风云三号微波观测资料的海雾同化模拟

吴晓京; 朱江; 王曦; 杨冰韵

doi:10.3878/j.issn.1006-9895.1610.16105

风云三号微波观测资料的海雾同化模拟

doi: 10.3878/j.issn.1006-9895.1610.16105

吴晓京^1,,
朱江^2, ,,
王曦¹,
杨冰韵¹

1.
国家卫星气象中心中国气象局中国遥感卫星辐射测量和定标重点开放实验室, 北京 100081
2.
中国科学院大气物理研究所国际气候与环境科学中心, 北京 100029

基金项目:

国家自然科学基金项目 41075057

国家自然科学基金项目 41675110

详细信息

作者简介:
吴晓京, 男, 1968年出生, 理学博士、正研级高工, 主要从事气象卫星资料天气应用研究.E-mail:wuxj8@cma.gov.cn

通讯作者:
朱江, E-mail:jzhu@mail.iap.ac.cn

中图分类号: P413
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- 被引次数: 0
出版历程
- 收稿日期: 2016-01-07
- 网络出版日期: 2016-11-10
- 刊出日期: 2017-05-15

Sea Fog Simulation with Assimilation of FY-3A Microwave Data

1.
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081
2.
International Center for Climate and Environment Science, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

Funds:

National Natural Science Foundation of China 41075057

National Natural Science Foundation of China 41675110

摘要

摘要: 数值模式边界层物理过程和初值场条件的欠缺是海雾模拟准确率偏低的主要原因。本文为改进模式初始场，开展针对海雾模拟的卫星观测资料同化试验，将质量控制和偏差订正后的FY-3A卫星微波湿度计（MWHS）和微波温度计（MWTS）的优选通道数据，经3DVar（Three-dimensional variational data assimilation）进入WRF模式以试验其对黄、渤海海雾模拟的影响。通过分析静止气象卫星检测到的海雾区模拟大气温、湿场同化分析增量，发现代表环境场条件的海雾类型及模式对其模拟能力的差异，显著影响了同化效果，表现为同化对模式模拟能力较强的平流冷型海雾改进明显，对模拟效果不甚理想的非典型混合过程中的暖型海雾阶段则基本没有改进效果。为寻找原因，对包括海雾区低层大气模拟场逆温结构在内的温湿度场与邻近探空观测进行了对比，分析了随时间演变的海雾格点温、湿场同化分析增量，发现冷型海雾区格点同化分析增量能弥补观测—模拟差异，使气温调减，相对湿度调增，同时水汽和液态水也出现负相关的变化，边界层相关热力动力场同化分析增量在垂直方向也有配合迹象，相比而言，主体是暖型海雾的非典型过程则未见此类现象和其他的有益调整迹象。
- 卫星资料 /
- 数据同化 /
- 海雾模拟 /
- 微波湿度计（MWHS） /
- 微波温度计（MWTS） /
- 边界层
Abstract: Inappropriate description of physical processes within the boundary layer and errors in the initial condition are two primary reasons for the low accuracy of sea fog simulation. In order to obtain more satellite data assimilation and simulation experience for improving the initial condition, the present paper investigates the impact of assimilation of FY-3A MicroWave Humidity Sounding (MWHS) and MicroWave Temperature Sounding (MWTS) data derived from optimal channels. The WRF-3DVar (Weather Research and Forecasting-Three-dimensional variational data assimilation) is applied to assimilate these data that have been quality controlled and bias corrected in simulation and prediction experiments of sea fog over the Yellow Sea and Bohai. Analysis of the increments of temperature and relative humidity from the assimilation system over the sea fog region detected by Geo-satellite indicates that differences in the model capability for sea fog simulation and various types of sea fog that are related to the environmental condition have significant influences on the assimilation effects, i.e. the model performance is greatly improved by assimilation for the simulation of cold-type advection sea fog, which the model already has a strong simulation ability. However, for warm-type sea fog that involves non-typical mixing processes, data assimilation results in little changes in the simulation. In order to determine the reasons, direct comparisons between the simulated temperature and relative humidity at a sea fog covered grid and standard rawinsonde observations nearby, and analysis of the evolution of the temperature and relative humidity and their increments from assimilation at all sea fog covered grids, are carried out. The results show that the increments from assimilation at the cold-type sea fog covered grids can make up the deviations between observations and simulation by reducing temperature and increasing relative humidity; meanwhile, the atmospheric water vapor and liquid water contents also appear to be adjusted by assimilation. Possible increments and adjustment in the boundary layer by assimilation are also found in the vertical direction. However, similar phenomena and other beneficial signs of adjustment are not found during the warm-type sea fog period.
- Satellite data /
- Data assimilation /
- Sea fog simulation /
- MicroWave Humidity Sounding (MWHS) /
- MicroWave Temperature Sounding (MWTS) /
- Boundary layer

