Model description paper
15 Oct 2020
Model description paper
| 15 Oct 2020
Dynamic Anthropogenic activitieS impacting Heat emissions (DASH v1.0): development and evaluation
Isabella Capel-Timms et al.
Related authors
No articles found.
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-85, https://doi.org/10.5194/gmd-2022-85, 2022
Preprint under review for GMD
Short summary
Short summary
We developed SHAFTS, a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
Mathew Lipson, Sue Grimmond, Martin Best, Winston Chow, Andreas Christen, Nektarios Chrysoulakis, Andrew Coutts, Ben Crawford, Stevan Earl, Jonathan Evans, Krzysztof Fortuniak, Bert G. Heusinkveld, Je-Woo Hong, Jinkyu Hong, Leena Järvi, Sungsoo Jo, Yeon-Hee Kim, Simone Kotthaus, Keunmin Lee, Valéry Masson, Joseph P. McFadden, Oliver Michels, Wlodzimierz Pawlak, Matthias Roth, Hirofumi Sugawara, Nigel Tapper, Erik Velasco, and Helen Claire Ward
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-65, https://doi.org/10.5194/essd-2022-65, 2022
Preprint under review for ESSD
Short summary
Short summary
A collection of weather observations from twenty urban locations around the world. The observations are valuable as they capture not only typical measurements (temperature, humidity, wind etc.) but also how energy is exchanged between the land and the atmosphere through radiation and turbulent heat fluxes. These can be used to improve our understanding of weather processes over cities, and to evaluate urban environmental models. Together the collection captures 50 years of observations.
Yiqing Liu, Zhiwen Luo, and Sue Grimmond
Atmos. Chem. Phys., 22, 4721–4735, https://doi.org/10.5194/acp-22-4721-2022, https://doi.org/10.5194/acp-22-4721-2022, 2022
Short summary
Short summary
Anthropogenic heat emission from buildings is important for atmospheric modelling in cities. The current building anthropogenic heat flux is simplified by building energy consumption. Our research proposes a novel approach to determine ‘real’ building anthropogenic heat emission from the changes in energy balance fluxes between occupied and unoccupied buildings. We hope to provide new insights into future parameterisations of building anthropogenic heat flux in urban climate models.
Hamidreza Omidvar, Ting Sun, Sue Grimmond, Dave Bilesbach, Andrew Black, Jiquan Chen, Zexia Duan, Zhiqiu Gao, Hiroki Iwata, and Joseph P. McFadden
Geosci. Model Dev., 15, 3041–3078, https://doi.org/10.5194/gmd-15-3041-2022, https://doi.org/10.5194/gmd-15-3041-2022, 2022
Short summary
Short summary
This paper extends the applicability of the SUEWS to extensive pervious areas outside cities. We derived various parameters such as leaf area index, albedo, roughness parameters and surface conductance for non-urban areas. The relation between LAI and albedo is also explored. The methods and parameters discussed can be used for both online and offline simulations. Using appropriate parameters related to non-urban areas is essential for assessing urban–rural differences.
Junxia Dou, Sue Grimmond, Shiguang Miao, Bei Huang, Huimin Lei, and Mingshui Liao
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-1022, https://doi.org/10.5194/acp-2021-1022, 2022
Preprint under review for ACP
Short summary
Short summary
Multi-timescale variations in surface energy fluxes at a suburb of Beijing are analyzed using 16-months observations. Compared to previous suburban areas, this study site has larger seasonal variability in energy partitioning, and summer and winter Bowen ratios are at the lower and higher end of other suburban sites, respectively. Our analysis indicates that precipitation, irrigation, crop/vegetation growth activity, and land use/cover all play critical roles in energy partitioning.
Will S. Drysdale, Adam R. Vaughan, Freya A. Squires, Sam J. Cliff, Stefan Metzger, David Durden, Natchaya Pingintha-Durden, Carole Helfter, Eiko Nemitz, C. Sue B. Grimmond, Janet Barlow, Sean Beevers, Gregor Stewart, David Dajnak, Ruth M. Purvis, and James D. Lee
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-982, https://doi.org/10.5194/acp-2021-982, 2022
Revised manuscript accepted for ACP
Short summary
Short summary
Measurements of nitrogen oxide and nitrogen dioxide (NOx) emissions are important for a good understanding of air quality. While there are many direct measurements of NOx concentration, there are very few measurements of its emission. Measurements of emissions provide constraints to emissions inventories and air quality models. This article presents measurements of NOx emission from the BT Tower in central London in 2017, and compares them with inventories, finding they underestimate by ~1.48 x.
Michael Biggart, Jenny Stocker, Ruth M. Doherty, Oliver Wild, David Carruthers, Sue Grimmond, Yiqun Han, Pingqing Fu, and Simone Kotthaus
Atmos. Chem. Phys., 21, 13687–13711, https://doi.org/10.5194/acp-21-13687-2021, https://doi.org/10.5194/acp-21-13687-2021, 2021
Short summary
Short summary
Heat-related illnesses are of increasing concern in China given its rapid urbanisation and our ever-warming climate. We examine the relative impacts that land surface properties and anthropogenic heat have on the urban heat island (UHI) in Beijing using ADMS-Urban. Air temperature measurements and satellite-derived land surface temperatures provide valuable means of evaluating modelled spatiotemporal variations. This work provides critical information for urban planners and UHI mitigation.
Claire E. Reeves, Graham P. Mills, Lisa K. Whalley, W. Joe F. Acton, William J. Bloss, Leigh R. Crilley, Sue Grimmond, Dwayne E. Heard, C. Nicholas Hewitt, James R. Hopkins, Simone Kotthaus, Louisa J. Kramer, Roderic L. Jones, James D. Lee, Yanhui Liu, Bin Ouyang, Eloise Slater, Freya Squires, Xinming Wang, Robert Woodward-Massey, and Chunxiang Ye
Atmos. Chem. Phys., 21, 6315–6330, https://doi.org/10.5194/acp-21-6315-2021, https://doi.org/10.5194/acp-21-6315-2021, 2021
Short summary
Short summary
The impact of isoprene on atmospheric chemistry is dependent on how its oxidation products interact with other pollutants, specifically nitrogen oxides. Such interactions can lead to isoprene nitrates. We made measurements of the concentrations of individual isoprene nitrate isomers in Beijing and used a model to test current understanding of their chemistry. We highlight areas of uncertainty in understanding, in particular the chemistry following oxidation of isoprene by the nitrate radical.
Wenhua Wang, Longyi Shao, Claudio Mazzoleni, Yaowei Li, Simone Kotthaus, Sue Grimmond, Janarjan Bhandari, Jiaoping Xing, Xiaolei Feng, Mengyuan Zhang, and Zongbo Shi
Atmos. Chem. Phys., 21, 5301–5314, https://doi.org/10.5194/acp-21-5301-2021, https://doi.org/10.5194/acp-21-5301-2021, 2021
Short summary
Short summary
We compared the characteristics of individual particles at ground level and above the mixed-layer height. We found that the particles above the mixed-layer height during haze periods are more aged compared to ground level. More coal-combustion-related primary organic particles were found above the mixed-layer height. We suggest that the particles above the mixed-layer height are affected by the surrounding areas, and once mixed down to the ground, they might contribute to ground air pollution.
Lisa K. Whalley, Eloise J. Slater, Robert Woodward-Massey, Chunxiang Ye, James D. Lee, Freya Squires, James R. Hopkins, Rachel E. Dunmore, Marvin Shaw, Jacqueline F. Hamilton, Alastair C. Lewis, Archit Mehra, Stephen D. Worrall, Asan Bacak, Thomas J. Bannan, Hugh Coe, Carl J. Percival, Bin Ouyang, Roderic L. Jones, Leigh R. Crilley, Louisa J. Kramer, William J. Bloss, Tuan Vu, Simone Kotthaus, Sue Grimmond, Yele Sun, Weiqi Xu, Siyao Yue, Lujie Ren, W. Joe F. Acton, C. Nicholas Hewitt, Xinming Wang, Pingqing Fu, and Dwayne E. Heard
Atmos. Chem. Phys., 21, 2125–2147, https://doi.org/10.5194/acp-21-2125-2021, https://doi.org/10.5194/acp-21-2125-2021, 2021
Short summary
Short summary
To understand how emission controls will impact ozone, an understanding of the sources and sinks of OH and the chemical cycling between peroxy radicals is needed. This paper presents measurements of OH, HO2 and total RO2 taken in central Beijing. The radical observations are compared to a detailed chemistry model, which shows that under low NO conditions, there is a missing OH source. Under high NOx conditions, the model under-predicts RO2 and impacts our ability to model ozone.
Rutambhara Joshi, Dantong Liu, Eiko Nemitz, Ben Langford, Neil Mullinger, Freya Squires, James Lee, Yunfei Wu, Xiaole Pan, Pingqing Fu, Simone Kotthaus, Sue Grimmond, Qiang Zhang, Ruili Wu, Oliver Wild, Michael Flynn, Hugh Coe, and James Allan
Atmos. Chem. Phys., 21, 147–162, https://doi.org/10.5194/acp-21-147-2021, https://doi.org/10.5194/acp-21-147-2021, 2021
Short summary
Short summary
Black carbon (BC) is a component of particulate matter which has significant effects on climate and human health. Sources of BC include biomass burning, transport, industry and domestic cooking and heating. In this study, we measured BC emissions in Beijing, finding a dominance of traffic emissions over all other sources. The quantitative method presented here has benefits for revising widely used emissions inventories and for understanding BC sources with impacts on air quality and climate.
Freya A. Squires, Eiko Nemitz, Ben Langford, Oliver Wild, Will S. Drysdale, W. Joe F. Acton, Pingqing Fu, C. Sue B. Grimmond, Jacqueline F. Hamilton, C. Nicholas Hewitt, Michael Hollaway, Simone Kotthaus, James Lee, Stefan Metzger, Natchaya Pingintha-Durden, Marvin Shaw, Adam R. Vaughan, Xinming Wang, Ruili Wu, Qiang Zhang, and Yanli Zhang
Atmos. Chem. Phys., 20, 8737–8761, https://doi.org/10.5194/acp-20-8737-2020, https://doi.org/10.5194/acp-20-8737-2020, 2020
Short summary
Short summary
Significant air quality problems exist in megacities like Beijing, China. To manage air pollution, legislators need a clear understanding of pollutant emissions. However, emissions inventories have large uncertainties, and reliable field measurements of pollutant emissions are required to constrain them. This work presents the first measurements of traffic-dominated emissions in Beijing which suggest that inventories overestimate these emissions in the region during both winter and summer.
Michael Biggart, Jenny Stocker, Ruth M. Doherty, Oliver Wild, Michael Hollaway, David Carruthers, Jie Li, Qiang Zhang, Ruili Wu, Simone Kotthaus, Sue Grimmond, Freya A. Squires, James Lee, and Zongbo Shi
Atmos. Chem. Phys., 20, 2755–2780, https://doi.org/10.5194/acp-20-2755-2020, https://doi.org/10.5194/acp-20-2755-2020, 2020
Short summary
Short summary
Ambient air pollution is a major cause of premature death in China. We examine the street-scale variation of pollutant levels in Beijing using air pollution dispersion and chemistry model ADMS-Urban. Campaign measurements are compared with simulated pollutant levels, providing a valuable means of evaluating the impact of key processes on urban air quality. Air quality modelling at such fine scales is essential for human exposure studies and for informing choices on future emission controls.
