Academic literature on the topic 'Urban climate model'
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Journal articles on the topic "Urban climate model":
Mills, G. "An urban canopy-layer climate model." Theoretical and Applied Climatology 57, no. 3-4 (1997): 229–44. http://dx.doi.org/10.1007/bf00863615.
HARAYAMA, Kazuya, Ryozo OOKA, Shuzo MURAKAMI, Shinji YOSHIDA, Masahiro SETOJIMA, and Hiroaki KONDO. "STUDY ON URBAN CLIMATE ANALYSIS BASED ON MESO-SCALE CLIMATE MODEL INCORPORATED WITH THE URBAN CANOPY MODEL." Journal of Environmental Engineering (Transactions of AIJ) 70, no. 592 (2005): 75–82. http://dx.doi.org/10.3130/aije.70.75_3.
Yilmaz, Didem Gunes. "Model Cities for Resilience: Climate-led Initiatives." Journal of Contemporary Urban Affairs 5, no. 1 (January 1, 2020): 47–58. http://dx.doi.org/10.25034/ijcua.2021.v5n1-4.
Früh, Barbara, Paul Becker, Thomas Deutschländer, Johann-Dirk Hessel, Meinolf Kossmann, Ingrid Mieskes, Joachim Namyslo, et al. "Estimation of Climate-Change Impacts on the Urban Heat Load Using an Urban Climate Model and Regional Climate Projections." Journal of Applied Meteorology and Climatology 50, no. 1 (January 1, 2011): 167–84. http://dx.doi.org/10.1175/2010jamc2377.1.
Kubilay, Aytaç, Jonas Allegrini, Dominik Strebel, Yongling Zhao, Dominique Derome, and Jan Carmeliet. "Advancement in Urban Climate Modelling at Local Scale: Urban Heat Island Mitigation and Building Cooling Demand." Atmosphere 11, no. 12 (December 4, 2020): 1313. http://dx.doi.org/10.3390/atmos11121313.
Chatzinikolaou, E., C. Chalkias, and E. Dimopoulou. "URBAN MICROCLIMATE IMPROVEMENT USING ENVI-MET CLIMATE MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 69–76. http://dx.doi.org/10.5194/isprs-archives-xlii-4-69-2018.
Cremades, Roger, and Philipp S. Sommer. "Computing climate-smart urban land use with the Integrated Urban Complexity model (IUCm 1.0)." Geoscientific Model Development 12, no. 1 (February 1, 2019): 525–39. http://dx.doi.org/10.5194/gmd-12-525-2019.
Salim, Mohamed Hefny, Sebastian Schubert, Bjorn Maronga, Christoph Schneider, and Mohamed Fathy Cidek. "Introducing the Urban Climate Model PALM System 6.0." International Journal of Applied Energy Systems 2, no. 1 (January 1, 2020): 15–18. http://dx.doi.org/10.21608/ijaes.2020.169937.
De Ridder, Koen, Dirk Lauwaet, and Bino Maiheu. "UrbClim – A fast urban boundary layer climate model." Urban Climate 12 (June 2015): 21–48. http://dx.doi.org/10.1016/j.uclim.2015.01.001.
Li, Zhiqiang, Yulun Zhou, Bingcheng Wan, Hopun Chung, Bo Huang, and Biao Liu. "Model evaluation of high-resolution urban climate simulations: using the WRF/Noah LSM/SLUCM model (Version 3.7.1) as a case study." Geoscientific Model Development 12, no. 11 (November 5, 2019): 4571–84. http://dx.doi.org/10.5194/gmd-12-4571-2019.
Dissertations / Theses on the topic "Urban climate model":
Bogart, Tianna A. "Sensitivity of a global climate model to the urban land unit." Thesis, University of Delaware, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3598618.