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图 1 2011年2月20日12:00到22日08:00每间隔4小时的同化MWHS资料后RH_2m分析增量与卫星检测的海雾区（黄色区域）叠加图。图中“+”、“·”和“Δ”符号分别表示RH_2m增加、减少和不变，增减变化幅度如色标所示；红圈为同化分析增量显著的海雾区

Figure 1. Evolution of RH_2m (relative humidity at 2-m height) increments by the MWHS radiance data assimilation overlapped with the sea fog area (yellow regions) detected by satellite at 4-hour interval from 1200 UTC 20 to 1200 UTC 22 February 2011. "+", "·", and "Δ" denote increases, decreases, and unchanged; red circle represents obvious increments of the sea fog area

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图 2 同图 1，但为同化MWTS资料后的结果，时间为2011年3月12日06:00至13日10:00。红圈和蓝圈分别为黄海北部、江苏中部沿海同化分析增量显著的海雾区

Figure 2. As in Fig. 1, but for the RH_2m increments by MWTS data assimilation for the period from 0600 UTC 12 to 1000 UTC 13 March 2011. Red circles and blue circles represent obvious increments of the sea fog area in northern Yellow Sea and along the coast of central Jiangsu Province, respectively

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图 3 2011年3月12日22:00到13日06:00同化前模拟的RH_2m和Wind_10m。填色区表示相对湿度RH_2m。白圈为黄海北部RH_2m相对低值区；黄圈为江苏中部沿海RH_2m相对低值区

Figure 3. Simulations of RH_2m and Wind_10m (wind at 10-m height) without assimilation for the period of 2200 UTC 12 to 0600 UTC 13 March 2011. Shaded areas denotes RH_2m. White and yellow circles represent low RH_2m in northern Yellow Sea and low RH_2m along the coast of central Jiangsu Province, respectively

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图 4 2011年3月12日05:00至13日12:00模式输出海雾格点的（a）RH_2m、T_2m和（b）1000 hPa Q_v、Q_c同化累计分析增量演变

Figure 4. Temporal evolutions of cumulative incremental values at sea fog grids for the period from 0500 UTC 12 to 1200 UTC 13 March 2011: (a) RH_2m and temperature at 2-m height (T_2m); (b) 1000-hPa water vapor content (Q_v) and cloud liquid water content (Q_c)

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图 5 山东省荣成站2月21日00:00观测点（37.1°N，122.29°E）的RH、T和邻近海雾格点（36.667°N，122.983°E）的同化前RH、T模拟值以及同化增量在600 hPa以下的垂直分布：（a）观测点RH、邻近海雾格点同化前RH（a图左）、同化增量（∆RH，a图右）；（b）观测点T、同化前T模拟值（b图左）、同化增量（∆T，b图右）。（c）、（d）同（a）、（b），但为3月13日00:00，邻近海雾格点（37.167°N，123.483°E）的值

Figure 5. Vertical distributions below 600 hPa of relative humidity (RH), temperature (T) at observation point (37.1°N, 122.29°E) and RH, T, RH increments, T increments at sea fog grid point (36.667°N, 122.983°E) at 0000 UTC 21 February: RH at observation point (left of Fig. a), RH without assimilation at sea fog grid point (left of Fig. a), RH increments (∆RH) with assimilation (right of Fig. a); T at observation point (left of Fig. b), T without assimilation at sea fog grid (left of Fig. b), T increments (∆T) with assimilation (right of Fig. b). (c, d) As in (a, b), but for 0000 UTC 13 March, sea fog grid point is (37.167°N, 123.483°E)

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图 6 模拟的（同化前）全部海雾格点上的2月（黑线）、3月（红线）过程平均的（a）温度和（b）垂直速度（w）随高度的变化曲线。2月过程平均值计算时段：20日12:00至22日12:00；3月过程平均值计算时段：12日05:00至13日12:00

Figure 6. Vertical distributions of simulated (without assimilation) (a) temperature and (b) vertical velocity (w) averaged over all the sea fog grid points during the periods of fog processes of February (black lines) and March (red lines). Statistical average period for February is from 1200 UTC 20 to 1200 UTC 22 February and that for March is from 0500 UTC 12 to 1200 UTC 13 March