Ting Sun and Sue Grimmond
Geosci. Model Dev., 12, 2781–2795, https://doi.org/10.5194/gmd-12-2781-2019, https://doi.org/10.5194/gmd-12-2781-2019, 2019
Short summary
Short summary
A Python-enhanced urban land surface model, SuPy (SUEWS in Python), is presented with its development (the SUEWS interface modification, F2PY configuration and Python frontend implementation), cross-platform deployment (PyPI, Python Package Index) and demonstration (online tutorials in Jupyter notebooks for users of different levels). SuPy represents a significant enhancement that supports existing and new model applications, reproducibility and enhanced functionality.
Zongbo Shi, Tuan Vu, Simone Kotthaus, Roy M. Harrison, Sue Grimmond, Siyao Yue, Tong Zhu, James Lee, Yiqun Han, Matthias Demuzere, Rachel E. Dunmore, Lujie Ren, Di Liu, Yuanlin Wang, Oliver Wild, James Allan, W. Joe Acton, Janet Barlow, Benjamin Barratt, David Beddows, William J. Bloss, Giulia Calzolai, David Carruthers, David C. Carslaw, Queenie Chan, Lia Chatzidiakou, Yang Chen, Leigh Crilley, Hugh Coe, Tie Dai, Ruth Doherty, Fengkui Duan, Pingqing Fu, Baozhu Ge, Maofa Ge, Daobo Guan, Jacqueline F. Hamilton, Kebin He, Mathew Heal, Dwayne Heard, C. Nicholas Hewitt, Michael Hollaway, Min Hu, Dongsheng Ji, Xujiang Jiang, Rod Jones, Markus Kalberer, Frank J. Kelly, Louisa Kramer, Ben Langford, Chun Lin, Alastair C. Lewis, Jie Li, Weijun Li, Huan Liu, Junfeng Liu, Miranda Loh, Keding Lu, Franco Lucarelli, Graham Mann, Gordon McFiggans, Mark R. Miller, Graham Mills, Paul Monk, Eiko Nemitz, Fionna O'Connor, Bin Ouyang, Paul I. Palmer, Carl Percival, Olalekan Popoola, Claire Reeves, Andrew R. Rickard, Longyi Shao, Guangyu Shi, Dominick Spracklen, David Stevenson, Yele Sun, Zhiwei Sun, Shu Tao, Shengrui Tong, Qingqing Wang, Wenhua Wang, Xinming Wang, Xuejun Wang, Zifang Wang, Lianfang Wei, Lisa Whalley, Xuefang Wu, Zhijun Wu, Pinhua Xie, Fumo Yang, Qiang Zhang, Yanli Zhang, Yuanhang Zhang, and Mei Zheng
Atmos. Chem. Phys., 19, 7519–7546, https://doi.org/10.5194/acp-19-7519-2019, https://doi.org/10.5194/acp-19-7519-2019, 2019
Short summary
Short summary
APHH-Beijing is a collaborative international research programme to study the sources, processes and health effects of air pollution in Beijing. This introduction to the special issue provides an overview of (i) the APHH-Beijing programme, (ii) the measurement and modelling activities performed as part of it and (iii) the air quality and meteorological conditions during joint intensive field campaigns as a core activity within APHH-Beijing.
Fushan Wang, Guangheng Ni, William J. Riley, Jinyun Tang, Dejun Zhu, and Ting Sun
Geosci. Model Dev., 12, 2119–2138, https://doi.org/10.5194/gmd-12-2119-2019, https://doi.org/10.5194/gmd-12-2119-2019, 2019
Short summary
Short summary
The current lake model in the Weather Research and Forecasting system was reported to be insufficient in simulating deep lakes and reservoirs. We thus revised the lake model by improving its spatial discretization scheme, surface property parameterization, diffusivity parameterization, and convection scheme. The revised model was evaluated at a deep reservoir in southwestern China and the results were in good agreement with measurements.
Tom V. Kokkonen, Sue Grimmond, Sonja Murto, Huizhi Liu, Anu-Maija Sundström, and Leena Järvi
Atmos. Chem. Phys., 19, 7001–7017, https://doi.org/10.5194/acp-19-7001-2019, https://doi.org/10.5194/acp-19-7001-2019, 2019
Short summary
Short summary
This is the first study to evaluate and correct the WATCH WFDEI reanalysis product in a highly polluted urban environment. It gives an important understanding of the uncertainties in reanalysis products in local-scale urban modelling in polluted environments and identifies and corrects the most important variables in hydrological modelling. This is also the first study to examine the effects of haze on the local-scale urban hydrological cycle.
Dantong Liu, Rutambhara Joshi, Junfeng Wang, Chenjie Yu, James D. Allan, Hugh Coe, Michael J. Flynn, Conghui Xie, James Lee, Freya Squires, Simone Kotthaus, Sue Grimmond, Xinlei Ge, Yele Sun, and Pingqing Fu
Atmos. Chem. Phys., 19, 6749–6769, https://doi.org/10.5194/acp-19-6749-2019, https://doi.org/10.5194/acp-19-6749-2019, 2019
Short summary
Short summary
This study provides source attribution and characterization of BC in the Beijing urban environment in both winter and summer. For the first time, the physically and chemically based source apportionments are compared to evaluate the primary source contribution and secondary processing of BC-containing particles. A method is proposed to isolate the BC from the transportation sector and coal combustion sources.
Roy M. Harrison, David C. S. Beddows, Mohammed S. Alam, Ajit Singh, James Brean, Ruixin Xu, Simone Kotthaus, and Sue Grimmond
Atmos. Chem. Phys., 19, 39–55, https://doi.org/10.5194/acp-19-39-2019, https://doi.org/10.5194/acp-19-39-2019, 2019
Short summary
Short summary
Particle number size distributions were measured simultaneously at five sites in London during a campaign. Observations are interpreted in terms of both evaporative shrinkage of traffic-generated particles and condensational growth, probably of traffic-generated particles under cool nocturnal conditions, as well as the influence of particles emitted from Heathrow Airport at a distance of about 22 km. The work highlights the highly dynamic behaviour of nanoparticles within the urban atmosphere.
Ting Sun, Zhi-Hua Wang, Walter C. Oechel, and Sue Grimmond
Geosci. Model Dev., 10, 2875–2890, https://doi.org/10.5194/gmd-10-2875-2017, https://doi.org/10.5194/gmd-10-2875-2017, 2017
Short summary
Short summary
The diurnal hysteresis behaviour found between the net storage heat flux and net all-wave radiation has been captured in the Objective Hysteresis Model (OHM). To facilitate use, and enhance physical interpretations of the OHM coefficients, we develop the Analytical Objective Hysteresis Model (AnOHM) using an analytical solution of the one-dimensional advection–diffusion equation of coupled heat and liquid water transport in conjunction with the surface energy balance relationship.
Wen-Yu Yang, Guang-Heng Ni, You-Cun Qi, Yang Hong, and Ting Sun
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-388, https://doi.org/10.5194/amt-2016-388, 2016
Revised manuscript has not been submitted
Short summary
Short summary
Using a dataset consisting of one-year measurements by an X-band radar and distrometer, we found that error corrections greatly improve X-band-radar-based rainfall estimation. Specifically, the greatest improvement is realized by the beam integration. Derivation of localized Z-R relationships for specific rainfall systems is also of great importance. Moreover, wind drift correction improves quantitative estimates and temporal consistency.
Carole Helfter, Anja H. Tremper, Christoforos H. Halios, Simone Kotthaus, Alex Bjorkegren, C. Sue B. Grimmond, Janet F. Barlow, and Eiko Nemitz
Atmos. Chem. Phys., 16, 10543–10557, https://doi.org/10.5194/acp-16-10543-2016, https://doi.org/10.5194/acp-16-10543-2016, 2016
Short summary
Short summary
There are relatively few long-term, direct measurements of pollutant emissions in urban settings. We present over 3 years of measurements of fluxes of CO, CO2 and CH4, study their respective temporal and spatial dynamics and offer an independent verification of the London Atmospheric Emissions Inventory. CO and CO2 were strongly controlled by traffic and well characterised by the inventory whilst measured CH4 was two-fold larger and linked to natural gas usage and perhaps biogenic sources.
Simone Kotthaus, Ewan O'Connor, Christoph Münkel, Cristina Charlton-Perez, Martial Haeffelin, Andrew M. Gabey, and C. Sue B. Grimmond
Atmos. Meas. Tech., 9, 3769–3791, https://doi.org/10.5194/amt-9-3769-2016, https://doi.org/10.5194/amt-9-3769-2016, 2016
Short summary
Short summary
Ceilometers lidars are useful to study clouds, aerosol layers and atmospheric boundary layer structures. As sensor optics and acquisition algorithms can strongly influence the observations, sensor specifics need to be incorporated into the physical interpretation. Here, recommendations are made for the operation and processing of profile observations from the widely deployed Vaisala CL31 ceilometer. Proposed corrections are shown to increase data quality and even data availability at times.
