This study examines the effectiveness of an ensemble Kalman filter based on the weather research and forecasting model to assimilate Doppler-radar radial-velocity observations for convection-permitting prediction of c...This study examines the effectiveness of an ensemble Kalman filter based on the weather research and forecasting model to assimilate Doppler-radar radial-velocity observations for convection-permitting prediction of convection evolution in a high-impact heavy-rainfall event over coastal areas of South China during the pre-summer rainy season. An ensemble of 40 deterministic forecast experiments(40 DADF) with data assimilation(DA) is conducted, in which the DA starts at the same time but lasts for different time spans(up to 2 h) and with different time intervals of 6, 12, 24, and 30 min. The reference experiment is conducted without DA(NODA).To show more clearly the impact of radar DA on mesoscale convective system(MCS)forecasts, two sets of 60-member ensemble experiments(NODA EF and exp37 EF) are performed using the same 60-member perturbed-ensemble initial fields but with the radar DA being conducted every 6 min in the exp37 EF experiments from 0200 to0400 BST. It is found that the DA experiments generally improve the convection prediction. The 40 DADF experiments can forecast a heavy-rain-producing MCS over land and an MCS over the ocean with high probability, despite slight displacement errors. The exp37 EF improves the probability forecast of inland and offshore MCSs more than does NODA EF. Compared with the experiments using the longer DA time intervals, assimilating the radial-velocity observations at 6-min intervals tends to produce better forecasts. The experiment with the longest DA time span and shortest time interval shows the best performance.However, a shorter DA time interval(e.g., 12 min) or a longer DA time span does not always help. The experiment with the shortest DA time interval and maximum DA window shows the best performance, as it corrects errors in the simulated convection evolution over both the inland and offshore areas. An improved representation of the initial state leads to dynamic and thermodynamic conditions that are more conducive to earlier initiation of the inland MCS and longer maintenance of the offshore MCS.展开更多
文摘利用常规气象观测资料、天气雷达以及NCEP FNL再分析资料,对2017年5月18日下午天水一次强冰雹天气的雷达回波结构演变特征和成因进行了详细分析。结果表明:(1)降雹对流单体低层反射率因子呈现出明显的"V"型缺口,最大回波强度出现在低层,为63 d Bz。反射率因子垂直剖面呈对流单体有界弱回波区和其上的回波悬垂,相应的径向速度垂直剖面呈中低层径向风有明显的辐合特征,高层转为辐散,尤其风暴顶附近。(2)对流单体发生在对流层中层河套地区至甘肃河东东部低涡及其附近冷区和河西中部高压脊及其东北部冷池和低层甘肃与宁夏交界处冷性低涡分别为干冷空气入侵和暖湿气流辐合抬升提供有利条件的环境背景下;较高CAPE值和低CIN值有利于强对流天气发生;对流层中低层深厚的上升气流,中层下沉气流和0℃层以上强气流上升有利于对流单体水汽输送以及生成、发展和维持;距地高度2600～2900 m的0℃层为大冰雹落地提供了环境条件。(3)冰雹临近预警的雷达参数化指标为最大反射率因子达55 d Bz,VIL最大值和VIL密度分别达25 kg·m-2和2. 3 g·m-3。
基金supported by the National Natural Science Foundation of China(Grant Nos.41405050,91437104&41461164006)the Public Welfare Scientific Research Projects in Meteorology(Grant No.GYHY201406013)the National Basic Research Program of China(Grant No.2014CB441402)
文摘This study examines the effectiveness of an ensemble Kalman filter based on the weather research and forecasting model to assimilate Doppler-radar radial-velocity observations for convection-permitting prediction of convection evolution in a high-impact heavy-rainfall event over coastal areas of South China during the pre-summer rainy season. An ensemble of 40 deterministic forecast experiments(40 DADF) with data assimilation(DA) is conducted, in which the DA starts at the same time but lasts for different time spans(up to 2 h) and with different time intervals of 6, 12, 24, and 30 min. The reference experiment is conducted without DA(NODA).To show more clearly the impact of radar DA on mesoscale convective system(MCS)forecasts, two sets of 60-member ensemble experiments(NODA EF and exp37 EF) are performed using the same 60-member perturbed-ensemble initial fields but with the radar DA being conducted every 6 min in the exp37 EF experiments from 0200 to0400 BST. It is found that the DA experiments generally improve the convection prediction. The 40 DADF experiments can forecast a heavy-rain-producing MCS over land and an MCS over the ocean with high probability, despite slight displacement errors. The exp37 EF improves the probability forecast of inland and offshore MCSs more than does NODA EF. Compared with the experiments using the longer DA time intervals, assimilating the radial-velocity observations at 6-min intervals tends to produce better forecasts. The experiment with the longest DA time span and shortest time interval shows the best performance.However, a shorter DA time interval(e.g., 12 min) or a longer DA time span does not always help. The experiment with the shortest DA time interval and maximum DA window shows the best performance, as it corrects errors in the simulated convection evolution over both the inland and offshore areas. An improved representation of the initial state leads to dynamic and thermodynamic conditions that are more conducive to earlier initiation of the inland MCS and longer maintenance of the offshore MCS.
