高级检索

京津冀典型寒潮事件下温度以及风特征和CMA-GFS预报性能评估

Evaluation of Temperature and Wind Characteristics and CMA-GFS Forecasting Performance under Cold Wave Events in the Beijing Tianjin Hebei Region

  • 摘要: 基于CMA-GFS数值模式预报、全球探空和地面加密自动站观测和ERA5再分析等多种资料,筛选2000年至2024年37例寒潮事件并统计分析了京津冀地区寒潮降温和风速分布特征;针对其中11例寒潮事件,开展了CMA-GFS高空高度、温度和地面2 m最高最低气温和10 m风场的提前0~72 h不同起报时间的空间和时间偏差多维检验,细致评估了多要素预报性能,进而讨论了地面温度模式预报偏差的可能成因。研究表明:(1)寒潮事件中平均最大降温幅度大于6℃的区域集中在北京北部、天津北部和南部、河北东南部地区,北京北部山区的降温幅度显著高于平原地区;最大降温幅度大于16℃区域主要在山区,其余大部分站点最大降温幅度集中在8~12℃;2分钟平均风速和阵风极大值分布类似,平原非沿海地区风速整体较小,山区及沿海地区风速整体较大。(2)CMA-GFS能较好预报寒潮事件中对流层中上层大气环流形势,但对温度的预报存在一定偏差,大部分事件温度预报偏差并未随起报时间临近而减小,个别事件表现出最临近起报时次预报偏差最大。对于地面最高气温预报优于最低气温。63.6%寒潮事件最高温度预报偏低,偏低的区域集中在北京、天津、河北中部,00 h起报的ME、RMSE、站点正确率和偏差离散度均优于其他起报时次;大部分寒潮事件最低气温预报都偏高,偏高区域集中在河北北部和西部、北京北部高海拔地区,提前36 h起报效果最优。京津冀平原地区10 m风速预报误差大部分站点在±2m/s之间,少数站点预报误差以偏大为主,提前36 h预报效果最优,00 h误差最大。(3)CMA-GFS模式地面温度预报偏差源于高空温度预报偏差,而部分温度预报准确的个例中可能与相对湿度等造成的非绝热项误差有关。

     

    Abstract: Based on various data such as CMA-GFS numerical model forecasting, global sounding and ground encrypted automatic station observations, and ERA5 reanalysis, 37 cold wave events from 2000 to 2024 were screened and the distribution characteristics of cold wave cooling and wind speed in the Beijing Tianjin Hebei region were statistically analyzed; A multidimensional test was conducted on the spatial and temporal deviations of CMA-GFS high-altitude, temperature, and the highest and lowest ground temperature at 2 meters, as well as the wind field at 10 meters, with different reporting times of 0-72 hours in advance, for 11 cold wave events. The performance of multi factor forecasting was carefully evaluated, and the possible causes of ground temperature model forecasting bias were discussed. Research has shown that: (1) regions with an average maximum cooling amplitude greater than 6 ℃ during cold wave events are concentrated in the northern and southern parts of Beijing, Tianjin, and southeastern Hebei. The cooling amplitude in the northern mountainous areas of Beijing is significantly higher than that in the plain areas; The areas with a maximum cooling amplitude greater than 16 ℃ are mainly located in mountainous areas, while the maximum cooling amplitude of most other stations is concentrated at 8-12 ℃; The distribution of 2-minute average wind speed and maximum gust values is similar, with overall lower wind speeds in non coastal areas of plains and higher wind speeds in mountainous and coastal areas. (2) CMA-GFS can effectively predict the atmospheric circulation situation in the upper troposphere during cold wave events, but there is a certain deviation in temperature prediction. Most event temperature prediction deviations do not decrease with the approaching reporting time, and some events show the maximum prediction deviation in the nearest reporting time. For the highest ground temperature forecast, it is better than the lowest temperature. The highest temperature forecast for 63.6% of the cold wave event is relatively low, with areas with low forecasts concentrated in Beijing, Tianjin, and central Hebei. The ME, RMSE, station accuracy, and deviation dispersion reported from 00 hours are all better than other reporting times; The minimum temperature forecast for most cold wave events is relatively high, with high altitude areas concentrated in the northern and western parts of Hebei and the northern part of Beijing. The best effect is to start reporting 36 hours in advance. The prediction error of wind speed at 10 meters in the Beijing Tianjin Hebei Plain area is mostly between ± 2m/s for most stations, and a few stations have a relatively large prediction error. The best prediction effect is achieved 36 hours in advance, and the maximum error is achieved at 00 hours. (3) The ground temperature prediction bias of the CMA-GFS model originates from the high-altitude temperature prediction bias, and in some cases where the temperature prediction is accurate, it may be related to non adiabatic errors caused by relative humidity and other factors.

     

/

返回文章
返回