Sensitivity Study of WRF Parameterization Schemes for the Spring Sea Fog in the Yellow Sea

Lu Xue; Gao Shanhong; Rao Lijuan; Wang Yongming

Vol.25, NO.3, 2014

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2014 > 25(3): 312-320

Lu Xue, Gao Shanhong, Rao Lijuan, et al. Sensitivity study of WRF parameterization schemes for the spring sea fog in the Yellow Sea. J Appl Meteor Sci, 2014, 25(3): 312-320.

Citation:

Lu Xue, Gao Shanhong, Rao Lijuan, et al. Sensitivity study of WRF parameterization schemes for the spring sea fog in the Yellow Sea. J Appl Meteor Sci, 2014, 25(3): 312-320.

Lu Xue, Gao Shanhong, Rao Lijuan, et al. Sensitivity study of WRF parameterization schemes for the spring sea fog in the Yellow Sea. J Appl Meteor Sci, 2014, 25(3): 312-320.

Citation:

Lu Xue, Gao Shanhong, Rao Lijuan, et al. Sensitivity study of WRF parameterization schemes for the spring sea fog in the Yellow Sea. J Appl Meteor Sci, 2014, 25(3): 312-320.

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Sensitivity Study of WRF Parameterization Schemes for the Spring Sea Fog in the Yellow Sea

Lu Xue^1,2,
Gao Shanhong^{1,2
,
,},
Rao Lijuan^1,2,
Wang Yongming^1,2

1.
Key Laboratory of Physical Oceanography, Ministry of Education, Ocean University of China, Qingdao 266100
2.
Key Laboratory of Ocean-Atmosphere Interaction and Climate in Universities of Shandong, Ocean University of China, Qingdao 266100

Received Date: 2013-07-09
Rev Recd Date: 2014-03-03
Publish Date: 2014-05-31

Abstract

Abstract

Sea fog is a water vapor condensation phenomenon, which happens in marine atmospheric boundary layer (MABL). Low atmospheric visibility caused by sea fog brings huge threat to maritime transportation, fishery and oil-drilling operations. Therefore, it is becoming increasingly important and being paid more and more attention. In recent years, meso-scale atmospheric numerical modeling has become a dominant way for the mechanism study and numerical modeling of sea fog.Previous studies on sea fog indicate that sea fog modeling is very sensitive to initial conditions, especially realistic representation of temperature and humidity profile in MABL. Besides initial conditions, turbulence process and cloud generating process are the other important aspects for sea fog modeling. In a meso-scale atmospheric numerical model, the turbulence process is described by planetary boundary layer (PBL) scheme, and the cloud generating process is determined by microphysics (MP) scheme. Due to the uncertainties of the modeling result and the complexities of turbulence and cloud microphysics processes, many options of PBL and MP schemes are available for choice focusing on different modeling purposes.Based on the Weather Research and Forecasting (WRF) model and cycling three-dimensional variational method, sensitivity study of WRF PBL and MP schemes for the Yellow Sea fog is conducted, focusing on 10 typical widely-spread sea fog cases. The result indicates that simulated sea fog area mostly depends on PBL scheme but little on MP scheme; density and depth of simulated sea fog are affected by MP scheme with cloud droplet number being predicted and how it is prescribed. The best combination of PBL and MP schemes is YSU and Lin, while the worst is Mellor-Yamada and WSM5. The Mellor-Yamada and QNSE scheme brings about much stronger turbulence simulation, resulting in much higher boundary layer, and therefore it's not favorable to the development and maintenance of sea fog, while turbulence intensity and boundary layer height produced by MYNN and YSU schemes benefit sea fog developing. MYNN scheme can match YSU scheme in general, however, the latter performs better in most cases while the former is better in certain ones. In depth investigation is needed to tell whether MYNN or YSU PBL scheme is better for a given sea fog case. These information can provide hints to choose and improve PBL and MP schemes of WRF for the Yellow Sea fog numerical prediction system in the near future.
- the Yellow Sea fog;
- microphysics scheme;
- PBL scheme;
- WRF sensitivity study

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References(39)

Figures and Tables

Fig. 1 Domains of WRF model numerical experiments

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Fig. 2 12-hour root mean square error (solid) and bias (dashed) vertical profile of water vapor mixing ratio (a) and temperature (b) in D2 of Fig.1

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Fig. 3 Horizontal distribution of planetary boundary layer heights (shaded) and fog frequencies (contour, unit:%) forecasted by the experiments with different planetary boundary layer schemes

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图 4 海雾过程个例3的YSU方案模拟结果沿图 1中AB的云水混合比 (a) 与R_i(b) 垂直剖面

(填充色表示R_i, 红色表示0 < R_i≤0.25, 黄色表示0.25 < R_i≤1, 灰色表示R_i＞1;等值线表示云水混合比，单位：g·kg^-1; 蓝色粗实线表示边界层高度，单位：km)

Fig. 4 Vertical sections of cloud mixing ratio (a) and R_i(b) along line AB in Fig.1 for the result from experiment with YSU scheme of example 3

(the shaded denotes R_i, red:0 < R_i≤0.25, yellow: 0.25 < R_i≤1, gray:R_i > 1;contour denotes cloud mixing ratios, unit:g·kg^-1; blue solid line denotos planetary boundary layer height, unit:km)

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Fig. 5 The same as in Fig.4, but it is for QNSE scheme (the shaded represents TKE distribution)

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Table 1 10 sea fog cases of the Yellow Sea for the numerical forecasting

海雾过程	预报起始时间	预报持续时间/h
个例1	2005-03-09T02:00	36
个例2	2006-03-06T08:00	48
个例3	2007-02-05T20:00	48
个例4	2007-05-27T14:00	48
个例5	2008-04-28T02:00	60
个例6	2008-05-25T20:00	42
个例7	2009-04-09T20:00	72
个例8	2009-05-02T20:00	66
个例9	2010-02-22T08:00	60
个例10	2011-03-12T14:00	30

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Table 2 Statistical result of critical success index

边界层方案	微物理方案
边界层方案	Kessler	Lin	WSM5	TP
MY	0.286	0.256	0.230	0.229
QNSE	0.300	0.271	0.242	0.251
YSU	0.363	0.350	0.340	0.342
MY2.5	0.334	0.333	0.317	0.322
MY3	0.328	0.335	0.330	0.329

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Table 3 Observed microphysical characteristics of sea fog around the Yellow Sea (from reference 28-30)

观测海域	雾滴数/(10⁶m^-3)			云水混合比/(g·kg^-1)
观测海域	最大值	最小值	平均值	最大值	最小值	平均值
青岛近海	42.9	0.6	12.5	0.15	0.01	0.04
青岛近海	248.0	5.4	82.4	0.15	0.001	0.07
浙江舟山海域	122.0	7.6	37.1	2.08		0.29
上海近海	518.4	23.6	173.0	1.19	0.01	0.20

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