东亚地区冬季地面气温延伸期概率预报研究

利用TIGGE资料中的ECMWF、NCEP、UKMO三个中心集合预报系统以及由此构成的多中心集合预报系统所提供的地面2 m气温10~15 d延伸期集合预报产品,建立贝叶斯模式平均(Bayesian Model Averaging,BMA)概率预报模型,对东亚地区冬季地面气温进行延伸期概率预报研究。采用距平相关系数、均方根误差、布莱尔评分、等级概率评分等指标分别对BMA确定性结果与概率预报进行评估。结果表明,BMA方法明显地改进了原始集合预报结果,预报技巧优于原始集合预报,且多中心BMA预报优于单中心BMA预报,最佳滑动训练期取35 d。BMA预报为气温的延伸期概率预报提供了更合理的概率分布,定量描述了预报的不确定性。     

Probabilistic Precipitation Forecasting Based on Ensemble Output Using Generalized Additive Models and Bayesian Model Averaging

A probabilistic precipitation forecasting model using generalized additive models (GAMs) and Bayesian model averaging (BMA) was proposed in this paper.GAMs were used to fit the spatial-temporal precipi...     

Projections of high resolution climate changes for South Korea using multiple-regional climate models based on four RCP scenarios. Part 1: surface air temperature

We projected surface air temperature changes over South Korea during the mid (2026-2050) and late (2076-2100) 21st century against the current climate (1981-2005) using the simulation results from five regional climate models (RCMs) driven by Hadley Centre Global Environmental Model, version 2, coupled with the Atmosphere- Ocean (HadGEM2-AO), and two ensemble methods (equal weighted averaging, weighted averaging based on Taylor’s skill score) under four Representative Concentration Pathways (RCP) scenarios. In general, the five RCM ensembles captured the spatial and seasonal variations, and probability distribution of temperature over South Korea reasonably compared to observation. They particularly showed a good performance in simulating annual temperature range compared to HadGEM2-AO. In future simulation, the temperature over South Korea will increase significantly for all scenarios and seasons. Stronger warming trends are projected in the late 21st century than in the mid-21st century, in particular under RCP8.5. The five RCM ensembles projected that temperature changes for the mid/late 21st century relative to the current climate are +1.54°C/+1.92°C for RCP2.6, +1.68°C/+2.91°C for RCP4.5, +1.17°C/+3.11°C for RCP6.0, and +1.75°C/+4.73°C for RCP8.5. Compared to the temperature projection of HadGEM2-AO, the five RCM ensembles projected smaller increases in temperature for all RCP scenarios and seasons. The inter-RCM spread is proportional to the simulation period (i.e., larger in the late-21st than mid-21st century) and significantly greater (about four times) in winter than summer for all RCP scenarios. Therefore, the modeled predictions of temperature increases during the late 21st century, particularly for winter temperatures, should be used with caution.     

基于贝叶斯原理降水订正的水文概率预报试验

利用淮河流域加密站点2008年6月1日—8月31日逐日降水资料、对应的T213模式的24 h, 48 h以及72 h集合预报,采用贝叶斯模型平均 (Bayesian Model Averaging,BMA) 方法对集合预报15个成员的降水预报进行了概率集成与偏差订正,采用排序概率评分 (CRPS)、平均绝对误差 (MAE) 对BMA的订正结果进行检验,并将订正后的降水预报输入VIC (Variable Infiltration Capacity) 水文模型中进行水文概率预报。结果表明:经BMA订正后的24 h, 48 h, 72 h降水预报精度较订正前有所提高;BMA模型给出的有效区间 (第25百分位数至第75百分位数) 预报将实况降水量包含在内的可能性比订正前更大;由水文概率预报检验指标分析可知,经BMA订正的降水集合预报,由VIC水文模型模拟得到的径流量变化趋势与实况较吻合。     

Improving Multi-Model Ensemble Forecasts of Tropical Cyclone Intensity Using Bayesian Model Averaging

