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Heavy rainfall is an important weather that causes flood disasters, and it is also one of the most important natural disasters in our country. With development of the high-resolution numerical models, numerical weather prediction has been the main method for heavy rainfall forecasting. However, the accuracy of the numerical prediction depends on the completeness of the atmospheric motion equations, the accuracy of the initial state, the reasonability of the physical process, and the robustness of the calculation method. As the atmosphere is a nonlinear chaotic system, the small errors in these aspects will cause significant uncertainty in the forecast results. Therefore, the improvement of the rainstorm numerical prediction is closely related to the development of data assimilation, parameterization of physical processes, and ensemble prediction, especially for the role of parameterization schemes of cloud microphysical processes that produce precipitation in numerical models. In addition, in order to improve and perfect the numerical model, the investigation of the evaluation method of the forecast results can also not be ignored and is a crucial part of the numerical prediction. This review describes the development of several key numerical weather prediction techniques. The four-dimensional ensemble variational assimilation method, the microphysics parameterization scheme, and the stochastic kinetic energy backscatter method of ensemble prediction model perturbation are highlighted. An evaluation method of model results based on kinetic energy spectrum analysis is also proposed. Finally, the future research directions in these aspects are summarized.

To improve the accuracy of short-term wind speed and power forecasts as well as reduce the impact of wind power uncertainty on the grid system, this study attempted to use wind speed observations to select the optimal numerical forecasting ensemble members that closely matched the actual wind speed within the forecast window. Those selected ensemble members then formed an optimized ensemble for training and testing machine learning models. Unlike the conventional method that relied solely on ensemble averaging, this method considered the forecast discrepancies among different ensemble members, avoided the introduction of members with large errors, and thus helped to improve wind speed prediction. The optimal number of ensemble members for wind farms with different altitudes and terrains in Henan and Gansu was determined based on the results of ensemble performance and sensitivity experiments. Comparative analyses demonstrated that the ensemble selection forecasts outperformed ensemble averaging in predicting wind speed fluctuations during different weather processes, closely aligning with actual wind speed observations. The sea-level pressure field estimates generated by the ensemble selection method exhibited a higher level of agreement with ERA5 data. Evaluation of wind speed and power prediction over eleven consecutive months in different wind farms showed that the ensemble selection method improved the accuracy of diurnal variation and monthly average wind speed compared to the original ensemble averaging method. Analysis of the observed data of upslope and downslope winds with different durations and speed changes in two wind farms showed that there were the most wind speed changes of 2 -4 m s-1 within 0-2 h and 2-4 h. Compared to ensemble averaging, the ensemble selection method significantly improved the prediction accuracy for these upslope and downslope winds. Furthermore, using the machine learning algorithm to train the optimal selection ensemble can further reduce the absolute deviation and root mean square error of wind speed, thereby effectively improving the accuracy of power prediction. [ABSTRACT FROM AUTHOR]

In order to understand more comprehensively the ensemble forecast results of rainfall with the convective scale ensemble prediction system and thus to further recommend them to the weather forecasters, this study carried out the analysis of the forecast performance of a rainstorm process with a convective scale ensemble prediction system. The results show that: (1) The forecast difference of each ensemble member increases with precipitation magnitude, and the threat score difference between the best and worst performed ensemble member is more than 0.3. (2) Probability-matched mean forecast performs better than control forecast and ensemble mean forecast for both rainstorm and heavy rainstorm precipitation. Ensemble mean is insensitive to extreme precipitation due to the smoothing effect of ensemble member forecast. Therefore, ensemble mean is not suitable for extreme precipitation forecast. (3) From the minimum forecast to the maximum forecast, with the increase of ensemble percentile, the probability of detection, false alarm rate, and frequency bias gradually increase. The forecast at 70% or 80% ensemble percentile performs the best, and it is better than the ensemble mean and probability-matched mean forecast. (4) For the heavy rainstorm precipitation in the west part of northeastern Chongqing, the long-time ensemble probability forecasts with leading-times up to 60 h all successfully predict certain precipitation probability of rainstorm, and the forecasted precipitation from the corresponding best performed ensemble member is close to the observation.

In order to provide more powerful support for forecasters in Sichuan and Chongqing with complicated terrain to carry out short-term (0-12 h) precipitation forecast, a convective scale ensemble forecast operational system is designed based on ensemble Kalman filter data assimilation method (31 ensemble samples) and WRF model with 3 km resolution (model domain: 24.5°-34.5°N, 99°-113°E) and lead time of 12 h, which is started by 3 h cycle. It is urgent to decide how many members should be used for the 12 h ensemble forecast to achieve the most representative probability distribution and optimal ensemble forecast skills. An ensemble forecast experiment is carried out on 16 heavy convective scale precipitation cases occurred in Sichuan and Chongqing with different amount of ensemble members, and the results are analyzed comprehensively. It is concluded that the precipitation forecast skills of the ensemble members for different magnitude of precipitation are roughly the same, so there is little difference in the totally averaged prediction skills of different ensemble size. Talagrand distribution becomes better with the increase of ensemble size first. However, when the ensemble size is larger than 17, the improvement by increasing ensemble size is no longer significant. Meanwhile, the forecast error probability becomes smaller with the increase of ensemble size first, but when the ensemble size reaches 16 to 18, the difference between the forecast error probability and the ideal value tends to be stable, indicating that the improvement by further increasing the ensemble size is no longer significant. The relative area of operational characteristic (AROC) score which represents the prediction probability forecast skills, improves gradually with the increase of ensemble size. However, when the ensemble size is large enough, the improvement by lager ensemble size is no longer significant and the AROC scores tend to be stable. The ensemble size required for stable AROC score increases with the magnitude of precipitation. Overall, when the AROC scores become stable, the ensemble size required for light rain, moderate rain, heavy rain, rainstorm (and heavy rainstorm) are 10, 14, 16 and 18, respectively. Based on the comparative analysis results and considering that there is generally little difference in forecasting skills when the number of members is different by 2, in order to achieve the most representative probability distribution and optimal ensemble forecast skills of precipitation, it is recommended to set the ensemble size of convective scale ensemble prediction system from 16 to 18.