With more than half of the world's population living in urban areas, it is important that the relationships between the urban environment and climate are better understood. The current research aims to continue the effort in assessing and understanding the urban environment through the use of a global climate model (GCM). Given the relative newness of the presence of an urban land type and model in a GCM, there are many more facets of the urban-climate relationship to be investigated. By comparing thirty-year ensembles of CAM4 coupled with CLM4 both with (U) and without (Un) the inclusion of the urban land type, the sensitivity of the atmospheric model to urban land cover is assessed. As expected, largest differences tend to be in the Northern Hemisphere due to the location of most of the globe's densest and expansive cities. Significant differences in the basic climate variables of temperature and precipitation are present at annual, seasonal, and monthly scales in some regions. Seasonality to the urban influence also exists with the transition months of Spring and Fall having the largest difference in temperatures. Of the eleven regions defined by Oleson (2012), three were most impacted by the presence of urban land cover in the model—Europe, Central Asia, and East Asia.
Since urban attributes can vary greatly within one world continent, the sensitivity of regional climates to the urban type parameters is also explored. By setting all urban land cover to only one urban density type, the importance of city composition on climate, even within the same city, is highlighted. While preserving the distinct urban regional characteristics and the geographical distribution of urbanized areas, the model is run with homogeneous urban types: high density and tall building district. As with the default urban and excluded urban runs, a strong seasonality to the differences between the solo-high-density simulation and default urban (UHD – U) and solo-tall-building-district-density simulation and default urban (UTBD – U) exists. Overall, the transition and winter months are most sensitive to changes in urban density type.
Stock, Zadie Stevy. "Modelling the impact of megacities in a global chemistry-climate model." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648380.
Pimentel, Franciele de Oliveira. "Clima urbano: o uso de modelos geoespaciais na investigação do comportamento térmico em Juiz de Fora- MG." Universidade Federal de Juiz de Fora (UFJF), 2017. https://repositorio.ufjf.br/jspui/handle/ufjf/5618.
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A preocupação com os chamados impactos ambientais urbanos fomentou um maior interesse nas pesquisas, principalmente aquelas voltadas para as análises climáticas, na escala urbana. A cidade por consequência de seu processo de organização e estruturação desenvolveu um clima totalmente particular, o clima urbano, isso é possível através da retirada da vegetação original e a inserção dos chamados equipamentos urbanos, como por exemplo, as vias impermeabilizadas, as construções, a verticalização, além da circulação de pessoas e veículos que irão contribuir para maior aquecimento da atmosfera local. Os materiais presentes no meio urbano vão apresentar diferentes valores de albedo, emissividade, absortividade e irradiação e consequentemente, estes condicionarão diferentes valores de temperatura de superfície e que influenciarão na temperatura do ar. O presente estudo tem por objetivo analisar o comportamento do clima urbano na cidade de Juiz de Fora- MG, onde foram trabalhadas 35 regiões urbanas, localizadas ao longo do curso do Rio Paraibuna. O estudo busca através da aplicação de um modelo geoespacial, interligar variáveis que possuem uma conexão direta com a temperatura de superfície e indireta com a temperatura do ar. Este conjunto de dados permitiu alcançar uma maior compreensão, viabilizou a espacialização e consequentemente uma visualização de como se distribuem as áreas e suas diferentes capacidades de criarem distintos campos térmicos na cidade.Além disso, para fins de validação do modelo, foi feita uma correlação estatística entre o modelo matemático proposto e a temperatura de superfície obtida na faixa do infravermelho termal. O modelo utilizado provou possuir consistência para ser adaptado a fim de ser replicado em diferentes cidades com especificidades térmicas além de ser viável a integração de outras informações e dados.
