Data assimilation (DA) is used extensively in numerical weather prediction (NWP) to improve forecast skill. Indeed, improvements in forecast skill in NWP models over the past 30 years have directly coincided with improvements in DA schemes. At present, due to data availability and technical challenges, DA is underused in space weather applications, particularly for solar wind prediction. This paper investigates the potential of advanced DA methods currently used in operational NWP centers to improve solar wind prediction. To develop the technical capability, as well as quantify the potential benefit, twin experiments are conducted to assess the performance of the Local Ensemble Transform Kalman Filter (LETKF) in the solar wind model ENLIL. Boundary conditions are provided by the Wang‐Sheeley‐Arge coronal model and synthetic observations of density, temperature, and momentum generated every 4.5 h at 0.6 AU. While in situ spacecraft observations are unlikely to be routinely available at 0.6 AU, these techniques can be applied to remote sensing of the solar wind, such as with Heliospheric Imagers or interplanetary scintillation. The LETKF can be seen to improve the state at the observation location and advect that improvement toward the Earth, leading to an improvement in forecast skill in near‐Earth space for both the observed and unobserved variables. However, sharp gradients caused by the analysis of a single observation in space resulted in artificial wavelike structures being advected toward Earth. This paper is the first attempt to apply DA to solar wind prediction and provides the first in‐depth analysis of the challenges and potential solutions.

中文翻译：

---

太阳风中的数据同化：挑战和初步结果。


资料同化（DA）广泛用于数值天气预报（NWP）以提高预报技能。事实上，过去 30 年 NWP 模型预报技能的提高与 DA 方案的改进直接同步。目前，由于数据可用性和技术挑战，DA 在空间天气应用中尚未得到充分利用，特别是在太阳风预测方面。本文研究了目前在运营 NWP 中心使用的先进 DA 方法在改进太阳风预测方面的潜力。为了开发技术能力并量化潜在效益，进行了双实验来评估太阳风模型 ENLIL 中的局域集成变换卡尔曼滤波器 (LETKF) 的性能。边界条件由 Wang-Sheeley-Arge 日冕模型和 0.6 天文单位每 4.5 小时生成的密度、温度和动量的综合观测数据提供。虽然原位航天器观测不太可能在 0.6 AU 范围内常规实现，但这些技术可应用于太阳风的遥感，例如使用日光层成像仪或行星际闪烁。可以看出，LETKF 改善了观测位置的状态，并将这种改善平流到地球，从而提高了近地空间对观测到和未观测到的变量的预测能力。然而，对太空中一次观测的分析所引起的急剧梯度导致人造波状结构被平流吹向地球。本文是将DA应用于太阳风预测的首次尝试，并对挑战和潜在解决方案进行了首次深入分析。