evaluation of the urban weather forecast over seoul metropolitan … · 2019. 7. 11. · 배...
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배 경 ISTP-2019
(2019. 5. 20-24, Toulouse)
Evaluation of the urban weather forecast over Seoul metropolitan area from KMA LDAPS
Jae-Young Byon ∙ Seonok Hong ∙ Jong-Chul Ha
Applied Meteorological Research Division,
Korea Meteorological Administration
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Introduction
Characteristics of urban
- High population
- Building, paved road
- Local meteorological phenomena
(UHI, urban flood, rainfall, pollution etc)
Vulnerability
- heat stress, high impact weather
Urban forecast, urban planner and decision maker
Importance of urban weather forecast,
Diagnose of operational weather forecast,
Suggestion of improvement method
https://www.eea.europa.eu/data-and-maps/figures/urban-and-rural-population-in
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Model
• Numerical model
- UM 10.1, Data assimilation : 3D VAR
(Surface observation, Upper air observation,
Radar, Satellite observation)
- Horizontal resolution : 1.5 km
- Runs four times at 00, 06, 12, 18UTC :36-hour forecast
00Z
36hour
06Z
12Z
18Z
36hour
36hour
36hour
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Simulation method
• Experiment and Analysis Period
1) Experiment 1
KMA LDAPS (operational model)
LDAPS-Best: 2016. 6 – 2017. 5
2) Experiment 2
Improvement experiment for KMA operational model
LDAPS-MORUSES (UCM) : 2017. 6 – 2017. 8
3) Experiment 3
LDAPS with new LU data : 2018. 8
Experiment 1
Experiment 2
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Validation data
• Validation data
1) KMA AWS data : 25 stations in Seoul (red dot) : Experiment 1 and 2
2) Wind lidar with UMS-Seoul : Experiment 1 and 3
Atmos. Meas. Tech. (2017, Park. et al)
29 stations/ 17 stations(Seoul)
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Validation data
Wind lidar(6 stations)
Variables : Vertical profile of wind speed and direction
Temporal and spatial resolution : 10 min, 50m, Altitude 5∼6km
Location & Time : Bucheon, Kwanghwamun, Jungnang(2015), Songdo, Ilsan, Guro(2017)
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Seoul Metropolitan Area
Atmos. Meas. Tech. (2017, Park. et al)
Topography Landuse
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Validation (Annual mean temperature) : Experiment 1
Observation Forecast
• Comparison of the annual averaged temperature
• Seoul temperature is higher in the industrial area than outskirts
• Forecast temperature is lower than observation
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Validation (Annual mean temperature)
Bias RMSE
• Statistical validation for surface temperature forecast against AWS observation
• Bias : Most of station shows negative bias, model forecast tends to underestimate
• RMSE : Increase of RMSE in the mountain and river area
Cold Warm
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Validation (Temperature)
Bias RMSE
• Monthly variation of the bias and RMSE
• Bias: Increase of negative bias in winter and decrease of negative bias in summer
• RMSE: Smaller in the summer than winter
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Validation (Annual mean wind speed)
Observation Forecast
• Comparison of the annual averaged surface wind speed
• Wind speed is higher in the west area than east region
• Forecast wind speed overestimates than observation
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Validation (Annual mean wind speed)
Bias RMSE
• Statistical validation for surface wind speed forecast against AWS observation
• Bias : Most of station shows positive bias, model forecast tends to overestimate
• Increase of Bias and RMSE in the mountain and river area
Under Over
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Validation (Wind speed)
Bias RMSE
• Monthly variation of the bias and RMSE
• Bias: Increase of positive bias in winter and decrease of bias in summer
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Improvement of KMA-LDAPS
Environmental Geographic Information Service
(EGIS)
-2007 -5 m resolution (South Korea) -SPOT-5 -Ministry of Environment, Korea
International Geosphere and Biosphere Programme
(IGBP) -1998 -1km resolution
1. Update land-use data
Seoul Seoul
River River
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Improvement of KMA-LDAPS
2. Urban Canopy Model
(Adapted from Ching et al, 2009. BAMS)
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Validation (Summer Temperature) : Experiment 2
Bias RMSE
LDAPS LDAPS-MORUSES
Cold Warm
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Validation (Summer Temperature) : Experiment 2
AWS(Observation)
LDAPS
LDAPS-MORUSES
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Validation (Summer Wind speed) : Experiment 2
Bias RMSE
LDAPS LDAPS-MORUSES
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Validation (Wind profile) : Experiment 1
Gwanghwamun Buchon Jungnang
Bias RMSE
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Validation (Wind profile) : Experiment 1
Gwanghwamun Jungnang
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Validation (Wind profile) : Experiment 3
EGIS
IGBP
LIDAR
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Summary
Prediction of the urban forecast using Best scheme (LDAPS) in Seoul
Temperature : Underestimation, Warm season RMSE decreases than winter
Wind : Overestimation tendency, error increases in mountain and river region
Improvement of urban weather forecast using new LU data and urban scheme
Temperature : Improvement of cold bias in the morning time
Wind speed : Surface wind speed is not improved, but wind speed within PBL
shows better results in the new LU data simulation
Future plan
Validation of temperature profile using microwave radiometer
Improvement of urban building information and AH
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Thank you !! ([email protected])