Targeted ocean sampling guidance for tropical cyclones

被引:9
|
作者
Chen, Sue [1 ]
Cummings, James A. [2 ]
Schmidt, Jerome M. [1 ]
Sanabia, Elizabeth R. [3 ]
Jayne, Steven R. [4 ]
机构
[1] Naval Res Lab, Monterey, CA 93943 USA
[2] Sci Applicat Int Corp, Monterey, CA USA
[3] US Naval Acad, Dept Oceanog, Annapolis, MD 21402 USA
[4] Woods Hole Oceanog Inst, Woods Hole, MA 02543 USA
基金
美国海洋和大气管理局;
关键词
tropical cyclone; ocean data assimilation; ocean sampling; air-sea interaction; HURRICANE INTENSITY; INTENSIFICATION; IMPACT; EDDY; FEATURES; SYSTEM;
D O I
10.1002/2017JC012727
中图分类号
P7 [海洋学];
学科分类号
0707 ;
摘要
A 3-D variational ocean data assimilation adjoint approach is used to examine the impact of ocean observations on coupled tropical cyclone (TC) model forecast error for three recent hurricanes: Isaac (2012), Hilda (2015), and Matthew (2016). In addition, this methodology is applied to develop an innovative ocean observation targeting tool validated using TC model simulations that assimilate ocean temperature observed by Airborne eXpendable Bathy Thermographs and Air-Launched Autonomous Micro-Observer floats. Comparison between the simulated targeted and real observation data assimilation impacts reveals a positive maximum mean linear correlation of 0.53 at 400-500 m, which implies some skill in the targeting application. Targeted ocean observation regions from these three hurricanes, however, show that the largest positive impacts in reducing the TC model forecast errors are sensitive to the initial prestorm ocean conditions such as the location and magnitude of preexisting ocean eddies, storm-induced ocean cold wake, and model track errors. Plain Language Summary A 3D variational ocean data assimilation adjoint approach is used to examine the impact of ocean observations on coupled tropical cyclone (TC) model forecast error for three recent hurricanes: Isaac (2012), Hilda (2015), and Matthew (2016). Targeted ocean observation regions from these three hurricanes, show that the largest positive impacts in reducing the TC model forecast errors are sensitive to the initial pre-storm ocean conditions such as the location and magnitude of pre-existing ocean eddies, storm-induced ocean cold wake, and model track errors.
引用
收藏
页码:3505 / 3518
页数:14
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