Reanalysis wind fields are obtained by correcting the numerically forecasted wind fields based on observation data (i.e., either remote sensing or in-situ observations, or both). Although they are more accurate than forecasted wind fields, reanalysis wind fields tend to have time latencies because they can only be released after the observations are obtained. In order to produce accurate estimates of wind fields in a more timely manner, we develop an adversarial learning approach to correcting forecasted wind fields to be close to reanalysis wind fields. The adversarial learning approach is conducted by an adversarial ConvLSTM network (ACLN) framework that consists of a corrector and a discriminator. The corrector aims at comprehensively capturing both spatial and temporal characteristics of a sequence of forecasted wind fields and producing a corrected forecast wind field for the final field in the sequence. The discriminator tries to distinguish corrected forecast wind field from its corresponding reanalysis wind field. The training of ACLN is alternate between the corrector and the discriminator in an adversarial fashion. The adversarial training mechanism enhances the corrector’s representational power. Additionally, the corrector exploits a residual learning architecture that effectively learns the differences between forecasted wind fields and the corresponding reanalysis wind fields. In this scenario, the well trained corrector requires neither reanalysis wind fields nor observations such that it can correct forecasted wind fields in a timely manner. Furthermore, corrected forecast wind fields are employed for oil spill drift prediction. Extensive experiments validate the effectiveness of the proposed ACLN framework in forecasted wind field correction along with oil spill drift prediction. Compared with ECMWF numerical forecasts, the ACLN achieves an average reduction of 6.2%, 6.9%, and 10.6% in RMSE, MAE, and MAPE, respectively. Compared with a basic drift prediction method, the ACLN based prediction method reduces the error by about 5000 m in the Sanchi oil spill accident. The source codes are available at https://github.com/liyongqingupc/ACLN-WindFieldCorrection, providing a baseline for correcting forecasted wind fields.

再分析风场是通过根据观测数据（即遥感或现场观测，或两者兼有）修正数值预报的风场来获得的。虽然它们比预测的风场更准确，但再分析风场往往有时间延迟，因为它们只有在获得观测数据后才能发布。为了更及时地准确估计风场，我们开发了一种对抗性学习方法来校正预测的风场，使其接近再分析风场。对抗性学习方法由对抗性 ConvLSTM 网络 (ACLN) 框架执行，该框架由校正器和鉴别器组成。校正器旨在全面捕获一系列预报风场的空间和时间特征，并为序列中的最后一个场生成校正后的预报风场。鉴别器试图将校正后的预测风场与其相应的再分析风场区分开来。ACLN 的训练以对抗的方式在校正器和鉴别器之间交替进行。对抗性训练机制增强了校正者的表征能力。此外，校正器利用残差学习架构，有效地学习预测风场和相应的再分析风场之间的差异。在这种情况下，训练有素的校正器既不需要重新分析风场，也不需要观察，以便及时校正预测的风场。此外，修正后的预测风场用于溢油漂移预测。大量实验验证了所提出的 ACLN 框架在预测风场校正以及溢油漂移预测中的有效性。与 ECMWF 数值预报相比，ACLN 的 RMSE、MAE 和 MAPE 分别平均降低了 6.2%、6.9% 和 10.6%。与基本漂移预测方法相比，基于ACLN的预测方法在三池溢油事故中的误差减少了约5000 m。源代码可在 https://github.com/liyongqingupc/ACLN-WindFieldCorrection 获得，为校正预测的风场提供了基线。此外，修正后的预测风场用于溢油漂移预测。大量实验验证了所提出的 ACLN 框架在预测风场校正以及溢油漂移预测中的有效性。与 ECMWF 数值预报相比，ACLN 的 RMSE、MAE 和 MAPE 分别平均降低了 6.2%、6.9% 和 10.6%。与基本漂移预测方法相比，基于ACLN的预测方法在三池溢油事故中的误差减少了约5000 m。源代码可在 https://github.com/liyongqingupc/ACLN-WindFieldCorrection 获得，为校正预测的风场提供了基线。此外，修正后的预测风场用于溢油漂移预测。大量实验验证了所提出的 ACLN 框架在预测风场校正以及溢油漂移预测中的有效性。与 ECMWF 数值预报相比，ACLN 的 RMSE、MAE 和 MAPE 分别平均降低了 6.2%、6.9% 和 10.6%。与基本漂移预测方法相比，基于ACLN的预测方法在三池溢油事故中的误差减少了约5000 m。源代码可在 https://github.com/liyongqingupc/ACLN-WindFieldCorrection 获得，为校正预测的风场提供了基线。大量实验验证了所提出的 ACLN 框架在预测风场校正以及溢油漂移预测中的有效性。与 ECMWF 数值预报相比，ACLN 的 RMSE、MAE 和 MAPE 分别平均降低了 6.2%、6.9% 和 10.6%。与基本漂移预测方法相比，基于ACLN的预测方法在三池溢油事故中的误差减少了约5000 m。源代码可在 https://github.com/liyongqingupc/ACLN-WindFieldCorrection 获得，为校正预测的风场提供了基线。大量实验验证了所提出的 ACLN 框架在预测风场校正以及溢油漂移预测中的有效性。与 ECMWF 数值预报相比，ACLN 的 RMSE、MAE 和 MAPE 分别平均降低了 6.2%、6.9% 和 10.6%。与基本漂移预测方法相比，基于ACLN的预测方法在三池溢油事故中的误差减少了约5000 m。源代码可在 https://github.com/liyongqingupc/ACLN-WindFieldCorrection 获得，为校正预测的风场提供了基线。基于ACLN的预测方法将三池溢油事故的误差减少了约5000 m。源代码可在 https://github.com/liyongqingupc/ACLN-WindFieldCorrection 获得，为校正预测的风场提供了基线。基于ACLN的预测方法将三池溢油事故的误差减少了约5000 m。源代码可在 https://github.com/liyongqingupc/ACLN-WindFieldCorrection 获得，为校正预测的风场提供了基线。