In order to study the performance differences of different ensemble forecast products for temperature and precipitation forecasts in Chongqing, based on the Chongqing Convective-scale Ensemble Prediction System (CQCEPS) which has been operationally implemented, a comprehensive comparison and analysis are carried out for the differences as well as temporal and spatial distribution characteristics in the forecast performance of ensemble forecast application products of surface elements such as the control forecast of 24 h cumulative precipitation and 2 m temperature, ensemble mean forecast and ensemble quantile forecast for the whole year of 2020, as well as the probability matched mean forecast of 24 h cumulative precipitation. The results show that (1) the precipitation prediction performance of each ensemble forecast product decreases with the increase of predicting lead time. When predicting lead time is consistent, ensemble mean forecast, probability matched mean forecast, and 60% and higher quantile forecasts are better than control forecast. The 90% quantile forecast is the best among quantile forecast products. (2) In summer and autumn, the forecast results of each product are quite different, and the 90% quantile forecast, probability matched mean forecast and ensemble mean forecast are better than others. (3) The SEEPS scores of control forecast, ensemble mean forecast, probability matched mean forecast and the 90% quantile forecast show higher accuracy in eastern Sichuan and poorer accuracy in the southeast and northeast of Chongqing, indicating that the prediction performance may have a certain relationship with the terrain. (4) For temperature forecasts with different lead times, overall, the ensemble mean forecast performs the best among all of the prediction products, and the 70% quantile forecast performs the best among all quantile products.

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The precipitation Extreme Forecast Index (EFI) derived from ECMWF global ensemble prediction system (EPS) from June 2016 to May 2017 is applied to evaluate the statistical relationship between the EFI and the severe precipitation as well as the climatic precipitation percentile in Zhejiang. Results show that there is an obvious positive correlation between the EFI and the observed precipitation in Zhejiang. With the increase of the EFI threshold, the frequency of rainstorm increases at first and then decreases, and the probability of rainstorm increases with the increase of EFI threshold. With the increase of the EFI, the frequency of rainstorm increases at first and then decreases, while the probability of rainstorm increases with the increase of the EFI. Considering the TS and BS scores comprehensively, the threshold of the EFI decreases with the increase of forecast time length and increase with the increase of precipitation. There is a significant positive correlation between the EFI value and the climatic percentile of precipitation. The greater the EFI is, the higher the percentile is. Thus, the precipitation amount can be estimated according to the corresponding climatic percentile of the observed precipitation.

Using the products of European Centre for Medium-range Weather Forecasts (ECMWF) ensemble prediction and rain gauge data with interval of 6 hours from July 1 to August12 in 2019, we have established a precipitation probability forecast model (hereinafter referred to as the BMA model) based on Bayesian Model Average, and then conducted a comparative analysis of the deterministic forecast and probability forecast of typhoon Lekima precipitation based on the BMA model with respect to the raw ensemble forecast of ECMWF. The main results are as follow. (1) In general, the BMA model has a good correction to the typhoon precipitation ensemble prediction, and it can improve the dispersion of ensemble prediction effectively. (2) Compared with the raw ensemble forecast, the averaged values of CRPS, MAE and RMSE of the BMA model were reduced by 13%, 34% and 25%, respectively, which improved the reliability of the forecast to some extent. However, the improvement degree gradually weakened as the precipitation grade increased. The BMA model can also reduce the false alarm of probability prediction of rainstorm and heavier precipitation to a certain extent. It can also improve the accuracy of typhoon Lekima rainstorm and heavier precipitation' s area forecast. (3) In this case, the 25th percentile to the 95th percentile forecast can be regarded as an effective prediction interval. The effective prediction interval revised by the BMA model has a stronger ability to capture precipitation observations, and the ensemble forecast capture rate revised by the BMA model is 13.3% higher than that of raw ensemble forecast.

The accuracy of the monthly runoff forecast plays a fairly important role in aspects such as optimal allocation of water resources,flood control and drought relief in a basin,water dispatching,and power generation optimization of reservoir groups.The commonly used methods for the monthly runoff forecast mainly include water balance models,mathematical statistics models,and artificial neural networks.Studies have shown that any single model cannot achieve the optimal monthly runoff forecast.Therefore,the multi-model ensemble method provides an effective way to eliminate model uncertainty and improve the accuracy of the monthly runoff forecast.Specifically,the research takes Pingshi,Lishi,Hengshi,and Shijiao stations in the Beijiang River Basin as the research object to analyze and compare the effects of the seasonal auto-regressive (SAR) model,two-parameter monthly water balance (TPMWB) model,and artificial neural network (ANN) model.Then,the multi-model ensemble method for the above-mentioned stations is proposed on the basis of the Bayesian model averaging (BMA) method.The research results reveal that compared with any of the three models,the multi-model ensemble method has significantly improved the accuracy of the monthly runoff forecast with a higher determination coefficient (DC) and a lower mean absolute percentage error (MAPE),and thus it can provide better support for decisions in dispatching in the basin.