scholarly journals In-Situ and Predicted Performance of a Certified Industrial Passive House Building under Future Climate Scenarios

Buildings ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 457
Author(s):  
Alison Conroy ◽  
Phalguni Mukhopadhyaya ◽  
Guido Wimmers
Keyword(s):  
Energy Use ◽  
Wood Products ◽  
Future Climate ◽  
Thick Wall ◽  
Exterior Wall ◽  
Climate Data ◽  
Performance Models ◽  
Passive House ◽  
Hygrothermal Performance

The Wood Innovation Research Lab was designed as a low energy-use building to facilitate the construction and testing of engineered wood products by the faculty and staff of the Master of Engineering in Integrated Wood Design Program at the University of Northern British Columbia in Prince George, BC, Canada. Constructed using a 533 mm thick-wall and 659 mm flat roof assembly, it received certification as Canada’s first industrial facility built to the International Passive House standard. Temperature and humidity sensors were installed in the north and south exterior wall assemblies to measure long-term hygrothermal performance. Data collected between 2018–2020 shows no record of long-term moisture accumulation within the exterior assemblies. Data collected during this time period was used to validate hygrothermal performance models for the building created using the WUFI® Plus software. Long-term performance models created using future climate data for five cities across Canada under two global warming scenarios shows favorable results, with an increase in average annual temperatures resulting in lower average relative humidity values at the interior face of the exterior sheathing board in the exterior wall assemblies.

scholarly journals Climate data analysis to assess resilience of wall assemblies to moisture loads arising from the effects of wind-driven rain

2020 ◽  
Vol 172 ◽  
pp. 11003
Author(s):  
Zhe Xiao ◽  
Michael A. Lacasse ◽  
A. Gaur ◽  
Elena Dragomirescu
Keyword(s):  
Geographic Location ◽  
Standard Test ◽  
Future Climate ◽  
Sufficient Information ◽  
Climate Data ◽  
Test Protocol ◽  
Changing Climate ◽  
Expected Performance ◽  
Moisture Performance

In North America, and abroad, there currently exist standard test protocols for assessing the watertightness of wall assemblies and fenestration components although most of these methods are not directly related to expectations of in-field conditions as might be experienced by a wall assembly over its intended service life. How useful might such test protocols be to help determine the longevity of wall assemblies to future climate loads? Existing walls may, depending on their geographic location, be vulnerable to future climate loads and thus risk premature deterioration. For the design of new wall assemblies consideration ought to given to the non-stationarity of the climate and implications on the moisture loads on walls and the expected performance over the long-term. To permit assessing the resilience of wall assemblies to the effects of a changing climate as may occur in the future, and indeed, perhaps heightened moisture loads, one requires sufficient information on the watertightness of the assembly in relation to specified wind-driven rain loads and wall air-leakage conditions from which wall moisture retention functions could readily be developed. Such moisture functions are the basis of input of moisture loads to hygrothermal models and from which the expected long-term wall moisture performance can subsequently be derived. In this paper, a description is provided of the strategies used to analyze the WDR load for generating experimental input for a watertightness test protocol under development to assess resilience of wall assemblies to moisture loads arising from the effects of wind-driven rain in consideration of both historical climate loads and those as may arise from a changing climate.

Experimental and theoretical study on hygrothermal long-term performance of outer assemblies in lightweight passive house

2017 ◽  
Vol 41 (4) ◽  
pp. 299-320 ◽  
Author(s):  
Jan Radon ◽  
Krzysztof Was ◽  
Agnieszka Flaga-Maryanczyk ◽  
Jacek Schnotale
Keyword(s):  
Theoretical Study ◽  
Theoretical Calculations ◽  
Building Structure ◽  
Weather Station ◽  
Passive House ◽  
Hygrothermal Performance ◽  
Calculation Results ◽  
Long Term Performance ◽  
Term Performance

