The efficacious forecasting of single-station atmospheric temperature profiles can provide essential support for the structural design and flight missions of spacecrafts in near space. However, empirical models and reference atmospheric models most are calculations of the average state of the atmosphere profiles. Numerical assimilation models require expensive computational costs to improve the accuracy for medium and long-term forecasting. It has been still a challenge to refined predict short-term temperature profiles of near space at a low-cost. We present a temperature profile operator method for refined modeling in the stratosphere by fusing the ability of Long Short-Term Memory (LSTM) networks or its variants- bidirectional LSTM (BiLSTM) to exploit time series correlated information and deep operator networks (DeepONets) to approximate the solution operator of temperature profiles. It consists of three subnetworks. The first subnetwork is used to approximate the discrete temperature profile function, the second net is applied to represent the spatial information of pressure heights, and the third branch is utilized to encode the time domain of the temperature profile operator. We first use the hourly low latitude temperature data (20–50 km) from ERA5 for training, verification and iterative testing in the next 48 hr. The results denote that the temperature profile operator network has a stable and low error of cumulative generalization, and the BiLSTM operator significantly outperform the other models. We also apply two scenarios to testing the refined applicability of the high latitude temperature profile operator and the mid latitude wind profile operator in the stratosphere. This work provides a novel perspective for us to study the refined single-station modeling of the upper and middle atmosphere.

中文翻译：

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基于神经网络算子学习的平流层大气温度廓线短期精细预报

单站大气温度廓线的有效预报可以为临近空间航天器的结构设计和飞行任务提供重要支撑。然而，经验模型和参考大气模型大多是对大气廓线平均状态的计算。数值同化模型需要昂贵的计算成本来提高中长期预测的准确性。以低成本精细预测邻近空间的短期温度剖面仍然是一个挑战。我们提出了一种温度剖面算子方法，通过融合长短期记忆(LSTM) 网络或其变体双向 LSTM (BiLSTM) 的能力来利用时间序列相关信息和深度算子网络(DeepONets) 来在平流层中进行精细建模。近似温度分布的解算子。它由三个子网组成。第一个子网络用于近似离散温度剖面函数，第二个子网络用于表示压力高度的空间信息，第三个分支用于对温度剖面算子的时域进行编码。我们首先使用ERA5每小时的低纬度温度数据（20-50公里）在接下来的48小时内进行训练、验证和迭代测试。结果表明，温度剖面算子网络具有稳定且较低的累积泛化误差，并且 BiLSTM 算子显着优于其他模型。我们还应用两个场景来测试平流层高纬度温度廓线算子和中纬度风廓线算子的精细化适用性。这项工作为我们研究上层和中层大气的精细单站建模提供了一个新颖的视角。