Challenge 18 - Evaluation tool for urban anthropogenic heat emissions: how important is local data?
Stream 1 - Data Visualization and visual narratives for Earth Sciences applications
Goal
Build an evaluation tool for anthropogenic heat emission models in urban areas. The tool should be available for any city globally, using different data sources, to compare model outputs of an urban energy balance model (e.g., Simplified Thermal Energy Balance for Buildings Scheme STEBBS, DAVE-transport, Speedy Algorithm for Radiative Transfer through Cloud Sides SPARTACUS-surface/ Surface Urban Energy and Water Balance Scheme SUEWS) with available observational data of surface fluxes, 2-metre air temperature or surface temperature. The system should be able to use initial data on a global level and incorporate better regional data when available, updating through time as cities and models change. The result should include a visualization of the input and output data (e.g., maps of the urban data) and a quantification of the model output error.
Mentors and skills
Mentors:
University of Reading: Sue Grimmond, Denise Hertwig, Megan McGrory, Ting Sun, Fredrik Lindberg
ECMWF: Birgit Sützl, Robin Hogan, Florentine Weber
Skills required:
Python or any other programming language suitable for spatial data processing
Plotting/Visualization of maps and statistical indicators
Quantitative science skills
Experience in land-surface or urban-climate modelling is an advantage but not necessary
Experience handling large spatial datasets (e.g., satellite data products) is an advantage
Challenge description
Anthropogenic heat flux, stemming from sources like vehicles and building systems, significantly impacts the urban energy balance. It can surpass net all-wave radiation in winter at high latitudes. Current approaches in weather prediction and climate modelling often ignore it or assign fixed monthly values globally. Neglecting this flux can alter local atmospheric stability, affecting air quality and, consequently, forecasts for public health. Urban services like building design are influenced by weather and climate, affecting energy usage and regional demand. The flux magnitude varies with model resolution, impacting urban decisions such as natural ventilation efficacy in design.
Modelling anthropogenic heat flux involves top-down approaches using global datasets (e.g. Flanner 2009, Allen et al. 2011) or bottom-up approaches considering city characteristics and meteorology (Capel-Timms et al. 2020, Liu et al. 2022, Grimmond and Oke 1999). Balancing model accuracy with computational demands is a challenge, requiring careful consideration of resolution.
This challenge addresses the discrepancy between top-down and bottom-up approaches and aims to quantify the level of sophistication needed for accurate representation of anthropogenic heat fluxes into Numerical Weather Prediction and climate models.
We suggest utilising a bottom-up approach to develop the capability to model anthropogenic heat flux emissions globally, starting with buildings as the largest source of anthropogenic heat emissions. Use available models, for example a building energy balance model like STEBBS, to calculate anthropogenic heat fluxes on varying spatial and temporal resolutions and assesses the sensitivity of the model regarding the following aspects:
1) Source data: build a framework that can compare the impact of global data sources to high-resolution local data sources. Evaluate the resulting anthropogenic heat fluxes with the two different input sources and demonstrate its global usability by showing results for different cities.
2) Spatial and temporal resolution: compare the model output for different spatial resolutions and temporal ranges (daily and seasonal variations).
3) Evaluation: benchmark the output of your simulations against observations (e.g., surface fluxes and 2-m temperature) and summarise which model setups give the most accurate results.
4) Demonstrate the capabilities of your evaluation tool for 5 different cities in the world.
These tests will help pinpoint where additional information could be incorporated and identify areas where certain resolutions can be omitted. If time allows, we suggest the following additional aspects:
5) Significance of variables and processes: examine the sensitivity of parameters within the model to varying spatial resolutions.
6) Assess at which spatial scales building energy-related fluxes become less important, leading to the prominence of other sources such as transport, or whether the entire flux can be deemed negligible (e.g., due to factors like grid resolution or building volume density).
