Using the spectral scaling exponent for validation of quantitative precipitation forecasts

This study evaluates the spectral scaling of a heavy rainfall event and assesses the performance of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) model in terms of the multiscale variability of rainfall in the temporal spectral domain. The event occurred over southern Malay Peninsula on 18 December 2006 and was simulated at high resolutions. 10, 5 and 1?min aggregate rainfall data from rain gauge stations in Singapore and simulated rainfall sampled at different evaluation points on 0.9, 0.3 and 0.1?km grids were utilized. The simulated and observed rain rates were compared via Fourier and wavelet analyses. A scaling regime was noted in the observed rainfall spectra in the timescales between 60?min and 2?min. The scaling exponent obtained from the observed spectra has a value of about 2, which may be indicative of the physics of turbulence and raindrop coalescence and might suggest the predominance of a characteristic raindrop size. At 0.9?km resolution, the model rainfall spectra showed similar scaling to the observed down to about 10?min, below which a fall-off in variance was noted as compared to observations. Higher spatial resolution of up to 0.1?km was crucial to improve the ability of the model to resolve the shorter timescale variability. We suggest that the evaluation of dynamical models in the spectral domain is a crucial step in the validation of quantitative precipitation forecasts and assessing the minimal grid resolution necessary to capture rainfall variability for certain short timescales may be important for hydrological predictions.     

基于频率匹配法的中国降水多模式预报订正研究

基于TIGGE资料中欧洲中期天气预报中心、日本气象厅、美国国家环境预报中心及英国气象局1～7 d日降水量预报以及中国自动站观测资料与CMORPH降水产品融合的逐时降水量网格数据集,利用频率匹配法(Frequency-Matching Method,FMM)对中国降水预报进行客观订正。首先利用卡尔曼滤波方法对降水频率进行调整,并根据不同区域降水强度差异将全国分为7个子区域分别进行频率匹配。结果表明,FMM可以有效减小降水量预报的误差。经过频率匹配法订正后各模式降水预报的平均绝对误差(Mean Absolute Error,MAE)大幅减小,且订正后各量级降水的雨区面积更加接近实际观测值。FMM对小于5 mm和大于15 mm的降水预报技巧改进明显。此外,FMM降低了模式预报的小雨空报率和大雨漏报率,并且明显提高了晴雨预报的准确率。FMM明显消除了大范围小雨空报区域,但是对强降水预报FMM仅能调整降水量大小,强降水落区预报并不能得到明显改善。     

基于TIGGE资料的地面气温和降水的多模式集成预报

利用TIGGE资料集下中国气象局(CMA)、欧洲中期天气预报中心(ECMWF)、日本气象厅(JMA)、美国国家环境预报中心(NCEP)和英国气象局(UKMO)5个中心集合预报结果,对多模式集成预报方法进行讨论。结果表明,多模式集成方法的预报效果优于单个中心的预报,但对于不同预报要素多模式集成方法的适用性存在差异。滑动训练期超级集合(R-SUP)对北半球地面气温的改进效果最优,但此方法对降水场的改进效果并不理想。在北半球中低纬24 h累积降水的回报试验中,消除偏差(BREM)的结果优于单个中心的预报,且此方法预报结果稳定。进一步利用滑动训练期消除偏差(R-BREM)集合平均对2008年1月中国南方极端雨雪冰冻过程进行多模式集成预报试验,结果表明,在固定误差范围内,R-BREM将中国南方大部分地区的地面气温预报时效由最优数值预报中心的96 h延长至192 h,且除个别时效外,小雨、中雨的TS评分得到明显提高。     

Station-based verification of qualitative and quantitative MM5 precipitation forecasts over Northwest Himalaya (NWH)

Weather forecasts by any forecast system are verified using either distributions-oriented or measures-oriented forecast verification measures. Both the forecast verification schemes represent different aspects of the forecast quality, and advantages of them can be utilized to get better insight and to identify particular strengths (deficiencies) in the forecast performance of any forecast system. Keeping this in view, multi-faced verification (binary and continuous) of quantitative precipitation forecasts for consecutive 3 days by a Regional Meso-scale Weather Simulation Model (MM5 Model) has been carried out to get complete insight into its performance. The MM5 model forecasts at 10-km resolution for 792 days of six winters (winter 2003/2004 to winter 2008/2009) are compared with the observational data of six stations in the complex topography of Northwest Himalaya (NWH) in India. The model forecasts are verified using binary categorical forecast verification measures such as Probability of Detection, False Alarm Rate, Miss Rate, Correct Non-occurrence, Critical Success Index and Percent correct, and continuous forecast verification measures such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). BIAS is computed to know over-forecast/under-forecast tendency of a precipitation day (PT day) by the MM5 model. MAE (RMSE) of the MM5 model is computed separately for all days, PT days and no precipitation days (NPT days). MAE (RMSE) of PT days is found to be relatively larger as compared to NPT days and all days. These findings indicate that MAE (RMSE) computed separately for all days, PT days and NPT days provides better insight into the performance of the MM5 model. Results also suggest that the MM5 model shows reasonably good performance for binary forecasts (PT days/NPT days) for day 1 (0–24 h), day 2 (24–48 h) and day 3 (48–72 h). However, large errors are seen in predicting the observed precipitation amount of PT days over NWH.     

