Extreme temperatures can vary greatly across communities due to differences in land use, shade availability, proximity to water, and elevation. Spatially detailed estimates of temperature are difficult to find - often they are stations that are not regularly spaced or are from satellite observations, which estimate only the surface temperature, which can be quite different from air temperature. The PRISM Climate Group at the Oregon State University have developed an 800-meter resolution climatology of temperature for the United States that provides enough detail for intra-city temperature comparisons. It is created by a downscaling model, Parameter-elevation Regressions on Independent Slopes Model (PRISM).The 1991-2020 climate normal for maximum temperature for the month of July was downloaded and analyzed in ArcGIS Pro. Zonal Statistics provide min, max, and mean summaries for county and census tracts (2020 version) geometries. All temperatures were converted from degrees Celsius to Fahrenheit. Additionally, in each layer the mean of the maximum temperature analysis for the next order of geometry is provided (e.g., county data in the tracts layer), which allows comparison of the observed temperature to a larger geographic average. Data Source: https://www.prism.oregonstate.edu/normals/Citation: PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu, data created 10 June 2022, accessed 10 June 2022

Additional analysis on the CAPA heat data with the For Us tree study area by Census tract. Summary Statistics: Compute minimum, mean, maximum, and range statistics by census tract for all three modeled temperature layers. Average Temperature Calculation: Determine the average temperature across the entire study area for each of the three times, excluding non-disadvantaged areas. Heat Anomaly Identification (Afternoon): Create a temperature anomaly metric for each census tract by comparing the average tract temperature to the overall study area average for each time period. Percentile Field: Add a field indicating the percentile rank of each census tract based on the afternoon average temperature. Morning Heat Comparison: Compute and add a field representing the maximum morning temperature difference by census tract compared to 80°F. Daytime Temperature Flux: Calculate and add a field showing the difference between morning and afternoon temperatures to represent daytime temperature change. Nighttime Temperature Flux: Calculate and add a field showing the difference between morning and evening temperatures to represent daytime temperature changeDisclaimer: This product is for informational purposes only and may not be suitable for legal, engineering, or surveying purposes. It does not represent an official survey and represents only the approximate relative location of features and boundaries. Mapping may not necessarily reflect on-the-ground conditions. This product and those involved in its production make no claims as to the accuracy or reliability of the data, and neither assumes, nor will accept liability for their use.

The dataset utilizes satellite imagery raster files from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MOD11A2) Version 6.1 product. This data provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a spatial resolution of 1 kilometer within a 1,200 by 1,200 km grid.The variables extracted from this dataset are Daytime and Nighttime land surface temperatures in degrees Celsius. The data covers the period from January 1, 2020, to December 31, 2023, offering a comprehensive view over four years. It includes quality control assessments, observation times, view zenith angles, clear-sky coverages, and emissivity values for different land cover types.The dataset integrates spatial boundaries from shapefiles with the imagery raster files to provide data at various geographical levels, including state, county, census tract, and zip code, although your specific analysis seems to focus on the state level in Oklahoma.Field NameDescriptionStateNameName of the stateStateFipsState FIPS codeCountyFipsCounty FIPS codeTractFipsCenstract FIPS code of geographic areaDateDate at which temperature is recorded in that geographical areaLST_Day_1kmDay land surface temperature in degree Celsius (°C)LST_Night_1kmNight land surface temperature in degree Celsius (°C)The data is updated every 8 days, aligning with the ground track repeat period of the Terra and Aqua platforms. The provided data link directs to the Earth Engine catalog, offering access to the MOD11A2 dataset.

Additional analysis on the 2024 CAPA heat data with the For Us Tree study area by Community Tabulation Area 2010. The two areas Willowridge and Four Corners are within the For Us Tree project area but they are not Kinder Rice Community Tabulation Areas.Summary Statistics: Compute minimum, mean, maximum, and range statistics by census tract for all three modeled temperature layers. Average Temperature Calculation: Determine the average temperature across the entire study area for each of the three times, excluding non-disadvantaged areas. Heat Anomaly Identification (Afternoon): Create a temperature anomaly metric for each census tract by comparing the average tract temperature to the overall study area average for each time period. Percentile Field: Add a field indicating the percentile rank of each census tract based on the afternoon average temperature. Morning Heat Comparison: Compute and add a field representing the maximum morning temperature difference by census tract compared to 80°F. Daytime Temperature Flux: Calculate and add a field showing the difference between morning and afternoon temperatures to represent daytime temperature change. Nighttime Temperature Flux: Calculate and add a field showing the difference between morning and evening temperatures to represent daytime temperature changeDisclaimer: This product is for informational purposes only and may not be suitable for legal, engineering, or surveying purposes. It does not represent an official survey and represents only the approximate relative location of features and boundaries. Mapping may not necessarily reflect on-the-ground conditions. This product and those involved in its production make no claims as to the accuracy or reliability of the data, and neither assumes, nor will accept liability for their use.

