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）模型中的物理参数化方案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。这种方法面临高昂的计算成本，且需为每个任务从头进行迭代搜索，导致序列校准场景下的效率低下。针对这一问题，我们提出由气象状态表征驱动的序列进化迁移优化（SEETO）算法。首先，为精确衡量校准任务间的物理相似性，引入气象状态表征提取器，将高维气象场映射为潜在表征。其次，基于潜在空间相似性，设计双层自适应知识迁移机制：在解层级，复用相似历史任务中的优质种群实现优化"热启动"；在模型层级，基于源任务数据构建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。跨越不同源-目标相似度的10个校准任务的广泛实验表明，SEETO具有卓越的效率。在严格限制20次昂贵评估的预算下，SEETO相较于两个最新基线方法，超体积（HV）指标平均提升6%。值得注意的是，为达到SEETO在此阶段的性能，对比算法分别需要额外64%和28%的评估次数。这为NWP模型参数的高效精准自动校准提供了新范式。