Geographical extrapolation of typical hourly weather data for energy calculation in buildings

Arens, Edward; Flynn, L. E.; Nall, D.N.; Ruberg, Kalev

doi:10.6028/nbs.bss.126

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thereby, to preserve the physically realistic temporal sequence of the source data, the present-day weather file is morphed given the predictions from global or regional climate models on monthly mean changes of the meteorological variables. However, the very concept of hour-by-hour adjustments of a source weather data is not a new endeavor and it should probably be attributed to the work of Arens et al (1980). Later on, the Belcher's approach was adapted by Ren et al (2014) to incorporate the local UHI effect into the surrounding rural weather data, defined as the baseline climate.…”

Section: Urban Weather Modelingmentioning

confidence: 99%

Urban weather modeling applications: A Vienna case study

2019

View full text Add to dashboard Cite

Recently, the interest in urban weather modeling methods has been steadily increasing. This is in part due to the insight that thermal building performance simulations are typically undertaken with standardized weather files that provide a rather general perspective on urban weather conditions. This may lead toward errors in conclusions drawn from modeling efforts. In this context, present contribution reports on the potential of different approaches to generate location-dependent urban meteorological data. We compare the meteorological output generated with the Weather Research and Forecasting (WRF) model, Urban Weather Generator, and morphing approach. These methods were compared based on empirical data (air temperature, humidity, and wind speed) collected from two distinct urban locations in Vienna, Austria, over 5 study periods. Our results suggest significant temporal and spatial discrepancies in resulting modeling output. Results further suggest better predictive performance in the case of high-density urban areas and under warmer and extreme conditions in spring and summer periods, respectively.

show abstract

Section: Urban Weather Modelingmentioning

confidence: 99%