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Mingjian Zeng

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Open access Sep 2026

An Integrated Multi‐Component Error Verification Method for Numerical Weather Prediction

Errors in weather forecasts remain a critical challenge for high‐resolution numerical weather prediction (NWP) and artificial intelligence forecast models. Traditional verification approaches, such as Threat Score (TS) and Equitable Threat Score (ETS), suffer from “double penalty” issues due to spatial‐temporal mismatches and lack of spatial distribution fidelity. In this study, we introduce an integrated multi‐component error verification approach (IMEV) that decomposes forecast errors into three physically interpretable components: accumulation error (systematic intensity discrepancy), area error (areal coverage and spatial extent mismatch), and pattern error (distributional dissimilarity and organization difference). Pattern error is evaluated by the similarity of intensity distribution features between the forecast and observation fields. These intensity distribution features are represented using probability density histograms of meteorological elements. Pattern error is quantified using Jensen‐Shannon divergence of probability density distributions, avoiding complex feature matching while capturing spatial pattern discrepancies. Two representative precipitation cases are analyzed to validate this method. Results demonstrate that the proposed method aligns closely with subjective analysis, outperforming traditional metrics like TS and ETS in capturing spatial distribution discrepancies. By prioritizing dominant error dimensions (accumulation, area, and pattern), the method downplays fine‐scale spatial details while preserving essential distributional characteristics through probability density analysis. Applying the IMEV algorithm within small‐sized sliding windows can, to a certain extent, allow for partial sensitivity to spatial displacements. This avoids computationally intensive feature matching, enabling efficient error quantification.

Xiyu Mu, Qi Xu, Guoqing Liu et al. · Journal of Geophysical Resea... · 0 citations

Open access Jul 2026

Physics-Guided Deep Learning for Short-Term Offshore Wind Power Forecasting

Given wind energy’s growing significance in the world’s energy structure, the demand for high-precision forecasting is more urgent than ever. However, wind power’s inherent non-stationarity is linked to complex and variable meteorological conditions, which pose significant challenges for accurate forecasting. The accuracy of short-term wind power forecasts hinges on estimated future wind speed. Systematic biases often degrade the accuracy of Weather Research and Forecasting (WRF) forecasts. A hybrid LSTM–LightGBM correction model is proposed to correct the WRF wind speed bias. The wind correction model significantly reduces the systematic wind speed bias in the WRF model and achieves a notable reduction in RMSE across the entire wind speed range, with the highest RMSE decreasing by 7%. A new physics-guided and deep learning-integrated model is designed for 24 h short-term wind power forecasting, which integrates physical laws with data-driven features, effectively alleviating the “black-box” problem of purely data-driven models and making prediction results more consistent with engineering practice. The model’s predicted wind power has an MAE of 103.94 kW and an R2 of 0.9853, demonstrating superior prediction capability. The results provide reliable technical support for the real-time scheduling of wind farms and support the reliable, stable operation of the power system.

Boni Wang, Mingjian Zeng, H. Shan et al. · Journal of Marine Science an... · 0 citations