Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SSP1-RCP2.6)
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As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment. This dataset contains the individual-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 1 and the representative concentration pathway (RCP) used was RCP 2.6. More information about SSP and RCP can be referred to O'Neill et al. (2020). Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location. More information about the six selected CMIP6 GCMs: ACCESS-CM2 -http://dx.doi.org/10.1071/ES19040BCC-CSM2-MR -https://doi.org/10.5194/gmd-14-2977-2021CNRM-ESM2-1-https://doi.org/10.1029/2019MS001791MPI-ESM1-2-HR -https://doi.org/10.5194/gmd-12-3241-2019MRI-ESM2-0 -https://doi.org/10.2151/jmsj.2019-051NorESM2-MM -https://doi.org/10.5194/gmd-13-6165-2020 Additional references:O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228. Please cite the following if this data is used in any research or project: Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: 10.1145/3600100.3626637 Cross-Model Version: Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719204, Feb 2024. [Data] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719178, Feb 2024. [Data] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10698921, Feb 2024. [Data] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (Cross-Model version-SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10420668, Dec 2023. [Data] Model-specific Version: Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729277, Feb 2024. [Data] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729279, Feb 2024. [Data] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729223, Feb 2024. [Data] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729201, Feb 2024. [Data] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729157, Feb 2024. [Data] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729199, Feb 2024. [Data] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (East and South – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8335814, Sept 2023. [Data] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (West and Midwest – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8338548, Sept 2023. [Data] Representative Cities Version: Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [Data]
随着全球碳排放与气温持续攀升，全球气候模型可对未来数年的气候变化趋势作出预测。这些模型预测结果可应用于多种终端场景，以更好地理解当前系统将如何受气候变化影响。不过，气候模型虽可逐年预测，但单一年份的结果未必具有代表性，因为可能存在异常年份。而采用单一年份数据代表多年时段的做法同样具备实用价值。目前，针对历史气象数据，典型气象年（Typical Meteorological Year, TMY）方法可解决上述两类问题：该方法通过统计学手段从多年序列中选取具有代表性的月份，将这些月份拼接为单一年份的数据集，以代表目标时段的气候特征。本研究采用该方法，基于气候模型预测结果构建未来典型气象年（Future Typical Meteorological Year, fTMY）。随后将生成的fTMY数据集整理为EnergyPlus天气（EnergyPlus Weather, EPW）文件格式，可用于建筑模拟，以评估气候情景对建成环境的影响。 本数据集包含美国本土3281个县的单气候模型版本fTMY文件。每个县的数据源自耦合模型比较计划第六阶段（Coupled Model Intercomparison Project Phase 6, CMIP6）的6种全球气候模型（Global Climate Models, GCMs），分别为ACCESS-CM2、BCC-CSM2-MR、CNRM-ESM2-1、MPI-ESM1-2-HR、MRI-ESM2-0、NorESM2-MM。按照Rastogi等人（2022）所述方法，针对历史时段1980–2014年与未来时段2015–2100年，在SSP585情景下对这6种气候模型进行了统计降尺度处理。此外，基于每日降尺度输出结果，通过山地微气候模拟模型（Mountain Microclimate Simulation Model, MTCLIM; Thornton and Running, 1999）生成逐小时气象数据。本分析采用的共享社会经济路径（Shared Socioeconomic Pathway, SSP）为SSP1，典型浓度路径（Representative Concentration Pathway, RCP）为RCP2.6。关于SSP与RCP的更多信息可参考O'Neill等人（2020）的研究。 