7) Assess how temporal resolutions impact the spatial resolutions that need to be resolved. This includes considerations such as variations across different seasons or times of day (such as rush hour), extreme heat waves/cold spells via composite analysis, or even the temporal evolution of urban areas.
A modular design approach will allow for the development and testing of different components as part of a cohesive system with measurable variables (e.g. surface temperature) in addition to the fluxes as outputs.
Challenge 18 - Evaluation tool for urban anthropogenic heat emissions: how important is local data?
Goal
Build an evaluation tool for anthropogenic heat emission models in urban areas. The tool should be available for any city globally, using different data sources, to compare model outputs of an urban energy balance model (e.g., Simplified Thermal Energy Balance for Buildings Scheme STEBBS, DAVE-transport, Speedy Algorithm for Radiative Transfer through Cloud Sides SPARTACUS-surface/ Surface Urban Energy and Water Balance Scheme SUEWS) with available observational data of surface fluxes, 2-metre air temperature or surface temperature. The system should be able to use initial data on a global level and incorporate better regional data when available, updating through time as cities and models change. The result should include a visualization of the input and output data (e.g., maps of the urban data) and a quantification of the model output error.
Mentors and skills
Challenge description
Anthropogenic heat flux, stemming from sources like vehicles and building systems, significantly impacts the urban energy balance. It can surpass net all-wave radiation in winter at high latitudes. Current approaches in weather prediction and climate modelling often ignore it or assign fixed monthly values globally. Neglecting this flux can alter local atmospheric stability, affecting air quality and, consequently, forecasts for public health. Urban services like building design are influenced by weather and climate, affecting energy usage and regional demand. The flux magnitude varies with model resolution, impacting urban decisions such as natural ventilation efficacy in design.
Modelling anthropogenic heat flux involves top-down approaches using global datasets (e.g. Flanner 2009, Allen et al. 2011) or bottom-up approaches considering city characteristics and meteorology (Capel-Timms et al. 2020, Liu et al. 2022, Grimmond and Oke 1999). Balancing model accuracy with computational demands is a challenge, requiring careful consideration of resolution.
This challenge addresses the discrepancy between top-down and bottom-up approaches and aims to quantify the level of sophistication needed for accurate representation of anthropogenic heat fluxes into Numerical Weather Prediction and climate models.
We suggest utilising a bottom-up approach to develop the capability to model anthropogenic heat flux emissions globally, starting with buildings as the largest source of anthropogenic heat emissions. Use available models, for example a building energy balance model like STEBBS, to calculate anthropogenic heat fluxes on varying spatial and temporal resolutions and assesses the sensitivity of the model regarding the following aspects:
1) Source data: build a framework that can compare the impact of global data sources to high-resolution local data sources. Evaluate the resulting anthropogenic heat fluxes with the two different input sources and demonstrate its global usability by showing results for different cities.
2) Spatial and temporal resolution: compare the model output for different spatial resolutions and temporal ranges (daily and seasonal variations).
3) Evaluation: benchmark the output of your simulations against observations (e.g., surface fluxes and 2-m temperature) and summarise which model setups give the most accurate results.
4) Demonstrate the capabilities of your evaluation tool for 5 different cities in the world.
These tests will help pinpoint where additional information could be incorporated and identify areas where certain resolutions can be omitted. If time allows, we suggest the following additional aspects:
5) Significance of variables and processes: examine the sensitivity of parameters within the model to varying spatial resolutions.
6) Assess at which spatial scales building energy-related fluxes become less important, leading to the prominence of other sources such as transport, or whether the entire flux can be deemed negligible (e.g., due to factors like grid resolution or building volume density).
7) Assess how temporal resolutions impact the spatial resolutions that need to be resolved. This includes considerations such as variations across different seasons or times of day (such as rush hour), extreme heat waves/cold spells via composite analysis, or even the temporal evolution of urban areas.
A modular design approach will allow for the development and testing of different components as part of a cohesive system with measurable variables (e.g. surface temperature) in addition to the fluxes as outputs.
References: