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Sensitivity Study of WRF Parameterization Schemes for the Spring Sea Fog in the Yellow Sea
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摘要: 利用2005—2011年10次春季黄海海雾个例开展WRF模式参数化方案敏感性研究。结果表明：边界层方案对WRF模式雾区模拟结果起决定作用，而微物理方案影响较小，它主要影响海雾浓度和高度。边界层与微物理方案的最佳组合为YSU与Lin方案，最差为Mellor-Yamada与WSM5方案；Mellor-Yamada和QNSE方案模拟的近海面湍流过强，导致边界层过高，不利于海雾的发展与维持；而MYNN与YSU方案刻画的湍流强度与边界层高度合适，有利于海雾发展与维持。MYNN方案虽与YSU方案相当，但在大多数海雾个例中，后者明显优于前者，而在有些个例中却刚好相反。因此对于某一具体海雾个例而言，所用边界层方案仍需在它们之中选择最优者。这些信息可为黄海海雾WRF模式边界层与微物理方案的选择与改进提供参考。
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关键词:
- 黄海海雾;
- 微物理方案;
- 边界层方案;
- WRF模式敏感性研究
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.-
Key words:
- the Yellow Sea fog;
- microphysics scheme;
- PBL scheme;
- WRF sensitivity study
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图 3 不同边界层方案试验的平均边界层高度 (阴影) 与海雾频次 (等值线，单位：%) 水平分布
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
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Ri(b) 垂直剖面
(填充色表示Ri, 红色表示0 < Ri≤0.25, 黄色表示0.25 < Ri≤1, 灰色表示Ri＞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 Ri(b) along line AB in Fig.1 for the result from experiment with YSU scheme of example 3
(the shaded denotes Ri, red:0 < Ri≤0.25, yellow: 0.25 < Ri≤1, gray:Ri > 1;contour denotes cloud mixing ratios, unit:g·kg-1; blue solid line denotos planetary boundary layer height, unit:km)
表 1 所选取的黄海10次海雾过程
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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表 2 临界成功指数统计结果
Table 2 Statistical result of critical success index
边界层方案 微物理方案 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) 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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