Cities in the U.S. are getting hotter, and that is causing significant health risks, especially to minorities, the elderly, and impoverished. There is significant spatial variation in temperature across a city due to changes in the landscape (elevation, tree cover, development, etc). NOAA has been engaged in a nationwide effort with CAPA Strategies to use a combination of Sentinel-2 satellite data along with temperature readings recorded from car- and bike-mounted sensors to generate detailed maps of the urban areas most impacted by heat. These measurements have been combined into single raster layers for morning, afternoon, and evening temperatures. As of 2020, 27 cities (26 in the U.S) have been mapped; a total of 50 cities will be mapped by the end of 2021. This layer shows the census tract (neighborhood) averages for those temperatures, along with additional information calculated for each neighborhood including:Temperature anomaly (neighborhood temperature compared to the citywide average based on the CAPA data)Impervious surfaceTree coverDemographicsTotal populationPopulation <5Population >65MinorityMedian incomePovertyCombining these different types of information can help planners identify areas at risk and help to develop mitigation and resilience plans to improve urban living conditions. More information about the campaign can be found in this Story Map by NOAA.

Urban Heat Island images:MorningAfternoonEveningTacoma Heat Island StudyData collected on 7/25/2018, collected by Dr. Vivek Shandas, Capa StrategiesWhat Earth Economics is working on:Through grant funding, Earth Economics is working on building out an approach and methodology using Urban Heat Island modeling (LANDSAT data) to assume health impacts (mortality rates) on a census tract level, using research on how demographics and UHI impact community health outcomes.Variables:Name: Census Block Group NamePop: Census Block Group populationIncome: Average individual Census Block Group level annual incomeOver 65: Population over age 65Under14: Population under age 14AF: Afternoon temperature (C), averaged to Census Block Group (July 25, 2018). Data collected by Dr. Vivek Shandas using this methodologyPm: Evening temperature (C), averaged to Census Block Group (July 25, 2018)Combtemp: Average of evening and afternoon temperatureHighRiskAgeGroup: Percent of population in a high risk age group for heat related illness (over age 65 and under age 14)Density: Population DensityCity of Tacoma Contact: Vanessa Simpson, Senior Technical GIS Analyst, Environmental Servicesvsimpson@cityoftacoma.org

