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Adapting rain data for hygrothermal models

Cornick, C., Dalgliesh, S. D. and Alan, W.
2009
Building and Environment, 44(5): 987-996
Author Hygrothermal-simulation; Modeling; Moisture; Building envelope; Wind-driven rain; Climate; Stochastic modeling, Author Hygrothermal-simulation; Modeling; Moisture; Building envelope; Wind-driven rain; Climate; Stochastic modeling


Cornick, C., Dalgliesh, S. D. and Alan, W., (2009), "Adapting rain data for hygrothermal models", Building and Environment, 44(5): 987-996.
Abstract:
Building and Environment

Volume 44, Issue 5, May 2009, Pages 987-996

Design for moisture control has now become an established part of building envelope design. Hygrothermal modeling tools, capable of simulating moisture transfer in materials, are a key element of the design process. There are three principle methods of moisture transfer in envelopes. They are, in order of magnitude, capillary action, vapour convection, and vapour diffusion. Wind-driven rain has the potential to deposit large amounts of liquid water on the exterior surface, as well inside walls through rain penetration, providing a significant source for moisture transport. Most hygrothermal models are capable of handling wind-driven rain impinging on or penetrating the surface of the envelope. Correct results presuppose the availability of reliable rain intensity data. Many data sets, however, do not record hourly rain intensities but qualitative intensities such as light, moderate, or heavy. This paper examines several methods for assigning quantitative values to weather observations available in Canada. Real data, such as data from rain gauges, is preferable although the latter have shortcomings. Differences in catch can be up to 50% depending on gauge type, size, and exposure. When only rain codes are available the values recommended by the local meteorological service can provide adequate estimates. In case where there is observer bias a better estimate can be obtained by adjusting the value for light rainfall. If very little information is available stochastic modeling of rainfall is possible though the accuracy, especially for individual months is low.

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