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Think about someone dying from extreme temperatures. You probably pictured someone passing out from heat stroke or dying from hypothermia.

But this is not how most people die from “heat”. They die from conditions such as cardiovascular or kidney disease, respiratory infections, or diabetes.1

Almost no one has “heat” or “cold” written on their death certificate, but sub-optimal temperatures lead to a large number of premature deaths. As we’ll see later, researchers estimate that it kills several million every year.

Older populations are usually most vulnerable to extreme temperatures. Most deaths occur in people older than 65. It’s important to consider what "death" means here and how deaths from extreme temperatures might compare to other causes. Being too hot or cold can increase our risk of developing certain health conditions or worsen existing ones. It can thereby lead to an earlier death than would have occurred if the temperatures were “optimal”.

How much time do hot or cold conditions take off someone’s life? It’s difficult to give precise estimates. One method that researchers often use is to look at excess death rates — which measure how many more people die in a given year compared to an “average” year — in a particularly warm or cold year. Looking at patterns of excess deaths gives some indication of whether temperature-related deaths were “brought forward” significantly or not.

A study by Nirandeep Rehill and colleagues examined death patterns in the United Kingdom over 50 years.2 It found that most cold-related deaths were among people who would not have died in the next 6 months. A later study looked at the impacts of high and low temperatures across a much larger sample of countries.3 It found that most temperature-related deaths reduced lifespans for at least one year. Most people died at least one year earlier, although there would be some that did lose less than this.

In this article, I will examine how many people die from heat and cold each year and how researchers estimate these numbers. In a follow-up article, I’ll look at how these risks could change in the future due to climate change.

A quick note on terminology: I will use the term “temperature-related deaths” from this point forward to refer to the combination of deaths from heat and cold conditions. When I use the term “heat”, I mean warm or hot.

Source: The Emergency Events Database (EM-DAT) , Centre for Research on the Epidemiology of Disasters (CRED) / Université catholique de Louvain (UCLouvain), Brussels, Belgium – www.emdat.be.Category: Climate and WeatherData series: Climate related disasters frequency, Number of Disasters: TOTAL  Climate related disasters frequency, Number of Disasters: Drought  Climate related disasters frequency, Number of Disasters: Extreme temperature  Climate related disasters frequency, Number of Disasters: Flood  Climate related disasters frequency, Number of Disasters: Landslide  Climate related disasters frequency, Number of Disasters: Storm  Climate related disasters frequency, Number of Disasters: Wildfire Climate related disasters frequency, People Affected: Drought  Climate related disasters frequency, People Affected: Extreme temperature  Climate related disasters frequency, People Affected: Flood  Climate related disasters frequency, People Affected: Landslide  Climate related disasters frequency, People Affected: Storm  Climate related disasters frequency, People Affected: Wildfire Climate related disasters frequency, People Affected: TOTAL  Disaster IntensityMetadata:EM-DAT: The International Disasters Database - Centre for Research on the Epidemiology of Disasters (CRED), part of the University of Louvain (UCLouvain) www.emdat.be, Brussels, Belgium. Only climate related disasters (Wildfire, Storm, Landslide, Flood, Extreme Temperature, and Drought) are covered. See the CID Glossary for the definitions. EM-DAT records country level human and economic losses for disasters with at least one of the following criteria: i.          Killed ten (10) or more people  ii.         Affected hundred (100) or more people  iii.        Led to declaration of a state of emergency iv.        Led to call for international assistance   The reported total number of deaths “Total Deaths” includes confirmed fatalities directly imputed to the disaster plus missing people whose whereabouts since the disaster are unknown and so they are presumed dead based on official figures. “People Affected” is the total of injured, affected, and homeless people. Injured includes the number of people with physical injuries, trauma, or illness requiring immediate medical assistance due to the disaster. Affected includes the number of people requiring immediate assistance due to the disaster. Homeless includes the number of people requiring shelter due to their house being destroyed or heavily damaged during the disaster. Disaster intensity is calculated by summing “Total Deaths” and 30% of the “People Affected”, and then dividing the result by the total population. For each disaster and its corresponding sources, the population referred to in these statistics and the apportionment between injured, affected, homeless, and the total is checked by CRED staff members. Nonetheless, it is important to note that these are estimates based on certain assumptions, which have their limitations. For details on the criteria and underlying assumptions, please visit https://doc.emdat.be/docs/data-structure-and-content/impact-variables/human/. Methodology:Global climate related disasters are stacked to show the trends in climate related physical risk factors.