Related subject area
Atmospheric sciences
A novel method for objective identification of 3-D potential vorticity anomalies
Multiple same-level and telescoping nesting in GFDL's dynamical core
Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties
Assessing the roles emission sources and atmospheric processes play in simulating δ15N of atmospheric NOx and NO3− using CMAQ (version 5.2.1) and SMOKE (version 4.6)
The Regional Coupled Suite (RCS-IND1): application of a flexible regional coupled modelling framework to the Indian region at kilometre scale
A comparative analysis for a deep learning model (hyDL-CO v1.0) and Kalman filter to predict CO concentrations in China
Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 1: assessing E3SM aerosol predictions using aircraft, ship, and surface measurements
Effects of vertical ship exhaust plume distributions on urban pollutant concentration – a sensitivity study with MITRAS v2.0 and EPISODE-CityChem v1.4
An emergency response model for the formation and dispersion of plumes originating from major fires (BUOYANT v4.20)
Description and evaluation of the community aerosol dynamics model MAFOR v2.0
Modeling the high-mercury wet deposition in the southeastern US with WRF-GC-Hg v1.0
Development of a deep neural network for predicting 6 h average PM2.5 concentrations up to 2 subsequent days using various training data
Chemistry Across Multiple Phases (CAMP) version 1.0: an integrated multiphase chemistry model
An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application
Earth system modeling of mercury using CESM2 – Part 1: Atmospheric model CAM6-Chem/Hg v1.0
Conservation laws in a neural network architecture: enforcing the atom balance of a Julia-based photochemical model (v0.2.0)
On the application and grid-size sensitivity of the urban dispersion model CAIRDIO v2.0 under real city weather conditions
Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16
Estimating aerosol emission from SPEXone on the NASA PACE mission using an ensemble Kalman smoother: observing system simulation experiments (OSSEs)
An ensemble-based statistical methodology to detect differences in weather and climate model executables
Multiphase processes in the EC-Earth model and their relevance to the atmospheric oxalate, sulfate, and iron cycles
Sensitivity of precipitation in the highlands and lowlands of Peru to physics parameterization options in WRFV3.8.1
Coupling a weather model directly to GNSS orbit determination – case studies with OpenIFS
Implementation of an ensemble Kalman filter in the Community Multiscale Air Quality model (CMAQ model v5.1) for data assimilation of ground-level PM2.5
Massive-Parallel Trajectory Calculations version 2.2 (MPTRAC-2.2): Lagrangian transport simulations on graphics processing units (GPUs)
Bedymo: a combined quasi-geostrophic and primitive equation model in σ coordinates
Simulation of organics in the atmosphere: evaluation of EMACv2.54 with the Mainz Organic Mechanism (MOM) coupled to the ORACLE (v1.0) submodel
An update on the 4D-LETKF data assimilation system for the whole neutral atmosphere
Determining the sensitive parameters of the Weather Research and Forecasting (WRF) model for the simulation of tropical cyclones in the Bay of Bengal using global sensitivity analysis and machine learning
A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models
Implementation of aerosol data assimilation in WRFDA (v4.0.3) for WRF-Chem (v3.9.1) using the RACM/MADE-VBS scheme
Representing low-intensity fire sensible heat output in a mesoscale atmospheric model with a canopy submodel: a case study with ARPS-CANOPY (version 5.2.12)
A machine-learning-guided adaptive algorithm to reduce the computational cost of integrating kinetics in global atmospheric chemistry models: application to GEOS-Chem versions 12.0.0 and 12.9.1
Deep-learning spatial principles from deterministic chemical transport models for chemical reanalysis: an application in China for PM2.5
Model development in practice: a comprehensive update to the boundary layer schemes in HARMONIE-AROME cycle 40
A parameterization of long-continuing-current (LCC) lightning in the lightning submodel LNOX (version 3.0) of the Modular Earth Submodel System (MESSy, version 2.54)
Air Control Toolbox (ACT_v1.0): a flexible surrogate model to explore mitigation scenarios in air quality forecasts
The Aerosol Module in the Community Radiative Transfer Model (v2.2 and v2.3): accounting for aerosol transmittance effects on the radiance observation operator
RAP-Net: Region Attention Predictive Network for Precipitation Nowcasting
The Flexible Modelling Framework for the Met Office Unified Model (Flex-UM, using UM 12.0 release)
Integration-based extraction and visualization of jet stream cores
Particle-filter-based volcanic ash emission inversion applied to a hypothetical sub-Plinian Eyjafjallajökull eruption using the Ensemble for Stochastic Integration of Atmospheric Simulations (ESIAS-chem) version 1.0
Evaluating the assimilation of S5P/TROPOMI near real-time SO2 columns and layer height data into the CAMS integrated forecasting system (CY47R1), based on a case study of the 2019 Raikoke eruption
Improvement of stomatal resistance and photosynthesis mechanism of Noah-MP-WDDM (v1.42) in simulation of NO2 dry deposition velocity in forests
Representation of the autoconversion from cloud to rain using a weighted ensemble approach: a case study using WRF v4.1.3
EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 1: Development of deep learning model
EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 2: Model application to different datasets
A new exponentially decaying error correlation model for assimilating OCO-2 column-average CO2 data using a length scale computed from airborne lidar measurements
Numerically consistent budgets of potential temperature, momentum, and moisture in Cartesian coordinates: application to the WRF model
An aerosol classification scheme for global simulations using the K-means machine learning method
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Roderick van der Linden, Michael Maier-Gerber, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 15, 4447–4468, https://doi.org/10.5194/gmd-15-4447-2022, https://doi.org/10.5194/gmd-15-4447-2022, 2022
Short summary
Short summary
Potential vorticity (PV) analysis plays a central role in studying atmospheric dynamics. For example, anomalies in the PV field near the tropopause are linked to extreme weather events. In this study, an objective strategy to identify these anomalies is presented and evaluated. As a novel concept, it can be applied to three-dimensional (3-D) data sets. Supported by 3-D visualizations, we illustrate advantages of this new analysis over existing studies along a case study.
Joseph Mouallem, Lucas Harris, and Rusty Benson
Geosci. Model Dev., 15, 4355–4371, https://doi.org/10.5194/gmd-15-4355-2022, https://doi.org/10.5194/gmd-15-4355-2022, 2022
Short summary
Short summary
The single-nest capability in GFDL's dynamical core, FV3, is upgraded to support multiple same-level and telescoping nests. Grid nesting adds a refined grid over an area of interest to better resolve small-scale flow features necessary to accurately predict special weather events such as severe storms and hurricanes. This work allows concurrent execution of multiple same-level and telescoping multi-level nested grids in both global and regional setups.
Clara Betancourt, Timo T. Stomberg, Ann-Kathrin Edrich, Ankit Patnala, Martin G. Schultz, Ribana Roscher, Julia Kowalski, and Scarlet Stadtler
Geosci. Model Dev., 15, 4331–4354, https://doi.org/10.5194/gmd-15-4331-2022, https://doi.org/10.5194/gmd-15-4331-2022, 2022
Short summary
Short summary
Ozone is a toxic greenhouse gas with high spatial variability. We present a machine-learning-based ozone-mapping workflow generating a transparent and reliable product. Going beyond standard mapping methods, this work combines explainable machine learning with uncertainty assessment to increase the integrity of the produced map.
Huan Fang and Greg Michalski
Geosci. Model Dev., 15, 4239–4258, https://doi.org/10.5194/gmd-15-4239-2022, https://doi.org/10.5194/gmd-15-4239-2022, 2022
Short summary
Short summary
A new emission input dataset that incorporates nitrogen isotopes has been used in the CMAQ (Community Multiscale Air Quality) modeling system simulation to qualitatively analyze the changes in δ15N values, due to the dispersion, mixing, and transport of the atmospheric NOx emitted from different sources. The dispersion, mixing, and transport of the atmospheric NOx were based on the meteorology files generated from the WRF (Weather Research and Forecasting) model.
Juan Manuel Castillo, Huw W. Lewis, Akhilesh Mishra, Ashis Mitra, Jeff Polton, Ashley Brereton, Andrew Saulter, Alex Arnold, Segolene Berthou, Douglas Clark, Julia Crook, Ananda Das, John Edwards, Xiangbo Feng, Ankur Gupta, Sudheer Joseph, Nicholas Klingaman, Imranali Momin, Christine Pequignet, Claudio Sanchez, Jennifer Saxby, and Maria Valdivieso da Costa
Geosci. Model Dev., 15, 4193–4223, https://doi.org/10.5194/gmd-15-4193-2022, https://doi.org/10.5194/gmd-15-4193-2022, 2022
Short summary
Short summary
A new environmental modelling system has been developed to represent the effect of feedbacks between atmosphere, land, and ocean in the Indian region. Different approaches to simulating tropical cyclones Titli and Fani are demonstrated. It is shown that results are sensitive to the way in which the ocean response to cyclone evolution is captured in the system. Notably, we show how a more rigorous formulation for the near-surface energy budget can be included when air–sea coupling is included.
Weichao Han, Tai-Long He, Zhaojun Tang, Min Wang, Dylan Jones, and Zhe Jiang
Geosci. Model Dev., 15, 4225–4237, https://doi.org/10.5194/gmd-15-4225-2022, https://doi.org/10.5194/gmd-15-4225-2022, 2022
Short summary
Short summary
We present an application of a hybrid deep learning (DL) model on prediction of surface CO in China from 2015 to 2020, which utilizes both convolutional neural networks and long short-term memory neural networks. The DL model performance is better than a Kalman filter (KF) system in the training period (2005–2018). Furthermore, the DL model demonstrates good temporal extensibility: the mean bias and correlation coefficients are 95.7 ppb and 0.93 in the test period (2019–2020) over eastern China.
Shuaiqi Tang, Jerome D. Fast, Kai Zhang, Joseph C. Hardin, Adam C. Varble, John E. Shilling, Fan Mei, Maria A. Zawadowicz, and Po-Lun Ma
Geosci. Model Dev., 15, 4055–4076, https://doi.org/10.5194/gmd-15-4055-2022, https://doi.org/10.5194/gmd-15-4055-2022, 2022
Short summary
Short summary
We developed an Earth system model (ESM) diagnostics package to compare various types of aerosol properties simulated in ESMs with aircraft, ship, and surface measurements from six field campaigns across spatial scales. The diagnostics package is coded and organized to be flexible and modular for future extension to other field campaign datasets and adapted to higher-resolution model simulations. Future releases will include comprehensive cloud and aerosol–cloud interaction diagnostics.
Ronny Badeke, Volker Matthias, Matthias Karl, and David Grawe
Geosci. Model Dev., 15, 4077–4103, https://doi.org/10.5194/gmd-15-4077-2022, https://doi.org/10.5194/gmd-15-4077-2022, 2022
Short summary
Short summary
For air quality modeling studies, it is very important to distribute pollutants correctly into the model system. This has not yet been done for shipping pollution in great detail. We studied the effects of different vertical distributions of shipping pollutants on the urban air quality and derived advanced formulas for it. These formulas take weather conditions and ship-specific parameters like the exhaust gas temperature into account.
Jaakko Kukkonen, Juha Nikmo, Kari Riikonen, Ilmo Westerholm, Pekko Ilvessalo, Tuomo Bergman, and Klaus Haikarainen
Geosci. Model Dev., 15, 4027–4054, https://doi.org/10.5194/gmd-15-4027-2022, https://doi.org/10.5194/gmd-15-4027-2022, 2022
Short summary
Short summary
A mathematical model has been developed for the dispersion of plumes originating from major fires. We have refined the model for the early evolution of the fire plumes; such a module has not been previously presented. We have evaluated the model against experimental field-scale data. The predicted concentrations agreed well with the aircraft measurements. We have also compiled an operational version of the model, which can be used for emergency contingency planning in the case of major fires.
Matthias Karl, Liisa Pirjola, Tiia Grönholm, Mona Kurppa, Srinivasan Anand, Xiaole Zhang, Andreas Held, Rolf Sander, Miikka Dal Maso, David Topping, Shuai Jiang, Leena Kangas, and Jaakko Kukkonen
Geosci. Model Dev., 15, 3969–4026, https://doi.org/10.5194/gmd-15-3969-2022, https://doi.org/10.5194/gmd-15-3969-2022, 2022
Short summary
Short summary
The community aerosol dynamics model MAFOR includes several advanced features: coupling with an up-to-date chemistry mechanism for volatile organic compounds, a revised Brownian coagulation kernel that takes into account the fractal geometry of soot particles, a multitude of nucleation parameterizations, size-resolved partitioning of semi-volatile inorganics, and a hybrid method for the formation of secondary organic aerosols within the framework of condensation and evaporation.
Xiaotian Xu, Xu Feng, Haipeng Lin, Peng Zhang, Shaojian Huang, Zhengcheng Song, Yiming Peng, Tzung-May Fu, and Yanxu Zhang
Geosci. Model Dev., 15, 3845–3859, https://doi.org/10.5194/gmd-15-3845-2022, https://doi.org/10.5194/gmd-15-3845-2022, 2022
Short summary
Short summary
Mercury is one of the most toxic pollutants in the environment, and wet deposition is a major process for atmospheric mercury to enter, causing ecological and human health risks. High-mercury wet deposition in the southeastern US has been a problem for many years. Here we employed a newly developed high-resolution WRF-GC model with the capability to simulate mercury to study this problem. We conclude that deep convection caused enhanced mercury wet deposition in the southeastern US.
Jeong-Beom Lee, Jae-Bum Lee, Youn-Seo Koo, Hee-Yong Kwon, Min-Hyeok Choi, Hyun-Ju Park, and Dae-Gyun Lee
Geosci. Model Dev., 15, 3797–3813, https://doi.org/10.5194/gmd-15-3797-2022, https://doi.org/10.5194/gmd-15-3797-2022, 2022
Short summary
Short summary
The predication of PM2.5 has been carried out using a numerical air quality model in South Korea. Despite recent progress of numerical air quality models, accurate prediction of PM2.5 is still challenging. In this study, we developed a data-based model using a deep neural network (DNN) to overcome the limitations of numerical air quality models. The results showed that the DNN model outperformed the CMAQ when it was trained by using observation and forecasting data from the numerical models.
