AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-1575-2017High-resolution urban observation network for user-specific meteorological
information service in the Seoul Metropolitan Area, South KoreaParkMoon-Soongeograph2@gmail.comhttps://orcid.org/0000-0003-0551-5129ParkSung-Hwahttps://orcid.org/0000-0003-3217-0541ChaeJung-HoonChoiMin-HyeokSongYunyoungKangMinsooRohJoon-Woohttps://orcid.org/0000-0003-0337-4019Weather Information Service Engine, Hankuk University of Foreign Studies, 17035, Gyeonggi-do, South KoreaMoon-Soo Park (ngeograph2@gmail.com)25April20171041575159425August201630August20166April20177April2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/10/1575/2017/amt-10-1575-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/1575/2017/amt-10-1575-2017.pdf
To improve our knowledge of urban meteorology, including
those processes applicable to high-resolution meteorological models in the
Seoul Metropolitan Area (SMA), the Weather Information Service Engine (WISE)
Urban Meteorological Observation System (UMS-Seoul) has been designed and
installed. The UMS-Seoul incorporates 14 surface energy balance (EB)
systems, 7 surface-based three-dimensional (3-D) meteorological observation
systems and applied meteorological (AP) observation systems, and the
existing surface-based meteorological observation network. The EB system
consists of a radiation balance system, sonic anemometers, infrared
CO2/H2O gas analyzers, and many sensors measuring the wind speed
and direction, temperature and humidity, precipitation, and air pressure.
The EB-produced radiation, meteorological, and turbulence data will be used
to quantify the surface EB according to land use and to improve
the boundary-layer and surface processes in meteorological models. The 3-D
system, composed of a wind lidar, microwave radiometer, aerosol lidar, or
ceilometer, produces the cloud height, vertical profiles of backscatter by
aerosols, wind speed and direction, temperature, humidity, and liquid water
content. It will be used for high-resolution reanalysis data based on
observations and for the improvement of the boundary-layer, radiation, and
microphysics processes in meteorological models. The AP system includes road
weather information, mosquito activity, water quality, and
agrometeorological observation instruments. The standardized metadata for
networks and stations are documented and renewed periodically to provide a
detailed observation environment. The UMS-Seoul data are designed to support
real-time acquisition and display and automatically quality check within 10 min from observation. After the quality check, data can be distributed
to relevant potential users such as researchers and policy makers. Finally,
two case studies demonstrate that the observed data have a great potential
to help to understand the boundary-layer structures more deeply, improve the
performance of high-resolution meteorological models, and provide useful
information customized based on the user demands in the SMA.
Introduction
The world population exceeded 7.3 billion in 2015; it is projected to
increase steadily and reach 9.7 billion by 2050 and 11.2 billion by 2100
(United Nations, 2015). The urban population also increased and is expected
to increase at an even greater rate. The ratio of the global urban
population was over 53 % in 2014 and is projected to grow to approximately
66 % by 2050 (United Nations, 2014). The high population density in urban
areas is inevitably vulnerable not only to disastrous meteorological and
environmental phenomena, such as heavy rain/snowfall, heat and cold waves,
air pollution, and strong wind, but also to manmade disasters such as the
explosion or release of toxic gases (OFCM, 2004; Razafindrabe et al., 2009).
Impervious surfaces in urban areas tend to amplify urban flash flooding
under heavy rainfall conditions, freezing rain or snowfall disrupt
transportation systems, and severe storms with lightning and high winds
might result in power failures. A high population density in urban areas
therefore leads to greater property damage and loss of life as a result of
disastrous events.
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).
Many countries and cities in Europe, North America, and Asia have conducted urban
meteorological experiments and/or intensive observation campaigns for
various purposes such as understanding urban meteorological processes and
improving the predictability of urban high-resolution meteorological
phenomena (Allwine et al., 2002; Cros et al., 2004; Rotach et al., 2005;
Schroeder et al., 2010; Basara et al., 2011; Koskinen et al., 2011; Hicks et
al., 2012; Wood et al., 2013; Nakatani et al., 2015; Tan et al., 2015). The
observed meteorological variables, spatial resolution for each instrument in
the network, and temporal resolution for each variable are determined
according to the needs of various users: surface meteorology and
service-oriented observation might be sufficient for real-time information
services such as the New York Mesonet (http://www.nysmesonet.org); surface energy balance (EB) and vertical profiles
are needed for advances in urban meteorology or high-quality and
high-resolution forecasts such as the Basel Urban Boundary Layer Experiment
(Rotach et al., 2005) and Shanghai Urban Integrated Meteorological
Observation Network (Tan et al., 2015); and radars are good for real-time
services for severe weather and short-term forecast such as the Tokyo
Metropolitan Area Convection Study for Extreme Weather Resilience (Nakatani
et al., 2013, 2015).
For the purpose of high-resolution meteorological information services, the
improvement of the supporting meteorological model is essential. Physics
schemes, including microphysics (interactions among water vapor, cloud
water, cloud ice, rain drop, snow, and graupel), cumulus (updraft,
downdraft, entrainment, and detrainment in clouds), radiation (absorption,
emission, scattering, reflection, and transpiration in the atmosphere for
radiative energy), surface (surface EB and energy/moisture
transfer between the surface and ground), and atmospheric boundary-layer
schemes (energy and moisture transfer between the surface and atmospheric
boundary layer), interact with each other (Dudhia, 1989). Irregular surface
morphologies and materials in urban areas affect the surface optical,
physical, and thermal properties such as thermal conductivity, heat
capacity, roughness, displacement length, albedo, and emissivity (Masson,
2006; Lee and Park, 2008; Grimmond et al., 2009). These properties change
the energy partition dramatically over urban surfaces compared with rural
surfaces. As a result, the modified sensible and latent heat fluxes change
the boundary-layer structure due to energy and moisture interactions between
the surface, underlying ground, and overlying atmosphere (Pielke, 2002).
In South Korea, the urban population ratio increased steadily from 21.4 %
in 1950 to 79.4 % in 2000 to 82.4 % in 2014; it is expected to reach
87.6 % in 2050 (United Nations, 2014, 2015). The Seoul Metropolitan Area
(SMA) was ranked to include the fifth largest urban area population in 2015
(Demographia, 2015). Meteorological data analyses for the period from 1960 to
2009 in the SMA show that the air temperature and precipitation increase,
relative humidity decreases, and heavy rainfall events with more than
20 mm h-1 also increase (Kim et al., 2011). Recently, the SMA has
experienced blackouts of more than 1.6 million houses due to failure in
electric power demand prediction after extremely hot weather in autumn,
massive damage from shallow landslides due to heavy rainfall in 2011
(D. W. Park et al., 2013), several inundations by flash floods (Kim et al.,
2014), building damage due to strong winds such as typhoons, traffic
accidents as a result of road ice, and deaths due to annual heat/cold waves
(Son et al., 2012).
The Weather Information Service Engine (WISE) project was launched in 2012
to meet the needs of high-resolution meteorological information to reduce
the damage to the citizens caused by extreme weather phenomena and provide
useful indices customized for each user's demand in the SMA (Choi et al.,
2013). To achieve these goals, scientific advances in urban meteorology and
development/improvement of high-resolution meteorological models and service-specific application models are needed (Baklanov, 2006; Baklanov et al.,
2008). To provide various users, such as researchers, administrators, and
policy makers, with observation-based meteorological information and support
the development or improvement of related models in fields of air quality,
flash flood, road weather, dispersion of released dangerous matter, ecology,
and energy use prediction with a horizontal resolution from several meters
to several kilometers, the high-resolution Urban Meteorological Observation System
network in the Seoul Metropolitan Area (UMS-Seoul) has been proposed and
established.
This study includes the background of UMS-Seoul through a description of the
geography, topography, and land cover of the SMA and a review of available
meteorological observation networks. We then present the objectives,
details, and applications of each meteorological observation system network
including the surface EB observation system, three-dimensional (3-D)
meteorological observation system, and applied meteorological (AP) observation
system network in UMS-Seoul. The usefulness of UMS-Seoul and applicability
to high-resolution meteorological information services are then demonstrated
using the detailed horizontal surface meteorological field and complex
boundary-layer structure.
Seoul Metropolitan Area
The SMA on the Korean Peninsula consists of three
administrative provinces: Seoul Special City, Incheon Metropolitan City, and
Gyeonggi Province (Fig. 1a). Seoul Special City, the capital city of South Korea,
is surrounded by the Gyeonggi Province and Incheon Metropolitan City, with
the highest population density of 16 188 km-2 (Table 1). Incheon
Metropolitan City is located between Seoul Special City and the Yellow Sea.
The Gyeonggi Province has the highest population (11.4 million people) and
the largest area (10 184 km2) but the lowest population density (1119 km-2; Table 1).
(a) Geography and topography of the Seoul Metropolitan Area
with administrative boundaries. (b) Enhanced geography and
topography with major mountains in a highly populated region shown by the
rectangle in panel (a).
The SMA has very complex geography, topography, and land cover. Gyeonggi Bay
is in the Yellow Sea in the west of the SMA and has a very irregular
coastline. The Yellow Sea is an important moisture source in the case of
heavy rainfall, snowfall, or heterogeneous reactions among
long-range-transporting air pollutants (Chung and Kim, 2008; Cayetano et al.,
2011; Cha et al., 2011; S.-U. Park et al., 2011, 2013; Jeong and Park, 2013).
The western part of the SMA comprises relatively low-lying farmland or urban
areas, while the eastern part contains high-altitude mountain ranges, some of
which are higher than 1000 m in Domain 1 (Fig. 1a). Most mountains in South Korea
are covered with forest. Highly populated areas range from Incheon Metropolitan
City to Seoul Special City, indicated in Domain 2 (Fig. 1b). The Han River
flows from east to west and divides the SMA from Seoul Capital City. Seoul
Capital City is surrounded by several high mountains > 500 m in
altitude: the Bukhan, Dobong, Surak, and Gwanak mountains in the northern
part and the Cheonggye and Bulam mountains in the southern part. There is a
small mountain (262 m high) in the center of Seoul Special City (Fig. 1b).
Area, population, and population density statistics in SMA
(http://kosis.kr).
Figure 2 shows the simplified land use in the SMA (Domain 1) and
highly populated area (Domain 2) with 90 m horizontal resolution. Forest
covers 41.9 % of the area; croplands including pasture and grassland cover
21.5 %; water bodies including seawater and inland water cover 20.9 %;
urban areas including residential, industrial, and commercial areas cover
8.6 %; and wetlands cover 5.0 % in Domain 1 (Table 2). In Domain 2,
forest covers 36.0 %, urban areas cover 28.3 %, croplands cover
20.6 %, wetlands cover 8.6 %, and water bodies cover 6.4 % of the area
(Table 2). Most wetlands are tidelands on the border between the continent
and Yellow Sea. More than 40 % of Seoul Capital City is residential or
commercial areas, approximately 30 % is covered with forest, and
∼ 10 % is covered with roads and rivers (Fig. 2b).
(a) Land use in the Seoul Metropolitan Area and
(b) zoomed image of the highly populated region shown by the rectangle
in panel (a).
Background meteorological observation network
Many background meteorological observation systems have already been
installed in the SMA (Fig. 3; Table 3). There are over 110 meteorological observation
stations operated by the Korea
Meteorological Administration (KMA), whose locations are selected based on the administrative district,
and more than 1000 meteorological observation stations operated by a subsidiary company of SK (largest telecommunication
company in South Korea, SKP), whose
locations are selected based on the daytime and resident populations in
Domain 1. The KMA-operated automatic synoptic observation system (ASOS)
observes the air pressure, evaporation, cloud amount, sunshine, snow depth,
surface and ground temperatures, and weather phenomena and the basic
meteorological variables of the grass surface with minimized obstacles. All
ASOS stations follow the guidelines of the World Meteorological Organization
(WMO, 2008). The KMA-operated automatic weather system (AWS) has a wind
speed/direction sensor at 7 or 10 m, a temperature sensor at 1.5–2 m,
precipitation detection, and a tipping-bucket-type rain gauge with heater,
while each SKP-operated AWS has an integrated meteorological sensor and
tipping-bucket-type rain gauge without heater, which is set to not measure
precipitation in winter. Some of the KMA-operated AWSs and most SKP-operated
meteorological stations are installed on the rooftops of buildings; few are
installed in street canyons due to space restrictions. Regardless of the
installation environment, if we choose any point with urban land cover in
Seoul Special City, the distance from that point to the nearest AWS will be
less than 1 km.
Percentage of land use in the SMA domain (Domain 1) and
highly populated domain (Domain 2).
Land useDomain 1Domain 2(%)(%)Cropland, pasture, grassland21.520.6Forest41.936.0Water bodies20.96.4Wetlands5.08.6Barren2.10.1Urban (low-intensity residential)4.312.8Urban (high-intensity residential)2.29.0Urban (industrial and commercial)2.06.5
Location of background radars, rawindsondes, wind profilers,
radiometers, automatic synoptic observation systems, automatic weather
systems, and SKP automatic weather systems in the (a) Seoul
Metropolitan Area and (b) highly populated region.
Specification and installed sensors of the existing meteorological
observation network in the Seoul Metropolitan Area.
There are two rawindsonde stations, two wind profiler and microwave
radiometer stations, and six radar stations in Domain 1 (Fig. 3). The Osan
(WMO station number 47122) and Baengnyeongdo (WMO station number 47102)
stations observe the upper-air meteorological variables four and two times a
day, respectively. A wind profiler and microwave radiometer are installed at
the Paju and Cheorwon stations to observe the vertical profile of wind,
temperature, and humidity. With respect to radar stations, KMA operates
three S-band and one C-band radar, the Korea Institute of Civil
Engineering and Building Technology
operates one X-band radar, the South Korean Air Force operates one C-band radar,
and the Ministry of Land, Infrastructure, and Transport operates one C-band
radar in Domain 1 (Fig. 3).
Sensors installed in the surface energy balance system and
specifications of instruments of the 3-D and applied meteorological
observation system. The manufacturer and model name for each 3-D instrument
and sensor used in the applied meteorological system are added.
SystemsSensor or specification Surface energySites14 (rural, residential, commercial, industrial, apartment, river)balance systemTower height4.0–18.5 mSensorTemperature, relative humidity, wind speed, wind direction, downward/upward or shortwave/longwave radiation, CO2/H2O infrared gas analyzer, sonic anemometer, surface temperature, rain gauge, water temperature (two stations), thermal infrared imagery (Jungnang, Gwanghwamun, Gajwa, Anyang, Guro – two sets)Temporal resolution1 min for meteorological variables 10 Hz sampling and 30 min averaging for turbulent flux (sonic anemometer, CO2/H2O gas analyzer)OptionLarge-aperture scintillometer (Kipp & Zonen, MK-II) – one set Surface temperature monitoring system – two sets3-D meteorological observation systemCeilometer (Vaisala, CL51)Two stations Wavelength: 910 nm Backscatter by aerosol (up to 15 km, 10 m vertical resolution); cloud base heights (three levels) (1 min temporal resolution) Accuracy: ±5 m for cloud base heightAerosol lidar (Raymetrics, LB210-D200)Two stations Wavelength: 532 nm (parallel, cross-polarized), 1064 nm Backscatter by aerosol (up to 16 km, 3.75 m vertical resolution), depolarization ratio, backscattering coefficient (2 min temporal resolution, 1 h average)Microwave radiometer (RPG, HATPRO-G4)Seven stations Water vapor (22–31 GHz, 7 channels), temperature (51–58 GHz, 7 channels) Atmospheric attenuation for each channel, vertical profile of temperature, humidity, liquid water content (1 min temporal resolution) Vertical resolution (m): 30 up to 1200 m, 200 up to 5000 m, 400 up to 10 000 m for temperature profile; 200 up to 2000 m, 400 up to 5000 m, 800 up to 10 000 m for humidity profile RMS accuracy: 0.25 K (up to 500 m), 0.50 K (up to 1200 m), 0.75 K (up to 4000 m), 1.00 K (up to 10 000 m) for temperature, 5 % for relative humidityWind lidar (Leosphere, Windcube-200)Six stations Wavelength: 1532 nm Wind speed and direction (up to 6000 m, 100 m vertical resolution) (21 s sample and 10 min average) RMS accuracy: 0.3 m s-1 for wind speed, 1.5∘ for wind directionApplied meteorological observation systemRoadSix stations/mobile road weather vehicle – one set Fixed: wind speed and direction (RM Young, 05103), temperature and humidity (Vaisala, HMP155), pressure (Vaisala, PTB110), precipitation (ELP, ERGH), precipitation detection (ELP, ERD100), insolation (Kipp & Zonen, CMP10), net radiometer (Kipp & Zonen, CNR4), road sensor (surface temperature and status, salinity, water depth) (Lufft, IRS31Pro) (1 min temporal resolution) Mobile: wind speed and direction (Vaisala, WMT703), temperature and humidity (Vaisala, HMP155), pressure (Vaisala, PTB330), precipitation (Vaisala, RG13H), precipitation detection (Vaisala, DRD11A), insolation (Kipp & Zonen, CMP11), net radiometer (Kipp & Zonen, CNR4), road sensor (surface temperature, status, salinity, and water depth) (Vaisala, DSP310), global positioning system (TRIMBLE, NetR9) (1 s temporal resolution)Water quality (HYDROLAB, DS5X)Two stations Water temperature, pH, conductivity, dissolved oxygen, salinity, turbidity, chlorophyll a, water depth (5 min temporal resolution)
Continued.
SystemsSensor or specification Mosquito (ETND, DMS VER III)Three stations Mosquito activity (1 h temporal resolution)Greenhouse gasOne station CH4 concentration (LI-COR, LI-7700), total radiation (Kipp & Zonen, CMP10), diffuse radiation (Kipp & Zonen, CMP10) with sun tracker (Kipp & Zonen, SOLYS2) (30 min temporal resolution)AgrometeorologyFour stations Net radiometer (Kipp & Zonen, CNR4), temperature and humidity (Vaisala, HMP155A), albedo (Kipp & Zonen, CMA6), leaf wetness (Campbell, LWS), soil moisture content (Campbell, CS616), wind speed and direction (RM Young, 05103), precipitation (Wedaen, WDR-202), soil temperature (Campbell, 107), ground heat flux (Hukseflux, HFP01), Quantum (LI-COR, LI190SB) (1 min temporal resolution)
Location of the UMS-Seoul urban meteorological observation system
networks in the highly populated region of the Seoul Metropolitan Area.
Land cover classification of major observation stations.
a Auer (1978): A1, metropolitan natural; A2, agricultural rural; A5, water surfaces; C1, commercial; R1, common residential;
R2, compact residential. b Davenport et al. (2000): N, north; E, east; S, south; W, west; 1, sea; 3, open; 4, roughly open; 5, rough; 6, very rough;
7, skimming; 8, chaotic. c Oke (2004) urban climate zones (UCZ): UCZ-1 intensely developed; UCZ-2 intensely developed high density; UCZ-3 highly developed
medium density; UCZ-6 mixed use with large buildings in open landscape; UCZ-7 semi-rural development with scattered houses. d Stewart and Oke (2012)
local climate zones (LCZ): 1, compact high-rise; 2, compact mid-rise; 3, compact low-rise; 4, open high-rise; 9, sparsely built; A, dense trees; B, scattered
trees; C, bush scrub; D, low plant; E, bare rock or paved; G, water.
Even though high-resolution and various meteorological observation systems
are located in the SMA, there are still many unknowns regarding urban
surface forcing and vertical profiles of temperature, humidity, and wind
that impede our understanding of the fundamentals of urban meteorological
phenomena in highly populated areas of the SMA. To counter this, the WISE
project was designed and the UMS-Seoul was installed in this area.
Urban meteorological observation network system (UMS-Seoul) and its
applications
The UMS-Seoul is composed of a surface EB observation network, a 3-D meteorological observation network, an
AP
observation network, and the existing surface-based meteorological
observation network in the SMA. Table 4 shows the simplified specifications
and installed sensors in each observation network.
Figure 4 shows the location of each meteorological observation network
station in UMS-Seoul. The locations are selected considering the surface
land cover and horizontal distribution of geography and topography. Table 5
shows the land cover of the major observation stations classified by Auer (1978), Davenport et al. (2000), Oke (2004), and Stewart and
Oke (2009,
2012). Each station represents a different type of land cover. The Yeouido
and Gwangjin stations, located on the border of the Han River, are
representative river sites. Typifying intensely developed and compact
high-rise sites, Gwanghwamun, Guro, and Songdo are located in the center of
Seoul Special City, southwest of Seoul Special City, and south of Incheon
Metropolitan City, respectively. The Songdo Station, in particular, is a
newly developed high-rise building complex. The Anyang and Nowon stations
are surrounded by apartment building complexes, one of the typical
residential types. The Jungnang, Gajwa, Gangnam, and Seongnam stations are
located in old residential areas in the SMA. As rural stations, Youngin and
Bucheon represent urban forest and crop field sites, respectively.
Manufacturer, model, measurement range, and accuracy or
sensitivity of sensors deployed in the surface energy balance system.
SensorManufacturer (model)Measurement rangeAccuracy3-D sonic anemometerCSI∗ (CSAT3B)u & v: ±60 m s-1, w: ±8 m s-1, Ts: -50–60 ∘Cu & v < ±0.08 m s-1, w < ±0.04 m s-1CO2 and H2O open- path gas analyzer and 3-D sonic anemometerCSI/LI-COR (EC150/ CSAT3A)CO2: 0–1798 mg m-3 at 25 ∘C, 1 atm H2O: 0–52.9 g m-3 at 25 ∘C, 1 atm 3-D sonic anemometer u & v: ±60 m s-1, w: ±8 m s-1, Ts: -50–60 ∘CCO2 precision (RMS): 0.2 mg m-3 Zero drift: ±0.55 mg m-3∘C-1 Gain drift: ±0.1 % of reading ∘C-1 H2O precision (RMS): 0.004 g m-3 Zero drift: ±0.037 g m-3∘C-1 Gain drift: ±0.3 % of reading ∘C-1 3-D sonic anemometer u & v < ±0.08 m s-1, w < ±0.04 m s-1Net radiometerKipp & Zonen (CNR4)Maximum irradiance: 4000 W m-2 (shortwave); 2000 W m-2 (longwave) Spectral range: 300–2800 nm (shortwave); 4500–42000 nm (longwave)Expected daily uncertainly: < 2 % (shortwave); < 10 % (longwave) Sensitivity: 7–20 µV W-1 m2 (shortwave); 5–10 µV W-1 m2 (longwave)Temperature and relative humidity probeCSI/Vaisala (HMP155A)Temperature: -80–60 ∘C Relative humidity: 0–100 %Temperature: ±0.3 ∘C Relative humidity: ±1 % at 15–25 ∘C, 0–90 %, ±1.7 % at 15–25 ∘C, 90–100 %Wind vaneVector Instruments0–360∘±3∘Three-cup anemometer(W300P/ A100M)0–75 m s-10.1 m s-1 at 0.1–10 m s-1; 1 % at 10–55 m s-1; 2 % at 55–75 m s-1Barometric pressureCSI/Vaisala (CS106)500–1100 hPa±0.3 hPa at 20 ∘C; ±0.6 hPa at 0–40 ∘C; ±1.0 hPa at -20–45 ∘CInfrared surface temperatureCSI/Apogee (SI-111)-40–70 ∘C±0.5 ∘C at -40–70 ∘CPrecipitation gaugeWedaen (WDSA-205)0.5 mm per 1 tip±3 mm at 150 mm h-1Soil heat flux plateCSI/Hukseflux (HFP01)-2000–2000 W m-2Sensitivity: 50 µV W-1 m2-15–5 % in most common soilSoil water contentCSI (CS655)Soil temperature: -10–70 ∘C Volumetric water content: 5–50 %Soil temperature: ±0.5 ∘C Volumetric water content: ±3 %Soil and water temperatureCSI (107)-35–50 ∘C±1.0 ∘CPropeller-type wind vaneRM Young (05103)Wind speed: 0–100 m s-1 Wind direction: 0–360∘Wind speed: ±0.3 m s-1 Wind direction: ±3∘Thermal infrared imageryNippon Avionics (TS9230)Spectral range: 8–14 µm Pixel: 320 × 240 Field of view: 21.7∘× 16.4∘
∗ CSI: Campbell Scientific, Inc.
All 14 surface EB observation systems are deployed on the
tower installed on the ground or on a rooftop of a building surrounded by
similar surface land cover (residential, commercial, industrial, mixed, and
rural). The measurement tower height ranges from 1.5 m (river side) to
18.5 m (Jungnang). Each system includes two or three temperature, relative
humidity, wind speed, and wind direction sensors, one to three
CO2/H2O infrared gas analyzers, two or three sonic anemometers, an
air pressure sensor, a precipitation gauge with heater, one to three surface
temperature sensors, and a four-component net radiometer (downward/upward and
shortwave/longwave radiometers). Figure 5 shows a typical EB measurement
tower, including sensors, representative of surface land cover in the SMA.
Manufacturer, model, measurement range, and accuracy of sensors are listed in
Table 6. The CR3000 is used for data logging and LoggerNet for operating the software
manufactured by Campbell Scientific, Inc. The performances of all
meteorological sensors were certified by the manufacturer before shipping. In
addition, most meteorological sensors were also certified by the Korea
Meteorological Industry Promotion Agency before installation. Performance
certificates have been issued and will be renewed every 3 years. The
H2O and CO2 infrared gas analyzer is calibrated every 6 months
according to the procedure suggested in the manual
(https://s.campbellsci.com/documents/af/manuals/ec150.pdf).
Additionally, a large-aperture scintillometer (manufactured by Kipp &
Zonen, model MK-II) and six thermal infrared imagery systems (manufactured
by Nippon Avionics, model TS9230) are installed to obtain the
line-averaged sensible heat flux and sub-building-scale spatial distributions
of the surface temperature, respectively.
(a) Sensor deployment of a typical surface energy balance
system and representative surface covers of (b-1) residential,
(b-2) apartment, (b-3) industrial area,
(b-4) urban rice paddy, (b-5) forest, and
(b-6) river areas.
Quality check algorithms for meteorological variables and flux data have previously been developed (Aubinet et al., 2012; Chae et
al., 2014; M.-S. Park et al., 2013, 2014a; Kim et al., 2015). Basic quality
checks for meteorological variables include missing check, physical limit
check, climate range check, and spike removal (Chae et al., 2014). 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).
The EB systems, installed on different land cover in urban areas, are
applied to determine not only the surface EB among the net
radiation, sensible heat flux, latent heat flux, heat storage or ground heat
flux, and anthropogenic heat flux but also the surface thermal, optical, and
physical properties such as thermal conductivity, heat capacity, albedo,
emissivity, roughness length, and displacement length. 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). Furthermore, they
determine the 30 min averaged carbon dioxide concentration and flux and
the sensible heat flux, latent heat flux, radiative flux, and heat storage.
The EB data are applied to verify the urban surface processes based on the
land use and to improve the urban surface processes in meteorological
models.