This dataset provides synthetic data on climate change impacts from 2000 to 2023.

It includes records of temperature anomalies, CO2 levels, and extreme weather events across various countries. The data also captures the economic impact of these events and the population affected.

Key features include:

1. Date: The date of the record. 2. Country: Country where the data was collected. 3. TemperatureAnomaly_C: Temperature anomaly in degrees Celsius. 4. CO2Level_ppm: Atmospheric CO2 concentration in parts per million. 5. ExtremeWeatherEvent: Type of extreme weather event (e.g., heatwave, flood). 6. EconomicImpact_USD: Estimated economic impact in USD. 7. PopulationAffected: Number of people affected by the event.

This dataset is designed to help analyze trends in climate change and its effects on different regions and populations.

Tornadoes in North America

Magnitude, Fatalities, Injuries, and Crop Loss Data

By Homeland Infrastructure Foundation [source]

About this dataset

This dataset compiles historical data on tornadoes in the United States, Puerto Rico, and the U.S. Virgin Islands – providing a critical resource to researchers and policy-makers alike. Obtained from the National Weather Service's Storm Prediction Center (SPC), it contains an intricate wealth of information that sheds light onto patterns of tornado outbreaks across time & geographical space yielding insights into factors like magnitude, fatalities/injuries caused and losses incurred by these devastating weather disasters. With attributes such as Start Longitude/Latitude, End Longitude/Latitude, Day of Origin & Time Zone – this dataset will enable a comprehensive analysis of changes over time in regards to both intensity & frequency for those interested in studying climate change and its impact on extreme weather events such as tornadoes. For disaster management personnel dealing with natural hazards like floods or hurricanes - a familiarity with this dataset can help identify areas prone to frequent storms - thereby empowering proactive measures towards their mitigation.*

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How to use the dataset

This dataset contains historical tornado tracks in the United States, Puerto Rico, and the U.S. Virgin Islands. The data was obtained from the National Weather Service's Storm Prediction Center (SPC). It includes thirty-seven columns of statistics which you can use to analyze when, where, and how frequently tornadoes occur in North America over time.

Research Ideas

• Creating a tornado watch and warning system using Geographic Information Systems (GIS) technology to track and predict the path of dangerous storms.
• Developing an insurance system that gives detailed information on historical data related to natural disasters including tornadoes, hurricanes, floods, etc., in order to better assess risk levels for insuring homes and businesses in vulnerable areas.
• Developing an app that provides real-time notifications for potential tornadoes by utilizing the dataset's coordinates and forecasting data from the National Weather Service (NWS). The app could even provide shelter locations near users based on their current location ensuring that people are aware of potential active threats nearby them quickly increasing safety levels as much as possible when these hazardous events occur

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Historical_Tornado_Tracks.csv | Column name | Description | |:--------------|:-------------------------------------| | OM | Origin Mode (Point or Line) (String) | | YR | Year (Integer) | | MO | Month (Integer) | | DY | Day (Integer) | | DATE | Date (String) | | TIME | Time (String) | | TZ | Time Zone (String) | | ST | State (String) | | STF | FIPS State Code (String) | | STN | State Name (String) | | MAG | Magnitude (Integer) | | INJ | Injuries (Integer) | | FAT | Fatalities (Integer) | | LOSS | Loss (Integer) | | CLOSS | Crop Loss (Integer) | | SLAT | Starting Latitude (Float) | | SLON | Starting Longitude (Float) | | ELAT | Ending Latitude (Float) | | ELON | Ending Longitude (Float) | | LEN | Length of Track (Float) ...