Metadata:Data Provider: Oklahoma MesonetData Link: Daily SummariesLast Update Time: October 10, 2023Start Date: January 1, 2010End Date: December 31, 2010Update Frequency: DailyAdmin Unit: CensustractMeasurement Criteria:Total Daily Solar Radiation: Calculated if 99% of the observations for the site and day are available.Maximum Wind Gust, Maximum Heat Index, Minimum Wind Chill: Calculated if at least 1 observation is available for the day.Wind Direction: Available when wind speed is greater than 2.5 mph.Other Variables: Require at least 90% of the observations to be computed.Unavailable Data: Values between +/-990-999 indicate data are not available.Variables in Data Table:CSV Column NameDescriptionStateNameState NameStateFIPSState FIPS CodeCntyNameCounty NameCntyFIPSCounty FIPS CodeTractFIPSCensus Tract FIPS CodeCentroid_LatCentroid LatitudeCentroid_LonCentroid LongitudeDateDateMEAN_TR05Calibrated change in temperature of soil over time after a heat pulse is introduced. Used to calculate soil water potential, fractional water index, or volumetric water.MEAN_TR25Calibrated change in temperature of soil over time after a heat pulse is introduced. Used to calculate soil water potential, fractional water index, or volumetric water.MEAN_TR60Calibrated change in temperature of soil over time after a heat pulse is introduced. Used to calculate soil water potential, fractional water index, or volumetric water.MEAN_R05BDNumber of errant 30-minute calibrated delta-t at 5 cm observations.MEAN_R25BDNumber of errant 30-minute calibrated delta-t at 25 cm observations.MEAN_R60BDNumber of errant 30-minute calibrated delta-t at 60 cm observations.MEAN_TAVGAverage of all 5-minute averaged temperature observations each day.MEAN_HAVGAverage of all 5-minute averaged humidity observations each day.MEAN_DAVGAverage of all 5-minute averaged dewpoint temperatures each day. Dewpoint temperature is derived from 1.5 m air temperature and the corresponding humidity value.MEAN_VDEFAverage of all 5-minute averaged vapor deficit estimates each day.MEAN_PAVGAverage of all 5-minute averaged station air pressure observations each day.MEAN_WSPDAverage of all 5-minute wind speed observations each day.MEAN_ATOTDaily accumulation of solar radiation each day.MEAN_BAVGAverage of all 15-minute averaged soil temperature observations each day. This variable is only available prior to December 1, 2013.MEAN_S5AVAverage of all 15-minute averaged soil temperature observations each day.Description:The Oklahoma Mesonet dataset provides detailed daily environmental measurements across Oklahoma. It includes various parameters such as temperature, humidity, dewpoint, vapor deficit, air pressure, wind speed, solar radiation, and soil temperature. Each parameter is averaged over specific time intervals (e.g., 5-minute, 15-minute) and provides a comprehensive overview of daily weather and environmental conditions.The Mesonet stations require a minimum percentage of observations for the day to be included in daily calculations. For instance, total daily solar radiation is calculated only if 99% of the observations for the site and day are available. Other parameters, such as maximum wind gust, maximum heat index, and minimum wind chill, require at least one observation to be recorded for the day. Wind direction data are only recorded when wind speeds exceed 2.5 mph, and most other variables require at least 90% of the observations to be computed.This dataset is essential for researchers, policymakers, and agricultural professionals, providing critical data to analyze environmental trends, assess climate patterns, and make informed decisions related to agriculture and environmental management.

Metadata:Data Provider: Oklahoma MesonetData Link: Daily SummariesLast Update Time: October 10, 2023Start Date: January 1, 2023End Date: December 31, 2023Update Frequency: DailyAdmin Unit: CensustractMeasurement Criteria:Total Daily Solar Radiation: Calculated if 99% of the observations for the site and day are available.Maximum Wind Gust, Maximum Heat Index, Minimum Wind Chill: Calculated if at least 1 observation is available for the day.Wind Direction: Available when wind speed is greater than 2.5 mph.Other Variables: Require at least 90% of the observations to be computed.Unavailable Data: Values between +/-990-999 indicate data are not available.Variables in Data Table:CSV Column NameDescriptionStateNameState NameStateFIPSState FIPS CodeCntyNameCounty NameCntyFIPSCounty FIPS CodeTractFIPSCensus Tract FIPS CodeCentroid_LatCentroid LatitudeCentroid_LonCentroid LongitudeDateDateMEAN_TR05Calibrated change in temperature of soil over time after a heat pulse is introduced. Used to calculate soil water potential, fractional water index, or volumetric water.MEAN_TR25Calibrated change in temperature of soil over time after a heat pulse is introduced. Used to calculate soil water potential, fractional water index, or volumetric water.MEAN_TR60Calibrated change in temperature of soil over time after a heat pulse is introduced. Used to calculate soil water potential, fractional water index, or volumetric water.MEAN_R05BDNumber of errant 30-minute calibrated delta-t at 5 cm observations.MEAN_R25BDNumber of errant 30-minute calibrated delta-t at 25 cm observations.MEAN_R60BDNumber of errant 30-minute calibrated delta-t at 60 cm observations.MEAN_TAVGAverage of all 5-minute averaged temperature observations each day.MEAN_HAVGAverage of all 5-minute averaged humidity observations each day.MEAN_DAVGAverage of all 5-minute averaged dewpoint temperatures each day. Dewpoint temperature is derived from 1.5 m air temperature and the corresponding humidity value.MEAN_VDEFAverage of all 5-minute averaged vapor deficit estimates each day.MEAN_PAVGAverage of all 5-minute averaged station air pressure observations each day.MEAN_WSPDAverage of all 5-minute wind speed observations each day.MEAN_ATOTDaily accumulation of solar radiation each day.MEAN_BAVGAverage of all 15-minute averaged soil temperature observations each day. This variable is only available prior to December 1, 2013.MEAN_S5AVAverage of all 15-minute averaged soil temperature observations each day.Description:The Oklahoma Mesonet dataset provides detailed daily environmental measurements across Oklahoma. It includes various parameters such as temperature, humidity, dewpoint, vapor deficit, air pressure, wind speed, solar radiation, and soil temperature. Each parameter is averaged over specific time intervals (e.g., 5-minute, 15-minute) and provides a comprehensive overview of daily weather and environmental conditions.The Mesonet stations require a minimum percentage of observations for the day to be included in daily calculations. For instance, total daily solar radiation is calculated only if 99% of the observations for the site and day are available. Other parameters, such as maximum wind gust, maximum heat index, and minimum wind chill, require at least one observation to be recorded for the day. Wind direction data are only recorded when wind speeds exceed 2.5 mph, and most other variables require at least 90% of the observations to be computed.This dataset is essential for researchers, policymakers, and agricultural professionals, providing critical data to analyze environmental trends, assess climate patterns, and make informed decisions related to agriculture and environmental management.

