Assimilation of a Coordinated Fleet of Uncrewed Aircraft System Observations in Complex Terrain: EnKF System Design and Preliminary Assessment
Assimilation of a Coordinated Fleet of Uncrewed Aircraft System Observations in Complex Terrain: EnKF System Design and Preliminary Assessment
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复杂地形中无人飞机系统观测协调机队的同化：EnKF 系统设计和初步评估
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发表时间：
2021
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通讯作者：
M. Steiner
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作者：
Anders Jensen;J. Pinto;S. Bailey;R. Sobash;G. Boer;A. Houston;P. Chilson;Tyler M. Bell;G. Romine;S. Smith;D. Lawrence;Cory Dixon;J. Lundquist;J. Jacob;Jack Elston;S. Waugh;M. Steiner
在2018年高空低大气过程研究-遥控飞机团队实验（LAPSE-RATE）现场活动期间收集的无人机系统（UAS）观测数据被同化到使用集合卡尔曼滤波器的天气研究和预报模型的高分辨率配置中。UAS观测的好处进行了评估的地形驱动（排水和upvalley）流事件发生在科罗拉多的圣路易斯谷（SLV）使用独立的观察。从Saguache峡谷排水流的强度、深度和水平范围的分析和预测，以及随后过渡到上游和上游峡谷流的分析和预测，相对于在没有数据同化（基准）和仅同化地面观测数据的情况下获得的分析和预测，得到了改进。UAS观测资料的同化大大改善了对SLV北方多个地点温度、相对湿度和风的垂直变化的分析，相对于基准运行，每个变量的偏差和均方根误差都减少了大约40%。尽管有这些显著的改进，但仍存在一些偏差，这些偏差与测量误差和/或边界层参数化对垂直传播观测的影响有关，这两个问题都需要进一步探讨。这里介绍的结果突出了如何获得的观测与车队的剖析无人机改善有限的区域，高分辨率的分析和复杂地形的短期预报。
Uncrewed aircraft system (UAS) observations collected during the 2018 Lower Atmospheric Process Studies at Elevation—a Remotely Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting Model using an ensemble Kalman filter. The benefit of UAS observations was assessed for a terrain-driven (drainage and upvalley) flow event that occurred within Colorado’s San Luis Valley (SLV) using independent observations. The analysis and prediction of the strength, depth, and horizontal extent of drainage flow from the Saguache Canyon and the subsequent transition to upvalley and up-canyon flow were improved relative to that obtained both without data assimilation (benchmark) and when only surface observations were assimilated. Assimilation of UAS observations greatly improved the analyses of vertical variations in temperature, relative humidity, and winds at multiple locations in the northern portion of the SLV, with reductions in both bias and the root-mean-square error of roughly 40% for each variable relative to the benchmark run. Despite these noted improvements, some biases remain that were tied to measurement error and/or the impact of the boundary layer parameterization on vertically spreading the observations, both of which require further exploration. The results presented here highlight how observations obtained with a fleet of profiling UAS improve limited-area, high-resolution analyses and short-term forecasts in complex terrain.