publications

2025

1. 

   Improved machine learning estimation of surface turbulent flux using interpretable model selection and adaptive ensemble algorithms over the Horqin Sandy Land area

   Jing Zhao, Yiyi Guo, Hongsheng Zhang, and 3 more authors

   Atmospheric Research, 2025

   Abs DOI

   The turbulent exchanges between the land surface and atmosphere, crucial for global climate change and atmospheric circulation, are typically represented through bulk formulae based on Monin-Obukhov similarity theory (MOST), using simple regression as a function of the non-dimensional stability parameter derived from limited field experiments, which leaves large uncertainties. Recently, machine learning is anticipated as an alternative or complement to bulk algorithms, leveraging its ability to detect nonlinear relationships in large datasets without constraints from the similarity relationships and self-correlations prescribed in MOST. However, there are still unresolved problems and gaps, even though common models like random forest and neural networks can be directly applied. This study proposes a hybrid approach for improved estimation of surface turbulent flux, consisting of meta-learner estimation, interpretable model selection, and adaptive model integration. Motivated by understanding how different machine learning algorithms perform as surface-layer flux estimators and further exploring how to utilize results from multiple meta-learners for better estimations, the method starts with eight different machine learning algorithms. Then, a combination of Elastic Net and Shapley Additive Explanations is developed as an interpretable model selection module, followed by an adaptive model integration using AdaBoost and extreme learning machine. Experiments at the continuous observation station in the Horqin Sandy Land area, Inner Mongolia, China, demonstrate that the proposed system delivers reliable and stable performance, significantly reducing estimation bias of three scaling parameters, with root mean square error reductions of 43.16%–56.97% compared to MOST, and outperforming the best single machine learning model with additional error reductions of 4.24%–7.90%.

2024

1. A novel dynamic ensemble of numerical weather prediction for multi-step wind speed forecasting with deep reinforcement learning and error sequence modeling

   Jing Zhao, Yiyi Guo, Yihua Lin, and 2 more authors

   Energy, 2024

   Abs DOI

   Accurate wind forecasts for one day ahead or longer periods have significant impacts on the safe and efficient dispatch of power grids, where Numerical Weather Prediction (NWP) serves as the essential tool, such as ensemble NWP integrating multiple single simulations. Typically, ensembles include all single members with fixed weights; however, the relative accuracy of each member may change over time. This study introduces an attractive idea: improving ensemble performance by dynamically recognizing and avoiding low-performing members. It proposes a dynamic ensemble strategy based on NWP, reinforcement learning and error sequence correction. The process begins with Weather Research and Forecasting ensemble simulations. A dynamic framework is then constructed by mapping the multi-step ensemble problem into a Markov decision process, which is further solved using deep deterministic policy gradient. Subsequently, a hybrid deep learning model, comprising temporal convolutional network and bidirectional long short-term memory, is constructed for error sequence estimation of dynamic ensemble, using the high-frequency information of NWP as input. Conducting experiments at two wind farms, and focusing on the 24-h wind speed forecast with a 15-min time resolution, the proposed system demonstrates a reliable and stable ensemble throughout the entire forecasting horizon, significantly reducing the probability of large forecasting errors.