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...     

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.     

CMIP5多模式对东亚地区地面气温年际变率的回报研究

对CMIP5全球气候模式中年代际回报试验的气温资料及其简单集合平均(Multi-model ensemble mean,EMN)和贝叶斯模式平均的结果(Bayesian Model Averaging,BMA)进行经验正交函数(Empirical Orthogonal Function,EOF)分解和Morlet小波分析,检验评估各个模式及其EMN和BMA对东亚地面气温的方差、气温时空分布特征及周期变化的回报能力。结果表明,10个模式、EMN、BMA都能很好地回报出1981—2010年东亚地面气温的方差分布,其中BMA回报效果最好。EOF分析表明,BMA能较好地回报出东亚地面气温第一模态的时空分布。MIROC5能较好地回报出第二模态的趋势变化,但却不能回报出气温的年际变率。绝大多数模式和EMN、BMA虽然能回报出东亚地面气温的变化趋势,但是对气温年际变率的回报仍然是比较困难的。CMCC-CM对气温变化主模态的3~5 a的周期变化特征回报效果最好,和NCEP资料的结果最为接近。     

地面气象要素多模式集成预报研究进展

目前,集合预报已成为天气预报业务的主要支撑。然而,由于数值模式本身的限制与不完善以及集合系统存在初值扰动、集合大小等方面的局限,常存在预报偏差。不同预报模式通常具有不同的物理过程参数化方案、初始条件等,导致其预报能力各有不同。为此,如何纠正预报偏差以及如何充分有效地利用不同模式的预报信息以获得更加准确的天气预报广受关注。近年来,利用统计理论与预报诊断,基于多个集合预报系统的多模式集成预报技术得到快速发展,已成为有效消除预报偏差从而提高天气预报技巧的一种统计后处理方法。针对气温、降水和风3个最基本的地面气象要素,首先依据预报形式将应用范围较广的简单集合平均、消除偏差集合平均、超级集合、贝叶斯模式平均、集合模式输出统计等加权或等权平均多模式集成技术,分成确定性预报和概率预报两大类,并做系统介绍。最后,讨论使用和发展多模式集成技术需要关注的问题,包括考虑参与集成的模式个数、发展降水及风速分级预报模型和发展基于机器学习的多模式集成新技术。     

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.     

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。     

集合模式定量降水预报的统计后处理技术研究综述

集合数值模式预报已在定量降水预报业务中广泛应用,以获得预报不确定性、最可能预报结果以及极端天气预警。由于集合系统的数值模式不完善,且不能提供所有的不确定性信息,常表现出系统性偏差以及欠离散或过离散（如对于多模式集合）。为此,需要发展统计后处理技术,在尽量保持集合预报解析度的条件下,提高预报的技巧和可靠性。近年来,各种集合预报统计后处理技术得到快速发展。针对定量降水预报,依据技术方法的途径和成熟度将后处理研究归纳为3方面进行总结,包括:（1）不基于统计模型的非参数化后处理,包括集合定量降水预报偏差订正、多成员或模式信息集成以及基于空间分析的对流尺度模式后处理;（2）基于概率分布统计模型的参数化后处理,包括集合模式输出统计和贝叶斯模型平均两种方法框架;（3）考虑预报量的时间、空间和多变量间依赖关系或结构的处理方法,包括参数化和经验连接概率法。最后,讨论发展统计后处理技术需要关注的问题,包括考虑不同来源、不同尺度的多模式信息集成;提供高质量、高分辨率的降水分析资料;发展再预报技术扩充训练样本;基于不同的订正目的和应用场景来使用不同的后处理技术;发展面向海量预报数据、捕捉极端降水以及考虑预报量结构的新技术。      

Prospects of using Bayesian model averaging for the calibration of one-month forecasts of surface air temperature over South Korea

In this study, we investigated the prospect of calibrating probabilistic forecasts of surface air temperature (SAT) over South Korea by using Bayesian model averaging (BMA). We used 63 months of simulation results from four regional climate models (RCMs) with two boundary conditions (NCEP-DOE and ERA-interim) over the CORDEX East Asia. Rank histograms and residual quantile-quantile (R-Q-Q) plots showed that the simulation skills of the RCMs differ according to season and geographic location, but the RCMs show a systematic cold bias irrespective of season and geographic location. As a result, the BMA weights are clearly dependent on geographic location, season, and correlations among the models. The one-month equal weighted ensemble (EWE) outputs for the 59 stations over South Korea were calibrated using the BMA method for 48 monthly time periods based on BMA weights obtained from the previous 15 months of training data. The predictive density function was calibrated using BMA and the individual forecasts were weighted according to their performance. The raw ensemble forecasts were assessed using the flatness of the rank histogram and the R-Q-Q plot. The results showed that BMA improves the calibration of the EWE and the other weighted ensemble forecasts irrespective of season, simulation skill of the RCM, and geographic location. In addition, deterministic-style BMA forecasts usually perform better than the deterministic forecast of the single best member.     

