本文将全球预报系统(GFS,Global forecast system)分析数据和预报数据作为训练集和测试集,利用BP(Back propagation)神经网络后报风场,将BP后报结果松弛逼近到天气研究和预报(WRF,Weather research and forecasting)模式的后报阶段,改善...本文将全球预报系统(GFS,Global forecast system)分析数据和预报数据作为训练集和测试集,利用BP(Back propagation)神经网络后报风场,将BP后报结果松弛逼近到天气研究和预报(WRF,Weather research and forecasting)模式的后报阶段,改善WRF模式对强降水的预报效果。以2018年5月22日青岛地区强降水为例,利用青岛地区7个气象站的观测数据和雷达回波图检验优化方法对强降水的后报效果。结果表明,松弛逼近BP后报风场后,降水强度有了明显改善,相比于不松弛逼近任何数据的WRF模式,松弛逼近BP后报风场的WRF模式24 h降水量误差减少了8.62 mm,但后报降水量仍弱于实际降水量。展开更多
The results from a hybrid approach that combines a mesoscale meteorological model with a diagnostic model to produce high-resolution wind fields in complex coastal topography are evaluated.The diagnostic wind model(Ca...The results from a hybrid approach that combines a mesoscale meteorological model with a diagnostic model to produce high-resolution wind fields in complex coastal topography are evaluated.The diagnostic wind model(California Meteorological Model,CALMET) with 100-m horizontal spacing was driven with outputs from the Weather Research and Forecasting(WRF) model to obtain near-surface winds for the 1-year period from 12 September 2003 to 11 September 2004.Results were compared with wind observations at four sites.Traditional statistical scores,including correlation coefficients,standard deviations(SDs) and mean absolute errors(MAEs),indicate that the wind estimates from the WRF/CALMET modeling system are produced reasonably well.The correlation coefficients are relatively large,ranging from 0.5 to 0.7 for the zonal wind component and from 0.75 to 0.85 for the meridional wind component.MAEs for wind speed range from 1.5 to 2.0 m s-1 at 10 meters above ground level(AGL) and from 2.0 to 2.5 m s-1 at 60 m AGL.MAEs for wind direction range from 30 to 40 degrees at both levels.A spectral decomposition of the time series of wind speed shows positive impacts of CALMET in improving the mesoscale winds.Moreover,combining the CALMET model with WRF significantly improves the spatial variability of the simulated wind fields.It can be concluded that the WRF/CALMET modeling system is capable of providing a detailed near-surface wind field,but the physics in the diagnostic CALMET model needs to be further improved.展开更多
文摘本文将全球预报系统(GFS,Global forecast system)分析数据和预报数据作为训练集和测试集,利用BP(Back propagation)神经网络后报风场,将BP后报结果松弛逼近到天气研究和预报(WRF,Weather research and forecasting)模式的后报阶段,改善WRF模式对强降水的预报效果。以2018年5月22日青岛地区强降水为例,利用青岛地区7个气象站的观测数据和雷达回波图检验优化方法对强降水的后报效果。结果表明,松弛逼近BP后报风场后,降水强度有了明显改善,相比于不松弛逼近任何数据的WRF模式,松弛逼近BP后报风场的WRF模式24 h降水量误差减少了8.62 mm,但后报降水量仍弱于实际降水量。
基金National Public Benefit Research Foundation of China (2008416048GYHY201006035)
文摘The results from a hybrid approach that combines a mesoscale meteorological model with a diagnostic model to produce high-resolution wind fields in complex coastal topography are evaluated.The diagnostic wind model(California Meteorological Model,CALMET) with 100-m horizontal spacing was driven with outputs from the Weather Research and Forecasting(WRF) model to obtain near-surface winds for the 1-year period from 12 September 2003 to 11 September 2004.Results were compared with wind observations at four sites.Traditional statistical scores,including correlation coefficients,standard deviations(SDs) and mean absolute errors(MAEs),indicate that the wind estimates from the WRF/CALMET modeling system are produced reasonably well.The correlation coefficients are relatively large,ranging from 0.5 to 0.7 for the zonal wind component and from 0.75 to 0.85 for the meridional wind component.MAEs for wind speed range from 1.5 to 2.0 m s-1 at 10 meters above ground level(AGL) and from 2.0 to 2.5 m s-1 at 60 m AGL.MAEs for wind direction range from 30 to 40 degrees at both levels.A spectral decomposition of the time series of wind speed shows positive impacts of CALMET in improving the mesoscale winds.Moreover,combining the CALMET model with WRF significantly improves the spatial variability of the simulated wind fields.It can be concluded that the WRF/CALMET modeling system is capable of providing a detailed near-surface wind field,but the physics in the diagnostic CALMET model needs to be further improved.