Matthew L. Dawson, Christian Guzman, Jeffrey H. Curtis, Mario Acosta, Shupeng Zhu, Donald Dabdub, Andrew Conley, Matthew West, Nicole Riemer, and Oriol Jorba
Geosci. Model Dev., 15, 3663–3689, https://doi.org/10.5194/gmd-15-3663-2022, https://doi.org/10.5194/gmd-15-3663-2022, 2022
Short summary
Short summary
Progress in identifying complex, mixed-phase physicochemical processes has resulted in an advanced understanding of the evolution of atmospheric systems but has also introduced a level of complexity that few atmospheric models were designed to handle. We present a flexible treatment for multiphase chemical processes for models of diverse scale, from box up to global models. This enables users to build a customized multiphase mechanism that is accessible to a much wider community.
Haibo Wang, Ting Yang, Zifa Wang, Jianjun Li, Wenxuan Chai, Guigang Tang, Lei Kong, and Xueshun Chen
Geosci. Model Dev., 15, 3555–3585, https://doi.org/10.5194/gmd-15-3555-2022, https://doi.org/10.5194/gmd-15-3555-2022, 2022
Short summary
Short summary
In this paper, we develop an online data coupled assimilation system (NAQPMS-PDAF) with the Eulerian atmospheric chemistry-transport model. NAQPMS-PDAF allows efficient use of large computational resources. The application and performance of the system are investigated by assimilating 1 month of vertical aerosol observations. The results show that NAQPMS-PDAF can significantly improve the performance of aerosol vertical structure simulation and reduce the uncertainty to a large extent.
Peng Zhang and Yanxu Zhang
Geosci. Model Dev., 15, 3587–3601, https://doi.org/10.5194/gmd-15-3587-2022, https://doi.org/10.5194/gmd-15-3587-2022, 2022
Short summary
Short summary
Mercury is a global pollutant that can be transported over long distance through the atmosphere. We develop a new online global model for atmospheric mercury. The model reproduces the observed global atmospheric mercury concentrations and deposition distributions by simulating the emissions, transport, and physicochemical processes of atmospheric mercury. And we find that the seasonal variations of atmospheric Hg are the result of multiple processes and have obvious regional characteristics.
Patrick Obin Sturm and Anthony S. Wexler
Geosci. Model Dev., 15, 3417–3431, https://doi.org/10.5194/gmd-15-3417-2022, https://doi.org/10.5194/gmd-15-3417-2022, 2022
Short summary
Short summary
Large air quality and climate models require vast amounts of computational power. Machine learning tools like neural networks can be used to make these models more efficient, with the downside that their results might not make physical sense or be easy to interpret. This work develops a physically interpretable neural network that obeys scientific laws like conservation of mass and models atmospheric composition more accurately than a traditional neural network.
Michael Weger, Holger Baars, Henriette Gebauer, Maik Merkel, Alfred Wiedensohler, and Bernd Heinold
Geosci. Model Dev., 15, 3315–3345, https://doi.org/10.5194/gmd-15-3315-2022, https://doi.org/10.5194/gmd-15-3315-2022, 2022
Short summary
Short summary
Numerical models are an important tool to assess the air quality in cities,
as they can provide near-continouos data in time and space. In this paper,
air pollution for an entire city is simulated at a high spatial resolution of 40 m.
At this spatial scale, the effects of buildings on the atmosphere,
like channeling or blocking of the air flow, are directly represented by diffuse obstacles in the used model CAIRDIO. For model validation, measurements from air-monitoring sites are used.
Patrick C. Campbell, Youhua Tang, Pius Lee, Barry Baker, Daniel Tong, Rick Saylor, Ariel Stein, Jianping Huang, Ho-Chun Huang, Edward Strobach, Jeff McQueen, Li Pan, Ivanka Stajner, Jamese Sims, Jose Tirado-Delgado, Youngsun Jung, Fanglin Yang, Tanya L. Spero, and Robert C. Gilliam
Geosci. Model Dev., 15, 3281–3313, https://doi.org/10.5194/gmd-15-3281-2022, https://doi.org/10.5194/gmd-15-3281-2022, 2022
Short summary
Short summary
NOAA's National Air Quality Forecast Capability (NAQFC) continues to protect Americans from the harmful effects of air pollution, while saving billions of dollars per year. Here we describe and evaluate the development of the most advanced version of the NAQFC to date, which became operational at NOAA on 20 July 2021. The new NAQFC is based on a coupling of NOAA's operational Global Forecast System (GFS) version 16 with the Community Multiscale Air Quality (CMAQ) model version 5.3.1.
Athanasios Tsikerdekis, Nick A. J. Schutgens, Guangliang Fu, and Otto P. Hasekamp
Geosci. Model Dev., 15, 3253–3279, https://doi.org/10.5194/gmd-15-3253-2022, https://doi.org/10.5194/gmd-15-3253-2022, 2022
Short summary
Short summary
In our study we quantify the ability of the future satellite sensor SPEXone, part of the NASA PACE mission, to estimate aerosol emissions. The sensor will be able to retrieve accurate information of aerosol light extinction and most importantly light absorption. We simulate SPEXone spatial coverage and combine it with an aerosol model. We found that SPEXone will be able to estimate species-specific (e.g. dust, sea salt, organic or black carbon, sulfates) aerosol emissions very accurately.
Christian Zeman and Christoph Schär
Geosci. Model Dev., 15, 3183–3203, https://doi.org/10.5194/gmd-15-3183-2022, https://doi.org/10.5194/gmd-15-3183-2022, 2022
Short summary
Short summary
Our atmosphere is a chaotic system, where even a tiny change can have a big impact. This makes it difficult to assess if small changes, such as the move to a new hardware architecture, will significantly affect a weather and climate model. We present a methodology that allows to objectively verify this. The methodology is applied to several test cases, showing a high sensitivity. Results also show that a major system update of the underlying supercomputer did not significantly affect our model.
Stelios Myriokefalitakis, Elisa Bergas-Massó, María Gonçalves-Ageitos, Carlos Pérez García-Pando, Twan van Noije, Philippe Le Sager, Akinori Ito, Eleni Athanasopoulou, Athanasios Nenes, Maria Kanakidou, Maarten C. Krol, and Evangelos Gerasopoulos
Geosci. Model Dev., 15, 3079–3120, https://doi.org/10.5194/gmd-15-3079-2022, https://doi.org/10.5194/gmd-15-3079-2022, 2022
Short summary
Short summary
We here describe the implementation of atmospheric multiphase processes in the EC-Earth Earth system model. We provide global budgets of oxalate, sulfate, and iron-containing aerosols, along with an analysis of the links among atmospheric composition, aqueous-phase processes, and aerosol dissolution, supported by comparison to observations. This work is a first step towards an interactive calculation of the deposition of bioavailable atmospheric iron coupled to the model’s ocean component.
Santos J. González-Rojí, Martina Messmer, Christoph C. Raible, and Thomas F. Stocker
Geosci. Model Dev., 15, 2859–2879, https://doi.org/10.5194/gmd-15-2859-2022, https://doi.org/10.5194/gmd-15-2859-2022, 2022
Short summary
Short summary
Different configurations of physics parameterizations of a regional climate model are tested over southern Peru at fine resolution. The most challenging regions compared to observational data are the slopes of the Andes. Model configurations for Europe and East Africa are not perfectly suitable for southern Peru. The experiment with the Stony Brook University microphysics scheme and the Grell–Freitas cumulus parameterization provides the most accurate results over Madre de Dios.
Angel Navarro Trastoy, Sebastian Strasser, Lauri Tuppi, Maksym Vasiuta, Markku Poutanen, Torsten Mayer-Gürr, and Heikki Järvinen
Geosci. Model Dev., 15, 2763–2771, https://doi.org/10.5194/gmd-15-2763-2022, https://doi.org/10.5194/gmd-15-2763-2022, 2022
Short summary
Short summary
Production of satellite products relies on information from different centers. By coupling a weather model and an orbit determination solver we eliminate the dependence on one of the centers. The coupling has proven to be possible in the first stage, where no formatting has been applied to any of the models involved. This opens a window for further development and improvement to a coupling that has proven to be as good as the predecessor model.
Soon-Young Park, Uzzal Kumar Dash, Jinhyeok Yu, Keiya Yumimoto, Itsushi Uno, and Chul Han Song
Geosci. Model Dev., 15, 2773–2790, https://doi.org/10.5194/gmd-15-2773-2022, https://doi.org/10.5194/gmd-15-2773-2022, 2022
Short summary
Short summary
An EnKF was applied to CMAQ for assimilating ground PM2.5 observations from China and South Korea. The EnKF performed better than that without assimilation and even superior to 3D-Var. The reduced MBs in 24 h predictions were 48 % and 27 % by improving ICs and BCs, respectively.
Lars Hoffmann, Paul F. Baumeister, Zhongyin Cai, Jan Clemens, Sabine Griessbach, Gebhard Günther, Yi Heng, Mingzhao Liu, Kaveh Haghighi Mood, Olaf Stein, Nicole Thomas, Bärbel Vogel, Xue Wu, and Ling Zou
Geosci. Model Dev., 15, 2731–2762, https://doi.org/10.5194/gmd-15-2731-2022, https://doi.org/10.5194/gmd-15-2731-2022, 2022
Short summary
Short summary
We describe the new version (2.2) of the Lagrangian transport model MPTRAC, which has been ported for application on GPUs. The model was verified by comparing kinematic trajectories and synthetic tracer simulations for the free troposphere and stratosphere from GPUs and CPUs. Benchmarking showed a speed-up of a factor of 16 of GPU-enabled simulations compared to CPU-only runs, indicating the great potential of applying GPUs for Lagrangian transport simulations on upcoming HPC systems.
Clemens Spensberger, Trond Thorsteinsson, and Thomas Spengler
Geosci. Model Dev., 15, 2711–2729, https://doi.org/10.5194/gmd-15-2711-2022, https://doi.org/10.5194/gmd-15-2711-2022, 2022
Short summary
Short summary
In order to understand the atmosphere, we rely on a hierarchy of models ranging from very simple to very complex. Comparing different steps in this hierarchy usually entails comparing different models. Here we combine two such steps that are commonly used in one modelling framework. This makes comparisons both much easier and much more direct.
Andrea Pozzer, Simon F. Reifenberg, Vinod Kumar, Bruno Franco, Matthias Kohl, Domenico Taraborrelli, Sergey Gromov, Sebastian Ehrhart, Patrick Jöckel, Rolf Sander, Veronica Fall, Simon Rosanka, Vlassis Karydis, Dimitris Akritidis, Tamara Emmerichs, Monica Crippa, Diego Guizzardi, Johannes W. Kaiser, Lieven Clarisse, Astrid Kiendler-Scharr, Holger Tost, and Alexandra Tsimpidi
Geosci. Model Dev., 15, 2673–2710, https://doi.org/10.5194/gmd-15-2673-2022, https://doi.org/10.5194/gmd-15-2673-2022, 2022
Short summary
Short summary
A newly developed setup of the chemistry general circulation model EMAC (ECHAM5/MESSy for Atmospheric Chemistry) is evaluated here. A comprehensive organic degradation mechanism is used and coupled with a volatility base model.
