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9 results for “heat indicators”
Social and Heat Vulnerability Indices in Phoenix, Arizona
Vulnerability indices and maps are commonly employed by researchers and practitioners to assess hazard risk by combining variables that are theoretically or empirically associated with hazard outcomes and spatially visualizing those combined variables. For this dataset, we followed established methods to produce two vulnerability indices for 358 census tracts in the City of Phoenix, Arizona for the year 2016: the all-hazards Social Vulnerability Index (SoVI) and a specific hazards Heat Vulnerability Index (HVI). For SoVI, we compiled 27 social variables from the 2012-2016 American Community Survey (ACS); for HVI, we compiled seven social variables from the 2012-2016 ACS, one variable regarding residential air conditioning prevalence from the Maricopa County Assessor’s Office, and two variables related to vegetation density from Landsat 8 remote sensing imagery. Lastly, we conducted principal components analysis on each of the indices respective variables and then summed the resulting component scores for each census tract to produce the index values which we then spatially joined to the Phoenix census tracts.
Figure 1. Heat map indicating the cumulative percent seed shatter across the participating states for a in Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 1: Broadleaf species
Figure 1. Heat map indicating the cumulative percent seed shatter across the participating states for a window starting from soybean physiological maturity to 4 wk past maturity in 2016 and 2017. States were included in these maps only if they conducted sampling during the week indicated (e.g., In 2017, Arkansas sampled on October 2, October 18, and November 3, none of which are within ±3 d of the October 10 maturity date or maturity þ2 wk on October 24 in the state that year. Hence only data from maturity þ3 wk are for Arkansas for 2017.)
Figure 1. Heat map indicating the cumulative percent seed shatter across the participating states for a in Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 2: Grass species
Figure 1. Heat map indicating the cumulative percent seed shatter across the participating states for a window starting from soybean physiological maturity to 4 wk past physiological maturity in 2016 and 2017. States were included in these maps only if they conducted sampling during the week indicated. (e.g., In 2017, Arkansas sampled on October 2, October 18, and November 3, none of which are within ±3 d of the October 10 maturity date or maturity +2 wk on October 24 in the state that year. Hence only data from maturity +3 wk are for Arkansas for 2017.)
Standard heat stress indices may not be appropriate for assessing marathons
<p>Code and data supporting the manuscript "Standard heat stress indices may not be appropriate for assessing marathons".</p> <p>These scripts rely on some of the data being present in a certain location. But to reproduce this based on the included data, you need only run pet_calculations.py as all the data area in comrades.nc</p> <p>The order in which the scripts were run was:</p> <p>1. get_utci.py - downloads ERA5-HEAT data<br> 2. unzip_era5_heat.sh - unzips that data<br> 3. query.py - subsets ERA5-HEAT data, and ERA5 data from CEDA<br> 4. pet_calculations.py - calculations and analysis</p> <p>pet_calculations.ipynb shows the results of pet_calculations.py in notebook form.</p>
Daily indices of heat at select U.S. military installations (1990 - 2019)
<p>R data files for annual indices of heat of 30 Continental U.S. (CONUS) U.S. military installations from 1990-2019.</p> <p>Daily indices were derived from hourly meteorological estimates from the North American Land Data Assimilation System 2 (NLDAS-2) forcing dataset served as the primary source of weather and atmospheric data. We extracted parameters from NLDAS grid cells containing the centroid of each installation based on shapefiles from the Department of Defense (DoD) Military Installations, Ranges, and Training Areas (MIRTA) Dataset. We calculated relative humidity from specific humidity, temperature, and atmospheric pressure; heat index (HI) from temperature and relative humidity based on a US National Weather Service algorithm; and outdoor WBGT from air temperature, relative humidity, solar irradiance, barometric pressure, and wind speed using the method of Liljegren <em>et al.</em></p> <p>`daily_indices.Rmd` (328402 observations of 18 variables) contains indices for daily maximum, mean, minimum, and 95th percentile values of temperature (deg F), heat index (deg F), and WBGT (deg C) with urban and non-urban inputs to the Liljegren model.</p> <p>`morning_indices.Rmd` (328364 observations of 5 variables) contains indices for daily 0600 hour (local time) values of temperature (deg F), heat index (deg F), and WBGT (deg C, urban model). </p>
Future Heat Stress Indicators for Johannesburg and Ekurhuleni
<p>This dataset is part of the scientific paper: Souverijns, N., De Ridder, K., Veldeman, N., Lefebre, F., Kusambiza-Kiingi, F., Memela, W., Jones, N.K.W., 2022. Urban heat in Johannesburg and Ekurhuleni, South Africa: A meter-scale assessment and vulnerability analysis. Urban Climate, 46, 101331. <a href="https://doi.org/10.1016/j.uclim.2022.101331">https://doi.org/10.1016/j.uclim.2022.101331</a></p> <p>Heat stress indicators for present and future climates for the cities of Johannesburg and Ekurhuleni (South Africa).at 30m spatial resolution calculated with UrbClim (De Ridder et al., 2015) for the following scenarios:</p> <p>- 2001-2020: present time (ERA5 input data)</p> <p>- 2021-2040 RCP4.5 (CMIP5 ensemble)</p> <p>- 2021-2040 RCP8.5 (CMIP5 ensemble)</p> <p>- 2041-2060 RCP4.5 (CMIP5 ensemble)</p> <p>- 2041-2060 RCP8.5 (CMIP5 ensemble)</p> <p>For each of the 23 indicators, an overview image is available at high resolution. Furthermore, georeferenced GeoTiffs are available to visualize & analyse the indicators on the users preferred system. The indicators are available in EPSG4326/WGS84. A description of each indicator is given:</p> <p>- Cooling degree hours: Annual number of hours during which the temperatures rises over 25°C, multiplied by the number of degrees the temperature rises above 25°C. This is an international standard to estimate the energy demand for air conditioning use.