Concern about so-called urban environmental impacts has fostered greater interest in research, especially those focused on climate analysis, on the urban scale. The city as a result of its process of organization and structuring has developed a totally particular climate, the urban climate, this is possible through the removal of the original vegetation and the insertion of so-called urban equipment, such as waterproofed roads, constructions, verticalization, besides the circulation of people and vehicles that will contribute to greater warming of the local atmosphere. The materials present in the urban environment will present different values of albedo, emissivity, absorptivity and irradiation and consequently, these will condition different values of surface temperature and that will influence the air temperature. The present study aims to analyze the behavior of the urban climate in the city of Juiz de Fora- MG, where 35 urban areas were located along the course of the Paraibuna River. The study searches through the application of a geospatial model, interconnecting variables that have a direct coexistence with the surface temperature and indirect with the air temperature. This dataset allowed to reach a greater understanding, made possible the spatialization and consequently a visualization of how the areas are distributed and their different capacities to create different thermal fields in the city. In addition, for purposes of validation of the model, a statistical correlation was made between the proposed mathematical model and the surface temperature obtained in the thermal infrared range. The model used proved to have consistency to be adapted in order to be replicated in different cities with thermal specificities besides being feasible the integration of other information and data.
Burghardt, René [Verfasser]. "Development of an ArcGIS extension to model urban climate factors / René Burghardt." Kassel : Universitätsbibliothek Kassel, 2015. http://d-nb.info/1069689327/34.
Sajjad, Sajjad Hussain. "Observational and modelling approaches to study urban climate : application on Pakistan." Phd thesis, Université de Strasbourg, 2013. http://tel.archives-ouvertes.fr/tel-01044727.
Moreno, Cherry. "Urban water demand model: the case study of Emilia Romagna (Italy)." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/5938/.
Zhang, Hengyue. "Using satellite remote sensing, field observations and WRF/single-layer urban canopy model simulation to analyze the Oklahoma City UHI effect." Thesis, San Jose State University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1594250.
The Urban Heat Island (UHI) was investigated using satellite data, ground observations, and simulations with an Urban Canopy Parameterization in a numerical weather prediction model. Satellite-observed surface skin temperatures at Xi'an City and Oklahoma City (OKC) were analyzed to compare the UHI intensity for the two inland cities. A larger population density and larger building density in Xi'an City creates a stronger skin-level UHI effect. However, ground observed 2-m surface air temperature (Tair) data showed an urban cooling island (UCI) effect that occurred over an urban region in OKC during the daytime of July 19, 2003.
The sensitivity and accuracy of an Urban Canopy Model were evaluated by comparing simulation results between the urban and rural areas of OKC. The model reproduced skin temperature differences between the rural and urban area and reproduced a UCI effect in OKC. Furthermore, the Weather Research and Forecasting (WRF)/Noah/Single-Layer Urban Canopy Model (SLUCM) simulations were also compared with ground observations, including wind speeds, wind directions, and energy fluxes. Although the WRF/SLCUM model failed to simulate these variables accurately, it reproduced the diurnal variations of surface temperatures, wind speeds, wind directions and energy fluxes reasonably well.
Mauree, Dasaraden. "Development of a multi-scale meteorological system to improve urban climate modeling." Phd thesis, Université de Strasbourg, 2014. http://tel.archives-ouvertes.fr/tel-01037982.
Kohler, Manon. "Assessement of the building energy requirements : added value of the use of the urban climate modeling." Thesis, Strasbourg, 2015. http://www.theses.fr/2015STRAH004/document.
Buildings represent 40 percent of the end-use energy. Thus, they constitute a key point of the energy saving policies. Recently, climate modeling systems that include a mesoscale atmospheric model, sophisticated urban parameterizations have been developed to account for the complexity of the urban climate and its interactions with the building energy loads. This study aims to assess the capability of such climate modeling systems to provide climate and energy guidelines to urban planners. For this, we used the research collaborative WRF/ARW-BEP+BEM climate modeling system and performed sensitivity tests considering the territory of the Eurodistrict in 2010, and then in 2030. The results reveal that the climate modeling system achieves estimating the building energy needs over the study area, but also indicate that the building energy needs are more sensitive to the building intrinsic properties and occupant behavior than to the urban forms and their induced urban heat island
Testori, Paolo. "Modelling the urban heat island in the city of Bologna: improvement of the surface parameters classification." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18806/.
