Impact of UAS Global Hawk Dropsonde Data on Tropical and Extratropical Cyclone Forecasts in 2016
Impact of UAS Global Hawk Dropsonde Data on Tropical and Extratropical Cyclone Forecasts in 2016
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UAS全球鹰下投探空仪数据对2016年热带和温带气旋预报的影响
DOI：
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发表时间：
2018
影响因子：
2.9
通讯作者：
Hongli Wang
中科院分区：
文献类型：
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作者：
A. Kren;L. Cucurull;Hongli Wang
使用国家环境预报中心 (NCEP) 全球资料同化系统 (GDAS) 对全球鹰 (GH) 下投式探空仪观测对热带和温带预报的影响进行了初步调查。实验是在高影响天气事件期间进行的，这些天气事件是 2016 年 NOAA 无人机系统 (UAS) 感知危险与操作无人机技术 (SHOUT) 实地活动的一部分进行的采样：1) 2016 年 2 月的三个温带系统和 2) 西大西洋的飓风马修和妮可。对于这些事件，还研究了卫星数据缺口情况下 GH 观测的好处。结果发现，GH下投式探空仪的同化减少了Matthew和Nicole的跟踪误差； 60 小时后，改进高达 20%。此外，局部下投式探空仪可将四个热带气旋的全球预报跟踪误差降低高达 9%。在卫星间隙情景下，结果好坏参半，其中只有马修飓风通过同化下投探空仪得到了改善。改进的风暴轨迹归因于更好地表示了转向流和大气中层模式。对于所有情况，下投式探空仪在 96 小时内可将温度、相对湿度、风和海平面压力的均方根误差降低 3%–8%。 GH 下投式探空仪还可以在降水方面获得额外的好处，美国东南部地区的技能得分比控制预报高出高达 8%，并且对于恶劣天气预测非常重要的低层参数也有好处。这项研究的结果是初步的，因此需要更多的案例才能具有统计意义。
A preliminary investigation into the impact of dropsonde observations from the Global Hawk (GH) on tropical and extratropical forecasts is performed using the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS). Experiments are performed during high-impact weather events that were sampled as part of the NOAA Unmanned Aerial Systems (UAS) Sensing Hazards with Operational Unmanned Technology (SHOUT) field campaigns in 2016: 1) three extratropical systems in February 2016 and 2) Hurricanes Matthew and Nicole in the western Atlantic. For these events, the benefits of GH observations under a satellite data gap scenario are also investigated. It is found that the assimilation of GH dropsondes reduces the track error for both Matthew and Nicole; the improvements are as high as 20% beyond 60 h. Additionally, the localized dropsondes reduce global forecast track error for four tropical cyclones by up to 9%. Results are mixed under a satellite gap scenario, where only Hurricane Matthew is improved from assimilated dropsondes. The improved storm track is attributed to a better representation of the steering flow and atmospheric midlevel pattern. For all cases, dropsondes reduce the root-mean-square error in temperature, relative humidity, wind, and sea level pressure by 3%–8% out to 96 h. Additional benefits from GH dropsondes are obtained for precipitation, with higher skill scores over the southeastern United States versus control forecasts of up to 8%, as well as for low-level parameters important for severe weather prediction. The findings from this study are preliminary and, therefore, more cases are needed for statistical significance.