The 3-D meteorological observation network provides the real-time vertical
profile of backscatter, wind speed and direction, temperature, and humidity
using two aerosol lidars (manufactured by Raymetrics, model LB210-D200),
two ceilometers (manufactured by Vaisala, model CL51), six wind lidars
(three are manufactured by Leosphere, Windcube-200; three are manufactured
by Laser Systems, Windex-2000), and seven microwave radiometers (manufactured
by RPG, HATPRO-G4; Table 5). The aerosol lidar provides the vertical
distribution of the aerosols and aerosol optical depth using the vertical
profile of the range-corrected backscatter signal and depolarization ratio
from 532 and 1064 nm wavelength lasers. The ceilometer provides three
levels of cloud base height and the vertical distribution of two-way
attenuated backscatter from a 910 nm wavelength laser. The wind lidar
provides the vertical profile of the wind speed and direction using the
Doppler beam swinging scanning technology of a 1532 nm wavelength laser
(Werner, 2005). The microwave radiometer provides the vertical profile of
the temperature and humidity using the observed atmospheric attenuation of
14 wavelength channels. Each instrument except for the microwave radiometer
has a vertical resolution of less than or equal to 50 m and a temporal
resolution of less than 10 min: the aerosol lidar has a 3.75 m vertical
resolution up to 16 km and 2 min temporal resolution; the ceilometer has a
10 m vertical resolution up to 15 km and 1 min temporal resolution; the
microwave radiometer has a dense vertical resolution at low altitude but a
coarse resolution at high altitude (30 m up to 1.2 km, 200 m up to 5 km, 400 m up to 10 km for temperature profile; 200 m up to 2 km, 400 m up to 5 km,
800 m up to 10 km for relative humidity profile) and 1 min temporal
resolution; and the wind lidar has a 50 m vertical resolution and a 10 min
temporal resolution. The accuracy and reproducibility of each surface-based
remote sensing instrument are also certified by the manufacturers before
shipping. The vertical profiles obtained with these instruments are compared
with boundary-layer structures obtained from the sonde before installation
or during the intensive sonde observation campaign period.
Horizontal distribution of the air temperature obtained with the SKP
surface meteorological observation system at (a) 06:00 LST,
(b) 12:00 LST, (c) 18:00 LST, and (d) 24:00 LST
on 18 May 2016, in the Seoul Metropolitan Area. The dotted rectangle denotes
the highly populated region in Fig. 1b.
The mixing-layer height is determined as the height with a minimum gradient
of attenuated backscatter or range-corrected backscatter obtained by a
ceilometer or an aerosol lidar (Eresmaa et al., 2006) or based on the
steepest decrease of the wind speed variance obtained with a wind lidar
(Emeis et al., 2008). These instruments are expected to improve the
knowledge about the effects of high-rise buildings and impervious and
manmade material on urban boundary-layer structures and to eventually
produce observation-based 3-D meteorological fields with high quality and
high resolution by combination with meteorological model results.
The AP observation system includes road weather,
water quality, mosquito, and agrometeorology information systems. Each AP
system is designed to support each applied purpose through the verification
and improvement of each service-capable model. For example, the Road Weather
Information System (RWIS) observed the road surface temperature and status,
water depth, salinity, and conductivity and net radiation, temperature,
humidity, wind speed, and wind direction on an open road in 2013, at the
entrance and exit of a tunnel in 2014, and on a complex road structure with
a bridge and joint cross section in 2015. These data will be used to verify
and improve the road surface temperature, status, and braking distance
prediction model (Yang et al., 2011; Park et al., 2014b). A mobile road
weather vehicle equipped with a global positioning system and sensors in
RWIS is operated to find the road sections vulnerable to road wetness and
ice. The water quality information system observes the turbidity, salinity,
conductivity of stream water, dissolved oxygen, and biological oxygen demand
to support the water quality information service. The mosquito activity
information system observes the number of mosquitoes and meteorological
variables for the purpose of a mosquito advisory and warning service. The
agrometeorological information system observes the soil moisture and
temperature, ground heat flux, evaporation, leaf wetness, and leaf
temperature, as well as basic meteorological variables, including sensible and
latent heat fluxes, to predict the productivity of crops.
Case studies
To demonstrate the usefulness of UMS-Seoul and applicability to the
meteorological information service customized user demands, two case studies
are conducted: (1) to determine the spatial distribution of meteorological
surface variables and evolution of urban boundary-layer structures during
3 consecutive days in spring; (2) to find the road sections vulnerable to
road ice using the surface temperature and status on a roadway obtained with
a mobile road weather vehicle.
Case study I: spring zonal anticyclone event
Meteorological surface variables and atmospheric boundary-layer structures
are investigated for the period from 18 to 20 May 2016. During this period,
a zonal anticyclone in northern Japan blocks an eastward-moving weather
system; the weather system is stagnant. As a result, the SMA indicates fine
weather at the edge of a high-pressure system. A short-lived and small
thermal low-pressure system driven by the thermal difference between the
continent and sea develops in the afternoon and disappears in the
evening on 19 and 20 May 2016.
Figure 6 shows the horizontal distribution of the air temperature obtained
by the SKP surface meteorological observation system at 06:00, 12:00, 18:00, and 24:00 LST on 18 May 2016. The temperature is corrected
according to the sea-level height of the sensor using the monthly mean lapse
rate (4.8 K km-1) of the free atmosphere observed by a rawindsonde at
the Osan Station (Fig. 3; Park et al., 2014a). At 06:00 and 24:00 LST,
the western sites show a relatively high temperature when there is no
surface heating, while the eastern sites show a relatively low temperature.
In contrast, at 12:00 and 18:00 LST, the eastern sites show a
relatively high temperature, while the western sites show a relatively low
temperature. The urban area shows higher temperatures than the surrounding
region throughout this period, which is mainly due to the heat capacity
difference between the urban and rural regions and anthropogenic heat
release. The temperature difference between the two land covers, that is,
the urban heat island effect, becomes stronger during the night (Khan and
Simpson, 2001; Freitas et al., 2007; Ryu and Baik, 2013). These temperature
differences imply the possibility of local circulation such as land–sea
breeze and urban–rural circulation.