The results show that the model reproduces most of the tracers and aerosols satisfactorily but shows discrepancies for oxygenated organic gases. It is also shown that this model configuration can be used for further research in atmospheric chemistry.
Dai Koshin, Kaoru Sato, Masashi Kohma, and Shingo Watanabe
Geosci. Model Dev., 15, 2293–2307, https://doi.org/10.5194/gmd-15-2293-2022, https://doi.org/10.5194/gmd-15-2293-2022, 2022
Short summary
Short summary
The 4D ensemble Kalman filter data assimilation system for the whole neutral atmosphere has been updated. The update includes the introduction of a filter to reduce the generation of spurious waves, change in the order of horizontal diffusion of the forecast model to reproduce more realistic tidal amplitudes, and use of additional satellite observations. As a result, the analysis performance has been greatly improved, even for disturbances with periods of less than 1 d.
Harish Baki, Sandeep Chinta, C Balaji, and Balaji Srinivasan
Geosci. Model Dev., 15, 2133–2155, https://doi.org/10.5194/gmd-15-2133-2022, https://doi.org/10.5194/gmd-15-2133-2022, 2022
Short summary
Short summary
WRF model accuracy relies on numerous aspects, and the model parameters are one of them. By calibrating the model parameters, we can improve the model forecast. However, there exist hundreds of parameters, and calibrating all of them is unimaginably expensive. Thus, there is a need to identify the sensitive parameters that influence the model output variables to reduce the parameter dimensionality. This study addresses the different methods and outcomes of parameter sensitivity analysis.
Jessica Keune, Dominik L. Schumacher, and Diego G. Miralles
Geosci. Model Dev., 15, 1875–1898, https://doi.org/10.5194/gmd-15-1875-2022, https://doi.org/10.5194/gmd-15-1875-2022, 2022
Short summary
Short summary
Air transports moisture and heat, shaping the weather we experience. When and where was this air moistened and warmed by the surface? To address this question, atmospheric models trace the history of air parcels in space and time. However, their uncertainties remain unexplored, which hinders their utility and application. Here, we present a framework that sheds light on these uncertainties. Our approach sets a new standard in the assessment of atmospheric moisture and heat trajectories.
Soyoung Ha
Geosci. Model Dev., 15, 1769–1788, https://doi.org/10.5194/gmd-15-1769-2022, https://doi.org/10.5194/gmd-15-1769-2022, 2022
Short summary
Short summary
In an effort to improve air quality forecasting, the WRFDA 3D-Var system is newly extended for the assimilation of surface PM2.5 and PM10 using the RACM/MADE-VBS chemistry in the WRF-Chem model. Through a case study during the Korea–United States Air Quality (KORUS-AQ) period, it is demonstrated that the analysis can lead to improving the prediction of surface PM concentrations up to 26 % for 24 h, diminishing most bias errors.
Michael T. Kiefer, Warren E. Heilman, Shiyuan Zhong, Joseph J. Charney, Xindi Bian, Nicholas S. Skowronski, Kenneth L. Clark, Michael R. Gallagher, John L. Hom, and Matthew Patterson
Geosci. Model Dev., 15, 1713–1734, https://doi.org/10.5194/gmd-15-1713-2022, https://doi.org/10.5194/gmd-15-1713-2022, 2022
Short summary
Short summary
We examine methods used to represent wildland fire sensible heat release in atmospheric models. A set of simulations are evaluated using observations from a low-intensity prescribed fire in the New Jersey Pine Barrens. The comparison is motivated by the need for guidance regarding the representation of low-intensity fire sensible heating in atmospheric models. Such fires are prevalent during prescribed fire operations and can impact the health and safety of fire personnel and the public.
Lu Shen, Daniel J. Jacob, Mauricio Santillana, Kelvin Bates, Jiawei Zhuang, and Wei Chen
Geosci. Model Dev., 15, 1677–1687, https://doi.org/10.5194/gmd-15-1677-2022, https://doi.org/10.5194/gmd-15-1677-2022, 2022
Short summary
Short summary
The high computational cost of chemical integration is a long-standing limitation in global atmospheric chemistry models. Here we present an adaptive and efficient algorithm that can reduce the computational time of atmospheric chemistry by 50 % and maintain the error below 2 % for important species, inspired by machine learning clustering techniques and traditional asymptotic analysis ideas.
Baolei Lyu, Ran Huang, Xinlu Wang, Weiguo Wang, and Yongtao Hu
Geosci. Model Dev., 15, 1583–1594, https://doi.org/10.5194/gmd-15-1583-2022, https://doi.org/10.5194/gmd-15-1583-2022, 2022
Short summary
Short summary
Data fusion is used to estimate spatially completed and smooth reanalysis fields from multiple data sources of observations and model simulations. We developed a well-designed deep-learning model framework to embed spatial correlation principles of atmospheric physics and chemical models. The deep-learning model has very high accuracy to predict reanalysis data fields from isolated observation data points. It is also feasible for operational applications due to computational efficiency.
Wim C. de Rooy, Pier Siebesma, Peter Baas, Geert Lenderink, Stephan R. de Roode, Hylke de Vries, Erik van Meijgaard, Jan Fokke Meirink, Sander Tijm, and Bram van 't Veen
Geosci. Model Dev., 15, 1513–1543, https://doi.org/10.5194/gmd-15-1513-2022, https://doi.org/10.5194/gmd-15-1513-2022, 2022
Short summary
Short summary
This paper describes a comprehensive model update to the boundary layer schemes. Because the involved parameterisations are all built on widely applied frameworks, the here-described modifications are applicable to many NWP and climate models. The model update contains substantial modifications to the cloud, turbulence, and convection schemes and leads to a substantial improvement of several aspects of the model, especially low cloud forecasts.
Francisco J. Pérez-Invernón, Heidi Huntrieser, Patrick Jöckel, and Francisco J. Gordillo-Vázquez
Geosci. Model Dev., 15, 1545–1565, https://doi.org/10.5194/gmd-15-1545-2022, https://doi.org/10.5194/gmd-15-1545-2022, 2022
Short summary
Short summary
This study reports the first parameterization of long-continuing-current lightning in a climate model. Long-continuing-current lightning is proposed to be the main precursor of lightning-ignited wildfires and sprites, a type of transient luminous event taking place in the mesosphere. This parameterization can significantly contribute to improving the implementation of wildfires in climate models.
Augustin Colette, Laurence Rouïl, Frédérik Meleux, Vincent Lemaire, and Blandine Raux
Geosci. Model Dev., 15, 1441–1465, https://doi.org/10.5194/gmd-15-1441-2022, https://doi.org/10.5194/gmd-15-1441-2022, 2022
Short summary
Short summary
We introduce the first toolbox that allows exploration of the benefits of air pollution mitigation scenarios in the every-day air quality forecasts through a web interface. The toolbox relies on the joint use of chemistry-transport models (CTMs) and surrogate modelling techniques.
Cheng-Hsuan Lu, Quanhua Liu, Shih-Wei Wei, Benjamin T. Johnson, Cheng Dang, Patrick G. Stegmann, Dustin Grogan, Guoqing Ge, Ming Hu, and Michael Lueken
Geosci. Model Dev., 15, 1317–1329, https://doi.org/10.5194/gmd-15-1317-2022, https://doi.org/10.5194/gmd-15-1317-2022, 2022
Short summary
Short summary
This article is a technical note on the aerosol absorption and scattering calculations of the Community Radiative Transfer Model (CRTM) v2.2 and v2.3. It also provides guidance for prospective users of the CRTM aerosol option and Gridpoint Statistical Interpolation (GSI) aerosol-aware radiance assimilation. Scientific aspects of aerosol-affected BT in atmospheric data assimilation are also briefly discussed.
Zheng Zhang, Chuyao Luo, Shanshan Feng, Rui Ye, Yunming Ye, and Xutao Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-19, https://doi.org/10.5194/gmd-2022-19, 2022
Revised manuscript accepted for GMD
Short summary
Short summary
In this paper, we develop a model to predict radar echo sequences and apply it in the precipitation nowcasting field. Different from existed models, we propose two new attention modules. By introducing them, the performance of RAP-Net outperforms other models especially in those regions with middle and high-intensity rainfall. Considering these regions would cause more threats to human activity, the research in our manuscript is significant to prevent natural disasters caused by heavy rainfall.
Penelope Maher and Paul Earnshaw
Geosci. Model Dev., 15, 1177–1194, https://doi.org/10.5194/gmd-15-1177-2022, https://doi.org/10.5194/gmd-15-1177-2022, 2022
Short summary
Short summary
Climate models do a pretty good job. But they are far from perfect. Fixing these imperfections is really hard because the models are complicated. One way to make progress is to create simpler models: think impressionism rather than realism in the art world. We changed the Met Office model to be intentionally simple and it still does a pretty good job. This will help to identify sources of model imperfections, develop new methods and improve our understanding of how the climate works.
Lukas Bösiger, Michael Sprenger, Maxi Boettcher, Hanna Joos, and Tobias Günther
Geosci. Model Dev., 15, 1079–1096, https://doi.org/10.5194/gmd-15-1079-2022, https://doi.org/10.5194/gmd-15-1079-2022, 2022
Short summary
Short summary
Jet streams are coherent air flows that interact with atmospheric structures such as warm conveyor belts (WCBs) and the tropopause. Individually, these structures have a significant impact on the weather evolution. A first step towards a deeper understanding of the meteorological processes is to extract jet stream core lines, for which we develop a novel feature extraction algorithm. Based on the line geometry, we automatically detect and visualize potential interactions between WCBs and jets.
Philipp Franke, Anne Caroline Lange, and Hendrik Elbern
Geosci. Model Dev., 15, 1037–1060, https://doi.org/10.5194/gmd-15-1037-2022, https://doi.org/10.5194/gmd-15-1037-2022, 2022
Short summary
Short summary
The paper proposes an ensemble-based analysis framework (ESIAS-chem) for time- and altitude-resolved volcanic ash emission fluxes and their uncertainty. The core of the algorithm is an ensemble Nelder–Mead optimization algorithm accompanied by a particle filter update. The performed notional experiments demonstrate the high accuracy of ESIAS-chem in analyzing the vertically resolved volcanic ash in the atmosphere. Further, the system is in general able to estimate the emission fluxes properly.
Antje Inness, Melanie Ades, Dimitris Balis, Dmitry Efremenko, Johannes Flemming, Pascal Hedelt, Maria-Elissavet Koukouli, Diego Loyola, and Roberto Ribas
Geosci. Model Dev., 15, 971–994, https://doi.org/10.5194/gmd-15-971-2022, https://doi.org/10.5194/gmd-15-971-2022, 2022
Short summary
Short summary
This paper describes the way that the Copernicus Atmosphere Monitoring Service (CAMS) produces forecasts of volcanic SO2. These forecasts are provided routinely every day. They are created by blending SO2 data from satellite instruments (TROPOMI and GOME-2) with the CAMS model. We show that the quality of the CAMS SO2 forecasts can be improved if additional information about the height of volcanic plumes is provided in the satellite data.
