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.
Citation: 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.
shu

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

DOI: 10.11898/1001-7313.20240403
  • 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
  • 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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  • Fig. 1  Horizontal correlation coefficients between point P and other grids(the shaded denotes terrain height)

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

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

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

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    Fig. 3  Schematic of correlation between two points under topography

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    Fig. 3  Schematic of correlation between two points under topography

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

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

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

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

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    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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    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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    Fig. 12  ETS of 6 h precipitation forecasts

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    Fig. 12  ETS of 6 h precipitation forecasts

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    • Received : 2024-03-30
    • Accepted : 2024-06-04
    • Published : 2024-07-31
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