This paper proposes a method for multi-model ensemble forecasting based on Bayesian model averaging (BMA), aiming to improve the accuracy of tropical cyclone (TC) intensity forecasts, especially forecasts of minimum surface pressure at the cyclone center (P_min). The multi-model ensemble comprises three operational forecast models: the Global Forecast System (GFS) of NCEP, the Hurricane Weather Research and Forecasting (HWRF) models of NCEP, and the Integrated Forecasting System (IFS) of ECMWF. The mean of a predictive distribution is taken as the BMA forecast. In this investigation, bias correction of the minimum surface pressure was applied at each forecast lead time, and the distribution (or probability density function, PDF) of P_min was used and transformed. Based on summer season forecasts for three years, we found that the intensity errors in TC forecast from the three models varied significantly. The HWRF had a much smaller intensity error for short lead-time forecasts. To demonstrate the proposed methodology, cross validation was implemented to ensure more efficient use of the sample data and more reliable testing. Comparative analysis shows that BMA for this three-model ensemble, after bias correction and distribution transformation, provided more accurate forecasts than did the best of the ensemble members (HWRF), with a 5%–7% decrease in root-mean-square error on average. BMA also outperformed the multi-model ensemble, and it produced “predictive variance” that represented the forecast uncertainty of the member models. In a word, the BMA method used in the multi-model ensemble forecasting was successful in TC intensity forecasts, and it has the potential to be applied to routine operational forecasting.     

基于TIGGE多模式集合的24小时气温BMA 概率预报

利用TIGGE(THORPEX Interactive Grand Global Ensemble)单中心集合预报系统(ECMWF、United Kingdom Meteorological Office、China Meteorological Administration和NCEP)以及由此所构成的多中心模式超级集合预报系统24小时地面日均气温预报,结合淮河流域地面观测率定贝叶斯模型平均(Bayesian model averaging,BMA)参数,从而建立地面日均气温BMA概率预报模型.由此针对淮河流域进行地面日均气温BMA概率预报及其检验与评估,结果表明BMA模型比原始集合预报效果好;单中心的BMA概率预报都有较好的预报效果,其中ECMWF最好.多中心模式超级集合比单中心BMA概率预报效果更好,采用可替换原则比普通的多中心模式超级集合BMA模型计算量小,且在上述BMA集合预报系统中效果最好.它与原始集合预报相比其平均绝对误差减少近7%,其连续等级概率评分提高近10%.基于采用可替换原则的多中心模式超级集合BMA概率预报,针对研究区域提出了极端高温预警方案,这对防范高温天气有着重要意义.     

Bias Correction and Ensemble Projections of Temperature Changes over Ten Subregions in CORDEX East Asia

Regional climate models (RCMs) participating in the Coordinated Regional Downscaling Experiment (CORDEX) have been widely used for providing detailed climate change information for specific regions under different emissions scenarios. This study assesses the effects of three common bias correction methods and two multi-model averaging methods in calibrating historical (1980?2005) temperature simulations over East Asia. Future (2006?49) temperature trends under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios are projected based on the optimal bias correction and ensemble averaging method. Results show the following: (1) The driving global climate model and RCMs can capture the spatial pattern of annual average temperature but with cold biases over most regions, especially in the Tibetan Plateau region. (2) All bias correction methods can significantly reduce the simulation biases. The quantile mapping method outperforms other bias correction methods in all RCMs, with a maximum relative decrease in root-mean-square error for five RCMs reaching 59.8% (HadGEM3-RA), 63.2% (MM5), 51.3% (RegCM), 80.7% (YSU-RCM) and 62.0% (WRF). (3) The Bayesian model averaging (BMA) method outperforms the simple multi-model averaging (SMA) method in narrowing the uncertainty of bias-corrected results. For the spatial correlation coefficient, the improvement rate of the BMA method ranges from 2% to 31% over the 10 subregions, when compared with individual RCMs. (4) For temperature projections, the warming is significant, ranging from 1.2°C to 3.5°C across the whole domain under the RCP8.5 scenario. (5) The quantile mapping method reduces the uncertainty over all subregions by between 66% and 94%.     

