OSSE Assessment of Underwater Glider Arrays to Improve Ocean Model Initialization for Tropical Cyclone Prediction
OSSE Assessment of Underwater Glider Arrays to Improve Ocean Model Initialization for Tropical Cyclone Prediction
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OSSE 评估水下滑翔机阵列以改进热带气旋预测的海洋模型初始化
DOI：
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发表时间：
2020
影响因子：
2.2
通讯作者：
R. Atlas
中科院分区：
文献类型：
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作者：
G. Halliwell;G. Goñi;M. Mehari;V. Kourafalou;M. Baringer;R. Atlas
可信的热带气旋（TC）强度预测耦合模式需要准确的预测从海洋到大气的焓通量，这反过来又需要准确的预测风暴下的海面温度冷却。初始海洋场必须准确地代表海洋中尺度特征和相关的热量和密度结构。观测系统模拟实验（OSSEs）进行定量评估同化配置文件的影响，从多个水下滑翔机部署在西北大西洋TC区域，强调从移动与固定平台的配置文件所获得的优势。同化在固定地点重复收集的海洋剖面，只有在每个剖面仪的~50公里范围内才能产生大的均方根误差减少，这主要有两个原因。首先，在各个更新周期期间执行的校正往往会引入非物理的涡流结构，这是由背景误差协方差矩阵的平滑特性和由局部化半径函数引起的新息的锥形化引起的。第二，平流产生快速的非线性误差增长在更大的距离从轮廓仪的位置。每一个单独的移动滑翔机的能力，以跨越梯度和地图中尺度结构在其附近大大减少了这种非线性误差的增长。滑翔机阵列的水平间距可以比固定位置剖面仪大50%-100%，以实现类似的中尺度误差减少。相比之下，通过部署间隔距离高达数百公里的剖面仪阵列，可以实现上层海洋热含量的大规模偏差减少，移动滑翔机只提供适度的额外改善。讨论了结果对研究区域和资料同化方法的预期敏感性。
Credible tropical cyclone (TC) intensity prediction by coupled models requires accurate forecasts of enthalpy flux from ocean to atmosphere, which in turn requires accurate forecasts of sea surface temperature cooling beneath storms. Initial ocean fields must accurately represent ocean mesoscale features and the associated thermal and density structure. Observing system simulation experiments (OSSEs) are performed to quantitatively assess the impact of assimilating profiles collected from multiple underwater gliders deployed over the western North Atlantic Ocean TC region, emphasizing advantages gained by profiling from moving versus stationary platforms. Assimilating ocean profiles collected repeatedly at fixed locations produces large root-mean-square error reduction only within ~50 km of each profiler for two primary reasons. First, corrections performed during individual update cycles tend to introduce unphysical eddy structure resulting from smoothing properties of the background error covariance matrix and the tapering of innovations by a localization radius function. Second, advection produces rapid nonlinear error growth at larger distances from profiler locations. The ability of each individual moving glider to cross gradients and map mesoscale structure in its vicinity substantially reduces this nonlinear error growth. Glider arrays can be deployed with horizontal separation distances that are 50%–100% larger than those of fixed-location profilers to achieve similar mesoscale error reduction. By contrast, substantial larger-scale bias reduction in upper-ocean heat content can be achieved by deploying profiler arrays with separation distances up to several hundred kilometers, with moving gliders providing only modest additional improvement. Expected sensitivity of results to study region and data assimilation method is discussed.