This dataset is no longer being maintained. To avoid duplication with the Heat Warnings issued by Environment and Climate Change Canada, the City of Toronto will no longer issue these warnings to the public as they are already communicated broadly. For historical/archived data, please see link below. Information about Heat Alerts and Extreme Heat Alerts is updated daily. Alerts are issued by Toronto's Medical Officer of Health. Toronto Public Health monitors the Heat Health Alert System every day from May 15 to September 30 each year, to alert those people most at risk of heat-related illness that hot weather conditions presently exist and to take appropriate precautions.

This scene contains the relative heat severity for every pixel for every city in the United States, from this source layer. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2018 and 2019.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this scene is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource Center: https://www.epa.gov/heat-islands/heat-island-resourcesDr. Ladd Keith, University of Arizona: https://www.laddkeith.com/ Dr. Ben McMahan, University of Arizona: https://www.climas.arizona.edu/about/people/ben-mcmahan Dr. Jeremy Hoffman, Science Museum of Virginia: https://jeremyscotthoffman.com/about-me-shift#about Dr. Hunter Jones, NOAA: https://cpo.noaa.gov/News/News-Article/ArtMID/6226/ArticleID/971/CPOs-Hunter-Jones-delivers-keynote-on-Climate-and-Extreme-Heat-at-Design-for-Risk-Reduction-Symposium-in-NYC Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and Resiliency: https://youtu.be/sAHlqGDU0_4 Disclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Pete.Aniello@tpl.org with feedback.

Storms Historical Reports

Analyzing the Impacts of Severe Weather Events

By US Open Data Portal, data.gov [source]

About this dataset

This intriguing dataset contains historical storm prediction reports from the US National Weather Service Storm Prediction Center, giving you a glimpse into some of mother nature's most jaw-dropping weather events! It includes detailed summaries of today's latest information about tornado and severe thunderstorm watches, mesoscale discussions, convective day 1-3 outlooks, and fire weather outlooks. Valuable for both weather professionals and students alike, this one-of-a-kind dataset can be used to gain insights into how extreme storms form and the potential dangers they present. Stay informed with these reliable reports -- last updated at 2019-12-05!

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Research Ideas

• Creating a geographical map of storm prediction areas, to help people prepare in advance for storms and floods.
• Using the data to develop predictive analytics models for forecasting tornado, thunderstorm or fire activity in specific areas ahead of time.
• Analyzing trends and patterns in severe weather occurrences over time; this could be useful for understanding how extreme weather events are becoming more frequent as climate change progresses

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: web-page-1.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 US Open Data Portal, data.gov.

These datasets contain survey data that was used to evaluate the effect of the exposure to heatwave news texts on people’s preference for climate mitigation and adaptation actions, as presented in the manuscript titled “How do news about a heatwave affect public prioritization of climate change adaptation and mitigation behaviors?”. Three versions of the dataset are available:

Original dataset: This version contains choice text as data points and includes all finished survey responses that passed the attention check questions (n=1209).

Original recoded dataset: This version was generated by recoding choice text into numerical values. The 'Income' variable, representing household income levels for both Canadian and US residents, was added by converting reported income ranges to a unified scale based on exchange rate equivalencies. The "Income_Canadians" and "Income_US" columns were subsequently removed to avoid repetitions.

Final dataset: This version excludes observations from participants who completed the survey in under four minutes and those who selected the same response for every item within each matrix-style question (also known as straight-lining). Additionally, responses with missing values in questions regarding political views, gender, and household income, as well as responses where participants identified as non-binary or indicated that their gender was not listed, were omitted (see “Methods” for more details). Dependent variables have been added based on the original responses, including personal-level mitigation and adaptation likelihoods, personal-level mitigation preference, and both non-weighted and weighted collective-level mitigation preference. Furthermore, the dataset includes a 'Climate Change Concern' variable, derived through principal component analysis of thirteen variables expressing participants’ climate change attitudes and efficacy beliefs concerning climate actions. Variables not used in the subsequent data analysis were removed. Age, political views, education, and income columns were standardized. The final dataset was used for the data analysis presented in the manuscript.