Comparison of Statistical Post-Processing Methods for Probabilistic Wind Speed Forecasting

In this study, the statistical post-processing methods that include bias-corrected and probabilistic forecasts of wind speed measured in PyeongChang, which is scheduled to host the 2018 Winter Olympics, are compared and analyzed to provide more accurate weather information. The six post-processing methods used in this study are as follows: mean bias-corrected forecast, mean and variance bias-corrected forecast, decaying averaging forecast, mean absolute bias-corrected forecast, and the alternative implementations of ensemble model output statistics (EMOS) and Bayesian model averaging (BMA) models, which are EMOS and BMA exchangeable models by assuming exchangeable ensemble members and simplified version of EMOS and BMA models. Observations for wind speed were obtained from the 26 stations in PyeongChang and 51 ensemble member forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF Directorate, 2012) that were obtained between 1 May 2013 and 18 March 2016. Prior to applying the post-processing methods, reliability analysis was conducted by using rank histograms to identify the statistical consistency of ensemble forecast and corresponding observations. Based on the results of our study, we found that the prediction skills of probabilistic forecasts of EMOS and BMA models were superior to the biascorrected forecasts in terms of deterministic prediction, whereas in probabilistic prediction, BMA models showed better prediction skill than EMOS. Even though the simplified version of BMA model exhibited best prediction skill among the mentioned six methods, the results showed that the differences of prediction skills between the versions of EMOS and BMA were negligible.     

Validation of precipitation over Japan during 1985–2004 simulated by three regional climate models and two multi-model ensemble means

We dynamically downscaled Japanese reanalysis data (JRA-25) for 60 regions of Japan using three regional climate models (RCMs): the Non-Hydrostatic Regional Climate Model (NHRCM), modified RAMS version 4.3 (NRAMS), and modified Weather Research and Forecasting model (TWRF). We validated their simulations of the precipitation climatology and interannual variations of summer and winter precipitation. We also validated precipitation for two multi-model ensemble means: the arithmetic ensemble mean (AEM) and an ensemble mean weighted according to model reliability. In the 60 regions NRAMS simulated both the winter and summer climatological precipitation better than JRA-25, and NHRCM simulated the wintertime precipitation better than JRA-25. TWRF, however, overestimated precipitation in the 60 regions in both the winter and summer, and NHRCM overestimated precipitation in the summer. The three RCMs simulated interannual variations, particularly summer precipitation, better than JRA-25. AEM simulated both climatological precipitation and interannual variations during the two seasons more realistically than JRA-25 and the three RCMs overall, but the best RCM was often superior to the AEM result. In contrast, the weighted ensemble mean skills were usually superior to those of the best RCM. Thus, both RCMs and multi-model ensemble means, especially multi-model ensemble means weighted according to model reliability, are powerful tools for simulating seasonal and interannual variability of precipitation in Japan under the current climate.     

Development of updateable model output statistics (UMOS) system for air temperature over South Korea

In this study, a Updateable Model Output Statistics (UMOS) system has been developed for the forecast of 3-h temperature over South Korea using two significantly different models’ (Regional Data Assimilation and Prediction System (RDAPS) and Korea Meteorological Administration (KMA) Weather Research and Forecasting (WRF) model (KWRF)) outputs based on the Canadian UMOS system (Wilson and Vallee, 2002; 2003). The UMOS system is designed to consider the local climatology and the model’s forecasting skills. The 20 most frequently selected potential predictors for each season, station, and forecast projection time from the 67 potential predictors of the Model Output Statistics (MOS) system, were used as potential predictors of the UMOS system. The UMOS equations are developed by a weighted blending of the new and old model data, with weights chosen to emphasize the new model data while including enough old model data in the development to ensure stable equations and a smooth transition to dependency on the new model. The UMOS equations were updated regularly at a predefined time interval to consider the changes of covariance structure between the new model output and observations as the new model data increase. The validation results showed that seasonal mean bias, Root Mean Square Error (RMSE), and correlation coefficients for the total forecast projection times are ?0.379～0.055°C, 1.951～2.078°C, and 0.741～0.965, respectively. Although, the forecasting skills of UMOS system are very consistent without regard to the season and geographic location, the performance is slightly better in autumn and winter than in spring and summer, and better in coastal regions than in inland region. When we take into account the significant differences of the RDAPS and KWRF, the UMOS system can be used as a supplementary forecasting tool of the MOS system for 3-h temperature over South Korea. However, the UMOS system is very sensitive to the selected number and/or types of predictors. Therefore, more work is needed to enable the use of the UMOS system in operation, including tuning of the number and types of potential predictors and automation of the updating processes of the UMOS equations.     

