Temporal downscaling meteorological variables to unseen moments: Continuous temporal downscaling via Multi-source Spatial–temporal-wavelet feature Fusion and Time-Continuous Manifold

Abstract

Accurate modeling of meteorological variables with high temporal resolution is crucial for simulations and decision-making in aviation, aerospace, and other engineering sectors. Conventional meteorological products typically have temporal resolutions exceeding one hour, hindering the characterization of short-term nonlinear evolutions in meteorological variables. Current temporal downscaling methods encounter challenges of insufficient multi-source data fusion, limited extrapolation capabilities of data distributions, and inadequate learning of spatiotemporal dependencies, leading to low modeling accuracy and difficulties in modeling meteorological environments with higher temporal resolutions than those in the training data. To address these issues, this study proposes MSF-TCMA (Multi-source Spatial–temporal-wavelet feature Fusion and Time-Continuous Manifold-based Algorithm) for continuous temporal downscaling. The algorithm introduces multiscale deep-wavelet feature extraction branch for integrating spatial dependence and the cross-modal spatiotemporal information fusing branch for fusing multi-source information and learning temporal dependence. The time-continuous manifold sampling branch is used to solve the problem of data distribution extrapolation. Finally, the algorithm’s continuous downscaling performance is optimized by employing multi-moment weighted meteorological state estimation-energy change deduction joint loss. Two regional case studies demonstrate that MSF-TCMA achieves modeling errors of less than 0.65 K for 2-meter temperature, less than 36.24 Pa for surface pressure, and less than 0.38 m/s for wind speed over a 6-hour time interval, with errors reduced by 3.99-99.64% compared to the comparison methods. Furthermore, two engineering experiments demonstrate that the method realizes continuous downscaling of multiple moments in a time interval (including for unseen moments during the algorithm training phase), and downscaling prediction of future meteorological states based on GFS forecast data. This study provides a new paradigm for high-precision and high-temporal resolution reconstruction of meteorological data, which is of great application value for optimization and risk control of complex engineering activities. The code is available at: https://github.com/shermo1415/MSF-TCMA/.