Data used in "A U.S. heat disaster? Intersection of social vulnerability and temperature extremes exacerbated by mid-century climate change and population shifts" [https://iopscience.iop.org/article/10.1088/2752-5309/adb902/meta]. Please see the paper for detailed methodology. Median projected acute (95% maximum temperature, hot days (>37.5C)) and chronic (cooling degree days) heat hazard are assessed in contemporary (1995-2014) and projected (2041-2060) epochs across 25+ climate models under three shared socioeconomic pathways (SSP245, SSP370, SSP585) against contemporary social vulnerability index (SVI). We aggregate historical and projected (2050) population at the census tract in addition to various heat measures. The product of heat measures and population is used to estimate population heat exposure. We estimate multiple heat hazard metrics across three shared socioeconomic pathways and 32 total global climate models (GCMs). We estimate cooling degree days as the annual average (per epoch) cumulative sum daily average temperature degrees over 24C and 18C thresholds. We estimate hot days as the annual number of days where the daily maximum temperature exceeds 37.5C across epochs, and alternatively as the annual number of days where the daily maximum heat index (derived from temperature and specific humidity) exceed 40C. We estimate the 95th percentile for temperature and heat index. Population heat hazard is estimated by combining heat exposure with population growth projections (Gao et al., 2020). Outputs here are provided at the census tract level for all models, and for the median heat hazard and population heat exposure across models. The median metrics are the main source of data used in the paper.

Cities in the U.S. are getting hotter, and that is causing significant health risks, especially to minorities, the elderly, and impoverished. There is significant spatial variation in temperature across a city due to changes in the landscape (elevation, tree cover, development, etc). NOAA has been engaged in a nationwide effort with CAPA Strategies to use a combination of Sentinel-2 satellite data along with temperature readings recorded from car- and bike-mounted sensors to generate detailed maps of the urban areas most impacted by heat. These measurements have been combined into single raster layers for morning, afternoon, and evening temperatures. As of 2020, 27 cities (26 in the U.S) have been mapped; a total of 50 cities will be mapped by the end of 2021. This layer shows the census tract (neighborhood) averages for those temperatures, along with additional information calculated for each neighborhood including:Temperature anomaly (neighborhood temperature compared to the citywide average based on the CAPA data)Impervious surfaceTree coverDemographicsTotal populationPopulation <5Population >65MinorityMedian incomePovertyCombining these different types of information can help planners identify areas at risk and help to develop mitigation and resilience plans to improve urban living conditions. More information about the campaign can be found in this Story Map by NOAA.

