The configuration of physical parameterization schemes in Numerical Weather Prediction (NWP) models plays a critical role in determining the accuracy of the forecast. However, existing parameter calibration methods typically treat each calibration task as an isolated optimization problem. This approach suffers from prohibitive computational costs and necessitates performing iterative searches from scratch for each task, leading to low efficiency in sequential calibration scenarios. To address this issue, we propose the SEquential Evolutionary Transfer Optimization (SEETO) algorithm driven by the representations of the meteorological state. First, to accurately measure the physical similarity between calibration tasks, a meteorological state representation extractor is introduced to map high-dimensional meteorological fields into latent representations. Second, given the similarity in the latent space, a bi-level adaptive knowledge transfer mechanism is designed. At the solution level, superior populations from similar historical tasks are reused to achieve a "warm start" for optimization. At the model level, an ensemble surrogate model based on source task data is constructed to assist the search, employing an adaptive weighting mechanism to dynamically balance the contributions of source domain knowledge and target domain data. Extensive experiments across 10 distinct calibration tasks, which span varying source-target similarities, highlight SEETO's superior efficiency. Under a strict budget of 20 expensive evaluations, SEETO achieves a 6% average improvement in Hypervolume (HV) over two state-of-the-art baselines. Notably, to match SEETO's performance at this stage, the comparison algorithms would require an average of 64% and 28% additional evaluations, respectively. This presents a new paradigm for the efficient and accurate automated calibration of NWP model parameters.

翻译：数值天气预报（NWP）模型中物理参数化方案的配置对预报精度具有决定性影响。然而，现有参数校准方法通常将每个校准任务视为独立的优化问题。这种方法存在计算成本过高的问题，且每个任务均需从头开始进行迭代搜索，导致序贯校准场景下的效率低下。为解决该问题，我们提出一种由气象状态表征驱动的序贯进化迁移优化（SEETO）算法。首先，为精确度量校准任务间的物理相似性，引入气象状态表征提取器，将高维气象场映射至隐式表征空间。其次，基于隐空间相似性设计双层自适应知识迁移机制：在解层面，复用相似历史任务的优质种群以实现优化"热启动"；在模型层面，构建基于源任务数据的集成代理模型辅助搜索，采用自适应加权机制动态平衡源域知识与目标域数据的贡献。在涵盖不同源-目标相似度的10个校准任务上的大量实验表明，SEETO具有显著效率优势。在严格限定20次昂贵评估的预算下，SEETO在超体积（HV）指标上较两种先进基线方法平均提升6%。值得注意的是，为达到SEETO在此阶段的性能，对比算法分别平均需要增加64%和28%的评估次数。这为NWP模型参数的高效精准自动化校准提供了新范式。