The following variables/columns can be found across the three versions of the dataset:

Dependent variables:

Starting with “Personal_Mitigation”: participant’s self-reported likelihood of taking selected personal-level climate change mitigation actions

Starting with “Personal_Adaptation”: participant’s self-reported likelihood of taking selected personal-level climate change adaptation actions

Starting with “Collective_Mitigation”: participant’s ranking of the collective-level climate change mitigation initiatives

Starting with “Collective_Adaptation”: participant’s ranking of the collective-level climate change adaptation initiatives

Personal_Mitigation_Likelihood: personal-level mitigation likelihood (present only in the final dataset)

Personal_Adaptation_Likelihood: personal-level adaptation likelihood (present only in the final dataset)

Personal_Preference: personal-level mitigation preference (present only in the final dataset)

Collective_Preference_Unweighted: non-weighted collective-level mitigation preference (present only in the final dataset)

Collective_Preference_Weighted: weighted collective-level mitigation preference (present only in the final dataset)

Independent variables:

Group: group that the participant was assigned to as part of the experimental intervention

Distance: indicates whether the participant was assigned to read about a heatwave occurring in their community or a city 6,000 km away (for experimental groups only)

Severity: indicates whether the participant was prompted to read about a heatwave without or with the mention of associated causalities (for experimental groups only)

Covariates and supporting variables:

Gender: gender identity

Identity: ethnic and/or racial identity

Age: age

Political_Views: position on the liberal-conservative continuum

Education: highest level of education

Country: country of residence

Canada_Province: province or territory of residence (for Canadian participants only)

US_State: state of residence (for US participants only)

Duration_Residence: duration of residence in the current community

Income_Canadians: annual household income in Canadian dollars (for Canadian participants only)

Income_US: annual household income in US dollars (for US participants only)

Income: annual household income for both Canadian and US residents derived by converting reported income ranges to a unified scale based on exchange rate equivalencies

Efficacy_Mitigation_Personal: belief regarding the response efficacy of personal-level climate change mitigation actions

Efficacy_Mitigation_Collective: belief regarding the response efficacy of collective-level climate change mitigation actions

Efficacy_Adaptation_Personal: belief regarding the response efficacy of personal-level climate change adaptation actions

Efficacy_Adaptation_Collective: belief regarding the response efficacy of collective-level climate change adaptation

Climate_Change_Importance: perception of climate change as a personally important issue

Climate_Change_Worry: level of worry about climate change

Starting with “Climate_Risk”: beliefs regarding the degree of harm that climate change will cause to plants and animal species (Climate_Risk_Animals_Plants), future generations of people (Climate_Risk_Future_Generations), people in developing countries (Climate_Risk_Developing_Countries), people in participant’s country (Climate_Risk_Country), people in participant’s community (Climate_Risk_Community), and the participant personally (Climate_Risk_Personal)

Climate_Change_Onset_Time: belief regarding when climate change will start harming people in their community

Six_Americas_Segment: the Global Warming's Six Americas segment participant aligns with derived based on the Six Americas Short SurveY (SASSY) Group Scoring Tool

Climate_Change_Concern: variable derived through PCA of thirteen variables expressing participants' climate change attitudes and efficacy beliefs pertaining to climate actions (present only in the final dataset)

Survey_Duration_Seconds: The amount of time it took the respondent to complete the survey