Metadata:Data Provider: Oklahoma MesonetData Link: Daily SummariesLast Update Time: October 10, 2023Start Date: January 1, 2011End Date: December 31, 2011Update Frequency: DailyAdmin Unit: CensustractMeasurement Criteria:Total Daily Solar Radiation: Calculated if 99% of the observations for the site and day are available.Maximum Wind Gust, Maximum Heat Index, Minimum Wind Chill: Calculated if at least 1 observation is available for the day.Wind Direction: Available when wind speed is greater than 2.5 mph.Other Variables: Require at least 90% of the observations to be computed.Unavailable Data: Values between +/-990-999 indicate data are not available.Variables in Data Table:CSV Column NameDescriptionStateNameState NameStateFIPSState FIPS CodeCntyNameCounty NameCntyFIPSCounty FIPS CodeTractFIPSCensus Tract FIPS CodeCentroid_LatCentroid LatitudeCentroid_LonCentroid LongitudeDateDateMEAN_TR05Calibrated change in temperature of soil over time after a heat pulse is introduced. Used to calculate soil water potential, fractional water index, or volumetric water.MEAN_TR25Calibrated change in temperature of soil over time after a heat pulse is introduced. Used to calculate soil water potential, fractional water index, or volumetric water.MEAN_TR60Calibrated change in temperature of soil over time after a heat pulse is introduced. Used to calculate soil water potential, fractional water index, or volumetric water.MEAN_R05BDNumber of errant 30-minute calibrated delta-t at 5 cm observations.MEAN_R25BDNumber of errant 30-minute calibrated delta-t at 25 cm observations.MEAN_R60BDNumber of errant 30-minute calibrated delta-t at 60 cm observations.MEAN_TAVGAverage of all 5-minute averaged temperature observations each day.MEAN_HAVGAverage of all 5-minute averaged humidity observations each day.MEAN_DAVGAverage of all 5-minute averaged dewpoint temperatures each day. Dewpoint temperature is derived from 1.5 m air temperature and the corresponding humidity value.MEAN_VDEFAverage of all 5-minute averaged vapor deficit estimates each day.MEAN_PAVGAverage of all 5-minute averaged station air pressure observations each day.MEAN_WSPDAverage of all 5-minute wind speed observations each day.MEAN_ATOTDaily accumulation of solar radiation each day.MEAN_BAVGAverage of all 15-minute averaged soil temperature observations each day. This variable is only available prior to December 1, 2013.MEAN_S5AVAverage of all 15-minute averaged soil temperature observations each day.Description:The Oklahoma Mesonet dataset provides detailed daily environmental measurements across Oklahoma. It includes various parameters such as temperature, humidity, dewpoint, vapor deficit, air pressure, wind speed, solar radiation, and soil temperature. Each parameter is averaged over specific time intervals (e.g., 5-minute, 15-minute) and provides a comprehensive overview of daily weather and environmental conditions.The Mesonet stations require a minimum percentage of observations for the day to be included in daily calculations. For instance, total daily solar radiation is calculated only if 99% of the observations for the site and day are available. Other parameters, such as maximum wind gust, maximum heat index, and minimum wind chill, require at least one observation to be recorded for the day. Wind direction data are only recorded when wind speeds exceed 2.5 mph, and most other variables require at least 90% of the observations to be computed.This dataset is essential for researchers, policymakers, and agricultural professionals, providing critical data to analyze environmental trends, assess climate patterns, and make informed decisions related to agriculture and environmental management.

National Risk Index Version: March 2023 (1.19.0)A Heat Wave is a period of abnormally and uncomfortably hot and unusually humid weather typically lasting two or more days with temperatures outside the historical averages for a given area. Annualized frequency values for Heat Waves are in units of event-days per year.The National Risk Index is a dataset and online tool that helps to illustrate the communities most at risk for 18 natural hazards across the United States and territories: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather. The National Risk Index provides Risk Index values, scores and ratings based on data for Expected Annual Loss due to natural hazards, Social Vulnerability, and Community Resilience. Separate values, scores and ratings are also provided for Expected Annual Loss, Social Vulnerability, and Community Resilience. For the Risk Index and Expected Annual Loss, values, scores and ratings can be viewed as a composite score for all hazards or individually for each of the 18 hazard types.Sources for Expected Annual Loss data include: Alaska Department of Natural Resources, Arizona State University’s (ASU) Center for Emergency Management and Homeland Security (CEMHS), California Department of Conservation, California Office of Emergency Services California Geological Survey, Colorado Avalanche Information Center, CoreLogic’s Flood Services, Federal Emergency Management Agency (FEMA) National Flood Insurance Program, Humanitarian Data Exchange (HDX), Iowa State University's Iowa Environmental Mesonet, Multi-Resolution Land Characteristics (MLRC) Consortium, National Aeronautics and Space Administration’s (NASA) Cooperative Open Online Landslide Repository (COOLR), National Earthquake Hazards Reduction Program (NEHRP), National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration's National Hurricane Center, National Oceanic and Atmospheric Administration's National Weather Service (NWS), National Oceanic and Atmospheric Administration's Office for Coastal Management, National Oceanic and Atmospheric Administration's National Geophysical Data Center, National Oceanic and Atmospheric Administration's Storm Prediction Center, Oregon Department of Geology and Mineral Industries, Pacific Islands Ocean Observing System, Puerto Rico Seismic Network, Smithsonian Institution's Global Volcanism Program, State of Hawaii’s Office of Planning’s Statewide GIS Program, U.S. Army Corps of Engineers’ Cold Regions Research and Engineering Laboratory (CRREL), U.S. Census Bureau, U.S. Department of Agriculture's (USDA) National Agricultural Statistics Service (NASS), U.S. Forest Service's Fire Modeling Institute's Missoula Fire Sciences Lab, U.S. Forest Service's National Avalanche Center (NAC), U.S. Geological Survey (USGS), U.S. Geological Survey's Landslide Hazards Program, United Nations Office for Disaster Risk Reduction (UNDRR), University of Alaska – Fairbanks' Alaska Earthquake Center, University of Nebraska-Lincoln's National Drought Mitigation Center (NDMC), University of Southern California's Tsunami Research Center, and Washington State Department of Natural Resources.Data for Social Vulnerability are provided by the Centers for Disease Control (CDC) Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index, and data for Community Resilience are provided by University of South Carolina's Hazards and Vulnerability Research Institute’s (HVRI) 2020 Baseline Resilience Indicators for Communities.The source of the boundaries for counties and Census tracts are based on the U.S. Census Bureau’s 2021 TIGER/Line shapefiles. Building value and population exposures for communities are based on FEMA’s Hazus 6.0. Agriculture values are based on the USDA 2017 Census of Agriculture.