请注意，若某一地点包含多个.EPW文件，则表明该地点内存在多个气象数据采集点。 关于所选6种CMIP6全球气候模型的更多信息： ACCESS-CM2 - http://dx.doi.org/10.1071/ES19040 BCC-CSM2-MR - https://doi.org/10.5194/gmd-14-2977-2021 CNRM-ESM2-1 - https://doi.org/10.1029/2019MS001791 MPI-ESM1-2-HR - https://doi.org/10.5194/gmd-12-3241-2019 MRI-ESM2-0 - https://doi.org/10.2151/jmsj.2019-051 NorESM2-MM - https://doi.org/10.5194/gmd-13-6165-2020 附加参考文献： O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). 气候变化情景框架的成就与需求. 《自然·气候变化》, 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0 Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). 降尺度技术与气象参考观测的选择如何影响未来水文气候预测？《地球未来》, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734 Thornton, P. E. and Running, S. W. (1999). 基于气温、湿度与降水观测估算日入射太阳辐射的改进算法. 《农业与森林气象学》, 93, 211-228. 若在任何研究或项目中使用本数据，请引用以下文献： Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). 《面向全美几乎所有县的多模型未来典型气象（fTMY）天气文件》. 第三届ACM智能建筑与城市大数据及机器学习研讨会暨第十届ACM建筑、城市与交通能效系统国际会议（BuildSys '23），2023年11月15-16日，土耳其伊斯坦布尔。DOI: 10.1145/3600100.3626637 跨模型版本： Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). 《面向美国本土所有县的建筑模拟用未来典型气象年（fTMY）美国天气文件（跨模型版本-SSP1-RCP2.6）》. 橡树岭国家实验室内部科学与技术信息（STI）报告, doi: 10.5281/zenodo.10719204, 2024年2月. [数据] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). 《面向美国本土所有县的建筑模拟用未来典型气象年（fTMY）美国天气文件（跨模型版本-SSP2-RCP4.5）》. 橡树岭国家实验室内部科学与技术信息（STI）报告, doi: 10.5281/zenodo.10719178, 2024年2月. [数据] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). 《面向美国本土所有县的建筑模拟用未来典型气象年（fTMY）美国天气文件（跨模型版本-SSP3-RCP7.0）》. 橡树岭国家实验室内部科学与技术信息（STI）报告, doi: 10.5281/zenodo.10698921, 2024年2月. [数据] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). 《面向全美所有县的建筑模拟用未来典型气象年（fTMY）美国天气文件（跨模型版本-SSP5-RCP8.5）》. 橡树岭国家实验室内部科学与技术信息（STI）报告, doi: 10.5281/zenodo.10420668, 2023年12月. [数据] 特定模型版本： Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). 《面向美国本土所有县的建筑模拟用未来典型气象年（fTMY）美国天气文件（西部与中西部-SSP1-RCP2.6）》. 橡树岭国家实验室内部科学与技术信息（STI）报告, doi: 10.5281/zenodo.10729277, 2024年2月. [数据] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). 《面向美国本土所有县的建筑模拟用未来典型气象年（fTMY）美国天气文件（东部与南部-SSP1-RCP2.6）》. 橡树岭国家实验室内部科学与技术信息（STI）报告, doi: 10.5281/zenodo.10729279, 2024年2月. [数据] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). 《面向美国本土所有县的建筑模拟用未来典型气象年（fTMY）美国天气文件（西部与中西部-SSP2-RCP4.5）》. 橡树岭国家实验室内部科学与技术信息（STI）报告, doi: 10.5281/zenodo.10729223, 2024年2月. [数据] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). 《面向美国本土所有县的建筑模拟用未来典型气象年（fTMY）美国天气文件（东部与南部-SSP2-RCP4.5）》. 橡树岭国家实验室内部科学与技术信息（STI）报告, doi: 10.5281/zenodo.10729201, 2024年2月. [数据] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). 《面向美国本土所有县的建筑模拟用未来典型气象年（fTMY）美国天气文件（西部与中西部-SSP3-RCP7.0）》. 橡树岭国家实验室内部科学与技术信息（STI）报告, doi: 10.5281/zenodo.10729157, 2024年2月. [数据] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). 《面向美国本土所有县的建筑模拟用未来典型气象年（fTMY）美国天气文件（东部与南部-SSP3-RCP7.0）》. 橡树岭国家实验室内部科学与技术信息（STI）报告, doi: 10.5281/zenodo.10729199, 2024年2月. [数据] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). 《面向全美所有县的建筑模拟用未来典型气象年（fTMY）美国天气文件（东部与南部–SSP5-RCP8.5）》. 橡树岭国家实验室内部科学与技术信息（STI）报告, doi: 10.5281/zenodo.8335814, 2023年9月. [数据] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). 《面向全美所有县的建筑模拟用未来典型气象年（fTMY）美国天气文件（西部与中西部–SSP5-RCP8.5）》. 橡树岭国家实验室内部科学与技术信息（STI）报告, doi: 10.5281/zenodo.8338548, 2023年9月. [数据] 代表性城市版本： Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). 《面向建筑模拟的美国未来典型气象年（fTMY）天气文件（1.0）[数据集]》. Zenodo, doi.org/10.5281/zenodo.6939750, 2022年8月. [数据]