This is an hourly future weather dataset for energy modeling applications. The dataset is primarily based on the output of a regional climate model (RCM), i.e., the Weather Research and Forecasting (WRF) model version 3.3.1. The WRF simulations are driven by the output of a general circulation model (GCM), i.e., the Community Climate System Model version 4 (CCSM4). This dataset is in the EPW format, which can be read or translated by more than 25 building energy modeling programs (e.g., EnergyPlus, ESP-r, and IESVE), energy system modeling programs (e.g., System Advisor Model (SAM)), indoor air quality analysis programs (e.g., CONTAM), and hygrothermal analysis programs (e.g., WUFI). It contains 13 weather variables, which are the Dry-Bulb Temperature, Dew Point Temperature, Relative Humidity, Atmospheric Pressure, Horizontal Infrared Radiation Intensity from Sky, Global Horizontal Irradiation, Direct Normal Irradiation, Diffuse Horizontal Irradiation, Wind Speed, Wind Direction, Sky Cover, Albedo, and Liquid Precipitation Depth. This dataset provides future weather data under two emissions scenarios - RCP4.5 and RCP8.5 - across two 10-year periods (2045-2054 and 2085-2094). It also includes simulated historical weather data for 1995-2004 to serve as the baseline for climate impact assessments. We strongly recommend using this built-in baseline rather than external sources (e.g., TMY3) for two key reasons: (1) it shares the same model grid as the future projections, thereby minimizing geographic-averaging bias, and (2) both historical and future datasets were generated by the same RCM, so their differences yield anomalies largely free of residual model bias. This dataset offers a spatial resolution of 12 km by 12 km with extensive coverage across most of North America. Due to the enormous size of the entire dataset, in the first stage of its distribution, we provide weather data for the centroid of each Public Use Microdata Area (PUMA), excluding Hawaii. PUMAs are non-overlapping, statistical geographic areas that partition each state or equivalent entity into geographic areas containing no fewer than 100,000 people each. The 2,378 PUMAs as a whole cover the entirety of the U.S. The weather data can be utilized alongside the large-scale energy analysis tools, ResStock and ComStock, developed by National Renewable Energy Laboratory, whose smallest resolution is at the PUMA scale. The authors observed an anomalous warming signal over the Great Plains in the end-of-century (2085 - 2094) RCP4.5 time slice. This anomaly is absent in the mid-century slice (2045 - 2054) under RCP4.5 and in both the mid- (2045 - 2054) and end-of-century (2085 - 2094) slices under RCP8.5. Consequently, we recommend that users exercise particular caution when using the RCP4.5 2085-2094 data, especially for analyses involving the Great Plains region.

Global climate change is increasing the frequency and intensity of extreme weather events such as heatwaves, droughts, and flooding. This is the primary way many individuals experience climate change, which has led researchers to investigate the influence of personal experience on climate change concern and action. However, existing evidence is still limited and in some cases contradictory. At the same time, behavioral decision research has highlighted the importance of pre-existing values and beliefs in shaping how individuals experience changes in environmental conditions. This is in line with theories of motivated reasoning, which suggest that people interpret and process information in a biased manner to maintain their prior beliefs. Yet, the evidence for directional motivated reasoning in the context of climate change beliefs has recently been questioned. In the current paper, we critically review the literature on the interrelationships between personal experience of local weather anomalies, extreme weather events and climate change beliefs. Overall, our review shows that there is some evidence that local warming can generate climate change concern, but the capacity for personal experience to promote action may rely upon the experience first being attributed to climate change. Rare extreme weather events will likely have limited impact on judgments and decisions unless they have occurred recently. However, even recent events may have limited impact among individuals who hold strong pre-existing beliefs rejecting the reality of climate change. We identify limitations of existing research and suggest directions for future work.