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.     

Operative Scheme for the Short-range Complex Forecasting of Surface Air Temperature and Humidity

The statistical scheme is proposed for the forecast of surface air temperature and humidity using operative weather forecasts with 3–5-day lead time from the best forecasting hydrodynamic models as well as the archives of forecasts of these models and observational data from 2800 weather stations of Russia, Eastern Europe, and Central Asia. The output of the scheme includes the forecasts of air temperature for the standard observation moments with the period of 6 hours and extreme temperatures with the lead times of 12–120 hours. The accuracy of temperature and humidity forecasts for the period from July 2014 till June 2017 is much higher than that for the forecasts of original hydrodynamic models. The skill scores for extreme temperature forecasts based on the proposed method are compared with the similar results of the Weather Element Computation (WEC) forecasting scheme and forecasts by weathermen.     

A study of effectiveness of the ensemble long-term forecasts of spring floods issued with physically based models of the river runoff formation

In this paper, possible ways to increase effectiveness of the long-term ensemble spring floods forecasting and to assess their uncertainty based on the physical-mathematical model of the runoff formation (for the Vyatka River case study) are studied. It is shown that deterministic forecasts issued by using this approach are more accurate than those obtained from the traditional forecasting methods based on regression relationships. Probabilistic methods of forecasting of the spring flood volume and maximum discharge, which are issued by using various ways of the weather ensembles setting, are compared. Reliability of probabilistic forecasts of the volume and maximum discharge is estimated.     

春季中国日最高气温延伸期预报误差分析及订正

数值模式直接输出和经模式后处理得到的预报误差比较,是延伸期逐日要素预报应用基础。针对中国2 583个站点在2020年春季11~30天的日最高温度预报,根据欧洲数值中心的集合预报输出,首先,使用BP-SM（Back-Propagation - Self memory）法和回归法,进行确定性预报订正效果比较;结果表明BP-SM法和回归法都明显降低了预报绝对误差;在11~14天预报中,BP-SM法得到的平均绝对误差为3.3~3.6 ℃,预报准确率超过35%,订正效果更优。其次,基于模式直接输出和BP-SM法获得的概率预报,使用CRPSS (continuous ranked probability skill score)进行了可预报性分析。结果表明,在地形复杂地区,经过订正,预报准确率明显改善。对于延伸期逐日要素预报,合理的模式后处理方法是降低预报误差和提高预报能力的重要环节。      

用一种动力－统计方法提高中国夏季降水的季节预报

A dynamical-statistical post-processing approach is applied to seasonal precipitation forecasts in China during the summer. The data are ensemble-mean seasonal forecasts in summer (June-August) from four atmospheric general circulation models (GCMs) in the second phase of the Canadian Historical Forecasting Project (HFP2) from 1969 to 2001. This dynamical-statistical approach is designed based on the relationship between the 500 geopotential height (Z500) forecast and the observed sea surface temperature (SST) to calibrate the precipitation forecasts. The results show that the post-processing can improve summer precipitation forecasts for many areas in China. Further examination shows that this post-processing approach is very effective in reducing the model-dependent part of the errors, which are associated with GCMs. The possible mechanisms behind the forecast's improvements are investigated.     

多模式降水分级最优化权重集成预报技术

为综合不同模式对不同量级降水的预报优势,设计一种全球模式与区域模式结合的降水分级最优化权重集成预报算法,集成经最优TS评分订正法（optimal threat score,OTS）订正后的欧洲中期天气预报中心降水预报产品（以下简称EC-OTS）和华东区域中尺度模式降水预报产品（以下简称SMS-OTS）。以泛长江区域（23°~39°N,101°~123°E）为研究范围,基于2018年不同降水量级的TS评分最优化确定SMS-OTS和EC-OTS在不同降水量级时的最优权重系数以及最优集成方案,并以2019年降水数据为独立样本进行预报试验。结果表明:对于最优权重系数,EC-OTS在低降水量级权重较大,随着降水量级的加大,SMS-OTS的权重也逐渐加大;最优集成方案为初始集成降水量预报取SMS-OTS,集成运算迭代3次;集成预报在几乎所有预报时效、所有降水量级的TS评分均高于EC-OTS和SMS-OTS,其平均绝对误差略小于EC-OTS,显著小于SMS-OTS;集成预报12 h累积降水预报的TS评分较省级预报员主观预报高-0.009~0.041,24 h累积降水预报的TS评分较国家气象中心预报员主观预报高0.009~0.023。     