The article presents results of long-term experimental study of hygrothermal performance of envelope assemblies in a passive house located in Boruszowice (Southern Poland). The building was constructed in 2010 using prefabricated, lightweight technology. The construction of the walls and roof had been carefully planned to test both traditional solutions with higher thermal insulation and modified ones to improve the hygrothermal performance. Altogether, eight different walls and two roof constructions were integrated into the building structure and tested from the beginning of 2011 to the end of 2015 under real climate and usage conditions. In all assemblies, temperature was measured in three and relative humidity in four points at the surface and inside. Inner climate was measured by thermohygrometers installed in the rooms and outer climate was recorded by a weather station located near the building. Theoretical calculations were made using WUFI® Plus software. Based on experimental and calculation results, the main hygrothermal phenomena depending on construction specifics and used materials are presented.

scholarly journals Maintenance of Passive House Standard in the Light of Long-Term Study on Energy Use in a Prefabricated Lightweight Passive House in Central Europe

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2801 ◽  
Author(s):  
Krzysztof Wąs ◽  
Jan Radoń ◽  
Agnieszka Sadłowska-Sałęga
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Ventilation System ◽  
Primary Energy ◽  
Operating Conditions ◽  
Design Parameters ◽  
Passive House ◽  
Long Term Study ◽  
House Standard

This article presents the results of experimental research on energy consumption of a prefabricated lightweight passive house located in the south of Poland. The key design parameters of the building were as follows: orientation maximizing heat gains from solar radiation, high thermal insulation of partitions, heat provided by ground source heat pump, and mechanical ventilation system with the heat exchanger. The measurements were performed in normal operating conditions in an inhabited building, throughout the years 2011–2019. For the year 2012, the article also presents the detailed structure of electricity used for particular devices. The objective of the research was to verify whether, in the long term, the building fulfils the energy consumption requirements for passive buildings. The measurements showed that energy consumption for heating was 50% lower than the value required from passive buildings. However, primary energy consumption for the entire building was exceeded already in the second year of research. This was caused by two factors: human behaviors and the type of primary energy source. The research concludes that the maintenance of passive house standard is vulnerable to human impact and difficult in the case of power source characterized by high index of expenditure on non-renewable primary energy. The article also presents recommendations on how to restore the passive house standard in the building.

scholarly journals Thermal comfort level in Chennai Metropolis under present and future climate scenarios

2019 ◽  
pp. 17-23 ◽  
Author(s):  
Anushiya Jeganathan ◽  
Ramachandran Andimuthu ◽  
Palanivelu Kandasamy
Keyword(s):  
Climate Change ◽  
Thermal Comfort ◽  
City Planning ◽  
Monsoon Season ◽  
Future Climate ◽  
Comfort Level ◽  
Climate Scenarios ◽  
Climate Data ◽  
Post Monsoon

Climate change poses unprecedented challenges to urban inhabitants. Thermal comfort is one of the major issues in cities and it is expected to change in future due to climate change. The change of climate parameters particularly, temperature and relative humidity will affect the thermal comfort environments of people. Discomfort levels are largely preventable and requires prior assessment. In this study, the observed and projected thermal comfort level of Chennai Metropolis are calculated using Thermo-Hygrometric Index (THI) under present and future climate scenarios. The observed climate data of Chennai Metropolis for the period 1951-2010 procured from IMD are used to find the long term changes in observed thermal comfort. Monthly trends of THI are calculated for different periods to understand the thermal comfort behaviour in recent decades. On long term observation, high discomfort level is noticed during May and June months followed by July, August, April and September months. While there is a sharp increase in THI during winter months of recent decades. There is a considerable increase in discomfort level notice in post-monsoon season especially in December and November months. Future THI is calculated using high-resolution future climate scenarios developed using PRECIS. The deviations of THI from baseline to mid-century (2041-2070) and end-century period (2071-2099) are calculated and geospatially mapped using ArcGIS. There would be 2.0°C increase of THI is expected during winter and post monsoon months in mid-century scenario. Changes in future THI warrants the need for better cooling requirements and city planning to adapt with the future trends of external environment. Thus the study urges urban planners to evolve climate smart adaptation strategies to provide the congenial climate for a better living.

scholarly journals Climate drivers of plague epidemiology in British India, 1898–1949

