Transcript TECO 2012, Brussels Belgium Assessment of Environmental Impact for AWS Observation Data Using a Computational Fluid Dynamics Model Jae-Jin Kim1, Do-Yong Kim1, Bok-Haeng Heo2,

TECO 2012, Brussels Belgium
Assessment of Environmental Impact for
AWS Observation Data Using a
Computational Fluid Dynamics Model
Jae-Jin Kim1, Do-Yong Kim1, Bok-Haeng Heo2, and Jae-Kwang Won2
1Pukyong
National University
2Korean Meteorological Administration
Background
▪ Korean Meteorological Administration (KMA) enacted a
law on ‘Weather Observation Standardization (WOS)’
in 2006.
▪ Currently, conducting WOS project for 26 observational
organs & 3,469 observational facilities.
▪ For scientific/objective evaluation & management, KMA
conducted a planning project on ‘Weather Observational
Environment Simulator (WOES)’ in 2010.
▪ This study has been performed from 2011, following up
the WOES project.
▪ Evaluation for 14 AWSs/ASOSs in 2011
Kangnam
AWS 400
Kangneong
ASOS 105
Yangcheon
AWS 405
N. Kangneong
ASOS 104
Pyoungteak
AWS 551
Seogu
AWS 846
Deagu
ASOS 143
Gochanggun
ASOS 251
Gochang
ASOS 174
Dongreagu
AWS 940
Jeju
ASOS 184
Juam
ASOS 256
Suncheon
ASOS 174
▪ 15 ASOSs in 2012
- focusing on wind and direct solar radiation
Seoul
ASOS
Icheon
ASOS
Chuncheon
ASOS
Deakwanryoung
ASOS
Deajeon
ASOS
Chupungryoung
ASOS
Jeonju
ASOS
Namwon
ASOS
Kwangju
ASOS
Boseong
AWS
Gosan
ASOS
urban
rural
standard
Uljin
ASOS
Gumi
ASOS
Ulsan
ASOS
Busan
ASOS
Meteorological Model
 urban flow/dispersion
- extremely complex
- highly nonlinear
 resolution
 terrain following, sigma
- building (obstacle) shape
Computational Fluid
Dynamics (CFD) model
 3D, nonhydrostatic,
nonrotating, Boussinesq
 k-e turbulence closure
sheme
▪ Target Areas
1) Kangnam AWS
– located in a highly congested area (urban)
2) Gochang ASOS
– transferred on May 15, 2007
– conducted as a standard observatory
3) Seoul ASOS
– class 1 for surface wind, class 4 for direct radiation
▪ 16 different inflow directions for AWSs & ASOSs
▪ wind data at AWS/ASOS are compared with inflow
N
inflow
AWS
W
E
AWS
S
Results and Discussion
▪ Kangnam AWS
Higher than AWS
AWS
▪ located in a highly congested area
▪ building complexes in the north, east, and west directions
▪ park in the south direction
▪ Inflow vs AWS
the same as inflow
[wind speed]
[wind direction]
the ESE (112.5°) and NW (315°) cases
▪ wind speed ratio to inflow for east-south-east (112.5°)
deceleration
acceleration
- larger decrease in wind speed but no change in wind direction
wind speed ratio
area fraction
~ 20( ~ 1.18 m s-1)
11.51
~ 40( ~ 2.37 m s-1)
10.39
~ 60( ~ 3.55 m s-1)
15.44
~ 80( ~ 4.73 m s-1)
19.56
~ 100( ~ 5.92 m s-1)
18.13
~ 120( ~ 7.10 m s-1)
23.30
~ 140( ~ 8.28 m s-1)
1.67
~ 160( ~ 9.46 m s-1)
0.01
~ 180( ~10.65 m s-1)
0.00
~ 200( ~11.83 m s-1)
0.00
▪ flow acceleration in the upwind region due to ‘channeling effect’
▪ flow deceleration in the downwind region due to ‘building drag’
▪ wind vector for north-west (315°)
- largest decrease in wind speed and large change in wind direction
(m)
▪ Reproducing AWS wind data using a WRF-CFD model
[period: Apr. 03 – Apr. 09, 2008]
wind direction
360
315
AWS
WRF
WRF-CFD
270
225
180
135
90
45
0
0
20
40
60
80
100
120
140
160
16
AWS
WRF
WRF-CFD
Col 1 vs Col 5
wind speed
14
12
10
RMSE
8
▪ WRF = 3.11 m s-1
6
▪ WRF-CFD = 1.35 m s-1
4
2
(43%)
0
0
20
40
60
80
100
120
140
160
time (hr)
▪ wind direction – very strong dependency on WRF
▪ wind speed – more realistic reproducing of AWS data than WRF
▪ Gochang ASOS – a standard observatory (2007. 05.)
