the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Sep 2023
WRF-Comfort: Simulating micro-scale variability of outdoor heat stress at the city scale with a mesoscale model
Abstract. Urban overheating, and its ongoing exacerbation due to global warming and urban development, leads to increased exposure to urban heat and increased thermal discomfort and heat stress. To quantify thermal stress, specific indices have been proposed that depend on air temperature, mean radiant temperature (MRT), wind speed, and relative humidity. While temperature and humidity vary on scales of hundreds of meters, MRT and wind speed are strongly affected by individual buildings and trees, and vary at the meter scale. Therefore, most numerical thermal comfort studies apply micro-scale models to limited spatial domains (commonly representing urban neighborhoods with building blocks) with resolutions on the order of 1 m and a few hours of simulation. This prevents the analysis of the impact of city-scale adaptation/mitigation strategies on thermal stress and comfort. To solve this problem, we develop a methodology to estimate thermal stress indicators and their subgrid variability in mesoscale models – here applied to the multilayer urban canopy parametrization BEP-BEM within the WRF model. The new scheme (consisting of three main steps) can readily assess intra-neighborhood scale heat stress distributions across whole cities and for time scales of minutes to years. The first key component of the approach is the estimation of MRT in several locations within streets for different street orientations. Second, mean wind speed, and its subgrid variability, are parameterized as a function of the local urban morphology based on relations derived from a set of microscale LES and RANS simulations across a wide range of realistic and idealized urban morphologies. Lastly, we compute the distributions of two thermal stress indices for each grid square combining all the subgrid values of MRT, wind speed, air temperature, and absolute humidity. From these distributions, we quantify the high and low tails of the heat stress distribution in each grid square across the city, representing the thermal diversity experienced in street canyons. In this contribution, we present the core methodology as well as simulation results for Madrid (Spain), which illustrate strong differences between heat stress indices and common heat metrics like air or surface temperature, both across the city and over the diurnal cycle.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(4183 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-1069', Anonymous Referee #1, 18 Nov 2023
General comments
Quantifying outdoor heat stress of urban inhabitants requires consideration of air temperature and humidity, mean radiant temperature (MRT) and wind speed. As noted in the abstract, temperature and relative humidity vary on scales of hundreds of meters but wind speed and MRT vary at the meter scale. As a result, current simulation workflows require use of a mesoscale simulation to provide boundary conditions for time-consuming microscale computational fluid dynamics (CFD) simulations that, in the absence of supercomputer resources, apply only to a specific neighborhood for a short period of time. The authors argue for an alternative approach that relies on an estimation of MRT at several locations in streets of varying orientation, microscale CFD simulations to determine mean wind speed and its variation across a range of urban morphologies, and mesoscale simulation of temperature and relative humidity to estimate thermal-stress indicators and their variation at the sub-grid level of the mesoscale simulation.
While the method is more efficient than applying microscale simulations at large temporal and spatial scales, it is more complex than simple estimates of air temperature and humidity, which existing mesoscale simulations can efficiently provide over long time scales, and more demanding than analysis of remotely estimated surface temperatures. Here the authors argue that heat stress depends on more than air temperature and humidity and that surface temperatures estimated from satellite measurements do not correlate with heat maps generated with multiple input variables.
The proposed approach has merit in meeting the needs of urban planners and designers to assess heat stress at fine spatial scale and expansive time scales on the order of years. As developed by the authors, the workflow has the rigor necessary to produce useful results.
The backbone of the workflow is the BEP-BEM multilayer urban canopy parameterization, developed by one of the authors, that includes a building energy model and is incorporated into the widely used mesoscale Weather Research and Forecasting (WRF) model. The authors parameterize MRT at locations within the geometry and orientation allowed in BEP-BEM. The
parameterization of wind speed is based on a large number of CFD simulations (both Reynolds Averaged Navier-Stokes and Large Eddy Simulations) that span realistic and idealized urban configurations, with wind speed parameterized by the building roof area or, more satisfactorily, vertical wall area, each normalized by the total horizontal area in a grid cell.
From an assumed variation of 1 oC in air temperature and computed variations in wind speed and MRT, the authors calculated 54 combinations from which they calculated two measures of heat stress and documented the 10th, 50th and 90th percentile values.
The paper effectively communicates the application of the method for a typical heatwave day in Madrid, Spain, with diurnal plots of MRT, air temperature and UTCI and Heat Index for high- and low-density neighborhoods and bar charts of thermal stress categories as estimated by UTCI and Heat Index. The conclusion is a crisp summary of the method and results, establishing the contributions of the research. In all, the paper is coherent and effectively claims a significant addition to the modeling of urban outdoor stress.