Figure 7 shows the time series of surface meteorological variables obtained
every minute by a surface EB system installed at the Jungnang
Station (Fig. 4) for the period from 00:00 LST on 18 May to 24:00 LST on
20 May 2016. During this period, it was so clear that the daily cloud cover
was recorded as 5, 0, and 6 % on 18, 19, and 20 May 2016,
respectively. The air pressure minimum occurred at ∼ 15:00–18:00 LST every day, accompanied by a wind speed, direction, and
vapor pressure change (Fig. 7a, b, d, and e). That is to say, after the low
air pressure passed the station, the wind direction abruptly changed to
northwesterly, the air temperature dropped down by 1.8 ∘C
(3.5 ∘C), and the vapor pressure jumped up by 2.6 hPa (8.0 hPa) on
19 (20) May. The diurnal variation of the wind speed and direction shows
that the station is affected by local circulations: northeasterly winds are
dominant at night, while other directional winds are dominant during the day
(Fig. 7c and d). The net radiation is negative at night and positive during
the day; the variation indicates that there are few clouds during this
period, except for the afternoon of 20 May (Fig. 7f).
Time series of the (a) air temperature, (b) vapor
pressure, (c) wind speed, (d) wind direction,
(e) air pressure, and (f) net radiation observed at the
Jungnang Station from 18 to 20 May 2016.
Time–height cross sections of (a) attenuated backscatter
obtained with a ceilometer and (b) wind speed and direction obtained
with a wind lidar at the Jungnang Station for the period from 00:00 LST on
18 May to 00:00 LST on 21 May 2016.
Figure 8 shows the backscattering coefficient observed by a ceilometer and
vertical profile of the wind observed by a wind lidar at the Jungnang
Station for the period from 00:00 LST on 18 May to 24:00 LST on 20 May
2016. The atmospheric boundary-layer structures defined by the attenuated
backscatter profile perfectly coincide with those defined by the wind: (1) the attenuated backscatter shows that there are two distinct layers before
10:00 LST on 18 May; the lower layer with a maximum height of 400 m contains
thick backscattering aerosols, while the upper layer is less dense up to 1.2 km. The wind profile also shows that the two layers have different origins:
easterly winds are dominant in the lower layer, while westerly winds are
dominant in the upper layer. (2) The convective atmospheric boundary layer
defined by attenuated backscatter evolves during the day on 3
consecutive days. Correspondingly, the winds become irregular in the same
layer. (3) The residual layer with a high attenuated backscatter at
∼ 2 km at night moves slowly downward until the next morning
and combines with the evolving convective boundary layer at noon on 19 May.
Southerly winds are in the upper residual layer, while northerly or
northeasterly winds are in the lower stable boundary layer; the highly
attenuated backscatter zones from 500 to 1500 m at 03:00–09:00 LST and
> 1000 m at ∼ 18:00 LST on 20 May correspond to the
wind convergence zone. The potential temperature and mixing ratio profiles
obtained by the microwave radiometer also support the similar atmospheric
boundary-layer structures (Fig. 9). In conclusion, the meteorological
surface variables and vertical profiles of the meteorological variables
observed with UMS-Seoul will be very helpful to produce a high-quality and
high-resolution meteorological field in the SMA.
Time–height cross sections of the (a) potential temperature
and (b) mixing ratio obtained with a microwave radiometer at the
Jungnang Station for the period from 00:00 LST on 18 May to 00:00 LST on
21 May 2016.
(a) Road surface temperature, (b) road surface
material and structure, and (c) elevation on the roadway obtained
with a mobile road weather vehicle in the Seoul Metropolitan Area for the
period from 10:00 to 14:40 LST on 2 December 2016.
Case study II: mobile road weather vehicle
Figure 10 shows an example of road surface temperature on a roadway
route in the Seoul Metropolitan Area observed by a mobile road weather
vehicle with road material, structure, and elevation for the period from
10:00 to 14:40 LST on 2 December 2016. The vehicle moves at a speed of 13.7 m s-1 on the 162.4 km route. The road material is classified as asphalt
(72.2 %) and concrete (27.8 %), while the road structure is classified
as overground (86.2 %), bridge (10.4 %), underpass (0.6 %), and
tunnel (2.8 %) roads (Table 7). The road elevation is observed using the
global positioning system sensor. The surface temperature is related to the
road elevation, surface material, road structure, sky-view, and horizontal
distribution of the surface land use. The surface temperature over the
concrete road is lower than that over the asphalt road due to the difference
in albedo and diffusivity (Fig. 11). The albedo and thermal properties of
typical road surface materials are obtained in advance based on a special
experiment using a four-component net radiometer and surface temperature. The
sky view is determined by a fish-eye view image; that over a bridge is
higher than that over an overground due to the difference of thermal heat
capacity and heat transfer processes. Based on these data, the roadway
sections vulnerable to icing on highways and major principal roads can be
determined by linearly detrended road surface temperature analysis (Fig. 12). These vulnerabilities can be used to provide alarms or advisories to
drivers. Also, these data will help to improve the road surface temperature
and status prediction system (Park et al., 2014b). Statistical and physical
approaches to determine road sections vulnerable to icing are now available.
Coverage fraction of road material and structure on the
roadway route observed between 10:00 and 14:40 LST on 2 December 2016.
