1. Fog Simulation with the (WRF+PAFOG) Coupled Model Utilizing Meteorological Tower Data
Seong Soo Yum1, Wonheung Kim1, Jae-In Song1 and Chang Ki Kim2
1Department of Atmospheric Sciences, Yonsei University, Korea
2New and Renewable Energy Resource Center, Korea Institute of Energy Research, Korea

2. Introduction
Prediction of fog using numerical models is difficult because fog formation is usually determined by local meteorological conditions that are hard to be measured and modeled with sufficient resolution.
In this study we uses the coupled system of the 1D PAFOG model and the 3D WRF model to simulate fogs formed at a southern coastal region of Korea, where the National Center for Intensive Observation of Severe Weather (NCIO) is located.
Here we demonstrate how the coupled model simulation of fog can be improved with the utilization of meteorological tower data.

3. The Models
3D MODEL
WRF V3.7
Horizontal Resolution
18, 6, 2 km
Vertical Layers
65 (top~20 km)
Initial field
NCEP FNL data
Radiation Process
RRTMg scheme (both SW & LW)
PBL Process
MYNN scheme
Surface physics
Unified Noah land-surface model
Microphysics
Morrison
1D MODEL (PAFOG, PArameterized FOG, Bott and Trautmann, 2002)
Vertical Layers
Dynamics
turbulence
Radiation
Microphysics
Vegetation
PAFOG
L400 (up to the model top, 2.5 km)
Bott et al.
(1990)
2.5 level model of Mellor and Yamada(1974)
Δ-two straem approximation of Zdunkowski et al.(1982)
Nickerson et al.(1986) and Chaumerloac et al. (1987)
Siebert et al.(1992)

4. Model Domain Coupling Method Selected Cases (2014.05.~2015.06.)
WRF_D1
WRF_D2
WRF_D3
Model Domain
Coupling Method
Number of Fog Cases 22
Selected Cases ( ~ )

5. Study Region Location: Boseong, Korea (34.76 o N, 127.16 o E).
Instrumentation : 300 m Met. Tower (T, Td every 30 m)
300 met. tower
Ka-band cloud radar
Microwave Radiometer
Ceilometer
Parsivel2

6. Average vertical profiles of Tz-T0 and RH (Met Tower)
Non-fog days
Fog days

7. Nudging Method Time Weighting Height Weighting Tower (t0min) (t30min)

8. Skill Scores HR CSI 0.05 ± 0.12 0.11 ± 0.23 0.27 ± 0.30 0.14 ± 0.18
~ (22 cases)
HR(Hit Rate) = 𝒂 𝒂+𝒄
counts
Observation
Yes No
Forecast a b c d
CSI(Critical Success Index) = 𝒂 𝒂+𝒃+𝒄 HR
CSI
WRF
0.05 ± 0.12
0.11 ± 0.23
WRF+PAFOG
0.27 ± 0.30
0.14 ± 0.18
(Initial _TOWER)
0.60 ± 0.48
0.23 ± 0.18
(nudging_TOWER)
0.81 ± 0.21
0.45 ± 0.22

9. Skill Scores HR CSI 0.05 ± 0.12 0.11 ± 0.23 0.27 ± 0.30 0.14 ± 0.18
~ (22 cases)
HR(Hit Rate) = 𝒂 𝒂+𝒄
count
Observations
Yes No
Forecasts a b c d
CSI(Critical Success Index) = 𝒂 𝒂+𝒃+𝒄 HR
CSI
WRF+PAFOG(nudging_TOWER) for radiation fog cases (13)
0.93 ± 0.24
0.65 ± 0.16
WRF+PAFOG(nudging_TOWER) for cases with prior precip. (9)
0.68 ± 0.24
0.26 ± 0.13 HR
CSI
WRF
0.05 ± 0.12
0.11 ± 0.23
WRF+PAFOG
0.27 ± 0.30
0.14 ± 0.18
(Initial _TOWER)
0.60 ± 0.48
0.23 ± 0.18
(nudging_TOWER)
0.81 ± 0.21
0.45 ± 0.22

10. Time Variation of Met. Variables
A case with prior precipitation ( )
Soil moisture variation
due to precipitation considered
Precip.

11. Skill Scores HR CSI 0.05 ± 0.12 0.11 ± 0.23 0.27 ± 0.30 0.14 ± 0.18
~ (22 cases)
HR(Hit Rate) = 𝒂 𝒂+𝒄
count
Observations
Yes No
Forecasts a b c d
CSI(Critical Success Index) = 𝒂 𝒂+𝒃+𝒄 HR
CSI
WRF+PAFOG(nudging_TOWER) for radiation fog cases (13)
0.93 ± 0.24
0.65 ± 0.16
WRF+PAFOG(nudging_TOWER) for cases with prior precip. (9)
0.68 ± 0.24
0.26 ± 0.13
WRF+PAFOG(nudging_TOWER) for cases with prior precip. (9) (soil moisture variation considered)
0.82 ± 0.07
0.50 ± 0.08 HR
CSI
WRF
0.05 ± 0.12
0.11 ± 0.23
WRF+PAFOG
0.27 ± 0.30
0.14 ± 0.18
WRF+PAFOG(Initial _TOWER)
0.60 ± 0.48
0.23 ± 0.18
WRF+PAFOG(nudging_TOWER)
0.81 ± 0.21
0.45 ± 0.22

