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Application of Topographic Impact Horizontal Correlation Model to CMA-MESO System
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摘要: 在背景误差水平相关模型中引入地形作用,研究复杂地形下近地面观测资料同化对分析和预报的影响。CMA-MESO三维变分系统中背景误差水平相关关系采用高斯相关模型描述,观测信息在高度追随坐标的模式面上各向同性传播。然而在地形复杂的近地面层,观测信息传播受到山脉阻挡,因而其背景误差协方差非均匀且各向异性,观测信息传播应随地形高度变化。为此,采用美国国家气象中心NMC方法统计复杂地形下背景误差水平相关结构,构建包含地形高度和地形梯度影响的高斯相关模型,并将改进的水平相关模型应用于CMA-MESO三维变分分析。理想试验表明:考虑地形项的水平相关模型方案使观测信息以随地形高度变化的各向异性形式传播,越过大地形观测信息影响明显减弱,分析增量更加合理。我国北方一次强降水过程分析预报试验表明:随地形高度变化的水平相关模型方案使地面观测信息各向异性传播,削弱了大地形处近地面的分析增量,对降水预报略有正贡献。针对华东地区降水过程进行5 d逐小时快速更新分析预报循环试验结果表明,随地形变化的水平相关模型方案对10 m风场和24 h时效内降水预报有正贡献。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 the 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 considering the terrain height error term allows the observation information to propagate in an anisotropic manner that varies with terrain height，and significantly reduces the influence of observation information across large terrain features, thereby achieving more reasonable analysis increments. Results of a forecast experiment for a heavy precipitation process in northern China indicate that the correlation modeling scheme 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.
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Key words:
- background error;
- horizontal correlation model;
- terrain;
- 3DVar;
- CMA-MESO
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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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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
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
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)
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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