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| 1 | A Regional Ensemble Forecast System for Stratiform Precipitation Events in the Northern China Region.Part Ⅱ:Seasonal Evaluation for Summer 2010显示文摘In this study,the Institute of Atmospheric Physics,Chinese Academy of Sciences-regional ensemble forecast system(IAP-REFS) described in Part I was further validated through a 65-day experiment using the summer season of 2010.The verification results show that IAP-REFS is skillful for quantitative precipitation forecasts(QPF) and probabilistic QPF,but it has a systematic bias in forecasting near-surface variables.Applying a 7-day running mean bias correction to the forecasts of near-surface variables remarkably improved the reliability of the forecasts.In this study,the perturbation extraction and inflation method(proposed with the single case study in Part I) was further applied to the full season with different inflation factors.This method increased the ensemble spread and improved the accuracy of forecasts of precipitation and near-surface variables.The seasonal mean profiles of the IAP-REFS ensemble indicate good spread among ensemble members and some model biases at certain vertical levels. | 朱江山 孔凡铀 雷恒池 | 2013 | Advances in Atmospheric Sciences2013，30，1： | 8 |
| 2 | GRAPES区域集合预报两种侧边界扰动方法对比试验显示文摘中国气象局数值预报中心自2014年建立了区域集合预报业务系统,其使用的侧边界扰动由全球集合预报系统动力降尺度得到。为深入了解侧边界扰动对区域集合预报的影响,基于15 km水平分辨率的区域集合预报模式,使用动力降尺度方法和尺度化滞后平均法(scaled lagged average forecasting,SLAF)设计构造了两种侧边界扰动方案,并开展了2015年7月共6天的集合预报试验,利用集合均方根误差、集合离散度、连续分级概率评分、离群值、Brier Score及相对作用特征曲线面积等概率预报检验方法进行了多方面检验,分析了两种侧边界扰动方案对区域集合预报质量的影响。结果表明:动力降尺度侧边界扰动方案(DOWN)的扰动总能量在各垂直层次均大于SLAF方案,使得边界上前者的离散度大于后者,集合扰动增长更为合理;对于等压面要素和地面要素,DOWN方案的离散度、Outlier、CRPS等评分优于SLAF方案,反映了DOWN方案构造的侧边界扰动更加合理;在降水概率预报技巧方面,SLAF方案在评分上具有一定优势,但评分的提高没有通过显著性水平检验,因此认为两种方案对降水预报的改进基本相当。 | 范宇恩 陈静 邓国 陈法敬 刘雪晴 徐致真 | 2019 | 气象2019，45，12： | 3 |
| 3 | Preface显示文摘The Flexible Global Ocean-Atmosphere-Land System model (FGOALS) is a coupled climate model that allows researchers to conduct fundamental research into the Earth’s past, present, near-term and long-term future climate states. FGOALS couples the ocean, atmosphere, land, and sea ice through a coupler that coordinates the component models and passes the exchange of energy, momentum, and water among them. | ZHOU Tianjun | 2013 | Advances in Atmospheric Sciences2013，30，3： | 3 |
| 4 | Bias-Corrected Short-Range Ensemble Forecasts for Near-Surface Variables during the Summer Season of 2010 in Northern China显示文摘A running mean bias(RMB) correction approach was applied to the forecasts of near-surface variables in a seasonal short-range ensemble forecasting experiment with 57 consecutive cases during summer 2010 in the northern China region. To determine a proper training window length for calculating RMB, window lengths from 2 to 20 days were evaluated, and 16 days was taken as an optimal window length, since it receives most of the benefit from extending the window length. The raw and 16-day RMB corrected ensembles were then evaluated for their ensemble mean forecast skills. The results show that the raw ensemble has obvious bias in all near-surface variables. The RMB correction can remove the bias reasonably well, and generate an unbiased ensemble. The bias correction not only reduces the ensemble mean forecast error, but also results in a better spreaderror relationship. Moreover, two methods for computing calibrated probabilistic forecast(PF) were also evaluated through the 57 case dates: 1) using the relative frequency from the RMB-corrected ensemble; 2) computing the forecasting probabilities based on a historical rank histogram. The first method outperforms the second one, as it can improve both the reliability and the resolution of the PFs, while the second method only has a small effect on the reliability, indicating the necessity and importance of removing the systematic errors from the ensemble. | ZHU Jiang-Shan KONG Fan-You LEI Heng-Chi | 2014 | Atmospheric and Oceanic Science Letters2014，7，4： | 2 |
| 5 | Index to Vol.29显示文摘 | | 2012 | Advances in Atmospheric Sciences2012，29，6： | 0 |
| 6 | A Methodological Study on Using Weather Research and Forecasting(WRF) Model Outputs to Drive a One-Dimensional Cloud Model显示文摘A new method for driving a One-Dimensional Stratiform Cold(1DSC) cloud model with Weather Research and Forecasting(WRF) model outputs was developed by conducting numerical experiments for a typical large-scale stratiform rainfall event that took place on 4–5 July 2004 in Changchun, China. Sensitivity test results suggested that, with hydrometeor profiles extracted from the WRF outputs as the initial input, and with continuous updating of soundings and vertical velocities(including downdraft) derived from the WRF model, the new WRF-driven 1DSC modeling system(WRF-1DSC) was able to successfully reproduce both the generation and dissipation processes of the precipitation event. The simulated rainfall intensity showed a time-lag behind that observed, which could have been caused by simulation errors of soundings, vertical velocities and hydrometeor profiles in the WRF output. Taking into consideration the simulated and observed movement path of the precipitation system, a nearby grid point was found to possess more accurate environmental fields in terms of their similarity to those observed in Changchun Station. Using profiles from this nearby grid point, WRF-1DSC was able to reproduce a realistic precipitation pattern. This study demonstrates that 1D cloud-seeding models do indeed have the potential to predict realistic precipitation patterns when properly driven by accurate atmospheric profiles derived from a regional shortrange forecasting system. This opens a novel and important approach to developing an ensemble-based rain enhancement prediction and operation system under a probabilistic framework concept. | JIN Ling Fanyou KONG LEI Hengchi HU Zhaoxia | 2014 | Advances in Atmospheric Sciences2014，31，1： | 0 |
| 7 | Using the Inverse of Expected Error Variance to Determine Weights of Individual Ensemble Members: Application to Temperature Prediction显示文摘The inverse of expected error variance is utilized to determine weights of individual ensemble members based on the THORPEX(The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble(TIGGE) forecast datasets. The weights of all ensemble members are thus calculated for summer 2012, with the NCEP final operational global analysis(FNL) data as the truth. Based on the weights of all ensemble members, the variable weighted ensemble mean(VWEM) of temperature of summer 2013 is derived and compared with that from the simple equally weighted ensemble mean. The results show that VWEM has lower root-mean-square error(RMSE) as well as absolute error, and has improved the temperature prediction accuracy. The improvements are quite notable over the Tibetan Plateau and its surrounding areas; specifically, a relative improvement rate of RMSE of more than 24% in 2-m temperature is demonstrated. Moreover, the improvement rates vary slightly with the prediction lead-time(24–96 h). It is suggested that the VWEM approach be employed in operational ensemble prediction to provide guidance for weather forecasting and climate prediction. | Xiaogong SUN Jinfang YIN Yan ZHAO | 2017 | Journal of Meteorological Research2017，31，3： | 0 |
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