480 27 8220 2 23242 document 8220 QRESY-PDSY.zip application/zip c3f52841c80a2f6e69847d934a3347cd MD5 163656 2018-03-07 10:57:22 https://researchdata.bath.ac.uk/480/1/QRESY-PDSY.zip 480 1 1 application/zip other en public cc_by

QRESY-PDSY.zip

data archive 6484 disk0/00/00/04/80 2018-03-27 09:42:34 2024-07-15 10:58:47 2018-03-27 09:42:34 data_collection show Herrera Fernandez Manuel amhf20@bath.ac.uk 0000-0001-9662-0017 University of Bath TRUE Ramallo-González Alfonso aprg20@bath.ac.uk 0000-0001-7021-0634 University of Murcia FALSE Eames Matt m.e.eames@exeter.ac.uk 0000-0002-0515-6878 University of Exeter FALSE Ferreira Aida A. 0000-0002-0322-6801 Pernambuco Federal Institute of Education, Science, and Technology FALSE Coley David D.A.Coley@bath.ac.uk 0000-0001-5744-1809 University of Bath FALSE dept_civ_eng The zip file contain 4 datasets in csv format. Each of them correspond to weather files of one hot summer year hourly data based on the weather observed over 40 (basis) years, 1974 - 2013. Two are the so-called probabilistic design summer years (PDSY) for the cities of London (UK) and Joao Pessoa (Brazil). The PDSY uses an overheating metric that is based on the number of hours in which the temperature is above a certain threshold when a building is occupied. Then, PDSY is created by selecting an entire year which contains the third hottest mean based on this overheating metric. PDSY is currently used in the UK as reference of warm summers. However it is the first time that a PDSY is created for Brazil. The other two weather files correspond to the new quantile ensemble regression summer year (QRESY) also aiming to represent hot summers both for London and Joao Pessoa. QRESY is created by combining observed summer extreme temperatures. This is done by endowing higher weights to quantiles away from the median for ensembles within upper quantiles. At the same time, it increases the importance of quantiles near to the median for combining lower quantiles. 2018-03-21 University of Bath public RightsHolder University of Bath Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/M021890/1 COLBE - The Creation of Localized Current and Future Weather for the Built Environment The Quantile Regression Ensemble Summer Year (QRESY) creation process starts by collecting hourly weather data over a long period. Typically, weather files attempt to be representative of periods around 20-40 years, and here we do the same, however much longer periods could be used. The existence, variables and quality of hourly weather data varies depending on the location. The variables usually include temperature, atmospheric pressure, cloud cover, wind speed and wind direction, precipitation, etc. For the QRESY process, preprocessing of this data is required to ensure it contains no long sequences of missing data. If large amount of data are missing in any of the variables, the whole year is removed from the analysis. At this point it is also necessary to decide the target level of extreme weather to work with. That is, to fix the quantile level for the subsequent construction of the Quantile Regression models depending on the distance to the median (quantile 50, Q50). Running a Quantile Regression model for every year under analysis is an ``embarrassingly parallel'' problem, as it is straightforward to separate the problem into a number of parallel tasks and the code run on a parallel machine. The set of regressions is combined in a unique year of hourly data. This is done by endowing higher weights to quantiles away from Q50 for ensembles within upper quantiles. At the same time, it increases the importance of quantiles near to Q50 for combining lower quantiles. The idea being to focus on explaining critical phases of summer temperatures. Each ensemble is thereby made over the predictors of a number of regression models corresponding to each of the years in the database. The ensemble parameters can be tuned by cross-validation over random partitions of the data into training and test summer periods. 1974-01-01 2013-12-31 London (UK) and Joao Pessoa (Brazil) en 1 10.15125/BATH-00480 https://doi.org/10.1016/j.envsoft.2018.03.007 pub open Data under Licence: Creative Commons Attribution 4.0 International