Specific comments
- BEP-BEM is limited to two street orientations, each with the same street width and building height distribution. This limitation defines the considered variation in MRT, which is computed for three positions (sidewalks on opposite sides of the street and street center) for each of the two street orientations. BEP view factors and shading algorithms are used to estimate shortwave reflection and longwave emission and reflection. MRT accounts for shortwave and longwave radiation reaching a pedestrian, weighting radiation received from body surfaces at different orientations. Not stated in the paper is the calculation of surface temperatures, which depends on a heat balance in which absorbed radiation can be emitted as longwave radiation or conducted into building material. Model results are validated by comparison with more detailed simulations made with the measurement-validated TUF-Pedestrian, but only over the designated locations in a street canyon. Modestly more explanation of the physics in the reference model would have better bolstered the asserted confidence in the streamlined methodology developed in the paper.
- Variations over a wider range of geometries derived from the detailed simulation would have determined whether the range of MRT as constrained by the BEP geometry is a reasonable approximation.
- Spatial maps at two times of the simulated day make a strong case that air and surface temperatures do not accurately predict UTCI values in hot-spot locations. The display of Heat Index is puzzling, because the paper promised the use of SET and UTCI but does not present simulated values of SET.
- What’s missing is a thoughtful discussion of limitations and, perhaps, a more detailed comparison with appropriate ground-truth simulations (with CFD and energy balances) for a single neighborhood. Do the authors think that the MRT model is adequate for all possible building materials and morphologies? What about the impact of trees? Similarly, is the parameterization of wind speed universally applicable or would city planners need to conduct or commission local simulations?
Editorial comments
- Line 47. Please replace 1 with one.
- Line 65 uses “autonomy” to characterize inhabitant choice of thermal environment, a different use of the word than in “spatial autonomy,” which refers to the extent, spatial or temporal” a space is thermally comfortable. People have agency to make choices, but spaces do not.
- Line 71. Please hyphenate “grid average” (proper when followed by a noun).
- Line 101. The sky is also a source of shortwave radiation, in which a portion of direct radiation from the Sun is scattered in the atmosphere.
- Line 119. In Equation 1, aK and aL are not defined.
- The text asserts excellent agreement between two models for shortwave radiation loading but the figures in Appendix A show peak differences as much as 100 W/m2.
- Line 147. Please replace “module” with “modulus.”
- Line 154. Please consider “Data are considered from over 173 microscale CFD simulations of urban airflow over realistic and idealized urban configurations,…”
- Line 177. Please consider deleting the commas that bracket “therefore.”
- Line 185. For consistency with other choices of tense, please replace “used” with “use.”
- Line 191. Please replace “Where” with “where.”
- Line 202. Please consider “that increasing cause severe heat stress….”
- Line 203. Please separate “27,9,3” with appropriate spaces.
- Line 228. Grid cell or grid point would appear to be better than grid, to describe a specific location.
- Lines 231, 232, 244 and 247 use heat stress index, Heat Index, Heat index and heat index; please consider more consistency.
- Line 263. The time here is stated as 9 UTC while in line 281 it is 09000 UTC. For consistency with the afternoon time of 1600 UTC and the caption to Figure 9, both should be 0900. Please consider starting the sentence with “In the dense region,….”
- Line 270. Please consider “…completely different pattern; on the city center at that time of day,….”
- Line 309. Please consider “…this has not been done before….”
- Line 311. Please delete the comma after “case.”
- Line 454. The caption for Figure A1 should define Short 1 and Short 2. In the caption for Figure A2, please replace “a E-W…” with “an E-W….”
Citation: https://doi.org/10.5194/egusphere-2023-1069-RC1 -
RC2: 'Comment on egusphere-2023-1069', Anonymous Referee #2, 14 Dec 2023
This manuscript describes how to calculate outdoor heat stress across a city using the atmospheric mesoscale model, WRF. This manuscript deals with spatial variabilities in mean radiant temperature (MRT) and wind speed in urban canyons in a decisive manner by simplifying their many aspects and assumptions. Originally such spatial variations cannot be resolved in the mesoscale model. The ideas proposed in this manuscript are interesting but simplified too much. Further investigation of this study needs important things below.
- The key idea of this study comes from the six-directional weighting method by (Thorsson et al., 2007). But this reference is missing in the manuscript and the description of this method is quite descriptive. This journal is for the code and model, and we expect more detailed information and code description.
- The similar problem also goes to wind speed calculation in 2.2.
Additionally, some information is vague. For example, what is the meaning of “close to the pedestrian height (~2.5 m)”? The symbol “~” stands for the approximation and why we need this approximation? So wind speed is at 2.5 m above the road?
