Application of Topographic Impact Horizontal Correlation Model to CMA-MESO System

Zhuang Zhaorong; Li Xingliang; Wang Ruichun; Gao Yudong

doi:10.11898/1001-7313.20240403

Vol.35, NO.4, 2024

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Article Navigation > Journal of Applied Meteorological Science > 2024 > 35(4): 414-428

Zhuang Zhaorong, Li Xingliang, Wang Ruichun, et al. Application of topographic impact horizontal correlation model to CMA-MESO system. J Appl Meteor Sci, 2024, 35(4): 414-428. DOI: 10.11898/1001-7313.20240403.

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Zhuang Zhaorong, Li Xingliang, Wang Ruichun, et al. Application of topographic impact horizontal correlation model to CMA-MESO system. J Appl Meteor Sci, 2024, 35(4): 414-428. DOI: 10.11898/1001-7313.20240403.

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Application of Topographic Impact Horizontal Correlation Model to CMA-MESO System

DOI: 10.11898/1001-7313.20240403

Zhuang Zhaorong^{1, 2, 3},
Li Xingliang^{1, 2, 3
,
,},
Wang Ruichun^{1, 2, 3},
Gao Yudong^{4, 5}

1.
CMA Earth System Modeling and Prediction Center, Beijing 100081
2.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
3.
CMA Key Laboratory of Earth System Modeling and Prediction, Beijing 100081
4.
Chongqing Institute of Meteorological Sciences, Chongqing 401147
5.
Key Laboratory of Core Tech on NWP-AI Integrated Forecast for Hazardous Precipitation, Chongqing 401147

Received Date: 2024-03-30
Rev Recd Date: 2024-06-04
Publish Date: 2024-07-31

Abstract

Abstract

The impact of near-surface observations on analysis and forecasting in complex terrain is studied by introducing the role of terrain in the background error horizontal correlation model. Observation information propagates isotropically on the model level in the height-based terrain-following coordinates since the background error horizontal correlation in CMA-MESO 3DVar system is characterized by an isotropic Gaussian correlation model. However, in the near-surface layer with complex topography, the propagation of observation information is blocked by mountain ranges, and thus its background error covariance is inhomogeneous and anisotropic, and furthermore, the propagation of observation information should vary with topography. The background error horizontal correlation coefficients in complex terrain are computed using NMC method by National Meteorological Center of USA. Results show that the blocking of large terrain causes the background error horizontal correlation coefficients to decrease more rapidly across mountain ranges, where the near-surface wind field is more localized than the temperature and humidity fields, with smaller horizontal correlation characteristic length scales, and the wind field information propagates over a closer distance. Based on the actual statistical structure, a Gaussian correlation model that includes effects of terrain height and terrain gradient is constructed, and the newly constructed horizontal correlation model accurately characterizes the decrease after mountain ranges are blocked. In CMA-MESO 3DVar analysis, the impact of terrain on the propagation of observational information is effectively incorporated by including a terrain height error term in the background error level correlation model. Idealized experiments show that the horizontal correlation modeling scheme that considering the terrain height error term allows the observation information to propagate anisotropically with the effect of observation information significantly diminishing across large terrain and the analysis increments more reasonable. Results of a forecast experiment for a heavy precipitation process in northern China indicate that the correlation modeling scheme that varying with the terrain height propagates the anisotropy of the ground observation information and weakens the analytical increment near the ground with large terrain, and thus makes a slightly biased and positive contribution to the precipitation forecast neutrality. Results of a 5-day hourly cycle rapid updating analysis and forecast for precipitation processes in East China show that the horizontal correlation modeling scheme with terrain elevation makes a positive contribution to 10-m wind field at the ground level and the precipitation forecast within 24 hours.
- background error;
- horizontal correlation model;
- terrain;
- 3DVar;
- CMA-MESO

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References(37)

Figures and Tables

图 1 观测点P与其他格点的背景误差水平相关系数分布(阴影为地形高度)

Fig. 1 Horizontal correlation coefficients between point P and other grids(the shaded denotes terrain height)

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图 2 观测点P与其他格点在南北方向和东西方向背景误差水平相关系数(灰色实线为地形高度)

Fig. 2 Zonal and meridional correlation coefficients between point P and other grids (the grey line denotes terrain height)

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图 3 地形下的两点相关关系示意图

Fig. 3 Schematic of correlation between two points under topography

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图 4 不同地形下的水平相关系数分布(阴影为地形)

Fig. 4 Horizontal correlation coefficients in different terrains(the shaded denotes topography)

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图 5 渭河平原观测点P风场观测的纬向风场分析增量(单位：m·s^-1)(阴影为地形高度)

Fig. 5 Analysis increments of zonal wind(unit:m·s^-1) with wind observation at point P(the shaded denotes terrain height)

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图 6 秦岭山顶Q点风场观测的纬向风场分析增量(单位：m·s^-1)(阴影为地形高度)

Fig. 6 Analysis increments of zonal wind(unit:m·s^-1) with wind observation at point Q(the shaded denotes terrain height)

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图 7 P点风场观测和温度观测在第1层110.0°E处的分析增量(灰色实线为地形高度)

Fig. 7 Analysis increments of zonal wind and potential temperature of 110.0°E at the first level with wind and temperature observations at point P(the grey line denotes terrain height)

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图 8 2021年10月3日00:00 850 hPa位势高度(黑色等值线，单位：dagpm)、风场(蓝色风矢量)及500 hPa位势高度(红色等值线，单位：dagpm)(a)和250 hPa位势高度(黑色等值线，单位：dagpm)、风场(蓝色风矢量)及3日00:00—4日00:00降水量(绿色等值线，单位：mm)(b)

Fig. 8 850 hPa height(the black contour,unit:dagpm) and wind(the blue vector) with 500 hPa height(the red contour, unit:dagpm) at 0000 UTC 3 Oct 2021(a), 250 hPa height(the black contour, unit:dagpm) and wind(the blue vector) with precipitation forecast(the green isoline, unit:mm) from 0000 UTC 3 Oct to 0000 UTC 4 Oct in 2021(b)

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图 9 两组试验的第1层温度分析增量及分析增量差异(蓝色等值线，单位：℃)及绝对偏差差异(彩色点)(阴影为地形高度)

Fig. 9 Analysis increments and difference for temperature at the first model level(the blue isoline, unit:℃) with absolute bias(the color dot)(the shaded denotes terrain height)

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图 10 2021年10月3日00:00—4日00:00 24 h累积降水量(填色)

Fig. 10 24 h precipitation(the shaded) from 0000 UTC 3 Oct to 0000 UTC 4 Oct in 2021

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图 11 改进试验与控制试验24 h累积降水预报绝对偏差差异(彩色点)(灰色阴影表示地形高度)

Fig. 11 Difference in absolute bias of 24 h precipitation between improve and control experiments(the color dot) (the shaded denotes terrain height)

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图 12 6 h累积降水预报ETS评分

Fig. 12 ETS of 6 h precipitation forecasts

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