before transfer
- conducted to May 14, 2007
- apartment complex (12 stories) in the
north and northeast, small buildings in
the west
- low mountain from south to north in the
east
after transfer
- conducted from May 15, 2007
- no higher building around
- ASOS built in flat terrain and
higher than around
▪ Inflow vs AWS
[wind speed]
before
after
[wind direction]
② 고창군 ASOS(ASOS 251) → 고창 ASOS(ASOS 172)
▪ wind vector for north
(360°) before transfer
- larger decrease in wind speed and no change in wind direction
inflow
(m)
ASOS
▪ wind speed ratio for south (180°) after transfer
- no change in wind direction but ~25 % decrease in wind speed
(%)
wind speed ratio
area fraction
inflow
~ 20( ~ 1.18 m s-1)
0.89
~ 40( ~ 2.37 m s-1)
0.17
~ 60( ~ 3.55 m s-1)
0.28
~ 80( ~ 4.73 m s-1)
2.94
~ 100( ~ 5.92 m s-1)
59.3
~ 120( ~ 7.10 m s-1)
36.43
~ 140( ~ 8.28 m s-1)
0.00
~ 160( ~ 9.46 m s-1)
0.00
~ 180( ~10.65 m s-1)
0.00
~ 200( ~11.83 m s-1)
0.00
▪ ASOS in the deceleration zone induced by far upwind buildings
▪ mostly (96%) equivalent to inflow
▪ wind speed ratio averaged for 16
before
after
▪ well representing background wind after transfer
▪ worthy of a standard observatory (surface wind)
▪ Seoul ASOS
ASOS
▪ ‘class 1 & 4’ for wind & DR from a survey using HemiView & NAOBS data
▪ no higher building than the observation filed except for one building
in the northwest (5 m) and an observatory building (5 m)
▪ open from southeast to northwest
 satisfying the obstacle restriction of the ‘class 1’ standard
▪ Inflow vs AWS
[wind speed]
Inflow speed
[wind direction]
45.0
22.5
360.0
337.5
315.0
292.5
Wind direction
270.0
247.5
225.0
202.5
180.0
157.5
135.0
112.5
90.0
67.5
45.0
22.5
0.0
0.0
22.5 45.0 67.5 90.0 112.5 135.0 157.5 180.0 202.5 225.0 247.5 270.0 292.5 315.0 337.5 360.0 22.5 45.0
Inflow direction
▪ slight change in wind direction but relatively large decrease in wind speed
▪ even in the cases of no building higher in the upwind region (from SE to NW)
▪ wind vector and wind speed ratio for southwest (225°)
▪ ASOS in deceleration zone behind the mountain in the south west
▪ resultantly ~45% decrease despite no higher building in the upwind region
▪ Model for direct radiation & sunshine duration
- using solar angle and buildings for special application to urban areas
KASI
Model
8:00 AM
E
S
S
E
E
S
N
N
W
W
5:00 PM
N
W
▪ validated against data from Korea Astronomy & Space Science Institute
(KASI)
▪ the same solar locations
▪ Application to Seoul ASOS
- no cloud day in winter (Dec. 6, 2008)
- shadow is long enough to investigate the obstacle’s interference
KASI & Model (no topo nor building)
latitude
longitude
height
17:13
07:32
▪ ASOS vs Model
direct radiation
- topography + buildings
- ASOS: hourly averaged (1 – full sunshine, 0 – no sunshine)
model – 1 min
ASOS
model – 1 hour average
1.0
0.0
07
08
09
10
11
12
13
14
15
16
17
18
time
08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00
ASOS
0
1
1
1
1
1
1
1
1
0.9
0
model(a
0.13
1
1
1
1
1
1
1
1
0.95
0
Slight difference results from model (building) resolution
vg)
Sunrise – 07:52, Sunset – 16:58
 Interference of topography and buildings
c.f.) KASI
sunrise – 07:32
sunset – 17:13
▪ Interference by buildings
VS
with buildings
without buildings
08:00
0.13
0.13
09:00
1
1
10:00
1
1
no interference by buildings
11:00
1
1
12:00
1
1
13:00
1
1
14:00
1
1
15:00
1
1
16:00
1
1
interference by buildings
17:00
0.95
1
18:00
0
0.21
17:13
▪ Late sunrise is caused by topography but early sunset is caused by buildings
▪ Shade (less than 30%, satisfied for class 4) by far upwind buildings
not by the observatory building or building in the northwest
▪ Survey study vs Model results
previous survey study
model result
· class 1 for surface wind?
- yes, for just the SE ~ NW cases
· large decrease in wind speed
even for the SE ~ NW cases
 sufficient for class 1?
· class 4 for direct radiation
- based on buildings just around
the observatory
· satisfying class 4 but caused by
far upwind buildings
discrepancy
resulted from considering only the obstacles near observatories
 More detail information is required, including obstacle’s orientation,
far upwind area information, site elevation/location, and so on.
Summary and Conclusion
▪ Evaluating the observational environment for AWSs &
ASOSs focusing on surface wind and direct radiation
▪ Systematic & quantitative analysis
KN – larger decrease in wind speed due to buildings
– WRF-CFD improved the RMSE
GC – well representing background wind as a standard observatory
after transfer
SU – discrepancy between the previous survey and this studies,
implying more systematic and detailed method required for
classification
▪ CFD model can be used for evaluation & classification of
AWS and/or ASOS
This study was supported by the national
meteorological observation-standardization
project of Korean Meteorological
Administration