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Volume 27 Issue 5

Sep.  2010

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2010 >  27(5): 1119-1130

A Composite Approach of Radar Echo Extrapolation Based on TREC Vectors in Combination with Model-Predicted Winds

  • 1.

    Guangdong Meteorological Observatory, Guangzhou 510080,Guangdong Meteorological Observatory, Guangzhou 510080,Guangdong Meteorological Observatory, Guangzhou 510080,Guangdong Meteorological Observatory, Guangzhou 510080,Guangzhou Institute of Tropical and Marine Meteorology, Guangzhou 510080,Guangdong Meteorological Observatory, Guangzhou 510080,Guangzhou Institute of Tropical and Marine Meteorology, Guangzhou 510080


doi: 10.1007/s00376-009-9093-4

  • Extending the lead time of precipitation nowcasts is vital to improvements in heavy rainfall warning, flood mitigation, and water resource management. Because the TREC vector (tracking radar echo by correlation) represents only the instantaneous trend of precipitation echo motion, the approach using derived echo motion vectors to extrapolate radar reflectivity as a rainfall forecast is not satisfactory if the lead time is beyond 30 minutes. For longer lead times, the effect of ambient winds on echo movement should be considered. In this paper, an extrapolation algorithm that extends forecast lead times up to 3 hours was developed to blend TREC vectors with model-predicted winds. The TREC vectors were derived from radar reflectivity patterns in 3 km height CAPPI (constant altitude plan position indicator) mosaics through a cross-correlation technique. The background steering winds were provided by predictions of the rapid update assimilation model CHAF (cycle of hourly assimilation and forecast). A similarity index was designed to determine the vertical level at which model winds were applied in the extrapolation process, which occurs via a comparison between model winds and radar vectors. Based on a summer rainfall case study, it is found that the new algorithm provides a better forecast.
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    • Manuscript History

    Manuscript received: 10 September 2010
    Manuscript revised: 10 September 2010
    通讯作者: 陈斌, bchen63@163.com
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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    A Composite Approach of Radar Echo Extrapolation Based on TREC Vectors in Combination with Model-Predicted Winds

    • 1. Guangdong Meteorological Observatory, Guangzhou 510080,Guangdong Meteorological Observatory, Guangzhou 510080,Guangdong Meteorological Observatory, Guangzhou 510080,Guangdong Meteorological Observatory, Guangzhou 510080,Guangzhou Institute of Tropical and Marine Meteorology, Guangzhou 510080,Guangdong Meteorological Observatory, Guangzhou 510080,Guangzhou Institute of Tropical and Marine Meteorology, Guangzhou 510080
    • Manuscript received: 2010-09-10
    • Manuscript revised: 2010-09-10

    Abstract: Extending the lead time of precipitation nowcasts is vital to improvements in heavy rainfall warning, flood mitigation, and water resource management. Because the TREC vector (tracking radar echo by correlation) represents only the instantaneous trend of precipitation echo motion, the approach using derived echo motion vectors to extrapolate radar reflectivity as a rainfall forecast is not satisfactory if the lead time is beyond 30 minutes. For longer lead times, the effect of ambient winds on echo movement should be considered. In this paper, an extrapolation algorithm that extends forecast lead times up to 3 hours was developed to blend TREC vectors with model-predicted winds. The TREC vectors were derived from radar reflectivity patterns in 3 km height CAPPI (constant altitude plan position indicator) mosaics through a cross-correlation technique. The background steering winds were provided by predictions of the rapid update assimilation model CHAF (cycle of hourly assimilation and forecast). A similarity index was designed to determine the vertical level at which model winds were applied in the extrapolation process, which occurs via a comparison between model winds and radar vectors. Based on a summer rainfall case study, it is found that the new algorithm provides a better forecast.

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