Comparison of prediction performance using statistical postprocessing methods

As the 2018 Winter Olympics are to be held in Pyeongchang, both general weather information on Pyeongchang and specific weather information on this region, which can affect game operation and athletic performance, are required. An ensemble prediction system has been applied to provide more accurate weather information, but it has bias and dispersion due to the limitations and uncertainty of its model. In this study, homogeneous and nonhomogeneous regression models as well as Bayesian model averaging (BMA) were used to reduce the bias and dispersion existing in ensemble prediction and to provide probabilistic forecast. Prior to applying the prediction methods, reliability of the ensemble forecasts was tested by using a rank histogram and a residualquantile-quantile plot to identify the ensemble forecasts and the corresponding verifications. The ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed. To correct such biases, statistical post-processing methods were applied using fixed and sliding windows. The prediction skills of methods were compared by using the mean absolute error, root mean square error, continuous ranked probability score, and continuous ranked probability skill score. Under the fixed window, BMA exhibited better prediction skill than the other methods in most observation station. Under the sliding window, on the other hand, homogeneous and non-homogeneous regression models with positive regression coefficients exhibited better prediction skill than BMA. In particular, the homogeneous regression model with positive regression coefficients exhibited the best prediction skill.     

基于贝叶斯理论的单站地面气温的概率预报研究

基于贝叶斯理论,建立了将确定性预报向概率预报转换的基本模式,并利用TIGGE资料中欧洲中期天气预报中心(ECMWF)地面气温预报资料及地面气温观测资料,对概率化后的预报进行了评估与释用。结果表明,概率化后的预报结果不但能提供丰富的预报产品,而且所提供的预报均值优于原始的确定性预报。应用贝叶斯模式平均法(BMA)将中国气象局(CMA)、美国国家环境预报中心(NCEP)和ECMWF 3个模式的预报结果进行多模式集成,得到了更为合理的概率分布,其中分布的均值可作为模式的预报结果,方差和置信区间反映了预报量的可变范围。因此,基于贝叶斯预报模式的概率预报相对于确定性预报,不但能够提供更高精度的预报,而且能提供更全面的预报信息。BMA集成预报结果不但优于集合平均预报,而且还能定量描述预报的不确定性。利用ECMWF预报中心51个预报成员进行集成贝叶斯概率预报试验,发现BMA预报融合了各成员对预报不确定性的描述,还对概率预报的均值进行了调整,使之与观测值更为接近。BMA预报的概率密度分布更能反映大气的真实分布情况。     

多模式集成的概率天气预报和气候预测研究进展

基于大气的混沌特性,单一的确定性预报逐步向多值的不确定性概率预报转化已成为一种趋势。本文系统地评述了概率天气预报产生的背景,介绍了概率预报的相关概念及国内外的研究状况,着重讨论了多模式集成的概率预报的两种集成方法,即贝叶斯模式平均(Bayesian model averaging,BMA)和多元高斯集合核拟合法(Gaussian ensemble kernel dressing,GEKD),并给出了两个例子的概率预报试验结果。利用BMA方法制作的概率预报的方差较小,减小了预报的不确定性,因此预报结果更接近大气的真实值。作为另一种多模式集成方法,多元高斯集合核拟合法回报的地面气温距平均值及趋势的概率预测结果与实测结果基本一致。利用此方法建立了地面气温年代际变化的概率多模式集合预测模型,并从中提取年代际气候变化特征,对东亚季风区年代际预测具有重要应用价值。     