Ming Chang, Jiachen Cao, Qi Zhang, Weihua Chen, Guotong Wu, Liping Wu, Weiwen Wang, and Xuemei Wang
Geosci. Model Dev., 15, 787–801, https://doi.org/10.5194/gmd-15-787-2022, https://doi.org/10.5194/gmd-15-787-2022, 2022
Short summary
Short summary
Despite the importance of nitrogen deposition, its simulation is still insufficiently represented in current atmospheric chemistry models. In this study, the improvement of the canopy stomatal resistance mechanism and the nitrogen-limiting schemes in Noah-MP-WDDM v1.42 give new options for simulating nitrogen dry deposition velocity. This study finds that the combined BN-23 mechanism agrees better with the observed NO2 dry deposition velocity, with the mean bias reduced by 50.1 %.
Jinfang Yin, Xudong Liang, Hong Wang, and Haile Xue
Geosci. Model Dev., 15, 771–786, https://doi.org/10.5194/gmd-15-771-2022, https://doi.org/10.5194/gmd-15-771-2022, 2022
Short summary
Short summary
An ensemble (EN) approach was designed to improve autoconversion (ATC) from cloud water to rainwater in cloud microphysics schemes. One unique feature of the EN approach is that the ATC rate is a mean value based on the calculations from several widely used ATC schemes. The ensemble approach proposed herein appears to help improve the representation of cloud and precipitation processes in weather and climate models.
Julian F. Quinting and Christian M. Grams
Geosci. Model Dev., 15, 715–730, https://doi.org/10.5194/gmd-15-715-2022, https://doi.org/10.5194/gmd-15-715-2022, 2022
Short summary
Short summary
Physical processes in weather systems importantly affect the midlatitude large-scale circulation. This study introduces an artificial-intelligence-based framework which allows the identification of an important weather system – the so-called warm conveyor belt (WCB) – at comparably low computational costs and from data at low spatial and temporal resolution. The framework thus newly enables the systematic investigation of WCBs in large data sets such as climate model projections.
Julian F. Quinting, Christian M. Grams, Annika Oertel, and Moritz Pickl
Geosci. Model Dev., 15, 731–744, https://doi.org/10.5194/gmd-15-731-2022, https://doi.org/10.5194/gmd-15-731-2022, 2022
Short summary
Short summary
This study applies novel artificial-intelligence-based models that allow the identification of one specific weather system which affects the midlatitude circulation. We show that the models yield similar results as their trajectory-based counterpart, which requires data at higher spatiotemporal resolution and is computationally more expensive. Overall, we aim to show how deep learning methods can be used efficiently to support process understanding of biases in weather prediction models.
David F. Baker, Emily Bell, Kenneth J. Davis, Joel F. Campbell, Bing Lin, and Jeremy Dobler
Geosci. Model Dev., 15, 649–668, https://doi.org/10.5194/gmd-15-649-2022, https://doi.org/10.5194/gmd-15-649-2022, 2022
Short summary
Short summary
The OCO-2 satellite measures many closely spaced column-averaged CO2 values around its orbit. To give these data proper weight in flux inversions, their error correlations must be accounted for. Here we lay out a 1-D error model with correlations that die out exponentially along-track to do so. A correlation length scale of ∼20 km is derived from column CO2 measurements from an airborne lidar flown underneath OCO-2 for use in this model. The model's performance is compared to previous ones.
Matthias Göbel, Stefano Serafin, and Mathias W. Rotach
Geosci. Model Dev., 15, 669–681, https://doi.org/10.5194/gmd-15-669-2022, https://doi.org/10.5194/gmd-15-669-2022, 2022
Short summary
Short summary
We present WRFlux, an open-source software that allows numerically consistent, time-averaged budget evaluation of prognostic variables for the numerical weather prediction model WRF as well as the transformation of the budget equations from the terrain-following grid of the model to the Cartesian coordinate system. We demonstrate the performance and a possible application of WRFlux and illustrate the detrimental effects of approximations that are inconsistent with the model numerics.
Jingmin Li, Johannes Hendricks, Mattia Righi, and Christof G. Beer
Geosci. Model Dev., 15, 509–533, https://doi.org/10.5194/gmd-15-509-2022, https://doi.org/10.5194/gmd-15-509-2022, 2022
Short summary
Short summary
The growing complexity of global aerosol models results in a large number of parameters that describe the aerosol number, size, and composition. This makes the analysis, evaluation, and interpretation of the model results a challenge. To overcome this difficulty, we apply a machine learning classification method to identify clusters of specific aerosol types in global aerosol simulations. Our results demonstrate the spatial distributions and characteristics of these identified aerosol clusters.
Cited articles
Allen, L., Lindberg, F., and Grimmond, C. S. B.: Global to city scale urban
anthropogenic heat flux: model and variability, Int. J. Climatol., 31,
1990–2005, 2011.
ASHRAE: ANSI/ASHRAE Standard 140-2017, Standard Method of Test for the
Evaluation of Building Energy Analysis Computer Programs, 2017.
Baetens, R. and Saelens, D.: Modelling uncertainty in district energy
simulations by stochastic residential occupant behaviour, J. Build. Perform.
Simu., 9, 431–447, https://doi.org/10.1080/19401493.2015.1070203, 2016.
BCO: Guide to Specification 2009, British Council for Offices, 2009.
BEIS: Department for Business, Energy & Industrial Strategy: Sub-national
electricity consumption data, available at:
https://www.gov.uk/government/collections/sub-national-electricity-consumption-data#lsoa/msoa-data,
last access: 14 July 2017a.
BEIS: Department for Business, Energy & Industrial Strategy: Sub-national
gas consumption data, available at:
https://www.gov.uk/government/collections/sub-national-gas-consumption-data,
last access: 14 July 2017b.
BEIS: Department for Business, Energy & Industrial Strategy: Sub-national
total final energy consumption data, available at:
https://www.gov.uk/government/collections/total-final-energy-consumption-at-sub-national-level,
last access: 14 July 2017c.
BEIS: Department for Business, Energy & Industrial Strategy: Sub-National
Consumption Statisitics: Methodology and guidance booklet, 2018.
Best, M. J. and Grimmond, C. S. B.: Investigation of the impact of
anthropogenic heat flux within an urban land surface model and PILPS-urban,
Theor. Appl. Climatol., 126, 51–60, https://doi.org/10.1007/s00704-015-1554-3,
2016.
Björkegren, A. and Grimmond, C. S. B.: Net carbon dioxide emissions from
central London, Urban Clim., 23, 131–158, https://doi.org/10.1016/j.uclim.2016.10.002,
2018.
Blitzstein, J. K. and Hwang, J.: Introduction to Probability, 2nd Edn.,
Chapman and Hall, CRC Press, 2019.
Bohnenstengel, S. I., Hamilton, I., Davies, M., and Belcher, S. E.: Impact of
anthropogenic heat emissions on London's temperatures, Q. J. Roy. Meteor.
Soc., 140, 687–698, https://doi.org/10.1002/qj.2144, 2014.
BSI: BS 6700: Specification for Design, installation, testing and
maintenance of services supplying water for domestic use within buildings
and their curtilages, British Standards Institution, 1997.
Bueno, B., Pigeon, G., Norford, L. K., Zibouche, K., and Marchadier, C.: Development and evaluation of a building energy model integrated in the TEB scheme, Geosci. Model Dev., 5, 433–448, https://doi.org/10.5194/gmd-5-433-2012, 2012.
Busby, J.: UK shallow ground temperatures for ground coupled heat
exchangers, Q. J. Eng. Geol. Hydroge., 48, 248–260,
https://doi.org/10.1144/qjegh2015-077, 2015.
Butcher, K. (Ed.): CIBSE Guide K: Electricity in buildings, Chartered
Institution of Building Services Engineers, 2004.
Butcher, K. (Ed.): CIBSE Guide F: Energy Efficiency in Buildings, Chartered
Institution of Building Services Engineers, 2012.
Butcher, K. (Ed.): CIBSE Guide G: Public health and plumbing engineering,
Chartered Institute of Building Service Engineers, 2014.
Butcher, K. and Craig, B. (Eds.): CIBSE Guide A: Environmental design,
Chartered Institute of Building Service Engineers, London, 2016.
Casey, H. J.: The law of retail gravitation applied to traffic engineering,
Traffic Q., 9, 313–321, 1955.
Capel-Timms, I., Smith, S. T., Sun, T., and Grimmond, S.: Dynamic Anthropogenic activitieS impacting Heat emissions (DASH v1.0): Development and evaluation (Version 1.2), Zenodo, https://doi.org/10.5281/zenodo.3936025, 2020.
Chrysoulakis, N., Grimmond, S., Feigenwinter, C., Lindberg, F.,
Gastellu-Etchegorry, J. P., Marconcini, M., Mitraka, Z., Stagakis, S.,
Crawford, B., Olofson, F., Landier, L., Morrison, W., and Parlow, E.: Urban
energy exchanges monitoring from space, Sci. Rep., 8, 1–8,
https://doi.org/10.1038/s41598-018-29873-x, 2018.
Cleveland, W. S.: LOWESS: A Program for Smoothing Scatterplots by Robust
Locally Weighted Regression, Am. Stat., 35, 54, https://doi.org/10.2307/2683591, 1988.
Cole, R. J. and Sturrock, N. S.: The convective heat exchange at the
external surface of buildings, Build. Environ., 12, 207–214,
https://doi.org/10.1016/0360-1323(77)90021-X, 1977.
Crawford, B., Grimmond, C. S. B., Ward, H. C., Morrison, W., and Kotthaus,
S.: Spatial and temporal patterns of surface–atmosphere energy exchange in
a dense urban environment using scintillometry, Q. J. Roy. Meteor. Soc.,
143, 817–833, https://doi.org/10.1002/qj.2967, 2017.
Crawley, D. B., Lawrie, L. K., Pedersen, C. O., and Winkelmann, F. C.:
EnergyPlus: Energy Simulation Program, ASHRAE J., 42, 49–56, 2000.
Crooks, A. T. and Heppenstall, A. J.: Introduction to Agent-Based Modelling,
in: Agent-Based Models of Geographical Systems, 85–105, 2012.
DECC: Non-Domestic National Energy Efficiency Data-Framework: Energy
Statistics 2006-12, Department of Energy and Climate Change, available at:
https://www.gov.uk/government/statistics/non-domestic-national-energy-efficiency-data-framework-energy-statistics-2006-12
(last access: 11 February 2020), 2015.
DECC and BRE: Energy Follow Up Survey (EFUS) 2011, Department of Energy and
Climate Change and Building Research Establishment, available at:
https://www.gov.uk/government/statistics/energy-follow-up-survey-efus-2011
(last access: 11 February 2020), 2016.
de Munck, C., Pigeon, G., Masson, V., Meunier, F., Bousquet, P., Tréméac, B., Merchat, M., Poeuf, P., and Marchadier, C.: How much can air conditioning increase air temperatures for a city like Paris, France?, Int. J. Climatol., 33, 210–227, https://doi.org/10.1002/joc.3415, 2013.
DfE: Schools, Pupils and their Characteristics, January 2019 – Accompanying
Table, Department for Education, available at:
https://www.gov.uk/government/statistics/schools-pupils-and-their-characteristics-january-2019
(last access: 11 February 2020), 2019.
DfT: Department for Transport, TRA0203 – Motor vehicle traffic (vehicle
kilometres) by road class and region and country in Great Britain, annual
2014, available at:
https://www.gov.uk/government/statistical-data-sets/road-traffic-statistics-tra
(last access: 10 February 2016), 2014a.
DfT: Department for Transport, TRA0204 – Road traffic (vehicle kilometres)
by vehicle type and road class in Great Britain, annual 2014,
available at:
https://www.gov.uk/government/statistical-data-sets/road-traffic-statistics-tra
(last access: 10 February 2016), 2014b.