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average difference between the 'lower' values before and after this update is 0.6.]What does the data show? The Annual Count of Summer Days is the number of days per year where the maximum daily temperature (the hottest point in the day) is above 25°C. It measures how many times the threshold is exceeded (not by how much) in a year. Note, the term ‘summer days’ is used to refer to the threshold and temperatures above 25°C outside the summer months also contribute to the annual count. The results should be interpreted as an approximation of the projected number of days when the threshold is exceeded as there will be many factors such as natural variability and local scale processes that the climate model is unable to represent.The Annual Count of Summer Days is calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of summer days to previous values. What are the possible societal impacts?An increase in the Annual Count of Summer Days indicates increased health risks from high temperatures. Impacts include:Increased heat related illnesses, hospital admissions or death for vulnerable people.Transport disruption due to overheating of railway infrastructure. Periods of increased water demand.Other metrics such as the Annual Count of Hot Summer Days (days above 30°C), Annual Count of Extreme Summer Days (days above 35°C) and the Annual Count of Tropical Nights (where the minimum temperature does not fall below 20°C) also indicate impacts from high temperatures, however they use different temperature thresholds.What is a global warming level?The Annual Count of Summer Days is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the 'Annual Count of Summer Days', an average is taken across the 21 year period. Therefore, the Annual Count of Summer Days show the number of summer days that could occur each year, for each given level of warming. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data? This data contains a field for each global warming level and two baselines. They are named ‘Summer Days’, the warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. E.g. ‘Summer Days 2.5 median’ is the median value for the 2.5°C warming level. Decimal points are included in field aliases but not field names e.g. ‘Summer Days 2.5 median’ is ‘SummerDays_25_median’. To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘Summer Days 2.0°C median’ values.What do the ‘median’, ‘upper’, and ‘lower’ values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Annual Count of Summer Days was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksThis dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report and uses the same temperature thresholds as the 'State of the UK Climate' report.Further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal

Notice: this is not the latest Heat Island Severity image service. For 2023 data, visit https://tpl.maps.arcgis.com/home/item.html?id=db5bdb0f0c8c4b85b8270ec67448a0b6. This layer contains the relative heat severity for every pixel for every city in the United States. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2018 and 2019.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of Arizona Dr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAADaphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

The Measurement template document is available at the archived version of this page on the UK Government Web Archive.

DfT Business plan

Geographical coverage: Great Britain

Information broken down by: Accident site. Data are available by geographical area, age, gender and type of road user.

Latest data

In 2013:

• 1,713 people were killed in reported road traffic accidents in Great Britain, 2% (41) fewer than in 2012. This is the lowest number of fatalities since national records began in 1926. The total number of people killed in 2013 was 39% lower than the 2005-09 baseline average
• the number of fatalities decreased for pedestrians, pedal cyclists and car occupants, by 5%, 8% and 2% respectively, but increased for motorcycle users by 1%. Over the same period motor vehicle traffic remained broadly stable, with a small increase of 0.4% between 2012 and 2013
• with the exception of 2011, road deaths have fallen every year since 2004. Adverse weather (heavy snow falls) experienced in the first and last quarters of 2010 but not in 2011, is likely to be the main factor behind the increase in fatalities recorded in 2011

The complete set of data is available for download.

Background information

The indicator can be broken down by any geographical area (eg country, region, local authority) since a grid reference is collected for each accident. Information is also available by age, gender, type of road user and road type. Numbers will be relatively small for more detailed breakdowns of the total and may therefore fluctuate from year to year. This needs to be taken into account when assessing trends.

• publishing schedule: annual
• last updated: September 2014
• next update: July 2015

Other related data and information

More detailed analysis and time series can be found in Reported road casualties Great Britain: annual report.

Record level data on accidents and casualties can be found in http://data.gov.uk/dataset/road-accidents-safety-data/">Record level data

Further information

• Business Plan 2012 to 2015
• Input and impact indicators
• Analyst contact: Anil Bhagat, roadacc.stats@dft.gsi.gov.uk

At present in the UK cold weather related illnesses and cold weather leads to a winter spike in hospitalisations and deaths. In the future, without adapting the way we live, higher summer temperatures and more frequent heatwaves will lead to increased numbers of people becoming ill or dying in the summer months too.

