Deep Learning to Estimate Model Biases in an Operational NWP Assimilation System
Deep Learning to Estimate Model Biases in an Operational NWP Assimilation System
复制标题
深度学习估计可操作 NWP 同化系统中的模型偏差
DOI：
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发表时间：
2022
影响因子：
6.8
通讯作者：
D. Hall
中科院分区：
文献类型：
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作者：
P. Laloyaux;T. Kurth;P. Dueben;D. Hall
模型偏差是提高使用最先进的大气模型进行数值天气预报的准确性和可靠性的主要障碍之一。为了处理模型偏差，我们开发了标准四维变分 (4D-Var) 算法的修改版，称为弱约束 4D-Var，其中在模型中引入了强迫项，以纠正沿模型轨迹累积的偏差。这种方法将平流层的温度偏差降低了 50%，并在欧洲中期天气预报中心业务预报系统中得到实施。尽管起源和应用不同，但数据同化（DA）和深度学习都能够通过观测来了解地球系统。在本文中，利用射电掩星（RO）测量的温度检索开发了一种用于模型偏差校正的深度学习方法。神经网络（NN）需要大量样本才能正确捕获温度初猜轨迹与模型偏差之间的关系。由于使用固定模型版本和实际分辨率长时间运行综合预报系统 (IFS) DA 系统在计算上非常昂贵，因此我们选择训练初始神经网络，然后使用 ERA5 再分析进行训练，然后在当前 IFS 模型上使用迁移学习 1 年。初步结果表明，卷积神经网络足以估计 RO 温度反演的模型偏差。讨论了深度学习和弱约束 4D-Var 的不同优点和缺点，强调了每种方法有效且自适应地学习模型偏差的潜力。
Model bias is one of the main obstacles to improved accuracy and reliability in numerical weather prediction conducted with state‐of‐the‐art atmospheric models. To deal with model bias, a modification of the standard four‐dimensional variational (4D‐Var) algorithm, called weak‐constraint 4D‐Var, has been developed where a forcing term is introduced into the model to correct for the bias that accumulates along the model trajectory. This approach reduced the temperature bias in the stratosphere by up to 50% and is implemented in the European Centre for Medium‐Range Weather Forecasts operational forecasting system. Despite different origins and applications, data assimilation (DA) and Deep Learning are both able to learn about the Earth system from observations. In this paper, a deep learning approach for model bias correction is developed using temperature retrievals from radio occultation (RO) measurements. Neural networks (NNs) require a large number of samples to properly capture the relationship between the temperature first‐guess trajectory and the model bias. As running the Integrated Forecasting System (IFS) DA system for extended periods of time with a fixed model version and at realistic resolutions is computationally very expensive, we have chosen to train, the initial NNs are trained using the ERA5 reanalysis before using transfer learning on 1 year of the current IFS model. Preliminary results show that convolutional NNs are adequate to estimate model bias from RO temperature retrievals. The different strengths and weaknesses of both deep learning and weak constraint 4D‐Var are discussed, highlighting the potential for each method to learn model biases effectively and adaptively.