Effects of Different Averaging Operators on the Urban Turbulent Fluxes

Abstract

The effects of different averaging operators and atmospheric stability on the turbulent fluxes are investigated using the vertical velocity, air temperature, carbon dioxide concentration, and absolute humidity data measured at 10 Hz by a 3-dimensional sonic anemometer and an open-path CO 2 /H 2 O infrared gas analyzer installed at a height of 18.5 m on the rooftop of the Jungnang KT building located at a typical residential area in Seoul, Korea. For this purpose, 7 different averaging operators including block average, linear regression, and moving averages during 100 s, 300 s, 600 s, 900 s, and 1800 s are considered and the data quality control procedure such as physical limit check and spike removal is also applied. It is found that as the averaging interval becomes shorter, turbulent fluxes computed by the moving average become smaller and the ratios of turbulent fluxes computed by the 100 s moving average to the fluxes by the 1800 s moving average under unstable stability are smaller than those under neutral stability. The turbulent fluxes computed by the linear regression are 85~92% of those computed by the 1800 s moving average and nearly the same as those computed by 900 s moving average, implying that the adequate selection of an averaging operator and its interval will be very important to estimate more accurate turbulent fluxes at urban area.

High-resolution urban observation network for user-specific meteorological information service in the Seoul Metropolitan Area, South Korea

“…The surface thermal conductivity, heat capacity, and emissivity are estimated and verified by comparison with the surface temperature determined based on the EB and heat transfer models with observed surface temperature (Monteith and Unsworth, 1990;Santillan-Soto et al, 2015). The surface roughness and displacement lengths are obtained by a micrometeorological method and verified with those obtained from urban morphology data such as mean building height, frontal area density, and plane area density from the geographical information system (Macdonald et al, 1998;Kwon et al, 2014). They are expected to produce high-resolution surface property maps, such as albedo, emissivity, and thermal conductivity, and surface roughness length and displacement (Yi et al, 2015;Jee et al, 2016).…”

“…Basic quality checks for meteorological variables include missing check, physical limit check, climate range check, and spike removal . Surface fluxes are computed from 10 Hz raw data using the following procedure: (1) physical limit check, (2) detection and removal of spike data (Vickers and Mahrt, 1997), (3) computation of the vertical flux with a 30 min block average (Kwon et al, 2014), and (4) Webb-Pearman-Leuning correction (Webb et al, 1980;Leuning, 2007).…”

“…It is well known that the urban surface material and building morphology affect meteorology in various ways including the increase in temperature, leading to the urban heat island effect (Bornstein, 1968;Oke, 1973;Landsberg, 1981;Arnfield, 2003;Kalnay and Cai, 2003;Kim and Baik, 2005;Grimmond, 2006); decrease or increase in the temporal variation of absolute humidity due to impervious surfaces and anthropogenic water use (Unger, 1999;Kuttler et al, 2007); increase in haze, cloud, and precipitation (Bornstein and Lin, 2000;Dixon and Mote, 2003;Shepherd, 2005;Carrio et al, 2010); decrease in visibility due to anthropogenic aerosols (Cheng and Tsai, 2000;Singh et al, 2008;Nichol et al, 2010); increase in the turbulent intensity and change of wind speed due to high-rise buildings (Roth, 2000;Arnfield, 2003;Grimmond et al, 2004;Barlow et al, 2011;Song et al, 2013); decrease in solar radiation due to manmade air pollutants (Peterson et al, 1978;Robaa, 2009); increase in the sensible heat flux and heat storage due to anthropogenic heat release from the urban surface; and decrease in the latent heat flux (Nunez and Oke, 1977;Christen and Vogt, 2004;Harman and Belcher, 2006;Grimmond et al, 2009;Nordbo et al, 2012;Park et al, 2014a). When the synoptic wind becomes strong, the area receiving most of the precipitation with strong upward motion moves more downwind (Bornstein and Lin, 2000;Lin et al, 2011;Han et al, 2014).…”

A Building-Block Urban Meteorological Observation Experiment (BBMEX) Campaign in Central Commercial Area in Seoul

“…where ρ is the air density, C p is the specific heat at a constant pressure, and ( ) indicates a deviation in a variable from a 30 min block average, and a b is the covariance between the two variables a and b [30].…”

Development of a Quality Check Algorithm for the WISE Pulsed Doppler Wind Lidar

“…차세대도시농림융합기상사업단(WISE, Weather Information Service Engine)에서는 도시에서 나타나는 돌발홍수, 노면 결빙과 같은 재해 기상에 의한 피해 를 저감시킬 목적으로 수도권에 고해상도 기상자료 산출을 위한 수도권 도시기상관측망을 구축하고 있으 며 (Choi et al, 2013;Chae et al, 2014;Kwon et al, 2014;Park et al, 2016) (Leosphere, 2012). 제조사에서는 한 달 동안…”