12. Skill Scores HR CSI 0.05 ± 0.12 0.11 ± 0.23 0.27 ± 0.30 0.14 ± 0.18
~ (22 cases)
HR(Hit Rate) = 𝒂 𝒂+𝒄
count
Observations
Yes No
Forecasts a b c d
CSI(Critical Success Index) = 𝒂 𝒂+𝒃+𝒄 HR
CSI
WRF+PAFOG(nudging_TOWER) for radiation fog cases (13)
0.93 ± 0.24
0.65 ± 0.16
WRF+PAFOG(nudging_TOWER) for cases with prior precip. (9)
0.68 ± 0.24
0.26 ± 0.13
WRF+PAFOG(nudging_TOWER) for cases with prior precip. (9) (soil moisture variation considered)
0.82 ± 0.07
0.50 ± 0.08 HR
CSI
WRF
0.05 ± 0.12
0.11 ± 0.23
WRF+PAFOG
0.27 ± 0.30
0.14 ± 0.18
(Initial _TOWER)
0.60 ± 0.48
0.23 ± 0.18
(nudging_TOWER)
0.81 ± 0.21
0.45 ± 0.22
WRF+PAFOG
(nudging_TOWER)
0.88 ± 0.18
0.59 ± 0.13

13. Skill Scores
~ (22 cases)
HR(Hit Rate) = 𝒂 𝒂+𝒄
count
Observations
Yes No
Forecasts a b c d
CSI(Critical Success Index) = 𝒂 𝒂+𝒃+𝒄 HR
CSI
WRF+PAFOG(nudging_TOWER) for radiation fog case
0.93 ± 0.24
0.65 ± 0.16
WRF+PAFOG(nudging_TOWER) for precipitation effect case
(not considered)
0.68 ± 0.24
0.26 ± 0.13
WRF+PAFOG(nudging_TOWER) for precipitation effect case (considered)
0.82 ± 0.07
0.50 ± 0.08 HR
CSI
WRF
0.05 ± 0.12
0.11 ± 0.23
WRF+PAFOG
0.27 ± 0.30
0.14 ± 0.18
(Initial _TOWER)
0.60 ± 0.48
0.23 ± 0.18
(nudging_TOWER)
0.81 ± 0.21
0.45 ± 0.22
But initial soil moisture values were fixed at minimum value for all cases!
WRF+PAFOG
(nudging_TOWER)
0.88 ± 0.18
0.59 ± 0.13

14. Time Variation of Met. Variables
Observed Soil Moisture: m3/m3
0.266 m3/m3
0.394 m3/m3
Observed Soil Moisture: m3/m3
0.266 m3/m3
0.394 m3/m3

15. Skill Scores HR CSI 0.88 ± 0.18 0.59 ± 0.13 HR CSI 0.89 ± 0.07
~ (22 cases)
HR(Hit Rate) = 𝒂 𝒂+𝒄
count
Observations
Yes No
Forecasts a b c d
CSI(Critical Success Index) = 𝒂 𝒂+𝒃+𝒄 HR
CSI
WRF+PAFOG
(nudging_TOWER)
Precipitation effect considered
0.88 ± 0.18
0.59 ± 0.13 HR
CSI
WRF+PAFOG
(nudging_TOWER) Initial soil moisture varied
0.89 ± 0.07
0.64 ± 0.13

16. (Soil moisture variation)
Skill Scores
~ (22 cases)
count
Observations
Yes No
Forecasts a b c d
WRF+PAFOG HR
CSI
0.27 ± 0.30
0.14 ± 0.18
Nudging effect VS Soil moisture effect
WRF+PAFOG
(nudging_TOWER) HR
CSI
0.81 ± 0.21
0.45 ± 0.22
WRF+PAFOG
(Soil moisture variation) HR
CSI
0.53 ± 0.35
0.27 ± 0.19

17. Comparison of WRF and UM Coupled Simulations
~ (22 sea fog cases over the sea near IIA)
UM_RE+PAFOG
UM_LO+PAFOG
WRF_CO+PAFOG
WRF_NE+PAFOG
MBE
RMSE T
-0.32 ± 0.91
1.70 ± 1.12
-0.75 ± 0.77
1.85 ± 1.16
3.20 ± 4.73
1.98 ± 1.14
3.38 ± 4.75
1.93 ± 1.38 Td
0.51 ± 0.61
1.59 ± 1.12
0.04 ± 0.74
1.44 ± 0.69
1.73 ± 2.93
1.96 ± 1.12
2.28 ± 3.06
2.03 ± 1.35
CSI rw
0.37 ± 0.34
0.22 ± 0.31
0.23 ± 0.32
0.17 ± 0.27

18. Summary
WRF+PAFOG coupled system was set up to use for fog simulation over the southern coastal region of Korea.
With the utilization of meteorological tower data, performance of the coupled model improved significantly.
For these coastal fog cases, soil moisture information was found to be critically important for better simulation.
Next step is to elucidate fog formation mechanism based on the model simulation results.
With proper adjustment, the coupled model system can be used as a fog prediction model during the PyeongChang Olympics.