New Orleans, LA Providence, RI Roanoke, VA San Jose, CA Seattle, WA Vancouver, BC Canada Boston, MA Fort Lauderdale, FL Honolulu, HI Boise, ID Nampa, ID Los Angeles, CA Yonkers, NY Oakland, CA Berkeley, CA San Juan, PR Sacramento, CA San Bernardino, CA Victorville, CA West Palm Beach, FL Worcester, MA Washington, D.C. Baltimore, MD Portland, ORCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Morning Heat Index Observations in Floating-Point (°F)

High resolution (10 meter) land surface temperature (LST) from September 1, 2022 is mapped for the seven-county metropolitan region of the Twin Cities. The goal of the map is to show the heat differences across the region and is not intended to show the maximum temperature that any specific area can reach. The raster dataset was computed at 30 meters using satellite imagery from Landsat 9 and downscaled to 10 meters using Copernicus Sentinel-2. These datasets were integrated using techniques modified from Ermida et al. 2020 and Onačillová et al. 2022). Open water was removed using ancillary data from OpenStreetMap and 2020 Generalized Land Use for the Twin Cities (Metropolitan Council).

First, Landsat 9 imagery taken at 11:59 am CDT on September 01, 2022 was processed into 30-meter resolution LST (based on Ermida et al. 2020). At this time, the air temperature was 88° F at the Minneapolis-St. Paul International Airport (NOAA). A model predicting LST based on spectral indices of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI) was created and applied to 10-meter Sentenel-2 imagery. Sentinel-2 imagery was also taken on September 1, 2022, and this resulted in a 10-meter downscaled LST image (based on Onačillová et al. 2022). To account for anomalies in NDVI on the primary image date of September 1 (e.g., recently harvested agricultural fields), maximum NDVI occurring between July 1, 2022 and September 1, 2022 was used for both Landsat and Sentinel image processing. Water bodies were removed for all processing steps (OpenStreetMap 2023, Metropolitan Council 2021).

This dataset is an update to the 2016 LST data for the Twin Cities Region (Metropolitan Council).

The code to create and processes this dataset is available at: https://github.com/Metropolitan-Council/extreme.heat

Sources:
Ermida, S.L., Soares, P., Mantas, V., Göttsche, F.-M., Trigo, I.F., 2020. Google Earth Engine open-source code for Land Surface Temperature estimation from the Landsat series. Remote Sensing, 12 (9), 1471; https://doi.org/10.3390/rs12091471.

Metropolitan Council. 2021. Generalized Land Use 2020. Minnesota Geospatial Commons. https://gisdata.mn.gov/dataset/us-mn-state-metc-plan-generl-lnduse2020

Metropolitan Council. 2017. Land Surface Temperature for Climate Vulnerability Analysis. Minnesota Geospatial Commons. https://gisdata.mn.gov/dataset/us-mn-state-metc-env-cva-lst2016

NOAA, National Oceanic and Atmospheric Administration, National Centers for Environmental Information, station USW00014922. September 1, 2022.

Onačillová, K., Gallay, M., Paluba, D., Péliová, A., Tokarčík, O., Laubertová, D. 2022. Combining Landsat 8 and Sentinel 2 data in Google Earth Engine to derive higher resolution land surface temperature maps in urban environment. Remote Sensing, 14 (16), 4076. https://doi.org/10.3390/rs14164076.

OpenStreetMap contributors. 2023. Retrieved from https://planet.openstreetmap.org on April 12, 2023.

Climate change is a major concern for governments and institutions worldwide, as is evident from the signing of the Paris Agreement in 2016, where most countries agreed to make efforts to achieve different climate-related goals. The evidence of climate change has been established, as extreme natural events occur more frequently and temperatures increase worldwide. A forecast for 2050 indicates that the temperature of the warmest month in Madrid would increase by 6.4 degrees Celsius, making it comparable to the temperatures of Marrakesh, Morocco.

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.

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.