Metadata:Data Provider: Oklahoma MesonetData Link: Daily SummariesLast Update Time: October 10, 2023Start Date: January 1, 2012End Date: December 31, 2012Update Frequency: DailyAdmin Unit: CensustractMeasurement Criteria:Total Daily Solar Radiation: Calculated if 99% of the observations for the site and day are available.Maximum Wind Gust, Maximum Heat Index, Minimum Wind Chill: Calculated if at least 1 observation is available for the day.Wind Direction: Available when wind speed is greater than 2.5 mph.Other Variables: Require at least 90% of the observations to be computed.Unavailable Data: Values between +/-990-999 indicate data are not available.Variables in Data Table:CSV Column NameDescriptionStateNameState NameStateFIPSState FIPS CodeCntyNameCounty NameCntyFIPSCounty FIPS CodeTractFIPSCensus Tract FIPS CodeCentroid_LatCentroid LatitudeCentroid_LonCentroid LongitudeDateDateMEAN_TR05Calibrated change in temperature of soil over time after a heat pulse is introduced. Used to calculate soil water potential, fractional water index, or volumetric water.MEAN_TR25Calibrated change in temperature of soil over time after a heat pulse is introduced. Used to calculate soil water potential, fractional water index, or volumetric water.MEAN_TR60Calibrated change in temperature of soil over time after a heat pulse is introduced. Used to calculate soil water potential, fractional water index, or volumetric water.MEAN_R05BDNumber of errant 30-minute calibrated delta-t at 5 cm observations.MEAN_R25BDNumber of errant 30-minute calibrated delta-t at 25 cm observations.MEAN_R60BDNumber of errant 30-minute calibrated delta-t at 60 cm observations.MEAN_TAVGAverage of all 5-minute averaged temperature observations each day.MEAN_HAVGAverage of all 5-minute averaged humidity observations each day.MEAN_DAVGAverage of all 5-minute averaged dewpoint temperatures each day. Dewpoint temperature is derived from 1.5 m air temperature and the corresponding humidity value.MEAN_VDEFAverage of all 5-minute averaged vapor deficit estimates each day.MEAN_PAVGAverage of all 5-minute averaged station air pressure observations each day.MEAN_WSPDAverage of all 5-minute wind speed observations each day.MEAN_ATOTDaily accumulation of solar radiation each day.MEAN_BAVGAverage of all 15-minute averaged soil temperature observations each day. This variable is only available prior to December 1, 2013.MEAN_S5AVAverage of all 15-minute averaged soil temperature observations each day.Description:The Oklahoma Mesonet dataset provides detailed daily environmental measurements across Oklahoma. It includes various parameters such as temperature, humidity, dewpoint, vapor deficit, air pressure, wind speed, solar radiation, and soil temperature. Each parameter is averaged over specific time intervals (e.g., 5-minute, 15-minute) and provides a comprehensive overview of daily weather and environmental conditions.The Mesonet stations require a minimum percentage of observations for the day to be included in daily calculations. For instance, total daily solar radiation is calculated only if 99% of the observations for the site and day are available. Other parameters, such as maximum wind gust, maximum heat index, and minimum wind chill, require at least one observation to be recorded for the day. Wind direction data are only recorded when wind speeds exceed 2.5 mph, and most other variables require at least 90% of the observations to be computed.This dataset is essential for researchers, policymakers, and agricultural professionals, providing critical data to analyze environmental trends, assess climate patterns, and make informed decisions related to agriculture and environmental management.

