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7 results for “urban heat island effect”

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zenodo40/100

Simulations of urban heat island effect in Paris Region during various types of heatwaves, and in different adaptation scenarios

Content - These data present air temperature, in the shade, 2m above grounds in Paris Region (projection: RGF93/Lambert 93, EPSG:2154) at different times of the day, for various heat waves conditions, and in different prospective scenarios for the built-up evolution and adaptation actions implementations. - more information can be found here : https://www.umr-cnrm.fr/ville.climat/spip.php?rubrique45 Classification of the data - the first 5 letters (e.g. "CDFFA") present the prospective scenario - the 4 following letters (e.g. "HW34") present the type of heat wave - the following 2 letters (e.g. "D8") present the length of the heat wave (number of days after the beginning of the heat wave) - the final letters (e.g. H15) represent the time (UTC : one hour should be added for French time) of the day Prospective scenarios - the first letter is always C - the second letter represents the expansion scenario. They are presented here : Lemonsu, A., Viguié, V., Daniel, M., Masson, V., 2015. Vulnerability to heat waves: Impact of urban expansion scenarios on urban heat island and heat stress in Paris (France). Urban Climate 14, 586–605.   - D stands for "dense development"   - F for business as usual scenario ("fil de l'eau" in French)   - V for a scenario with 10% more parks - the third letter represents the building evolution scenario   - F stands for business as usual scenario   - V for a scenario with more insulation and reflective roofs - the third letter represents AC use   - F stands for strong AC use   - M for moderate AC use   - N for no AC use - the fourth letter represents vegetation watering   - N stands for no watering   - A for watering Heat waves - the figure (e.g. "34" in "HW34") represents the intensity class, in °C of the heat wave. It is more precisely the maximum daily temperature observed without the impact of the urban heat island effect. (Tmax=34, 38, 42, or 46°C). Other information - see the file "aggregated data.xls" for more information and data about energy consumption for AC, and averages of temperatures in the city over the entire day.

opencc-by-4.0Mar 2019View details →

dryad40/100

Data from: Physiological thermal limits predict differential responses of bees to urban heat-island effects

Open the record for dataset details and reuse information.

publicFeb 2023View details →

zenodo36/100

Ottawa climate data for building simulations with urban heat island effects and nature-based solutions

As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Ottawa airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions. This dataset contains hourly historical and future weather files for use in building simulations for the city of Ottawa, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned.  The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R.     The historical and future global warming thresholds and their corresponding time periods are as follows: <table> <tbody> <tr> <td> Global Warming Scenario </td> <td> Time Period </td> </tr> <tr> <td> Historical </td> <td> 1991-2021 </td> </tr> <tr> <td> Global Warming 0.5ºC </td> <td> 2003-2033 </td> </tr> <tr> <td> Global Warming 1.0ºC </td> <td> 2014-2044 </td> </tr> <tr> <td> Global Warming 1.5ºC </td> <td> 2024-2054 </td> </tr> <tr> <td> Global Warming 2.0ºC </td> <td> 2034-2064 </td> </tr> <tr> <td> Global Warming 2.5ºC </td> <td> 2042-2072 </td> </tr> <tr> <td> Global Warming 3.0ºC </td> <td> 2051-2081 </td> </tr> <tr> <td> Global Warming 3.5ºC </td> <td> 2064-2094 </td> </tr> </tbody> </table>   The following variables are included in the files: <table> <tbody> <tr> <td>Variable</td> <td>Description</td> </tr> <tr> <td>RUN</td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td>YEAR</td> <td>Year associated with the record</td> </tr> <tr> <td>MONTH</td> <td>Month associated with the record</td> </tr> <tr> <td>DAY</td> <td>Day of the month associated with the record</td> </tr> <tr> <td>HOUR</td> <td>Hour associated with the record</td> </tr> <tr> <td>YDAY</td> <td>Day of the year associated with the record</td> </tr> <tr> <td>DRI_kJPerM2</td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DHI_kJperM2</td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DNI_kJperM2</td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>GHI_kJperM2</td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>TCC_Percent</td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td>RAIN_Mm</td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>WDIR_ClockwiseDegFromNorth</td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td>WSP_MPerSec</td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td>RHUM_Percent</td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td>TEMP_K</td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td>ATMPR_Pa</td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td>SnowC_Yes1No0 </td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td>SNWD_Cm</td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>

opencanada-crownMay 2024View details →

zenodo36/100

Climatologial analysis of urban heat island effects in Swiss cities

Dataset, namelists and python scripts for the paper "Climatologial analysis of urban heat island effects in Swiss cities".

opencc-by-4.0Aug 2023View details →

zenodo32/100

Montreal climate data for building simulations with urban heat island effects and nature-based solutions