To understand the observational environment of the stations in detail and
maintain the networks and stations more efficiently, metadata of the
UMS-Seoul are standardized based on a comparison with data established by
the World Meteorological Organization, the Korea Meteorological
Administration, and previous studies (WMO, 2008; KMA, 2013; Muller et al.,
2013; Song et al., 2014). The UMS-Seoul metadata include network and station
information composed of general, local-scale, micro-scale, and visual
information (Muller et al., 2013). Figure 13 shows the structure of
UMS-Seoul metadata and an example of general station information metadata
(Song et al., 2014). The metadata contain static information, such as urban
structure, surface land cover, metabolism, roof type (roofing tile, slope
angle, and material), telecommunication between instrument and data server,
moisture/heat sources, and traffic and updated information on environmental
changes, maintenance, replacement, and/or the calibration of sensors. The
urban structure includes information such as spaces between buildings,
building density, street widths, tree species, and tree height information
(Muller et al., 2013). The telecommunication for data transmission includes
telecommunication type, name, password, network owner, and contact person.
The network and station metadata are required to be documented and updated
every year. These metadata are documented based on an on-site survey and
panoramic photos taken at the station, fish-eye view image, satellite image,
map investigation, and literature investigation.
Box plot of the observed road surface temperatures according to road
material and structure on the roadway obtained with a mobile road weather
vehicle in the Seoul Metropolitan Area for the period from 10:00 to
14:40 LST on 2 December 2016. The first character stands for road material
type: A, asphalt; C, concrete. The second character denotes the road
structure type: G, overground; B, bridge; U, underpass; and T, tunnel.
Data acquisition and display system
All data are collected, displayed, and quality-checked in real time (Fig. 14). When the data are sampled on minute zero of every hour, they are
transmitted into a main server within 5 min using machine-to-machine
(M2M) technology and code division multiple access (CDMA) or a long-term
evolution (LTE) telecommunication network; the subsequent automated quality
checks are conducted within 10 min. The data are then ready to be
distributed to the relevant users. UMS-Seoul quality checks are divided into
automated and manual steps. Because each instrument has its own data
characteristics with its own format, temporal resolution, and vertical
resolution, it should have its own quality check algorithm. For example, the
wind lidar has the following quality check procedure: carrier-to-noise
check, data availability check, and vertical gradient of horizontal wind
check in addition to the basic missing check (Park and Choi, 2016).
Detailed surface temperature and material and structure based on a
satellite image at road sections (a) from 126.88 to
126.98∘ E and
(b) from 126.68 to 126.76∘ E in Fig. 10. The first
character stands for the road material type: A, asphalt; C, concrete. The
second character denotes the road structure type: G, overground; B, bridge;
U, underpass; and T, tunnel.
(a) The structure of UMS-Seoul metadata and station
metadata and (b) an example of station general information metadata
for the surface energy balance system at Jungnang Station.
UMS-Seoul data flow from observation to users via data acquisition
and quality checks.
Not only the current values and time series for the automated
quality-checked data are displayed in real time but also the derived
variables, such as albedo, atmospheric boundary-layer height, and net
radiative flux, to monitor the past and current states of atmospheric
variables.
Data obtained with UMS-Seoul are now available to limited researchers and
users in South Korea but will be available to all relevant researchers around the
world soon.
Summary and discussion
The UMS-Seoul is one of the most intensively integrated urban observation
networks in the world for user-specific meteorological information services,
such as flash floods, road status and surface temperature, urban heat
islands, and air quality, based on the retrieval of high-resolution and
high-quality meteorological data in the SMA in South Korea.
Although the existing surface meteorological observation network provides
very high-resolution information, it focuses primarily on surface
meteorology and radar echoes. The UMS-Seoul includes additional 14 surface
energy balance systems, 2 aerosol lidar systems and 2 ceilometers, 6
wind lidars, 7 microwave radiometers, and meteorological observation
systems. The surface energy balance system determines the surface energy
forcing and physical, optical, and thermal properties according to the urban
surface land cover in detail. The ceilometers and aerosol lidars provide the
aerosol vertical profile and the boundary-layer structure, while wind lidars
and microwave radiometers provide the vertical profiles of wind,
temperature, humidity, and liquid water in real time. The AP observation system includes road, water quality, greenhouse
gas, and mosquito data to improve the performance of weather information
customized based on the user demand. The metadata are standardized to
provide detailed network and station information. Also, real-time data
acquisition, quality checks, and a display system are constructed to monitor
the horizontal and vertical distribution of meteorological variables and
maintain observed data. Qualified data are then ready to be distributed to
researchers or policy makers. The UMS-Seoul data help to understand the
cause and effect for each weather event more deeply and to produce a 3-D
meteorological field with sufficiently high resolution and quality.
In addition to fixed meteorological observation systems, intensive upper-air
observation experimental campaigns are conducted repeatedly. They are aimed
at a deeper comprehension of the urban meteorology and produce the data
necessary for a high-resolution meteorological information service. The
station locations are optimized using the analysis of the observing system
simulation experiment (OSSE), a model-based experiment for the purpose of
assessing the potential impact of the would-be observation station for any
instrument and/or sensor (Zhang and Pu, 2010) providing a more stable
meteorological information service. Based on the OSSE results, some stations
or instruments might be added, removed, or moved to other locations.
As a leading urban experimental complex, the UMS-Seoul is expected to
contribute to the improvement of high-resolution meteorological information
technology and the alleviation of damage from disastrous weather phenomena
in high-population-density urban areas around the world.
Most data obtained with UMS-Seoul can be provided upon
request; for further inquiries please contact either Moon-Soo Park
(ngeograph2@gmail.com) or Sung-Hwa Park
(torch1407@gmail.com).
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was funded by the Weather Information Service Engine (WISE) Program
of the Korea Meteorological Administration under grant KMIPA-2012-0001-01.
All data used in this study were produced under the WISE Program.
Edited by: D. Ruffieux
Reviewed by: two anonymous referees
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