一种新的集合预报权重平均方法

提出了一种新的考虑权重的集合预报成员平均方法。使用气候等概率区间来对集合成员进行分组, 并根据气候等概率区间的大小及其中的成员数, 对集合成员的权重进行调整, 得到了一种改进的集合平均预报结果。检验表明, 它可以进一步提高集合平均预报的效果。相对于提高模式分辨率或发展庞大的集合预报系统, 这种方法的效果是显著的。     

Projections of high resolution climate changes for South Korea using multiple-regional climate models based on four RCP scenarios. Part 2: precipitation

Precipitation changes over South Korea were projected using five regional climate models (RCMs) with a horizontal resolution of 12.5 km for the mid and late 21st century (2026-2050, 2076- 2100) under four Representative Concentration Pathways (RCP) scenarios against present precipitation (1981-2005). The simulation data of the Hadley Centre Global Environmental Model version 2 coupled with the Atmosphere-Ocean (HadGEM2-AO) was used as boundary data of RCMs. In general, the RCMs well simulated the spatial and seasonal variations of present precipitation compared with observation and HadGEM2-AO. Equal Weighted Averaging without Bias Correction (EWA_NBC) significantly reduced the model biases to some extent, but systematic biases in results still remained. However, the Weighted Averaging based on Taylor’s skill score (WEA_Tay) showed a good statistical correction in terms of the spatial and seasonal variations, the magnitude of precipitation amount, and the probability density. In the mid-21st century, the spatial and interannual variabilities of precipitation over South Korea are projected to increase regardless of the RCP scenarios and seasons. However, the changes in area-averaged seasonal precipitation are not significant due to mixed changing patterns depending on locations. Whereas, in the late 21st century, the precipitation is projected to increase proportionally to the changes of net radiative forcing. Under RCP8.5, WEA_Tay projects the precipitation to be increased by about +19.1, +20.5, +33.3% for annual, summer and winter precipitation at 1-5% significance levels, respectively. In addition, the probability of strong precipitation (≥ 15 mm d^-1) is also projected to increase significantly, particularly in WEA_Tay under RCP8.5.     

Comparison of four ensemble methods combining regional climate simulations over Asia

A number of uncertainties exist in climate simulation because the results of climate models are influenced by factors such as their dynamic framework, physical processes, initial and driving fields, and horizontal and vertical resolution. The uncertainties of the model results may be reduced, and the credibility can be improved by employing multi-model ensembles. In this paper, multi-model ensemble results using 10-year simulations of five regional climate models (RCMs) from December 1988 to November 1998 over Asia are presented and compared. The simulation results are derived from phase II of the Regional Climate Model Inter-comparison Project (RMIP) for Asia. Using the methods of the arithmetic mean, the weighted mean, multivariate linear regression, and singular value decomposition, the ensembles for temperature, precipitation, and sea level pressure are carried out. The results show that the multi-RCM ensembles outperform the single RCMs in many aspects. Among the four ensemble methods used, the multivariate linear regression, based on the minimization of the root mean square errors, significantly improved the ensemble results. With regard to the spatial distribution of the mean climate, the ensemble result for temperature was better than that for precipitation. With an increasing number of models used in the ensembles, the ensemble results were more accurate. Therefore, a multi-model ensemble is an efficient approach to improve the results of regional climate simulations.     

Probabilistic temperature forecasting with statistical calibration in Hungary

Weather forecasting is based on the outputs of deterministic numerical weather forecasting models. Multiple runs of these models with different initial conditions result in forecast ensembles which are used for estimating the distribution of future atmospheric variables. However, these ensembles are usually under-dispersive and uncalibrated, so post-processing is required. In the present work, Bayesian model averaging (BMA) is applied for calibrating ensembles of temperature forecasts produced by the operational limited area model ensemble prediction system of the Hungarian Meteorological Service (HMS). We describe two possible BMA models for temperature data of the HMS and show that BMA post-processing significantly improves calibration and probabilistic forecasts although the accuracy of point forecasts is rather unchanged.     

