The Influence of Cavity Ventilation on the Performance of Brick Veneer Panels When Exposed to Wind Driven Rain: an Experimental Analysis

This study investigates the influence of cavity ventilation on the wind driven rain (WDR) performance of brick veneer walls. Two types of walls (type C and D) both bonded with N-type mortar were studied. The volume and frequency of WDR was based on weather station data from York University. Cavity conditions were mocked with a cavity chamber and ventilation was simulated with a fan providing air suction out of the cavity. Ventilation rates were simulated at 0, 5 and 10 ACH. Higher ventilation rates resulted in more efficient drying and lower RH within the cavity chamber. Wall type C exhibited more absorption with increased ventilation rates. Moisture content readings were generally irrelevant due to failure of the prescribed method. Measuring the influence of cavity ventilation on the amount of penetrated water should be further investigated by applying different ventilation rates to the same wall specimens to reduce the impact of physical variations within the same brick type.

The Influence of Cavity Ventilation on the Performance of Brick Veneer Panels When Exposed to Wind Driven Rain: an Experimental Analysis

This study investigates the influence of cavity ventilation on the wind driven rain (WDR) performance of brick veneer walls. Two types of walls (type C and D) both bonded with N-type mortar were studied. The volume and frequency of WDR was based on weather station data from York University. Cavity conditions were mocked with a cavity chamber and ventilation was simulated with a fan providing air suction out of the cavity. Ventilation rates were simulated at 0, 5 and 10 ACH. Higher ventilation rates resulted in more efficient drying and lower RH within the cavity chamber. Wall type C exhibited more absorption with increased ventilation rates. Moisture content readings were generally irrelevant due to failure of the prescribed method. Measuring the influence of cavity ventilation on the amount of penetrated water should be further investigated by applying different ventilation rates to the same wall specimens to reduce the impact of physical variations within the same brick type.

Sensitivity of precipitation and temperature over the Mount Kenya area to physics parameterization options in a high-resolution model simulation performed with WRFV3.8.1

Several sensitivity experiments with the Weather Research and Forecasting (WRF) model version 3.8.1 have been performed to find the optimal parameterization setup for precipitation amounts and patterns around Mount Kenya at a convection-permitting scale of 1 km. Hereby, the focus is on the cumulus scheme, with tests of the Kain–Fritsch, the Grell–Freitas, and no cumulus parameterizations. In addition, two longwave radiation schemes and two planetary boundary layer parameterizations are evaluated, and different nesting ratios and numbers of nests are tested. The precipitation amounts and patterns are compared against a large amount of weather station data and three gridded observational data sets. The temporal correlation of monthly precipitation sums show that fewer nests lead to a more constrained simulation, and hence the correlation is higher. The pattern correlation with weather station data confirms this result, but when comparing it to the most recent gridded observational data set the difference between the number of nests and nesting ratios is marginal. The precipitation patterns further reveal that using the Grell–Freitas cumulus parameterization in the domains with resolutions >5 km provides the best results when it comes to precipitation patterns and amounts. If no cumulus parameterization is used in any of the domains, the temporal correlation between gridded and in situ observations and simulated precipitation is especially poor with more nests. Moreover, even if the patterns are captured reasonably well, a clear overestimation in the precipitation amounts is simulated around Mount Kenya when using no cumulus scheme in all domains. The experiment with the Grell–Freitas cumulus parameterization in the domains with resolutions >5 km also provides reasonable results for 2 m temperature with respect to gridded observational and weather station data.

Evaluating the association between extreme heat and mortality in urban Southwestern Ontario using different temperature data sources

Urban areas have complex thermal distribution. We examined the association between extreme temperature and mortality in urban Ontario, using two temperature data sources: high-resolution and weather station data. We used distributed lag non-linear Poisson models to examine census division-specific temperature–mortality associations between May and September 2005–2012. We used random-effect multivariate meta-analysis to pool results, adjusted for air pollution and temporal trends, and presented risks at the 99th percentile compared to minimum mortality temperature. As additional analyses, we varied knots, examined associations using different temperature metrics (humidex and minimum temperature), and explored relationships using different referent values (most frequent temperature, 75th percentile of temperature distribution). Weather stations yielded lower temperatures across study months. U-shaped associations between temperature and mortality were observed using both high-resolution and weather station data. Temperature–mortality relationships were not statistically significant; however, weather stations yielded estimates with wider confidence intervals. Similar findings were noted in additional analyses. In urban environmental health studies, high-resolution temperature data is ideal where station observations do not fully capture population exposure or where the magnitude of exposure at a local level is important. If focused upon temperature–mortality associations using time series, either source produces similar temperature–mortality relationships.

Comparing Kriging Estimators Using Weather Station Data and Local Greenhouse Sensors

In order to minimize the impacts of climate change on various crops, farmers must learn to monitor environmental conditions accurately and effectively, especially for plants that are particularly sensitive to the weather. On-site sensors and weather stations are two common methods for collecting data and observing weather conditions. Although sensors are capable of collecting accurate weather information on-site, they can be costly and time-consuming to install and maintain. An alternative is to use the online weather stations, which are usually government-owned and free to the public; however, their accuracy is questionable because they are frequently located far from the farmers’ greenhouses. Therefore, we compared the accuracy of kriging estimators using the weather station data (collected by the Central Weather Bureau) to local sensors located in the greenhouse. The spatio-temporal kriging method was used to interpolate temperature data. The real value at the central point of the greenhouse was used for comparison. According to our results, the accuracy of the weather station estimator was slightly lower than that of the local sensor estimator. Farmers can obtain accurate estimators of environmental data by using on-site sensors; however, if they are unavailable, using a nearby weather station estimator is also acceptable.

Experimental technology for very-short-range numerical weather prediction based on a nonhydrostatic mesoscale meteorological model with assimilation of radar and ground-based observational data

A brief description of the very-short-range numerical weather prediction technology based on the WRF-ARW nonhydrostatic model is presented. Skill scores are provided for the short- and very-short-range forecasts of temperature, precipitation and wind of various intensity, which were calculated with this model as a result of its integration on the nested grid with a spacing of 3 km, with the direct simulation of deep convection and the assimilation of radar and ground-based weather station data. The forecasts for a location were verified for the central part of European Russia using radar and weather station data for the summer of 2020. It is demonstrated that the model adequately simulates mesoscale convective systems and the related zones of heavy precipitation, strong winds, and thunderstorms. Possible reasons for forecast biases and the ways to reduce the value of spatial and temporal errors are discussed. Keywords: numerical very-short-range forecasting, mesoscale meteorological model, radar data assimilation, precipitation, severe weather events, active convection