基于集合预报系统的日最高和最低气温预报

根据欧洲中心集合预报系统2 m气温预报的集合统计值,提出了BP-SM方法,针对中国512个台站2016年3月的日最高（低）气温做预报分析。将集合预报系统的模式直接输出、BP和BP-SM方法得到的日最高（低）气温进行了比较,结果表明:预报时效越长,BP-SM方法较之BP方法的预报优势也更明显;在1至5 d的预报中,BP-SM方法显著降低了预报绝对误差,误差在2℃以内的准确率大部分在60%以上,部分站点达到90%;正技巧评分均值大多高于30%,在青藏高原东部和南部地区超过了60%。预报正技巧站点次数在绝对误差≤2℃（1℃）范围内有所提高,对日最高气温预报准确率的提高略好于日最低气温;BP-SM方法有效地降低了预报系统偏差,较大预报误差出现次数显著减少。      

Assessment of probability multimodel seasonal forecast based on the APCC model data

The probability multimodel forecast system based on the Asia-Pacific Economic Cooperation Climate Center (APCC) model data is verified. The winter and summer seasonal mean fields T ₈₅₀ and precipitation seasonal totals are estimated. To combine the models into a multimodel ensemble, the probability forecast is calculated for each of single models first, and then these forecasts are combined using the total probability formula. It is shown that the multimodel forecast is considerably more skilful than the single-model forecasts. The forecast quality is higher in the tropics compared to the mid- and high latitudes. The multimodel ensemble temperature forecasts outperform the random and climate forecasts for Northern Eurasia in the above- and below-normal categories. Precipitation forecast is less successful. For winter, the combination of single-model ensembles provides the precipitation forecast skill exceeding that of the random forecast for both Northern Eurasia and European Russia.     

基于BMA方法的地面气温的10～15 d延伸期概率预报研究

利用TIGGE资料提供的欧洲中期天气预报中心(ECMWF)、美国国家环境预报中心(NCEP)、英国气象局(UKMO)三个预报中心2013年6月1日至8月31日的地面2 m气温10~15 d预报资料,对延伸期地面气温进行贝叶斯模式平均(Bayesian Model Averaging,BMA)预报试验。结果表明,BMA方法的预报效果随训练期长度而改变,训练期长度为30 d时预报效果最优。BMA方法可提供全概率密度函数,定量描述预报不确定性的大小,且陆地上预报不确定性大于海洋上的预报不确定性,高纬度地区预报不确定性大于低纬度地区的预报不确定性。利用CRPS评分对BMA概率预报技巧进行评估,发现预报技巧随预报时效的延长降低,且预报技巧在海洋上优于陆地、低纬度地区优于高纬度地区。此外,3 d、5 d和7 d滑动平均的预报值反映某些天气过程的平均要素预报,对于提高10~15 d延伸期概率预报技巧有一定效果,且滑动天数越长,预报效果越好。     

ConvLSTM Based Temperature Forecast Modification Model for North China

The correction of model forecast is an important step in evaluating weather forecast results. In recent years, post-processing models based on deep learning have become prominent. In this paper, a deep learning model named ED-ConvLSTM based on encoder-decoder structure and ConvLSTM is developed, which appears to be able to effectively correct numerical weather forecasts. Compared with traditional post-processing methods and convolutional neural networks, ED-ConvLSTM has strong collaborative extraction ability to effectively extract the temporal and spatial features of numerical weather forecasts and fit the complex nonlinear relationship between forecast field and observation field. In this paper, the post-processing method of ED-ConvLSTM for 2 m temperature prediction is tested using The International Grand Global Ensemble dataset and ERA5-Land data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Root mean square error and temperature prediction accuracy are used as evaluation indexes to compare ED-ConvLSTM with the method of model output statistics, convolutional neural network postprocessing methods, and the original prediction by the ECMWF. The results show that the correction effect of ED-ConvLSTM is better than that of the other two postprocessing methods in terms of the two indexes, especially in the long forecast time.     

Long-term ensemble forecast of snowmelt runoff with the help of the physics-based models of runoff generation

Applicability of the physically based models of runoff generation has been shown for the long-term ensemble forecasts of snowmelt runoff volumes and peak discharges. For the deterministic forecast, the ensembles of runoff hydrographs have been modeled by different combinations of measured and calculated indices of the basin conditions on the date of the forecast issue and by the mean weather for the lead time of the forecast. For the probabilistic forecast, the ensembles of runoff hydrographs have been modeled either by the ensembles of weather, which were observed for the lead-time periods of different years, or by the ensembles of weather generated by the Monte Carlo method with the help of a stochastic weather generator. The criteria have been suggested to compare the effectiveness of the probabilistic forecasts of snowmelt runoff characteristics and peak discharges and the different forecasting approaches have been compared. The study has been carried out for the Seim and Sosna river basins.