DfT: Department for Transport, National Travel Survey, 2002–2016, [data
collection], 12th Edn., UK Data Service, SN: 5340, https://doi.org/10.5255/UKDA-SN-5340-8,
2017.
DfT and DVLA: Department for Transport (DfT) and Driver and Vehicle
Licensing Agency (DVLA), Data on all licensed and registered vehicles
(VEH01), available at:
https://www.gov.uk/government/statistical-data-sets/all-vehicles-veh01
(last access: 10 February 2020), 2019.
Dong, Y., Varquez, A. C. G., and Kanda, M.: Global anthropogenic heat flux
database with high spatial resolution, Atmos. Environ., 150, 276–294,
https://doi.org/10.1016/j.atmosenv.2016.11.040, 2017.
Druckman, A. and Jackson, T.: Household energy consumption in the UK: A
highly geographically and socio-economically disaggregated model, Energ.
Policy, 36, 3167–3182, https://doi.org/10.1016/j.enpol.2008.03.021, 2008.
EnergyPlus: Knowledgebase: Downloads, Testing and validation, ANSI/ASHRAE
Standard 140 models, Knowledgebase Downloads, available at:
http://energyplus.helpserve.com/Knowledgebase/List/Index/49, last access: 30
June 2020.
Evans, S., Liddiard, R., and Steadman, P.: Modelling a whole building stock:
domestic, non-domestic and mixed use, Build. Res. Inf., 47, 156–172,
https://doi.org/10.1080/09613218.2017.1410424, 2019.
Ferreira, M. J., de Oliveira, A. P., and Soares, J.: Anthropogenic heat in
the city of São Paulo, Brazil, Theor. Appl. Climatol., 104,
43–56, https://doi.org/10.1007/s00704-010-0322-7, 2011.
Field, J.: TM46: Energy Benchmarks, Chartered Institute of Building Service
Engineers, 2008.
Firth, S., Lomas, K., Wright, A., and Wall, R.: Identifying trends in the use
of domestic appliances from household electricity consumption measurements,
Energ. Buildings, 40, 926–936, https://doi.org/10.1016/j.enbuild.2007.07.005, 2008.
Fisher, K. and Gershuny, J.: Coming full circle – introducing the
multinational Time Use Study Simple File, Electron. Int. J. Time Use Res.,
10, 91–96, https://doi.org/10.1016/j.physbeh.2017.03.040, 2013.
Flamco: Indirect Water Heaters (mains water systems), available
at: https://flamcogroup.com/media/files/documentation/EXP17_PSTEST_LR_v09022017_Chapter_6.pdf (last access: 7 March 2019), 2017.
Foucquier, A., Robert, S., Suard, F., Stéphan, L., and Jay, A.: State of
the art in building modelling and energy performances prediction: A review,
Renew. Sust. Energ Rev., 23, 272–288, https://doi.org/10.1016/j.rser.2013.03.004,
2013.
Gabey, A. M., Grimmond, C. S. B., and Capel-Timms, I.: Anthropogenic heat
flux: advisable spatial resolutions when input data are scarce, Theor. Appl.
Climatol., 135, 791–807, https://doi.org/10.1007/s00704-018-2367-y, 2019.
Gershuny, J. and Sullivan, O.: United Kingdom Time Use Survey, 2014–2015,
[data collection], UK Data Service, SN: 8128,
https://doi.org/10.5255/UKDA-SN-8128-1, 2017.
GLA: Greater London Authority, Statistical GIS Boundary Files for London,
available at:
https://data.london.gov.uk/dataset/statistical-gis-boundary-files-london
(last access: 11 February 2020), 2011.
GLA: Greater London Authority, London Schools Atlas, available
at: https://data.london.gov.uk/dataset/london-schools-atlas (last access: 7 September 2020), 2014.
Google: Google Directions API, available at:
https://developers.google.com/maps/documentation/directions/start (last access:
31 January 2020), 2019.
Greenshields, B. D., Bibbins, J. R., Channing, W. S., and Miller, H. H.: A
Study of Traffic Capacity, in Proceedings of the highway research board,
Highway Research Board, Washington, D. C., 14, 448–477, 1935.
Grimmond, C. S. B.: The Suburban Energy Balance?: Methodological
Considerations and Results for a Mid-Latitude West, Int. J., 12, 481–497,
https://doi.org/10.1002/joc.3370120506, 1992.
Grimmond, C. S. B. and Oke, T. R.: Aerodynamic properties of urban areas
derived from analysis of surface form, J. Appl. Meteorol., 38, 1262–1292, https://doi.org/10.1175/1520-0450(1999)038<1262:apouad>2.0.co;2, 1999.
Grimmond, C. S. B., Cleugh, H. A., and Oke, T. R.: An objective urban heat
storage model and its comparison with other schemes, Atmos. Environ., 25, 311–326, https://doi.org/10.1016/0957-1272(91)90003-W, 1991.
Grimmond, C. S. B., Potter, S. K., Zutter, H. N., and Souch, C.: Rapid
methods to estimate sky view factors applied to urban areas, Int. J. Climatol.,
21, 903–913, https://doi.org/10.1002/joc.659, 2001.
Hawkins, G.: Rules of Thumb, Guidelines for Building Services, 5th Edn.,
BSRIA, Building Services Research and Information Association, 2011.
HCA: Employment Densities Guide: 2nd Edition, Homes and Communities Agency,
available at:
https://www.gov.uk/government/publications/employment-densities-guide (last access: 7 September 2020), 2010.
Heaviside, C., Vardoulakis, S., and Cai, X.-M.: Attribution of mortality to
the urban heat island during heatwaves in the West Midlands, UK, Environ.
Health, 15 Suppl 1, 50–59, https://doi.org/10.1186/s12940-016-0100-9, 2016.
Heiple, S. and Sailor, D. J.: Using building energy simulation and
geospatial modeling techniques to determine high resolution building sector
energy consumption profiles, Energ. Buildings, 40, 1426–1436,
https://doi.org/10.1016/j.enbuild.2008.01.005, 2008.
Hermanns, H.: Interactive Markov Chains: The Quest for Quantified Quality,
Springer, 2003.
Highways Agency: Traffic Capcity of Urban Roads. Design Manual for Roads and
Bridges: TA 79/99, Highways Agency, available at:
http://www.standardsforhighways.co.uk/ha/standards/dmrb/vol5/section1/ta7999.pdf
(last access: 10 February 2020), 2017.
Hinkel, K. M., Nelson, F. E., Klene, A. E., and Bell, J. H.: The urban heat
island in winter at Barrow, Alaska, Int. J. Climatol., 23, 1889–1905,
https://doi.org/10.1002/joc.971, 2003.
Iamarino, M., Beevers, S., and Grimmond, C. S. B.: High-resolution (space,
time) anthropogenic heat emissions: London 1970–2025, Int. J. Climatol.,
32, 1754–1767, https://doi.org/10.1002/joc.2390, 2012.
IOP: Institute of Plumbing. Plumbing Services Engineering Design Guide ISBN
9781871956405, 2002.
Kikegawa, Y., Genchi, Y., Yoshikado, H., and Kondo, H.: Development of a
numerical simulation system toward comprehensive assessments of urban
warming countermeasures including their impacts upon the urban buildings'
energy-demands, Appl. Energ., 76, 449–466,
https://doi.org/10.1016/S0306-2619(03)00009-6, 2003.
Kikegawa, Y., Tanaka, A., Ohashi, Y., Ihara, T., and Shigeta, Y.: Observed
and simulated sensitivities of summertime urban surface air temperatures to
anthropogenic heat in downtown areas of two Japanese Major Cities, Tokyo and
Osaka, Theor. Appl. Climatol., 117, 175–193,
https://doi.org/10.1007/s00704-013-0996-8, 2014.
Kim, Y. S. and Srebric, J.: Impact of occupancy rates on the building
electricity consumption in commercial buildings, Energ. Buildings, 138,
591–600, https://doi.org/10.1016/j.enbuild.2016.12.056, 2017.
Klein, S. A., Duffie, J. A., and Mitchell, J. C.: TRNSYS 18: A Transient
System Simulation Program, Solar Energy Laboratory, University of Wisconsin,
available at: https://sel.me.wisc.edu/trnsys/ (last access: 31
January 2020), 2017.
Knudsen, S.: Heat transfer in a “tank in tank” combi store, BYG Rapport, No. R-025, 2002.
Kotthaus, S. and Grimmond, C. S. B.: Identification of Micro-scale
Anthropogenic CO2, heat and moisture sources – Processing eddy covariance
fluxes for a dense urban environment, Atmos. Environ., 57, 301–316,
https://doi.org/10.1016/j.atmosenv.2012.04.024, 2012.
Kotthaus, S. and Grimmond, C. S. B.: Energy exchange in a dense urban
environment – Part I: Temporal variability of long-term observations in
central London, Urban Clim., 10, 261–280,
https://doi.org/10.1016/j.uclim.2013.10.002, 2014.
Lee, S. H., Song, C. K., Baik, J. J., and Park, S. U.: Estimation of
anthropogenic heat emission in the Gyeong-In region of Korea, Theor. Appl.
Climatol., 96, 291–303, https://doi.org/10.1007/s00704-008-0040-6, 2009.
LGA: Local Government Association website, available at:
https://www.local.gov.uk/about/what-local-government, last access: 26 November
2019.
Lindberg, F., Grimmond, C. S. B., Yogeswaran, N., Kotthaus, S., and Allen,
L.: Impact of city changes and weather on anthropogenic heat flux in Europe
1995–2015, Urban Clim., 4, 1–15, https://doi.org/10.1016/j.uclim.2013.03.002,
2013.
London Datastore: London Atmospheric Emissions Inventory (LAEI) 2013 –
Supporting information: key GIS geographies and road traffic flows and
vehicle-kilometres, available at:
https://data.london.gov.uk/dataset/london-atmospheric-emissions-inventory-2013
(last access: 11 February 2020), 2014.
Lu, Y., Wang, Q., Zhang, Y., Sun, P., and Qian, Y.: An estimate of
anthropogenic heat emissions in China, Int. J. Climatol., 36, 1134–1142,
https://doi.org/10.1002/joc.4407, 2016.
Macal, C. and North, M.: Tutorial on agent-based modelling and simulation, J.
Simul., 4, 151–162, https://doi.org/10.1057/jos.2010.3, 2010.
McKenna, E., Krawczynski, M., and Thomson, M.: Four-state domestic building
occupancy model for energy demand simulations, Energ. Buildings, 96, 30–39,
https://doi.org/10.1016/j.enbuild.2015.03.013, 2015.
MWS: McDonald Water Storage Ltd: Hot Water Tanks Specifications and Sizing,
available at:
https://www.mcdonaldwaterstorage.com/rectangular-tank-sizing-specifications,
last access: 7 March 2019.
National Bureau of Statistics of China: China Statistical Information
Network, available at:
http://www.stats.gov.cn/english/statisticaldata/censusdata/ (last access: 11
February 2020), 2017.
NG: National Grid: Transmission operational data, available at:
https://www.nationalgridgas.com/data-and-operations/transmission-operational-data,
last access: 28 November 2015.
Nie, W. S., Sun, T., and Ni, G. H.: Spatiotemporal characteristics of
anthropogenic heat in an urban environment: A case study of Tsinghua Campus,
Build. Environ., 82, 675–686, https://doi.org/10.1016/j.buildenv.2014.10.011, 2014.