The Climate Change and Human Health Literature Portal is a bibliographic database created by the National Institute of Environmental Health Sciences(NIEHS) that contains a collection of scientific research on the health impacts of climate change. It compiles literature including studies on extreme weather events, heat waves, air pollution, infectious diseases and more. The mission of the NIEHS is "to discover how the environment affects people in order to promote healthier lives." The portal draws from biomedical and environmental databases like PubMed to compile studies and collect data across various aspects of the environment and population groups. The database includes citations and links to 22,695 studies published from 2007 to 2023, and users can filter studies to search based on exposure, health impact, geographic location, geographic feature, model/methodology, model timescale, special topic, resource type and year published. Citations with links to articles (not the articles themselves) are included in the database.The web portal was no longer available as of February 2025, and the bibliographic records in this dataset capture what was behind the portal. A copy of the portal is available in the Internet Archive.Data is provided in two formats:1. A JSONL file that captures the original structure of the records as they were published in the web portal. Also available from the Internet Archive at: https://archive.org/details/cchhl_2025-02-10.2. A CSV file that represents a flattened version of the JSON, with the removal of duplicate fields and renamed and reordered columns. Columns with the suffix "terms" contain lists of subject terms that are separated with a pipe character '|'.A README file with codebook is included.

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average difference between the 'lower' values before and after this update is 0.0.]What does the data show? The Annual Count of Tropical Nights is the number of days per year where the minimum daily temperature is above 20°C. It measures how many times the threshold is exceeded (not by how much). It measures how many times the threshold is exceeded (not by how much) in a year. The results should be interpreted as an approximation of the projected number of days when the threshold is exceeded as there will be many factors such as natural variability and local scale processes that the climate model is unable to represent.The Annual Count of Tropical Nights is calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of tropical nights to previous values. What are the possible societal impacts?The Annual Count of Tropical Nights indicates increased health risks and heat stress due to high night-time temperatures. It is based on exceeding a minimum daily temperature of 20°C, i.e. the temperature does not fall below 20°C for the entire day. Impacts include:Increased heat related illnesses, hospital admissions or death for vulnerable people.Increased heat stress, it is important the body has time to recover from high daytime temperatures during the lower temperatures at night.Other metrics such as the Annual Count of Summer Days (days above 25°C), Annual Count of Hot Summer Days (days above 30°C) and the Annual Count of Extreme Summer Days (days above 35°C) also indicate impacts from high temperatures, however they use different temperature thresholds.What is a global warming level?The Annual Count of Tropical Nights is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming.The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Annual Count of Tropical Nights, an average is taken across the 21 year period. Therefore, the Annual Count of Tropical Nights show the number of tropical nights that could occur each year, for each given level of warming. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?This data contains a field for each global warming level and two baselines. They are named ‘Tropical Nights’, the warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. E.g. ‘Tropical Nights 2.5 median’ is the median value for the 2.5°C warming level. Decimal points are included in field aliases but not field names e.g. ‘Tropical Nights 2.5 median’ is ‘TropicalNights_25_median’. To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘Tropical Nights 2.0°C median’ values.What do the ‘median’, ‘upper’, and ‘lower’ values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Annual Count of Tropical Nights was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksThis dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report and uses the same temperature thresholds as the 'State of the UK Climate' report.Further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

This layer contains the relative heat severity for every pixel for every city in the United States. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2019 and 2020.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Pete.Aniello@tpl.org with feedback.Terms of UseYou understand and agree, and will advise any third party to whom you give any or all of the data, that The Trust for Public Land is neither responsible nor liable for any viruses or other contamination of your system arising from use of The Trust for Public Land’s data nor for any delays, inaccuracies, errors or omissions arising out of the use of the data. The Trust for Public Land’s data is distributed and transmitted "as is" without warranties of any kind, either express or implied, including without limitation, warranties of title or implied warranties of merchantability or fitness for a particular purpose. The Trust for Public Land is not responsible for any claim of loss of profit or any special, direct, indirect, incidental, consequential, and/or punitive damages that may arise from the use of the data. If you or any person to whom you make the data available are downloading or using the data for any visual output, attribution for same will be given in the following format: "This [document, map, diagram, report, etc.] was produced using data, in whole or in part, provided by The Trust for Public Land."