Books on the topic "Urban climate model":
Bogart, Tianna Anise. Sensitivity of a global climate model to the urban land unit. Middletown, Delaware: Legates Consulting Llc, 2013.
C, Hughes Trevor, Wang Yi-Min, and United States. Bureau of Reclamation. Provo Projects Office., eds. Impacts of projected climate change on urban water use: An application using the Wasatch Front water demand and supply model. Provo, Utah: U.S. Dept. of the Interior, Bureau of Reclamation, Provo Projects Office, 1994.
Hansen, Roger. Impacts of projected climate change on urban water use: An application using the Wasatch Front water demand and supply model. Provo, Utah: U.S. Dept. of the Interior, Bureau of Reclamation, Provo Projects Office, 1994.
Schuhmacher, Peter. Messung und numerische Modellierung des Windfeldes über einer Stadt in komplexer Topographie. Zürich: Verlag der Fachvereine Zürich, 1992.
Rotach, Mathias W. Turbulence within and above an urban canopy. Zürich: Verlag der Fachvereine Zürich, 1991.
Carlson, Toby N. A remotely sensed index of deforestation/urbanization for use in climate models: Annual performance report for the period 1 January 1995 - 31 December 1995. University Park, PA: Pennsylvania State University, 1995.
Carlson, Toby N. A remotely sensed index of deforestation/urbanization for use in climate models: Annual performance report for the period 1 January 1995 - 31 December 1995. University Park, PA: Pennsylvania State University, 1995.
Kim, Chŏng-gon. Ŭiryo illyŏk chagyŏk sangho injŏng ŭl wihan chŏngchʻaek panghyang: Han-Mi myŏnhŏ kwalli chʻaegye pigyo rŭl chungsim ŭro. Sŏul Tʻŭkpyŏlsi: Taeoe Kyŏngje Chŏngchʻaek Yŏnʼguwŏn, 2006.
Impacts of projected climate change on urban water use: An application using the Wasatch Front water demand and supply model. Provo, Utah: U.S. Dept. of the Interior, Bureau of Reclamation, Provo Projects Office, 1994.
Chubarova, Natalia, Yekaterina Zhdanova, Yelizaveta Androsova, Alexander Kirsanov, Marina Shatunova, Yulia Khlestova, Yelena Volpert, et al. THE AEROSOL URBAN POLLUTION AND ITS EFFECTS ON WEATHER, REGIONAL CLIMATE AND GEOCHEMICAL PROCESSES. LLC MAKS Press, 2020. http://dx.doi.org/10.29003/m1475.978-5-317-06464-8.
Book chapters on the topic "Urban climate model":
Nicholls, M. E., R. A. Pielke, J. L. Eastman, C. A. Finley, W. A. Lyons, C. J. Tremback, R. L. Walko, and W. R. Cotton. "Applications of the RAMS Numerical Model to Dispersion over Urban Areas." In Wind Climate in Cities, 703–32. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-017-3686-2_34.
Lim, Tian Kuay, Nyuk Hien Wong, Marcel Ignatius, Miguel Martin, Hann-Ming Henry Juang, Jing Lou, and Robert Lee Kong Tiong. "Singapore: An Integrated Multi-scale Urban Microclimate Model for Urban Planning in Singapore." In Urban Climate Science for Planning Healthy Cities, 189–217. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87598-5_9.
Gangwisch, Marcel, and Andreas Matzarakis. "Comparison of Thermal Indices in Urban Environments with SkyHelios Model." In Climate Change and Cooling Cities, 215–31. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3675-5_12.
Ortiz, L., A. Mustafa, B. Rosenzweig, and Timon McPhearson. "Modeling Urban Futures: Data-Driven Scenarios of Climate Change and Vulnerability in Cities." In Resilient Urban Futures, 129–44. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63131-4_9.
