Zhang Yunji, Sieron Scott B, Lu Yinghui, Chen Xingchao, Nystrom Robert G, Minamide Masashi, Chan Man-Yau, Hartman Christopher M, Yao Zhu, Ruppert James H, Okazaki Atsushi, Greybush Steven J, Clothiaux Eugene E, Zhang Fuqing
Department of Meteorology and Atmospheric Science Center for Advanced Data Assimilation and Predictability Techniques The Pennsylvania State University University Park PA USA.
I.M. Systems Group Inc. (IMSG) College Park MD USA.
Geophys Res Lett. 2021 Dec 28;48(24):e2021GL096410. doi: 10.1029/2021GL096410. Epub 2021 Dec 26.
Ensemble-based data assimilation of radar observations across inner-core regions of tropical cyclones (TCs) in tandem with satellite all-sky infrared (IR) radiances across the TC domain improves TC track and intensity forecasts. This study further investigates potential enhancements in TC track, intensity, and rainfall forecasts via assimilation of all-sky microwave (MW) radiances using Hurricane Harvey (2017) as an example. Assimilating Global Precipitation Measurement constellation all-sky MW radiances in addition to GOES-16 all-sky IR radiances reduces the forecast errors in the TC track, rapid intensification (RI), and peak intensity compared to assimilating all-sky IR radiances alone, including a 24-hr increase in forecast lead-time for RI. Assimilating all-sky MW radiances also improves Harvey's hydrometeor fields, which leads to improved forecasts of rainfall after Harvey's landfall. This study indicates that avenues exist for producing more accurate forecasts for TCs using available yet underutilized data, leading to better warnings of and preparedness for TC-associated hazards in the future.
将热带气旋（TC）内核区域的雷达观测数据与整个TC区域的卫星全天空红外（IR）辐射数据进行基于集合的数据同化，可改善TC路径和强度预报。本研究以2017年飓风哈维为例，通过同化全天空微波（MW）辐射数据，进一步探究TC路径、强度和降雨预报方面的潜在改进。除了GOES - 16全天空红外辐射数据外，同化全球降水测量星座全天空微波辐射数据，与仅同化全天空红外辐射数据相比，可减少TC路径、快速增强（RI）和峰值强度的预报误差，包括RI的预报提前期增加24小时。同化全天空微波辐射数据还改善了哈维的水凝物场，这使得哈维登陆后的降雨预报得到改善。本研究表明，利用现有但未充分利用的数据来生成更准确的TC预报是可行的，这将为未来TC相关灾害提供更好的预警和防范。
