Forecasting short-term methane based on corrected numerical weather prediction outputs

Abstract

Methane (CH4) represents a significant greenhouse gas, and the control of its emissions is crucial in impacting global climate change. Accurate forecasting of CH4 emissions is important for identifying future sources of high CH4 emissions, rational design of experiments, and information gathering. This study presented a method to improve numerical weather prediction (NWP) outputs using the Extreme Gradient Boosting (XGB) algorithm. In contrast to previous conventional correction methods, the XGB model greatly enhances the accuracy and stability of the NWP outputs. Short-term (1-3 d) CH4 forecasting models were built for 53 stations worldwide using three machine learning algorithms: Convolutional Neural Network (CNN), Random Forest (RF), and XGB. The models were validated with flux data from the stations. The results showed that the CNN model performed best in forecasting CH4 for all lead times (mean NRMSE=0.34), followed by the RF model (mean NRMSE=0.68) and the XGB model performed worst (mean NRMSE=0.74). For land cover types (e.g., savanna (WSA) and forest (EBF)), the accuracy of the model may be greater for 2 d lead times than for 1 d lead times, which is closely related to the regional climate and terrain conditions. For lead times, the CNN model achieves high CH4 forecast accuracy at 1-2 d lead times. In addition, the RF and XGB models with 1 d lead times exhibit significant overestimation at most stations in North America (PBIAS>0.5). This work quantified the performance of machine learning models for correcting NWP outputs, providing a technical approach to the correction of NWP outputs; and assessed the performance of deep learning models for forecasting short-term CH4, providing a reference for model selection for CH4 forecasting.