Cangelli, Eliana. "Climate Change: New Ways to Inhabit the Earth." In The Urban Book Series, 537–45. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29515-7_48.
Du, Yuhan, and Kun Sang. "Evaluating Users’ Satisfaction on Urban Railway Based on Service Quality Model: The Study on KLIA Express in Malaysia." In Urban Resilience, Livability, and Climate Adaptation, 267–75. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54911-3_16.
Giovanardi, Matteo, Matteo Trane, and Riccardo Pollo. "Environmental Sensing and Simulation for Healthy Districts: A Comparison Between Field Measurements and CFD Model." In The Urban Book Series, 921–33. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29515-7_82.
Milardi, Martino. "Adaptive Building Technologies for Building Envelopes Under Climate Change Conditions." In The Urban Book Series, 695–702. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29515-7_62.
Mahdavi Estalkhsari, Bonin, Pir Mohammad, and Alireza Karimi. "Land Use and Land Cover Change Dynamics and Modeling Future Urban Growth Using Cellular Automata Model Over Isfahan Metropolitan Area of Iran." In Springer Climate, 495–516. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15501-7_19.
Danumah, Jean Homian, Samuel Nii Odai, Mahaman Bachir Saley, Joerg Szarzynski, Kwaku Adjei, and Fernand Koffi Kouame. "A Stochastic Weather Generator Model for Hydroclimatic Prevision in Urban Floods Risk Assessment in Abidjan District (Cote d’Ivoire)." In Climate Change Management, 211–23. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25814-0_15.
Conference papers on the topic "Urban climate model":
Teggi, Pralhad P., Santhi Natarajan, and Bharathi Malakreddy. "Intelligent FORecasting Model for Climate Variations (InFORM): An Urban Climate Case Study." In 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2020. http://dx.doi.org/10.23919/indiacom49435.2020.9083720.
"Urban Heat Island Minimisation, Local Climate Zones and Parametric Optimisation: A “Grasshopper” Based Model." In Countermeasures to Urban Heat Islands. BS Publications, 2022. http://dx.doi.org/10.37285/bsp.ic2uhi.23.
Heldens, Wieke, Bjorn Maronga, Julian Zeidler, Farah Kanani-Suhring, Wiebke Hanke, and Thomas Esch. "Remote sensing-supported generation of surface descriptors for a highly detailed urban climate model." In 2019 Joint Urban Remote Sensing Event (JURSE). IEEE, 2019. http://dx.doi.org/10.1109/jurse.2019.8809010.
Henning, Johanna, and Matthias Winkler. "Developing climate change adaptation measures for urban planning in the city of Munich (Germany) using the urban climate model PALM-4U." In 2023 Building Simulation Conference. IBPSA, 2023. http://dx.doi.org/10.26868/25222708.2023.1616.
Kamal, Athar, Ibrahim Hassan, Liangzhou (Leon) Wang, and Mohammad Azizur Rahman. "Estimating Combined Impact of Urban Heat Island Effect and Climate Change on Cooling Requirements of Tall Residential Buildings in Hot-Humid Locations." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-94272.
Sharma, Ashish, Harindra J. S. Fernando, Jessica Hellmann, and Fei Chen. "Sensitivity of WRF Model to Urban Parameterizations, With Applications to Chicago Metropolitan Urban Heat Island." In ASME 2014 4th Joint US-European Fluids Engineering Division Summer Meeting collocated with the ASME 2014 12th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/fedsm2014-21292.
Conry, Patrick, H. J. S. Fernando, L. S. Leo, Ashish Sharma, Mark Potosnak, and Jessica Hellmann. "Multi-Scale Simulations of Climate-Change Influence on Chicago Heat Island." In ASME 2014 4th Joint US-European Fluids Engineering Division Summer Meeting collocated with the ASME 2014 12th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/fedsm2014-21581.
