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arXiv 2308.03373astro-ph.IM
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迈向不同尺度的业务化光学湍流预报系统

Towards operational optical turbulence forecast systems at different scales

Elena Masciadri, Alessio Turchi, Luca Fini

AI总结:

本文将在LBT的ALTA Center中已实现的自回归短时预报方法扩展至VLT台址,覆盖视宁度、波前相干时间、等晕角和地面层比例,证明其在1-2小时尺度上优于持续性预测和基于现场测量的机器学习方法,并计划部署于VLT业务化预报系统。

摘要:

1至2小时时间尺度的预报对于所有由自适应光学支持、并将主要以服务模式运行的新一代设施(ELTs)仪器至关重要。在最近的一项研究(Masciadri et al. 2020)中,我们已经表明,可以使用自回归方法在此类短时间尺度上预报视宁度和大气参数,取得了前所未有的模型精度,并且相对于前一天(即在较长时间尺度上)利用大气中尺度模式获得的预报有显著增益。同样,我们展示了相对于在相同短时间尺度(1-2小时)上简单使用现场实时测量的持续性方法也有增益。该自回归方法利用中尺度大气模式所做的预报和实时测量,自2019年起已在支持LBT观测的业务化预报系统ALTA Center中实现。在本文中,我们将相同的方法应用于VLT台址,将该方法扩展到所有主要的天文气候参数,即视宁度、波前相干时间、等晕角和地面层比例。我们证明,这种方法为所有天文气候参数提供了前所未有的预报精度,并且相对于持续性预测具有明显增益。初步计算还表明,其精度优于使用现场测量的基于机器学习的方法所获得的精度。我们很快将把这种方法应用于我们为VLT构想的业务化预报系统中。

英文摘要:

The forecast on a time scale of 1 or 2 hours is crucial for all kind of new generation facilities (ELTs) instrumentation supported by the adaptive optics that will be mainly operated in Service Mode. In a recent study (Masciadri et al. 2020) we have showed that we can forecast the seeing and atmospheric parameters at such short time scales using an autoregressive method achieving unprecedented model accuracies with a substantial gain with respect to forecasts performed the day before (i.e. on longer time scales) obtained with an atmospheric mesoscale model. Equally we showed a gain with respect to the method by persistence using simply real-time measurements in situ on the same short time scale (1-2 hours). The auto-regressive method makes use of the forecasts done with mesoscale atmospheric models and real-time measurements and since 2019 has been implemented in the operational forecast system ALTA Center supporting LBT observations. In this contribution we apply the same approach to the VLT site extending the method to all the main astroclimatic parameters i.e. the seeing, the wavefront coherence time, the isoplanatic angle and the ground layer fraction. We prove that such a method offers unprecedented forecast accuracies for all the astroclimatic parameters with clear gains with respect to the prediction by persistence. Preliminary calculations indicate also better accuracies than those obtained with the machine learning based approach using in situ measurements. We will apply soon such a method in an operational forecast system that we conceived for the VLT.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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