</p> <p>- CSDI: Cold spell duration index. Annual number of days with at least six consecutive days when the night temperature < 10th percentile</p> <p>- Heatwave days: Number of heatwave days per year calculated following the definition of the South African Weather Service. If the maximum temperature at a particular town is expected to meet or exceed 5 degrees C above the average maximum temperature of “the hottest month” for that particular place (this is 32°C for Johannesburg & Ekurhuleni), as well as persisting in that mode for 3 days or more (https://www.weathersa.co.za/home/weatherques)</p> <p>- T2M_daily_mean_max: Average daily maximum temperature</p> <p>- T2M_daily_mean_max_topography: Daytime Urban Heat Island corrected for topography</p> <p>- T2M_daily_mean_min: Average daily minimum temperature</p> <p>- T2M_daily_mean_min_topography: Nighttime Urban Heat Island corrected for topography</p> <p>- T2M_dayover25: Annual number of days attaining a maximum temperature > 25°C</p> <p>- T2M_dayover25_duetourban: Annual number of days attaining a maximum temperature > 25°C that are caused by urban canopy</p> <p>- T2M_dayover30: Annual number of days attaining a maximum temperature > 30°C</p> <p>- T2M_dayover30_duetourban: Annual number of days attaining a maximum temperature > 30°C that are caused by urban canopy</p> <p>- T2M_max: Absolute maximum temperature</p> <p>- T2M_mean: Absolute average temperature</p> <p>- T2M_min: Absolute minimum temperature</p> <p>- T2M_nightover20: Number of nights attaining a minimum temperature not dropping below 20°C</p> <p>- T2M_nightover25: Number of nights attaining a minimum temperature not dropping below 25°C</p> <p>- TN10P: Annual number of days when the night temperature < 10th percentile</p> <p>- TN90P: Annual number of days when the night temperature > 90th percentile</p> <p>- TN_max: Annual minimum nighttime warmest temperature</p> <p>- TX10P: Annual number of days when the day temperature < 10th percentile</p> <p>- TX90P: Annual number of days when the day temperature > 90th percentile</p> <p>- TX_min: Annual maximum daytime coolest temperature</p> <p>- WSDI: Warm Spell Duration Index. Annual number of days with at least six consecutive days when daytime temperatues > 90th percentile</p>
Annual indices of heat and humidity, U.S. Army installations, 1990-2018
<p>R data files for annual indices of heat of 25 Continental U.S. (CONUS) U.S. Army installations from 1990-2018 in list and long formats.</p> <p>Annual indices were derived from hourly meteorological estimates from the North American Land Data Assimilation System 2 (NLDAS-2) forcing dataset served as the primary source of weather and atmospheric data. We selected NLDAS grid cells containing the centroid of each installation based on shapefiles from the Department of Defense (DoD) Military Installations, Ranges, and Training Areas (MIRTA) Dataset. We calculated relative humidity from specific humidity, temperature, and atmospheric pressure; heat index (HI) from temperature and relative humidity based on a US National Weather Service algorithm; and outdoor WBGT from air temperature, relative humidity, solar irradiance, barometric pressure, and wind speed using the method of Liljegren <em>et al.</em></p>
Performance Indicators of Photovoltaic Heat Pumps
<p>Qualitative and quantitaive results included in the review 'Performance Indicators of Photovoltaic Heat Pumps' (DOI:10.1016/j.heliyon.2019.e02691) are given, together with the original data and the corresponding calculations. The files with the simulation info for the SISIFO simulation tool are also available. </p>
Urban Heat: Forward-Looking Climate Modelling for West-Africa: extra indicator
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for present-day and future conditions under selected climate scenarios. This dataset serves as a supplement to the previous two datasets: https://zenodo.org/doi/10.5281/zenodo.11085333 and https://zenodo.org/doi/10.5281/zenodo.11073297. It contains an extra indicator Heat Index (HI) based on the apparent temperature (AT) for 12 cities in west Africa. </p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for HI across three scenarios (<strong>present, SSP2-4.5, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2031-2050, and 2051-2070</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>The indicator is available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats. It is named as HIAT in the file name with extra information such as scenario, resolution, projection, etc. </li> <li>The indicator is calculated at a resolution from <strong>100 m </strong>to <strong>200 m </strong>(depending on the size of the city). Additionally, downscaled versions of the indicator is provided at a resolution of <strong>30 m</strong>.</li> <li>The Heat Index is calculated as the <strong>yearly average number of days when apparent temperature reaches 105 F</strong>. More information regarding the definition and calculation of HI can be found: Rohat, G., Flacke, J., Dosio, A., Dao, H., & Van Maarseveen, M. (2019). Projections of human exposure to dangerous heat in African cities under multiple socioeconomic and climate scenarios. <em>Earth's Future</em>, <em>7</em>(5), 528-546.</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format are available. </li> <li>The indicators in NetCDF and GeoTiff format as well as the visualized PNG files can be found in the <strong>{city}_HIAT.zip </strong>(for the cities with future projection) or <strong>{city}</strong><strong>_present_HIAT.zip </strong>(for those cities without future projection).</li> <li>More information about other indicators, including the simulation, methodology, all available data list, contact information, etc. can be found in the other two datasets.</li> </ul>
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