Forecast Performance of the Pre-operational CMA-TRAMS (EPS) in South China During April - September 2020

The mesoscale ensemble prediction system based on the Tropical Regional Atmosphere Model for the South China Sea (CMA-TRAMS (EPS)) has been pre-operational since April 2020 at South China Regional Meteorological Center (SCRMC), which was developed by the Guangzhou Institute of Tropical and Marine Meteorology (GITMM). To better understand the performance of the CMA-TRAMS (EPS) and provide guidance to forecasters, we assess the performance of this system on both deterministic and probabilistic forecasts from April to September 2020 in this study through objective verification. Compared with the control (deterministic) forecasts, the ensemble mean of the CMATRAMS (EPS) shows advantages in most non-precipitation variables. In addition, the threat score indicates that the CMA-TRAMS (EPS) obviously improves light and heavy rainfall forecasts in terms of the probability-matched mean. Compared with the European Center for Medium-range Weather Forecasts operational ensemble prediction system (ECMWF-EPS), the CMA-TRAMS (EPS) improves the probabilistic forecasts of light rainfall in terms of accuracy, reliability and discrimination, and this system also improves the heavy rainfall forecasts in terms of discrimination. Moreover, two typical heavy rainfall cases in south China during the pre-summer rainy season are investigated to visually demonstrate the deterministic and probabilistic forecasts, and the results of these two cases indicate the differences and advantages (deficiencies) of the two ensemble systems.     

Intercomparison of prediction skills of ensemble methods using monthly mean temperature simulated by CMIP5 models

This study focuses on an objective comparison of eight ensemble methods using the same data, training period, training method, and validation period. The eight ensemble methods are: BMA (Bayesian Model Averaging), HMR (Homogeneous Multiple Regression), EMOS (Ensemble Model Output Statistics), HMR+ with positive coefficients, EMOS+ with positive coefficients, PEA_ROC (Performance-based Ensemble Averaging using ROot mean square error and temporal Correlation coefficient), WEA_Tay (Weighted Ensemble Averaging based on Taylor’s skill score), and MME (Multi-Model Ensemble). Forty-five years (1961-2005) of data from 14 CMIP5 models and APHRODITE (Asian Precipitation- Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources) data were used to compare the performance of the eight ensemble methods. Although some models underestimated the variability of monthly mean temperature (MMT), most of the models effectively simulated the spatial distribution of MMT. Regardless of training periods and the number of ensemble members, the prediction skills of BMA and the four multiple linear regressions (MLR) were superior to the other ensemble methods (PEA_ROC, WEA_Tay, MME) in terms of deterministic prediction. In terms of probabilistic prediction, the four MLRs showed better prediction skills than BMA. However, the differences among the four MLRs and BMA were not significant. This resulted from the similarity of BMA weights and regression coefficients. Furthermore, prediction skills of the four MLRs were very similar. Overall, the four MLRs showed the best prediction skills among the eight ensemble methods. However, more comprehensive work is needed to select the best ensemble method among the numerous ensemble methods.     

Evaluation of the CORDEX regional climate models performance in simulating climate conditions of two catchments in Upper Blue Nile Basin

This study was targeted at evaluating the performance of six Regional Climate Models (RCMs) used in Coordinated Regional Climate Downscaling Experiment (CORDEX). The evaluation is on the bases of how well the RCMs simulate the seasonal mean climatology, interannual variability and annual cycles of rainfall, maximum and minimum temperature over two catchments in western Ethiopia during the period 1990–2008. Observed data obtained from the Ethiopian National Meteorological Agency was used for performance evaluation of the RCMs outputs. All Regional Climate Models (RCMs) have simulated seasonal mean annual cycles of precipitation with a significant bias shown on individual models; however, the ensemble mean exhibited better the magnitude and seasonal rainfall. Despite the highest biases of RCMs in the wet season, the annual cycle showed the prominent features of precipitation in the two catchments. In many aspects, CRCM5 and RACMO22 T simulate rainfall over most stations better than the other models. The highest biases are associated with the highest error in simulating maximum and minimum temperature with the highest biases in high elevation areas. The rainfall interannual variability is less evident in Finchaa with short rainy season experiencing a larger degree of interannual variability. The differences in performance of the Regional Climate Models in the two catchments show that all the available models are not equally good for particular locations and topographies. In this regard, the right regional climate models have to be used for any climate change impact study for local-scale climate projections.