Offerle, B., Grimmond, C. S. B., and Fortuniak, K.: Heat storage and
anthropogenic heat flux in relation to the energy balance of a central
European city centre, Int. J. Climatol., 25, 1405–1419,
https://doi.org/10.1002/joc.1198, 2005.
Oke, T. R.: The urban energy balance, Prog. Phys. Geogr., 12, 471–508,
https://doi.org/10.1177/030913338801200401, 1988.
ONS: Office for National Statistics, QS406EW – Household size, available at: https://www.nomisweb.co.uk/census/2011/qs406ew (last access: 10
February 2020), 2011.
ONS: Office for National Statistics, WP101EW Population (Workplace
population), available at:
https://www.nomisweb.co.uk/query/construct/summary.asp?reset=yes&mode=construct&dataset=1300&version=0&anal=1&initsel=%0D
(last access: 31 January 2020), 2014a.
ONS: Office for National Statistics, WU03UK Location of usual residence and
place of work by method of travel to work, available at:
https://www.nomisweb.co.uk/query/construct/summary.asp?reset=yes&mode=construct&dataset=1207&version=0&anal=1&initsel=
(last access: 31 January 2020), 2014b.
ONS: Office for National Statistics, Mid-2015 Population Estimates for
Census Output Areas in London by Single Year of Age and Sex, available at:
https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/censusoutputareaestimatesinthelondonregionofengland,
last access: 25 May 2015.
ONS: Office for National Statistics, Census Geography, available
at:
https://www.ons.gov.uk/methodology/geography/ukgeographies/censusgeography,
last access: 11 February 2017a.
ONS: Office for National Statistics, Labour force survey – Families and
Households, available at:
https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/families/datasets/familiesandhouseholdsfamiliesandhouseholds,
last access: 30 October 2017b.
ONS: Estimated average calorific values of fuels 2017 – Digest of UK Energy
Statistics (DUKES): calorific values, available at:
https://www.gov.uk/government/statistics/dukes-calorific-values (last access: 31
January 2020), 2018.
ONS: Office for National Statistics, UK Business Counts – local units by
industry and employment size band, available at:
https://www.nomisweb.co.uk/datasets/idbrlu (last access: 17 January 2020), 2019.
OpenStreetMap: OpenStreetMap data of Greater London, available at: https://www.openstreetmap.org, last access: 31 January 2017.
OS: OS MasterMap®, available at:
http://digimap.edina.ac.uk (last access: 11 October 2015), 2014.
OS: London digital speed limit map and private communication, Ordnance
Survey, 2015.
OS: OS Open Roads, available at:
https://www.ordnancesurvey.co.uk/opendatadownload/products.html (last access: 30
August 2016), 2016.
O'Sullivan, D., Millington, J., Perry, G., and Wainwright, J.: Agent-Based
Models – Because They're Worth It?, in: Agent-Based Models of Geographical
Systems, 109–123, 2012.
Page, J., Robinson, D., Morel, N., and Scartezzini, J. L.: A generalised
stochastic model for the simulation of occupant presence, Energ. Buildings,
40, 83–98, https://doi.org/10.1016/j.enbuild.2007.01.018, 2008.
Palmer, E. (Ed.): CIBSE Guide B1: Heating, Chartered Institution of Building
Services Engineers, 2016.
Pigeon, G., Legain, D., Durand, P., and Masson, V.: Anthropogenic heat
release in an old European agglomeration (Toulouse, France), Int. J.
Climatol., 27, 1969–1981, 2007.
Reilly, W. J.: The Law of Retail Gravitation (1931), 2nd Edn., Pilsbury
Publishers, New York, 1953.
Richardson, I., Thomson, M., and Infield, D.: A high-resolution domestic
building occupancy model for energy demand simulations, Energ. Buildings,
40, 1560–1566, https://doi.org/10.1016/j.enbuild.2008.02.006, 2008.
Richardson, I., Thomson, M., Infield, D., and Clifford, C.: Domestic
electricity use: A high-resolution energy demand model, Energ. Buildings,
42, 1878–1887, https://doi.org/10.1016/j.enbuild.2010.05.023, 2010.
Sailor, D. J.: A review of methods for estimating anthropogenic heat and
moisture emissions in the urban environment, Int. J. Climatol., 31,
189–199, https://doi.org/10.1002/joc.2106, 2011.
Sailor, D. J. and Lu, L.: A top-down methodology for developing diurnal and
seasonal anthropogenic heating profiles for urban areas, Atmos. Environ.,
38, 2737–2748, https://doi.org/10.1016/j.atmosenv.2004.01.034, 2004.
Salamanca, F. P., Georgescu, M., Mahalov, A., Moustaoui, M., and Wang, M.: Anthropogenic heating of the urban environment due to air conditioning, J. Geophys. Res.-Atmos., 119, 5949–5965, https://doi.org/10.1002/2013jd021225, 2014.
Salter, R. J.: The relationship between space, flow and density of a highway
traffic stream, in: Highway Traffic Analysis and Design,
Palgrave Macmillan, 119–120, 1989.
Santamouris, M., Papanikolaou, N., Livada, I., Koronakis, I., Georgakis, C.,
Argiriou, A., and Assimakopoulos, D. N.: On the impact of urban climate on
the energy consumption of buildings, Sol. Energy, 70, 201–216,
https://doi.org/10.1016/S0038-092X(00)00095-5, 2001.
Schoetter, R., Masson, V., Bourgeois, A., Pellegrino, M., and Lévy, J.-P.: Parametrisation of the variety of human behaviour related to building energy consumption in the Town Energy Balance (SURFEX-TEB v. 8.2), Geosci. Model Dev., 10, 2801–2831, https://doi.org/10.5194/gmd-10-2801-2017, 2017.
SciPy: Numpy random sampling, available at:
https://docs.scipy.org/doc/numpy/reference/random/index.html, last access: 30
November 2019.
Sellers, W. D.: Physical Climatology, 4th Edn., Univeristy of Chicago Press,
Ltd, 1972.
Sericola, B.: Markov chains: theory, algorithms and applications, John Wiley
& Sons, Inc., 2013.
Smith, C., Lindley, S. and Levermore, G.: Estimating spatial and temporal
patterns of urban anthropogenic heat fluxes for UK cities: The case of
Manchester, Theor. Appl. Climatol., 98, 19–35,
https://doi.org/10.1007/s00704-008-0086-5, 2009.
Spitler, J. D.: Thermal Load and Energy performance prediction, in: Building
performance simulation for design and operation, edited by: Hensen, J. L. and
Lamberts, R., Spon Press, 2011.
Statistics Bureau of Japan: Japanese census data resolution, available at: https://www.stat.go.jp/english/data/index.html (last access: 7 September 2020), 2017.
Statistics Canada: Statistical Area Classification (SAC), available at:
https://www150.statcan.gc.ca/n1/pub/92-195-x/2016001/other-autre/sac-css/sac-css-eng.htm
(last access: 11 February 2020), 2017.
Steadman, P., Bruhns, H. R., and Rickaby, P. A.: An introduction to the
national Non-Domestic Building Stock database, Environ. Plann. B,
27, 3–10, https://doi.org/10.1068/bst2, 2000.
Stewart, I. D., Oke, T. R., and Krayenhoff, E. S.: Evaluation of the “local
climate zone” scheme using temperature observations and model simulations,
Int. J. Climatol., 34, 1062–1080, https://doi.org/10.1002/joc.3746, 2014.
Sun, T. and Grimmond, S.: A Python-enhanced urban land surface model SuPy (SUEWS in Python, v2019.2): development, deployment and demonstration, Geosci. Model Dev., 12, 2781–2795, https://doi.org/10.5194/gmd-12-2781-2019, 2019.
TfL: Transport for London. Bus service usage, passengers and kilometres
operated by route (2014–2015), available at:
https://tfl.gov.uk/corporate/publications-and-reports/buses#on-this-page-1
(last access: 12 February 2020), 2018.
TfL: Transport for London. Number of Buses by Type of Bus in London,
available at:
https://data.london.gov.uk/dataset/number-buses-type-bus-london (last access: 11
February 2020), 2019.
Thorsson, S., Rocklöv, J., Konarska, J., Lindberg, F., Holmer, B.,
Dousset, B., and Rayner, D.: Mean radiant temperature – A predictor of heat
related mortality, Urban Clim., 10, 332–345,
https://doi.org/10.1016/j.uclim.2014.01.004, 2014.
Underwood, C. P. and Yik, F.: Modelling methods for energy in buildings,
Blackwell Publishing, Malden, 2004.
U.S. Department of Energy: EnergyPlusVersion 9.3.0: Engineering Reference,
2020.
US Census Bureau: US Census Geography, available at:
https://www.census.gov/data.html (last access: 11 February 2020), 2019.
VOA: Dwellings by Property Build Period and Type, available at:
https://data.london.gov.uk/dataset/property-build-period-lsoa (last access: 31
January 2020), 2015.
Ward, H. C. and Grimmond, C. S. B.: Assessing the impact of changes in
surface cover, human behaviour and climate on energy partitioning across
Greater London, Landscape Urban Plan., 165, 142–161, 2017.
Ward, H. C., Kotthaus, S., Järvi, L., and Grimmond, C. S. B.: Surface
Urban Energy and Water Balance Scheme (SUEWS): Development, Evaluation and
Application, Urban Clim., 18, 1–32, https://doi.org/10.1016/j.uclim.2016.05.001, 2016.
Widén, J. and Wäckelgård, E.: A high-resolution stochastic model
of domestic activity patterns and electricity demand, Appl. Energ., 87,
1880–1892, https://doi.org/10.1016/j.apenergy.2009.11.006, 2010.
Widén, J., Nilsson, A. M., and Wäckelgård, E.: A combined
Markov-chain and bottom-up approach to modelling of domestic lighting
demand, Energ. Buildings, 41, 1001–1012,
https://doi.org/10.1016/j.enbuild.2009.05.002, 2009a.
Widén, J., Lundh, M., Vassileva, I., Dahlquist, E., Ellegård, K., and
Wäckelgård, E.: Constructing load profiles for household electricity
and hot water from time-use data-Modelling approach and validation, Energ.
Buildings, 41, 753–768, https://doi.org/10.1016/j.enbuild.2009.02.013, 2009b.
Wu, N.: A new Approach for Modeling of Fundamental Diagrams and its
Applications, Transp. Res. B, 36, 867–884, 2000.
Yohanis, Y. G., Mondol, J. D., Wright, A., and Norton, B.: Real-life energy
use in the UK: How occupancy and dwelling characteristics affect domestic
electricity use, Energ. Buildings, 40, 1053–1059,
https://doi.org/10.1016/j.enbuild.2007.09.001, 2008.
Zheng, Y. and Weng, Q.: High spatial- and temporal-resolution anthropogenic
heat discharge estimation in Los Angeles County, California, J. Environ.
Manage., 206, 1274–1286, https://doi.org/10.1016/j.jenvman.2017.07.047, 2017.
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(7491 KB) - Full-text XML
Short summary
Thermal emissions or anthropogenic heat fluxes (QF) from human activities impact the local- and larger-scale urban climate. DASH considers both urban form and function in simulating QF by use of an agent-based structure that includes behavioural characteristics of city populations. This allows social practices to drive the calculation of QF as occupants move, varying by day type, demographic, location, activity, and socio-economic factors and in response to environmental conditions.
Thermal emissions or anthropogenic heat fluxes (QF) from human activities impact the local- and...