As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Montreal airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions. This dataset contains hourly historical and future weather files for use in building simulations for the city of Montreal, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned.  The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R.     The historical and future global warming thresholds and their corresponding time periods are as follows: <table> <tbody> <tr> <td> Global Warming Scenario </td> <td> Time Period </td> </tr> <tr> <td> Historical </td> <td> 1991-2021 </td> </tr> <tr> <td> Global Warming 0.5ºC </td> <td> 2003-2033 </td> </tr> <tr> <td> Global Warming 1.0ºC </td> <td> 2014-2044 </td> </tr> <tr> <td> Global Warming 1.5ºC </td> <td> 2024-2054 </td> </tr> <tr> <td> Global Warming 2.0ºC </td> <td> 2034-2064 </td> </tr> <tr> <td> Global Warming 2.5ºC </td> <td> 2042-2072 </td> </tr> <tr> <td> Global Warming 3.0ºC </td> <td> 2051-2081 </td> </tr> <tr> <td> Global Warming 3.5ºC </td> <td> 2064-2094 </td> </tr> </tbody> </table>   The following variables are included in the files: <table> <tbody> <tr> <td>Variable</td> <td>Description</td> </tr> <tr> <td>RUN</td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td>YEAR</td> <td>Year associated with the record</td> </tr> <tr> <td>MONTH</td> <td>Month associated with the record</td> </tr> <tr> <td>DAY</td> <td>Day of the month associated with the record</td> </tr> <tr> <td>HOUR</td> <td>Hour associated with the record</td> </tr> <tr> <td>YDAY</td> <td>Day of the year associated with the record</td> </tr> <tr> <td>DRI_kJPerM2</td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DHI_kJperM2</td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DNI_kJperM2</td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>GHI_kJperM2</td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>TCC_Percent</td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td>RAIN_Mm</td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>WDIR_ClockwiseDegFromNorth</td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td>WSP_MPerSec</td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td>RHUM_Percent</td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td>TEMP_K</td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td>ATMPR_Pa</td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td>SnowC_Yes1No0 </td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td>SNWD_Cm</td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>

opencanada-crownSep 2024View details →

zenodo32/100

Toronto climate data for building simulations with urban heat island effects and nature-based solutions

As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Toronto airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions. This dataset contains hourly historical and future weather files for use in building simulations for the city of Toronto, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned.  The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R.     The historical and future global warming thresholds and their corresponding time periods are as follows: <table> <tbody> <tr> <td> Global Warming Scenario </td> <td> Time Period </td> </tr> <tr> <td> Historical </td> <td> 1991-2021 </td> </tr> <tr> <td> Global Warming 0.5ºC </td> <td> 2003-2033 </td> </tr> <tr> <td> Global Warming 1.0ºC </td> <td> 2014-2044 </td> </tr> <tr> <td> Global Warming 1.5ºC </td> <td> 2024-2054 </td> </tr> <tr> <td> Global Warming 2.0ºC </td> <td> 2034-2064 </td> </tr> <tr> <td> Global Warming 2.5ºC </td> <td> 2042-2072 </td> </tr> <tr> <td> Global Warming 3.0ºC </td> <td> 2051-2081 </td> </tr> <tr> <td> Global Warming 3.5ºC </td> <td> 2064-2094 </td> </tr> </tbody> </table>   The following variables are included in the files: <table> <tbody> <tr> <td>Variable</td> <td>Description</td> </tr> <tr> <td>RUN</td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td>YEAR</td> <td>Year associated with the record</td> </tr> <tr> <td>MONTH</td> <td>Month associated with the record</td> </tr> <tr> <td>DAY</td> <td>Day of the month associated with the record</td> </tr> <tr> <td>HOUR</td> <td>Hour associated with the record</td> </tr> <tr> <td>YDAY</td> <td>Day of the year associated with the record</td> </tr> <tr> <td>DRI_kJPerM2</td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DHI_kJperM2</td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DNI_kJperM2</td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>GHI_kJperM2</td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>TCC_Percent</td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td>RAIN_Mm</td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>WDIR_ClockwiseDegFromNorth</td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td>WSP_MPerSec</td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td>RHUM_Percent</td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td>TEMP_K</td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td>ATMPR_Pa</td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td>SnowC_Yes1No0 </td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td>SNWD_Cm</td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>

opencanada-crownSep 2024View details →

zenodo32/100

Effect of urban heat island mitigation strategies on precipitation and temperature in Montreal, Canada: case studies - Data from numerical experiments

Surface data from the numerical experiments for the paper : "Effect of urban heat island mitigation strategies on precipitation and temperature in Montreal, Canada: case studies", submitted in PLOS Climate.  The data is in NetCDF, structure as follow: 1 file per initialization date that contains the model outputs for the surface variables (air temperature, dew-point, relative humidity) and 1 file per initialization date that contains the accumulated precipitation.

opencc-by-4.0Mar 2023View details →

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