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3 results for “UTCI”
UTCI-to-DTS
<p>Mapping values of the Universal Thermal Climate Index (UTCI) to Dynamic Thermal Sensation (DTS), cf. Fig. 10 in https://doi.org/10.1007/s00484-011-0454-1<br> Tab-delimited ASCII file with the two columns UTCI (in °C) and DTS representing values from the 7-point thermal sensation scale (-3:cold; -2:cool:; -1:slightly cool; 0:neutral; +1:slightly warm; +2:warm; +3:hot)</p> <p>Headers in line 3, first data row in line 4</p>
UTCI-Test-Data
<p>Independent data for testing the accuracy of approximations to values of the Universal Thermal Climate Index (UTCI) calculated by the UTCI-Fiala model, cf. Fig. 12 in https://doi.org/10.1007/s00484-011-0454-1</p> <p>File name: UTCI-Test-Data.txt<br> Purpose: Providing independent test data for assessing the accuracy of UTCI approximations <br> This TAB-delimited file tabulates values of UTCI and the Offset (= UTCI - Ta) in °C calculated by the UTCI-Fiala model in comparison to UTCI approximated by the table look-up approach and the regression polynomial, respectively, for 1000 conditions with different input values of:<br> Ta: air temperature in°C (range: -50 °C to +42 °C)<br> Tr-Ta: difference between mean radiant temperature (Tr) and air temperature in °C (-17 °C to +50 °C)<br> va: wind speed in m/s measured 10 m above ground level (1.0 m/s to 30 m/s)<br> rH: relative humidity in % (5% to 100%)<br> pa: water vapour pressure in kPa (0 kPa to 3.4 kPa)<br> Calculated variables:<br> Offset: Offset (= UTCI - Ta) in °C calculated by the UTCI-Fiala model (range -62 to 13 °C)<br> UTCI: UTCI in °C calculated by the UTCI-Fiala model (range -111 to 43 °C)<br> UTCI_Table: UTCI in °C approximated by the look-up approach using the data table ESM4 from the above referred publication<br> UTCI_polynomial: UTCI in °C approximated by the polynomial regression function from ESM3 of the above referred publication</p> <p>Headers in line 34, first data row in line 35</p>
2-meter Universal Thermal Climate Index (UTCI) and Human Heat Health Index (H3I) hazard for Austin, Texas
<p>Universal Thermal Climate Index (UTCI) is a physiological temperature that is widely used in biometeorological studies to assess the heat stress felt by humans. UTCI considers the shortwave and longwave radiation incident on humans from the six cubical directions as well as air temperature, humidity, wind speed and clothing. As a part of NOAA National Integrated Heat Health Information System (NIHHIS) and NASA Interdisciplinary Research in Earth Science (IDS) project, we have generated the UTCI data for Austin, Texas and surrounding peri-urban area at 2-meters spatial resolution for the year 2017. Details on data generation and methodology can be found in Kamath et al., (2023) but are summarized here. </p> <p><strong>1. Datasets and model used</strong></p> <p>The solar and longwave environmental irradiance geometry (SOLWEIG) model was used to simulate shadows, mean radiant temperature (T<sub>MRT</sub>) and the UTCI (Lindberg et al., 2008). T<sub>MRT</sub> is the equivalent temperature due to exposure to absorbed shortwave and longwave radiation from all directions in a standing position. SOLWEIG was forced using near-surface ERA-5 data available at a spatial resolution of 0.25°x 0.25°. Building, vegetation heights, and digital terrain model were again derived from 3DEP LiDAR point cloud data. SOLWEIG was run using the urban multi-scale environment predictor (UMEP) (Lindberg et al., 2018) plug-in with QGIS. </p> <p><strong>2. Data availability</strong></p> <p>Diurnal UTCI data were calculated for typical meteorological clear sky days corresponding to Summer and Fall. The typical clear sky day was selected using the 10-year Typical meteorological Year (TMY) for Austin, Texas (30.2672° N, 97.7431° W) provided by National Solar Radiation Database (NSRDB). More details on TMY files can be found at: https://nsrdb.nrel.gov/data-sets/tmy</p> <p>Additionally, data is developed for heat hazard for daytime Human Heat Health Index (H3I) calculation as defined by Kamath et al., (2023). Briefly, this heat hazard is defined as the fraction of the day when the UTCI exceeds certain threshold. The threshold used to calculate heat hazard for Summer and Fall were 35° C and 32°C, respectively that imply strong heat stress (Jendritzky et al., 2012). Note that UTCI is on a different scale compared to air temperature, and could yield different heat stress levels.</p> <p><strong>3. Data format</strong></p> <p>The georeferenced UTCI and heat hazard data are available in the geoTIFF file format. The files can be readily visualized using GIS software such as QGIS and ArcGIS, as well as programing languages such as Python.</p> <p> <strong>4. Companion dataset</strong></p> <p>Based on the calculated UTCI here, the potential locations for tree planting were calculated to increase the shade to reduce heat vulnerability for Austin, Texas. [https://doi.org/10.5281/zenodo.6363494]</p> <p><strong>References</strong></p> <ol> <li>Kamath, H. G., Martilli, A., Singh, M., Brooks, T., Lanza, K., Bixler, R. P., ... & Niyogi, D. (2023). Human heat health index (H3I) for holistic assessment of heat hazard and mitigation strategies beyond urban heat islands. Urban Climate, 52, 101675.</li> <li>Lindberg, F., Holmer, B., & Thorsson, S. (2008). SOLWEIG 1.0–Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. <em>International journal of biometeorology</em>, <em>52</em>, 697-713.</li> <li>Lindberg, F., Grimmond, C. S. B., Gabey, A., Huang, B., Kent, C. W., Sun, T., ... & Zhang, Z. (2018). Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services. <em>Environmental modelling & software</em>, <em>99</em>, 70-87.</li> <li>Jendritzky, G., de Dear, R., & Havenith, G. (2012). UTCI—why another thermal index?. <em>International journal of biometeorology</em>, <em>56</em>, 421-428.</li> <li>Bixler, R. P., Coudert, M., Richter, S. M., Jones, J. M., Llanes Pulido, C., Akhavan, N., ... & Niyogi, D. (2022). Reflexive co-production for urban resilience: Guiding framework and experiences from Austin, Texas. Frontiers in Sustainable Cities, 4, 1015630.</li> <li>Lanza, K., Jones, J., Acuña, F., Coudert, M., Bixler, R. P., Kamath, H., & Niyogi, D. (2023). Heat vulnerability of Latino and Black residents in a low-income community and their recommended adaptation strategies: A qualitative study. <em>Urban Climate</em>, <em>51</em>, 101656.</li> </ol>
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