- What are the implications and limitations to use spatially averaged wind speed with estimation of MRT at three different locations? We need more considerate discussion on many assumptions and parameter values used in this study.
- Figure 2 for the model evaluation may not be useful because the proposed model is based on the two-street orientation. We can also argue that parameters in the proposed model is calibrated in some sense to match the results. It will be quite useful if there is comparison between the model and in-situ data in a city.
- Please check carefully if description on variables, abbreviation, and indices are well described in the manuscript.
Citation: https://doi.org/10.5194/egusphere-2023-1069-RC2 -
AC1: 'Comment on egusphere-2023-1069', Alberto Martilli, 10 Jan 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1069/egusphere-2023-1069-AC1-supplement.pdf
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1069', Anonymous Referee #1, 18 Nov 2023
General comments
Quantifying outdoor heat stress of urban inhabitants requires consideration of air temperature and humidity, mean radiant temperature (MRT) and wind speed. As noted in the abstract, temperature and relative humidity vary on scales of hundreds of meters but wind speed and MRT vary at the meter scale. As a result, current simulation workflows require use of a mesoscale simulation to provide boundary conditions for time-consuming microscale computational fluid dynamics (CFD) simulations that, in the absence of supercomputer resources, apply only to a specific neighborhood for a short period of time. The authors argue for an alternative approach that relies on an estimation of MRT at several locations in streets of varying orientation, microscale CFD simulations to determine mean wind speed and its variation across a range of urban morphologies, and mesoscale simulation of temperature and relative humidity to estimate thermal-stress indicators and their variation at the sub-grid level of the mesoscale simulation.
While the method is more efficient than applying microscale simulations at large temporal and spatial scales, it is more complex than simple estimates of air temperature and humidity, which existing mesoscale simulations can efficiently provide over long time scales, and more demanding than analysis of remotely estimated surface temperatures. Here the authors argue that heat stress depends on more than air temperature and humidity and that surface temperatures estimated from satellite measurements do not correlate with heat maps generated with multiple input variables.
The proposed approach has merit in meeting the needs of urban planners and designers to assess heat stress at fine spatial scale and expansive time scales on the order of years. As developed by the authors, the workflow has the rigor necessary to produce useful results.
The backbone of the workflow is the BEP-BEM multilayer urban canopy parameterization, developed by one of the authors, that includes a building energy model and is incorporated into the widely used mesoscale Weather Research and Forecasting (WRF) model. The authors parameterize MRT at locations within the geometry and orientation allowed in BEP-BEM. The
parameterization of wind speed is based on a large number of CFD simulations (both Reynolds Averaged Navier-Stokes and Large Eddy Simulations) that span realistic and idealized urban configurations, with wind speed parameterized by the building roof area or, more satisfactorily, vertical wall area, each normalized by the total horizontal area in a grid cell.
From an assumed variation of 1 oC in air temperature and computed variations in wind speed and MRT, the authors calculated 54 combinations from which they calculated two measures of heat stress and documented the 10th, 50th and 90th percentile values.
The paper effectively communicates the application of the method for a typical heatwave day in Madrid, Spain, with diurnal plots of MRT, air temperature and UTCI and Heat Index for high- and low-density neighborhoods and bar charts of thermal stress categories as estimated by UTCI and Heat Index. The conclusion is a crisp summary of the method and results, establishing the contributions of the research. In all, the paper is coherent and effectively claims a significant addition to the modeling of urban outdoor stress.
Specific comments
- BEP-BEM is limited to two street orientations, each with the same street width and building height distribution. This limitation defines the considered variation in MRT, which is computed for three positions (sidewalks on opposite sides of the street and street center) for each of the two street orientations. BEP view factors and shading algorithms are used to estimate shortwave reflection and longwave emission and reflection. MRT accounts for shortwave and longwave radiation reaching a pedestrian, weighting radiation received from body surfaces at different orientations. Not stated in the paper is the calculation of surface temperatures, which depends on a heat balance in which absorbed radiation can be emitted as longwave radiation or conducted into building material. Model results are validated by comparison with more detailed simulations made with the measurement-validated TUF-Pedestrian, but only over the designated locations in a street canyon. Modestly more explanation of the physics in the reference model would have better bolstered the asserted confidence in the streamlined methodology developed in the paper.
- Variations over a wider range of geometries derived from the detailed simulation would have determined whether the range of MRT as constrained by the BEP geometry is a reasonable approximation.