The variables extracted from this dataset are Daytime and Nighttime land surface temperatures in degrees Celsius. The data covers the period from January 1, 2020, to December 31, 2023, offering a comprehensive view over four years. It includes quality control assessments, observation times, view zenith angles, clear-sky coverages, and emissivity values for different land cover types.The dataset integrates spatial boundaries from shapefiles with the imagery raster files to provide data at various geographical levels, including state, county, census tract, and zip code, although your specific analysis seems to focus on the state level in Oklahoma.The data is updated every 8 days, aligning with the ground track repeat period of the Terra and Aqua platforms. The provided data link directs to the Earth Engine catalog, offering access to the MOD11A2 dataset.Column NameDescriptionStateNameName of the stateStateFipsState FIPS codeZipCodeZipcode of geographical areaDateDate at which temperature is recordedLST_Day_1kmDay land surface temperature in degrees CelsiusLST_Night_1kmNight land surface temperatureEach column represents a different attribute of the data, such as the name of the state, its FIPS code, zipcode, date of temperature recording, daytime land surface temperature, and nighttime land surface temperature.

Extreme heat events, or heat waves, are on the rise and becoming more intense according to the U.S. Environmental Protection Agency (EPA). These events are more than just an annoyance and can lead to illness and death, particularly among vulnerable populations including seniors and young people. The EPA also states prolonged exposure to these heat events can lead to other impacts such as damaging crops or killing livestock. Climate resilience planning is one approach to preparing for and mitigating the effects of extreme heat. Climate resilience planning in local communities involves several steps including assessing vulnerability and risk.© 2024 Adobe Stock. All rights reserved.It is a fact that trees can lower the surrounding air temperature through evapotranspiration, providing shade, and taking up space that might otherwise be converted to pavement. Lots of pavement, blacktop roads, and concrete buildings absorb the sun's heat and radiate that heat into the surrounding air. This is especially evident in highly developed urban areas which can get up to 20 degrees warmer than surrounding vegetated areas. These hot zones are referred to as Urban Heat Islands. One way to reduce the warmer temperatures in urban areas is to plant trees and other vegetation. This layer displays census tracts that are ranked according to which would benefit most from tree planting. The ranking is based upon a composite index built with the following attributes:High Summer Average Surface Temperature (°F)Percent of Tract Covered by Tree Canopy (%)Population Density (ppl/km2)These attribute links take you to the original data sources. Preprocessing was needed to prepare many of these inputs for inclusion in our index. The links are provided for reference only.This layer is one of a series developed to support local climate resilience planning. Intended as planning tools for policy makers, climate resilience planners, and community members, these layers highlight areas of the community that are most likely to benefit from the resilience intervention the map supports. Each layer focuses on one specific heat resilience intervention intended to help mitigate against the climate hazard.Planting trees along streets and over dark surfaces in urban areas is proven to reduce air temperature which helps to mitigate the impacts of urban heat islands. For more resources on extreme heat visit heat.gov where you can learn about the impacts of tree planting campaigns. The heat resilience index (HRI) and methodology were developed in collaboration with the U.S. Centers for Disease Control and Prevention (CDC) and the UC Davis, Department of Public Health.Layers in the Extreme Heat hazard intervention series include Where Will a Buddy Program Improve Urban Heat Health?Where Will Tree Planting Improve Urban Heat Health? Where Will Cooling Centers Improve Urban Heat Health?Did you know you can build your own climate resilience index or use ours and customize it? The Customize a climate resilience index Tutorial provides more information on the index and also walks you through steps for taking our index and customizing it to your needs so you can create intervention maps better suited to your location and sourced from your own higher resolution data. For more information about how Esri enriched the census tracts with exposure, demographic, and environmental data to create composite indices called intervention indices, please read this technical reference.This feature layer was created from the Climate Resilience Planning Census Tracts hosted feature layer view and is one of 18 similar intervention layers, all of which can be found in ArcGIS Living Atlas of the World.