Determination of Driving Rain Index in Korea

Ra, Ji-Eun

doi:10.5658/wood.2017.45.1.36

Cited by 4 publications

(6 citation statements)

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“…In previous research performed by Ra (2017), the DRI was determined by using daily climate data. Some studies have used daily data instead of hourly data to calculate driving rain index because it is difficult to obtain the climate data for the consecutive periods of 30 years that is known as climatological normals (Chand and Bhargava, 2002).…”

Section: Domestic Hourly Climatic Data From 2009 To 2017mentioning

confidence: 99%

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Determination of Driving Rain Index by Using Hourly Weather Data for Developing a Good Design of Wooden Buildings

Ra¹

2018

Journal of the Korean Wood Science and Technology

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This research was performed to supplement the previous research about the driving rain index (DRI) for Korea determined by using daily weather data for 30 years. The average annual driving rain index (AADRI) was calculated from the hourly weather data, and the magnitude of DRI was investigated according to wind directions. The hourly climate data were obtained from the Korea Meteorological Administration (KMA) for the period 2009 to 2017. Of 82 locations investigated, seven were classified into regions where the level of exposure of walls to rain was high. The result showed quite a difference from the previous results, in which no high exposure regions were observed. Since the hourly-based and the daily-based annual driving rain index (ADRI) values showed only a slight difference, the result may be explained by the length of the periods used in both studies. The change of DRI according to wind directions showed that there was a certain range of wind directions in which driving rain easily approached building walls. It suggests that the consideration of wind directions with high DRI would be useful to develop a good design of wooden buildings from the point of wood preservation and maintenance.

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Section: Domestic Hourly Climatic Data From 2009 To 2017mentioning

confidence: 99%

“…In Korea Ra (2017) investigated DRI for 82 locations spread all over the country by using daily-based climate data and reported that there were no high exposure regions. However, there was a need for additional research because the hourly-based climate data can gives a better estimation than the daily-based data.…”

Section: Introductionmentioning

confidence: 99%

Determination of Driving Rain Index by Using Hourly Weather Data for Developing a Good Design of Wooden Buildings

Ra¹

2018

Journal of the Korean Wood Science and Technology

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“…Based on its results, it is possible to build an optimal and logically justified chain of all logging operations (Sushkov et al, 2015). Despite the relevance and importance of these studies, an obvious drawback should be noted -their use is limited by the static environment (Ra, 2017;. At the same time, the forest area is a dynamically changing environment.…”

Section: Introductionmentioning

confidence: 99%

“…At the same time, the forest area is a dynamically changing environment. The constantly changing environmental conditions also influence the efficiency of the logging process (Kim and Ra, 2014;Ra, 2017;. Thus, there is a need to develop theoretical research precisely taking into account the dynamics of environmental change.…”

Section: Introductionmentioning

confidence: 99%

Design of Logging Infrastructure in Consideration of the Dynamically Changing Environment

Mokhirev¹,

Rukomojnikov²,

Герасимова³

et al. 2021

Journal of the Korean Wood Science and Technology

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Using forest resources involves solving complex and diverse tasks. At the same time, one of the key goals in the field is improving the quality of forest infrastructure. This direction requires adequate mathematical and economic justification. Moreover, creating an effective infrastructure will not only increase the accessibility and usage volumes of wood and other forest resources, but also contribute to the development of continuous and sustainable forest management. The existing practice of making decisions in terms of the organizational and technological aspects of logging, based on the personal experiences of managers or leading specialists in enterprises, hinders the achievement of constant optimal efficiency. The paper presents results that are a continuation of the research cycle of the authors' team in the fields of optimization and algorithmization of various logging processes. The focus of the study lies in the processing and movement of wood resources, the most valuable products of the investigated groups of enterprises. To this end, the paper presents a developed algorithm for determining an effective technological chain of transportation in logging operations, and for improving loading and unloading processing operations under dynamic natural and production conditions. This algorithm serves as the methodological basis for designing logging infrastructure in a dynamically changing environment.

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“…Currently, several climate indexes based on temperature-rainfall combinations have been developed to help designers and builders (Lstiburek, 2001;Russo, 1971). They were determined based on either wind-driven rain or the decay risk of wood used for exterior aboveground applications (Boyd, 1963;Kim et al, 2011;Ra, 2013, 2014;Ra, 2017;Setliff, 1986). Although these climate classifications were useful, they were not developed to evaluate potential moisture loading on the envelopes of materials, such as wood.…”

Section: Introductionmentioning

confidence: 99%

Determination of Moisture Index in Korea

Ra¹

2018

Journal of the Korean Wood Science and Technology

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This study aimed to obtain basic climate information for effective moisture control in wood in Korea. Two independent climate indexes, namely drying index (DI) and wetting index (WI), were determined using hourly weather data for 82 locations recorded from 2009 to 2017. These data were collected from the Korea Meteorological Administration (KMA). Hourly data had not been measured prior to 2009. DI and WI revealed that all regions were cold and wet except Baengnyeongdo, which was classified as a cold and dry region. DI and WI were normalized assuming that wetting and drying were equally important phenomena. Then, the normalized indexes were combined into moisture index (MI) to rank the moisture loading of the regions. The MIs showed that Seogwipo had the greatest moisture loading in Korea, followed by Seongsan, Namhae, and Geoje. The MIs suggested that Korea exhibited severe moisture loading. Further studies are required to investigate the relation between MI and moisture content on wood surfaces from a wood maintenance point of view.

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Determination of Driving Rain Index in Korea

Cited by 4 publications

References 1 publication

Determination of Driving Rain Index by Using Hourly Weather Data for Developing a Good Design of Wooden Buildings

Determination of Driving Rain Index by Using Hourly Weather Data for Developing a Good Design of Wooden Buildings

Design of Logging Infrastructure in Consideration of the Dynamically Changing Environment

Determination of Moisture Index in Korea

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