- Spatial maps at two times of the simulated day make a strong case that air and surface temperatures do not accurately predict UTCI values in hot-spot locations. The display of Heat Index is puzzling, because the paper promised the use of SET and UTCI but does not present simulated values of SET.
- What’s missing is a thoughtful discussion of limitations and, perhaps, a more detailed comparison with appropriate ground-truth simulations (with CFD and energy balances) for a single neighborhood. Do the authors think that the MRT model is adequate for all possible building materials and morphologies? What about the impact of trees? Similarly, is the parameterization of wind speed universally applicable or would city planners need to conduct or commission local simulations?
Editorial comments
- Line 47. Please replace 1 with one.
- Line 65 uses “autonomy” to characterize inhabitant choice of thermal environment, a different use of the word than in “spatial autonomy,” which refers to the extent, spatial or temporal” a space is thermally comfortable. People have agency to make choices, but spaces do not.
- Line 71. Please hyphenate “grid average” (proper when followed by a noun).
- Line 101. The sky is also a source of shortwave radiation, in which a portion of direct radiation from the Sun is scattered in the atmosphere.
- Line 119. In Equation 1, aK and aL are not defined.
- The text asserts excellent agreement between two models for shortwave radiation loading but the figures in Appendix A show peak differences as much as 100 W/m2.
- Line 147. Please replace “module” with “modulus.”
- Line 154. Please consider “Data are considered from over 173 microscale CFD simulations of urban airflow over realistic and idealized urban configurations,…”
- Line 177. Please consider deleting the commas that bracket “therefore.”
- Line 185. For consistency with other choices of tense, please replace “used” with “use.”
- Line 191. Please replace “Where” with “where.”
- Line 202. Please consider “that increasing cause severe heat stress….”
- Line 203. Please separate “27,9,3” with appropriate spaces.
- Line 228. Grid cell or grid point would appear to be better than grid, to describe a specific location.
- Lines 231, 232, 244 and 247 use heat stress index, Heat Index, Heat index and heat index; please consider more consistency.
- Line 263. The time here is stated as 9 UTC while in line 281 it is 09000 UTC. For consistency with the afternoon time of 1600 UTC and the caption to Figure 9, both should be 0900. Please consider starting the sentence with “In the dense region,….”
- Line 270. Please consider “…completely different pattern; on the city center at that time of day,….”
- Line 309. Please consider “…this has not been done before….”
- Line 311. Please delete the comma after “case.”
- Line 454. The caption for Figure A1 should define Short 1 and Short 2. In the caption for Figure A2, please replace “a E-W…” with “an E-W….”
Citation: https://doi.org/10.5194/egusphere-2023-1069-RC1 -
RC2: 'Comment on egusphere-2023-1069', Anonymous Referee #2, 14 Dec 2023
This manuscript describes how to calculate outdoor heat stress across a city using the atmospheric mesoscale model, WRF. This manuscript deals with spatial variabilities in mean radiant temperature (MRT) and wind speed in urban canyons in a decisive manner by simplifying their many aspects and assumptions. Originally such spatial variations cannot be resolved in the mesoscale model. The ideas proposed in this manuscript are interesting but simplified too much. Further investigation of this study needs important things below.
- The key idea of this study comes from the six-directional weighting method by (Thorsson et al., 2007). But this reference is missing in the manuscript and the description of this method is quite descriptive. This journal is for the code and model, and we expect more detailed information and code description.
- The similar problem also goes to wind speed calculation in 2.2.
Additionally, some information is vague. For example, what is the meaning of “close to the pedestrian height (~2.5 m)”? The symbol “~” stands for the approximation and why we need this approximation? So wind speed is at 2.5 m above the road?
- What are the implications and limitations to use spatially averaged wind speed with estimation of MRT at three different locations? We need more considerate discussion on many assumptions and parameter values used in this study.
- Figure 2 for the model evaluation may not be useful because the proposed model is based on the two-street orientation. We can also argue that parameters in the proposed model is calibrated in some sense to match the results. It will be quite useful if there is comparison between the model and in-situ data in a city.
- Please check carefully if description on variables, abbreviation, and indices are well described in the manuscript.
Citation: https://doi.org/10.5194/egusphere-2023-1069-RC2 -
AC1: 'Comment on egusphere-2023-1069', Alberto Martilli, 10 Jan 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1069/egusphere-2023-1069-AC1-supplement.pdf
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Cited
Alberto Martilli
Negin Nazarian
E. Scott Krayenhoff
Jacob Lachapelle
Jiachen Lu
Esther Rivas
Alejandro Rodriguez-Sanchez
Beatriz Sanchez
Jose Luis Santiago
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(4183 KB) - Metadata XML