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图 7 （a）2月过程和（b）3月过程海雾格点模拟（同化前）和同化后RH_2m值随时间的演变。柱状代表同化前的RH_2m，红色线代表同化MWHS资料后RH_2m的分析增量，蓝色线代表同化MWTS资料后RH_2m的分析增量

Figure 7. Temporal evolutions of RH_2m averaged over all the sea fog grid points from simulations without and with assimilation for the (a) February process and (b) March process. Red lines represent analysis increment of RH_2m with assimilation of MWHS data, blue lines represent analysis increment of RH_2m with assimilation of MWTS data

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图 8 同化MWHS资料和MWTS资料后2月过程和3月过程海雾的（a）T、（b）RH、（c）Q_c、（d）Q_v、（e）w的900 hPa以下垂直增量廓线。统计时间段：2月20日12:00至22日12:00；3月12日05:00至13日12:00

Figure 8. Vertical distributions of increments of (a) temperature (T), (b) relative humidity (RH), (c) Q_c, (d) Q_v, and (e) vertical wind speed (w) below 900 hPa after assimilation of MWHS data and MWTS data for the February process and March process. Statistical period for February is from 1200 UTC 20 to 1200 UTC 22 February and that for March is from 0500 UTC 12 to 1200 UTC 13 March

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表 1 MWHS仪器通道特征

Table 1. Channel characteristics of MWHS (MicroWave Humidity Sounding)

通道号	中心频率/ GHz	带宽/ MHz	星下点分辨率/km	噪声等效温差/K	权重峰值高度
1	150（V）	1000×2	15	0.90	地表
2	150（H）	1000×2	15	0.90	地表
3	183±1(V)	500×2	15	1.10	400 hPa
4	183±3(V)	1000×2	15	0.90	600 hPa
5	183±7(V)	2000×2	15	0.90	800 hPa
注：H表示水平极化，V表示垂直极化。

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表 2 MWTS仪器通道特征

Table 2. Channel characteristics of MWTS (MicroWave Temperature Sounding)

通道号	中心频率/GHz	带宽/ MHz	星下点分辨率/km	噪声等效温差/K	权重峰值高度
1	50.30	180	62	0.50	地表
2	53.596±0.115	2×170	62	0.40	700 hPa
3	54.94	400	62	0.40	300 hPa
4	57.29	330	62	0.40	70 hPa

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表 3 同化模拟试验使用的WRF模式设置

Table 3. WRF model configurations of assimilation and forecast experiments

模式选项	设置方案
区域	中心点纬度30°N，经度125°E；东西方向141个格点，南北方向121个格点
分辨率	水平分辨率45 km
时间步长	180 s
边界层方案	YSU方案
积云方案	Kain-Fritsch方案
微物理方案	WSM 3简单冰方案
辐射方案	长波：RRTM方案；短波：Dudhia方案
陆面过程	NOAH陆面过程方案
近地面层	Monin-Obukhov方案

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表 4 2011年2月20~22日地面观测风速、风向及气温统计

Table 4. Surface observations of wind speed, direction, and temperature from 20 to 22 February 2011

时间	山东省成山头站（站号54776）			江苏省吕泗站（站号58265）
时间	风速/m s⁻¹	风向	气温/℃	风速/m s⁻¹	风向	气温/℃
2月20日12:00	4	北	−1	2	北	4
2月20日18:00	4	北	−4	2	北	3
2月21日00:00	3	北	−3	2	西	1
2月21日06:00	3	北	1	2	北	5
2月21日12:00	0	静	−3	1	西	4
2月21日18:00	3	南	−3	1	北	3
2月21日00:00	4	南	−2	2	东	2
2月22日06:00	5	南	2	5	东	10
2月22日12:00	5	南	0	3	东	7

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表 5 2011年3月11~13日地面观测风速、风向及气温统计

Table 5. Surface observations of wind speed, direction, and temperature from 11 to 13 March 2011

时间	山东省成山头站（站号54776）			江苏省吕泗站（站号58265）
时间	风速/m s⁻¹	风向	气温/℃	风速/m s⁻¹	风向	气温/℃
3月11日12:00	5	南	3	3	南	9
3月11日18:00	5	南	6	4	南	8
3月12日00:00	5	南	4	5	南	10
3月12日06:00	10	南	4	3	南	18
3月12日12:00	9	南	3	5	南	10
3月12日18:00	8	南	6	3	南	9
3月13日00:00	8	南	6	4	南	9
3月13日06:00	6	南	3	6	南	18
3月13日12:00	6	南	2	4	东	11

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