In recent years, ensemble forecasting has become a vital tool for major global weather forecasting centres. However, ensemble forecasts often exhibit underdispersion and systematic biases, making the application of statistical post-processing methods essential. The standardized anomalies model output statistics (SAMOS) is a commonly used post-processing technique that provides a complete description of the forecast distribution. Nevertheless, SAMOS typically relies only on predictors either directly related to the forecast variable or selected based on subjective judgment, potentially overlooking other valuable predictors. Moreover, directly incorporating too many predictors into SAMOS may lead to overfitting. Therefore, effectively selecting key predictors from numerous variables provided by forecast models remains a significant challenge. Boosting-based variable selection and optimization algorithms have proven to be effective in mitigating overfitting and identifying the most important predictors. This study proposes the standardized anomalies gradient boosting (SABST) method by integrating the strength of SAMOS with a boosting-based variable selection algorithm. SABST is applied to calibrate ensemble forecasts of 2 m air temperature, 2 m relative humidity, and 10  m wind speed. The SABST model is developed using the European Centre for Medium-Range Weather Forecasts (ECMWF) high-resolution ensemble forecast (ensemble prediction system, ENS) products during 2019—2020 and is systematically compared with SAMOS, focusing on its calibration performance and bias correction ability. Results show that compared to ENS and SAMOS, SABST performs better in addressing underdispersion in probabilistic forecasts and improving the accuracy of deterministic forecasts. Based on the continuous ranked probability skill score (CRPSS), SABST improves the average CRPSS by 9.5%, 15.3%, and 4.6% across all forecast lead time compared to SAMOS. These findings demonstrate the advantage of introducing additional potential predictors in the SABST framework and highlight the method's potential for application in ensemble forecast post-processing.

Systematic biases in numerical weather prediction commonly require post-processing correction. Ensemble Model Output Statistics (EMOS) is a post-processing method for ensemble forecasts. In recent years, two other variations of EMOS (gEMOS and SAMOS) have been proposed to improve EMOS. This paper aims to evaluate their performance. A comparative study has been conducted for 2 m temperature, relative humidity, 10 m wind speed, and 3 h cumulative precipitation in North China using five numerical models, i.e., the Global Ensemble Prediction System (GEPS), the Global Forecast System (GFS), the Regional Ensemble Prediction System (REPS), and two mesoscale weather numerical forecasts (MESO-10 km, MESO-3 km) with different spatial resolutions from the China Meteorological Administration (CMA). Results show that all the three post-processing methods can reduce forecast errors of the CMA models across these variables. Specifically, (1) the EMOS method, which independently calculates parameters for each station, retains the unique characteristics of individual stations, resulting in optimal performance; (2) gEMOS underperforms EMOS due to its neglect of inter-station independence; (3) for variables such as temperature, humidity, and wind speed, which accurately simulate climatological distribution, SAMOS performance is comparable to that of EMOS. For precipitation, SAMOS's performance is constrained by climatological precipitation distribution simulation; the forecast error of SAMOS is larger than that of EMOS, yet it is still smaller than that of gEMOS.