2020 ◽  
Vol 287 (1928) ◽  
pp. 20200538
Author(s):  
Warren S. D. Tennant ◽  
Mike J. Tildesley ◽  
Simon E. F. Spencer ◽  
Matt J. Keeling
Keyword(s):  
Predictive Models ◽  
Control Measures ◽  
Climate Data ◽  
British India ◽  
Middle Income ◽  
Middle Income Countries ◽  
Complex Interactions ◽  
The World ◽  
Spatio Temporal

Plague, caused by Yersinia pestis infection, continues to threaten low- and middle-income countries throughout the world. The complex interactions between rodents and fleas with their respective environments challenge our understanding of human plague epidemiology. Historical long-term datasets of reported plague cases offer a unique opportunity to elucidate the effects of climate on plague outbreaks in detail. Here, we analyse monthly plague deaths and climate data from 25 provinces in British India from 1898 to 1949 to generate insights into the influence of temperature, rainfall and humidity on the occurrence, severity and timing of plague outbreaks. We find that moderate relative humidity levels of between 60% and 80% were strongly associated with outbreaks. Using wavelet analysis, we determine that the nationwide spread of plague was driven by changes in humidity, where, on average, a one-month delay in the onset of rising humidity translated into a one-month delay in the timing of plague outbreaks. This work can inform modern spatio-temporal predictive models for the disease and aid in the development of early-warning strategies for the deployment of prophylactic treatments and other control measures.

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

scholarly journals The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

scholarly journals A method for simulating risk profiles of wheat yield in data-sparse conditions

2021 ◽  
pp. 1-12
Author(s):  
G. Bracho-Mujica ◽  
P.T. Hayman ◽  
V.O. Sadras ◽  
B. Ostendorf
Keyword(s):  
Reference Site ◽  
Climate Impacts ◽  
Weather Data ◽  
Climate Data ◽  
High Quality ◽  
Risk Profiles ◽  
Record Length ◽  
Daily Weather Data ◽  
Study Sites

Abstract Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

scholarly journals Determining future aflatoxin contamination risk scenarios for corn in Southern Georgia, USA using spatio-temporal modelling and future climate simulations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruth Kerry ◽  
Ben Ingram ◽  
Esther Garcia-Cela ◽  
Naresh Magan ◽  
Brenda V. Ortiz ◽  
...  
Keyword(s):  
Strong Association ◽  
Growth Period ◽  
Aflatoxin Contamination ◽  
Adaptation Strategies ◽  
Future Climate ◽  
Climate Data ◽  
Emissions Scenarios ◽  
Rcp 8.5 ◽  
Maximum Temperatures ◽  
Southern Georgia

AbstractAflatoxins (AFs) are produced by fungi in crops and can cause liver cancer. Permitted levels are legislated and batches of grain are rejected based on average concentrations. Corn grown in Southern Georgia (GA), USA, which experiences drought during the mid-silk growth period in June, is particularly susceptible to infection by Aspergillus section Flavi species which produce AFs. Previous studies showed strong association between AFs and June weather. Risk factors were developed: June maximum temperatures > 33 °C and June rainfall < 50 mm, the 30-year normals for the region. Future climate data were estimated for each year (2000–2100) and county in southern GA using the RCP 4.5 and RCP 8.5 emissions scenarios. The number of counties with June maximum temperatures > 33 °C and rainfall < 50 mm increased and then plateaued for both emissions scenarios. The percentage of years thresholds were exceeded was greater for RCP 8.5 than RCP 4.5. The spatial distribution of high-risk counties changed over time. Results suggest corn growth distribution should be changed or adaptation strategies employed like planting resistant varieties, irrigating and planting earlier. There were significantly more counties exceeding thresholds in 2010–2040 compared to 2000–2030 suggesting that adaptation strategies should be employed as soon as possible.

Backcasting long-term climate data: evaluation of hypothesis

2017 ◽  
Vol 132 (3-4) ◽  
pp. 717-726 ◽  
Author(s):  
Bahram Saghafian ◽  
Sara Ghasemi Aghbalaghi ◽  
Mohsen Nasseri
Keyword(s):  
Data Evaluation ◽  
Climate Data
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