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.

More Datasets

For more datasets, click here.

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 | |:------------------...

Introduction

This dataset has been generated by combining two publically available datasets on weather data and city coordinates and demographics. In a nutshell, it uses a city's coordinates to identify the nearest weather station and then extracts the historical weather data of this station.

A dataset on weather patterns is generated here by using the following open/public datasets:

i)**Global Historical Climatology Network (GHCN) (https://www.ncdc.noaa.gov/ghcn-daily-description):** World Cities Database (https://simplemaps.com/data/world-cities): ** "GHCN (Global Historical Climatology Network)-Daily is an integrated database of daily climate summaries from land surface stations across the globe. Like its monthly counterpart (GHCN-Monthly) , GHCN-Daily is comprised of daily climate records from numerous sources that have been integrated and subjected to a common suite of quality assurance reviews.

GHCN-Daily contains records from over 100,000 stations in 180 countries and territories. NCEI provides numerous daily variables, including maximum and minimum temperature, total daily precipitation, snowfall, and snow depth; however, about one half of the stations report precipitation only. Both the record length and period of record vary by station and cover intervals ranging from less than a year to more than 175 years.

ii) World Cities Database (https://simplemaps.com/data/world-cities): A database of 26000 cities containing city names, geographical co-ordinates, population, density and country names. The database is available under Creative Commons Attribution 4.0 license.

1. Advantages:

The dataset generated and the code in this notebook has applications beyond the current competition.

i) Ease of Access: The dataset makes climate data on thousands of cities instantly accessible through year-wise CSV files. Additionally , a 'key file' which links each city to the nearest weather station makes it easy to retrieve weather data with only the city and country name. It is presented in a human readable format. It can be easily manipulated using Python, R or even Microsoft Excel/ Google Sheets.

ii) Scope and Usage The dataset covers thousands of cities beyond the CDP competition dataset. Hence, its potential uses extend to individuals and organizations engaged in sustainability, climate change, metereology etc. Although the notebook generates data for only the last 5 years, it can be modified further to obtain data for any other years from the NOAA database.

iii) Size NOAA GHCN annual files sizes range from 1-2 GB. The extracted dataset files sizes are in the range of 150-200 MB i.e. almost 10-20% of the original files

2. Motivation:

i) Climate change is known to induce high global temperatures, changes in precipitation patterns, shortened frost/winter season, extreme weather events, heat waves, droughts etc.

ii) Local data provides perspective: From NASAs Global Climate Change: "...the extent of climate change effects on individual regions will vary over time and with the ability of different societal and environmental systems to mitigate or adapt to change." Hence, analysing localized weather patterns, population and changes over and around the cities participating in CDP surveys can provide perspective on adaptation and mitigation measures chosen by a city, infrastructure priorities, social equity programs and policies, gender biases, effect on health of the population etc. National level population and weather data may not account for local variations especially in large diverse countries like USA, China, India, Brazil etc. Hence, localized data is prefereable to national level data.

iii) High Quality Measurable data: Weather and demographic data are primary, unbiased, recorded, historical data reflecting the on-ground situation. Local weather data like temperature, precipitation, snowfall etc. are regularly measured with quality assurances in place. Similarly, demographic data like population and density have been regularly measured for decades.

iv) Unbiased: Other data types like projections, self reported data, extrapolations suffer from bias/assumptions of the reporter or analyst/estimator. Indices and basket/combination indicators suffer from biases in weightage given to different parameters. We need to be cautious while using projections, indices etc as they can inject existing biases in current analysis. Organizations and administrations can and are known to 'game' such indicators. Hence, it is preferable to use primary, unbiased and measurable data like weather and population demographics to build KPIs

3. Method

I) Obtain files from NOAA and Simplemaps

II) Use the co-ordinates of each City and weather station to find the weather station closest to each city

III) Extract the data for these weather stations and shape it in a human readable...