Gottlieb, Avi. "Urban Climate Change Mitigation and Adaptation: Testing a conceptual model in four world cities." In 3rd Annual International Conference on Political Science, Sociology and International Relations (PSSIR 2013). Global Science and Technology Forum Pte Ltd, 2013. http://dx.doi.org/10.5176/2251-2403_pssir13.35.
Carmeliet, Jan, Aytaç Kubilay, Dominik Strebel, and Dominique Derome. "Urban climate simulation: coupling of mesoscale meteorological model with building-resolved neighborhood CFD simulation." In 2021 Building Simulation Conference. KU Leuven, 2021. http://dx.doi.org/10.26868/25222708.2021.31125.
PASSE, ULRIKE. "Assessing a Community-Engaged Decision Framework for Increased Urban Neighborhoods Resilience in a Warming Climate." In 2021 AIA/ACSA Intersections Research Conference. ACSA Press, 2021. http://dx.doi.org/10.35483/acsa.aia.inter.21.29.
Reports on the topic "Urban climate model":
Brandt, Leslie A., Cait Rottler, Wendy S. Gordon, Stacey L. Clark, Lisa O'Donnell, April Rose, Annamarie Rutledge, and Emily King. Vulnerability of Austin’s urban forest and natural areas: A report from the Urban Forestry Climate Change Response Framework. U.S. Department of Agriculture, Northern Forests Climate Hub, October 2020. http://dx.doi.org/10.32747/2020.7204069.ch.
Oakil, Abu Toasin, Ahm Mehbub Anwar, Alma Alhussaini, Nourah Al Hosain, Abdelrahman Muhsen, and Anvita Arora. Urban Transport Energy Demand Model for Riyadh: Methodology and Preliminary Analysis. King Abdullah Petroleum Studies and Research Center, June 2023. http://dx.doi.org/10.30573/ks--2023-mp03.
Hasan, Abdulghani. Flood Modelling Tool : an integrated GIS and hydrological modelling tool for planning nature-based solutions in the urban environment. Faculty of Landscape Architecture, Horticulture and Crop Production Science, Swedish University of Agricultural Sciences, 2024. http://dx.doi.org/10.54612/a.5s9t2ca774.
Chisari, Omar O., and Sebastián J. Miller. Climate Change and Migration: A CGE Analysis for Two Large Urban Regions of Latin America. Inter-American Development Bank, March 2016. http://dx.doi.org/10.18235/0011724.
Chiara Tornaghi, Chiara Tornaghi, and Michiel Dehaene Michiel Dehaene. AGROECOLOGICAL URBANISM: What is it, why we need it, and the role of UN-Habitat. Coventry University, June 2024. http://dx.doi.org/10.18552/cawr/2024/0001.
Rezaie, Shogofa, Fedra Vanhuyse, Karin André, and Maryna Henrysson. Governing the circular economy: how urban policymakers can accelerate the agenda. Stockholm Environment Institute, September 2022. http://dx.doi.org/10.51414/sei2022.027.
Guerrero Compeán, Roberto. Weather and Welfare: Health and Agricultural Impacts of Climate Extremes, Evidence from Mexico. Inter-American Development Bank, February 2013. http://dx.doi.org/10.18235/0011450.
Roa, Julio, Joseph Oldham, and Marina Lima. Recognizing the Potential to Reduce GHG Emissions Through Air Transportation Electrification. Mineta Transportation Institute, July 2023. http://dx.doi.org/10.31979/mti.2023.2223.
Klobucar, Blaz. Urban Tree Detection in Historical Aerial Imagery of Sweden : a test in automated detection with open source Deep Learning models. Faculty of Landscape Architecture, Horticulture and Crop Production Science, Swedish University of Agricultural Sciences, 2024. http://dx.doi.org/10.54612/a.7kn4q7vikr.
Tran, My-Thu, and Bo Yang. Using Thermal Remote Sensing to Quantify Impact of Traffic on Urban Heat Islands during COVID. Mineta Transportation Institute, April 2023. http://dx.doi.org/10.31979/mti.2023.2207.