Continuous long-term eddy covariance (EC) measurements of CO₂ fluxes (NEE) in a variety of terrestrial ecosystems are critical for investigating the impacts of climate change on ecosystem carbon cycling. However, due to a number of issues, approximately 30–60% of annual flux data obtained at EC flux sites around the world are reported as gaps. Given that the annual total NEE is mostly determined by variations in the NEE data with time scales longer than one day, we propose a novel framework to perform gap filling in NEE data based on machine learning (ML) and time series decomposition (TSD). The novel framework combines the advantages of ML models in predicting NEE with meteorological and environmental inputs and TSD methods in extracting the dominant varying trends in NEE time series. Using the NEE data from 25 AmeriFlux sites, the performance of the proposed framework is evaluated under four different artificial scenarios with gap lengths ranging in length from one hour to two months. The combined approach incorporating random forest and moving average (MA-RF) is observed to exhibit better performance than other approaches at filling NEE gaps in scenarios with different gap lengths. For the scenario with a gap length of seven days, the MA-RF improves the R² by 34% and reduces the root mean square error (RMSE) by 55%, respectively, compared to a traditional RF-based model. The improved performance of MA-RF is most likely due to the reduction in data variability and complexity of the variations in the extracted low-frequency NEE data. Our results indicate that the proposed MA-RF framework can provide improved gap filling for NEE time series. Such improved continuous NEE data can enhance the accuracy of estimations regarding the ecosystem carbon budget.

Hybrid data assimilation (DA) methods have received extensive attention in the field of numerical weather prediction. In this study, a hybrid gain data assimilation (HGDA) method that combined the gain matrices of ensemble and variational methods was first applied in the mesoscale version of the Global/Regional Assimilation and Prediction System (GRAPES_Meso). To evaluate the performance of the HGDA method in the GRAPES_Meso model, different DA schemes, including the three-dimensional variational (3DVAR), local ensemble transform Kalman filter (LETKF), and HGDA schemes, were compared across eight tropical cyclone (TC) cases, and FY-4A atmospheric motion vectors were assimilated. The results indicated that the HYBRID scheme outperformed the 3DVAR and LETKF schemes in TC position forecasting, and with ensemble forecasting techniques, the HYBRID scheme promoted the accuracy of the prediction TC intensity. The threat score (TS) values for the light and medium precipitation forecasts obtained in the HYBRID experiment were higher than those for the forecasts obtained in the 3DVAR and LETKF experiments, which may be attributed to the forecasting accuracy for the TC position. Regarding heavy and extreme rainfall, the HYBRID scheme achieved a more stable effect than those of the 3DVAR and LETKF schemes. The results demonstrated the superiority of the HGDA scheme in TC prediction with the GRAPES_Meso model.

For this paper, a new global atmospheric model (Global-to-Regional Integrated forecast SysTem; GRIST) with improved sub-grid scale orographic parameterization was verified and assessed, with an emphasis on the precipitation caused by typhoons. Four typical typhoon cases were selected for the verification of the model. The results indicate that, compared to the control experiments, the sensitivity experiments consistently simulated the trends in the three-hour cumulative precipitation changes and the high-value regions of total precipitation better. However, the improved experiments only had an ameliorating effect on the cumulative precipitation modelling biases for Typhoon LEKIMA and Typhoon HAGUPIT, not all of them. Precipitation bias is smaller on flat land than that on mountainous land, but the precipitation bias on windward/leeward slopes depends on the typhoon case. Precipitation modelling accuracy varies considerably between flat and mountainous terrain but very little between windward and leeward slopes. The precipitation simulation is poor for all terrains, with large precipitation thresholds in three typhoon cases, but for Typhoon HOTA, after improving the terrain, the model has the ability to forecast the heavy rainfall scenarios of the mountainous terrain.