北京地区高分辨率快速循环同化预报系统性能检验和评估

针对高分辨率数值模式降水预报检验中存在的问题,探讨模式分辨率的改变对模式降水评分的影响。对同一区域人为改变测站疏密度,讨论测站的疏密对降水预报评分的影响,从检验结果来看,降水评分与模式预报的水平分辨率有较强的关联,对测站的疏密程度也有很明显的依赖性,测站的疏密度不同,评分的差距可能很大。

     

Temporal statistical downscaling of precipitation and temperature forecasts using a stochastic weather generator

Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society(IRI). In conjunction with the GLM(generalized linear modeling) weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts(the IRI format only specifies probabilities for three categories: below normal, near normal, and above normal) into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.     

21世纪中国极端降水事件预估

评估分析了欧洲中期天气预报中心（European Centre for Medium-range Weather Forecasts,ECMWF）细网格模式（以下简称EC-thin）在长三角地区汛期（5-9月）的暴雨预报评分及ECMWF降水极端天气预报指数（EFI）对暴雨预警的指示作用。研究发现：（1）EC-thin降水和降水EFI对暴雨预报的ETS评分随着预报时效的延长而明显降低,在短时效内,细网格模式降水预报占优,超过60 h后,降水EFI的评分相对更好。（2）对EC-thin降水而言,在不同的预报时效采用不同的降水阈值来预报暴雨,可望达到最佳的评分效果。短期时效内该阈值随着预报时效的延长,大致从55 mm逐渐下降到35 mm。（3）对于降水EFI而言,12-36 h内EFI为0.65~0.7时,暴雨预报ETS评分最高。随着预报时效的延长逐渐下降,60-84 h内EFI为0.55~0.6时,暴雨预报ETS评分最高。（4）在不同预报时效内,采用合理的方式和阈值综合考虑EC-thin降水和降水EFI,可望得到更高的暴雨预报评分。

     

Study of method for synthetic precipitation data for ungauged sites using quantitative precipitation model

A method was developed to estimate a synthetic precipitation record for ungauged sites using irregular coarse observations. The proposed synthetic precipitation data were produced with ultrahigh hourly resolution on a regular 1 × 1 km grid. The proposed method was used to analyze selected real-time observational data collected in South Korea from 2010 to the end of 2014. The observed precipitation data were measured using the Automatic Weather System and Automated Synoptic Observing System. The principal objective of the proposed method was to estimate the additional effects of orography on precipitation introduced by ultrahigh- resolution (1 × 1 km) topography provided by a digital elevation model. The Global Forecast System analysis of the National Centers for Environmental Prediction was used for the upper-atmospheric conditions, necessary for estimating the orographic effects. Precipitation data from 48 of the more than 600 observation sites used in the study, which matched the grid points of the synthetic data, were not included in the synthetic data estimation. Instead, these data were used to evaluate the proposed method by direct comparison with the real observations at these sites. A bias score was investigated by comparison of the synthetic precipitation data with the observations. In this comparison, the number of Hit, False, Miss, and Correct results for 2010-2014 was 74738, 25778, 7544, and 367981, respectively. In the Hit cases, the bias score was 1.22 and the correlation coefficient was 0.74. The means of the differences between the synthetic data and the observations were 0.3, -3.9, -14.4, and -34.9 mm h^-1 and the root mean square errors (RMSEs) were 2.7, 8.3, 19.3, and 39.6 mm h^-1 for the categories of 0.5-10.0, 10.0-30.0, 30.0-50.0, and 50.0-100.0 mm h^-1, respectively. In addition, in each range, the 60% difference between the synthetic precipitation data and the observation data was -1.5 to +1.5, -5.0 to +5.0, -17.0 to +17.0, and -33.0 to +33.0 mm h^-1, respectively. Overall, the correlation coefficient of the synthetic precipitation data was > 0.7 for 43 of the 48 test stations and the RMSE was < 4 mm h^-1 at 31 stations. The results are significant at all evaluation stations at the 0.05 significance level.     

Improved seasonal climate forecasts for the Caribbean region using the Florida State University Synthetic Superensemble

Summary Climate variations in the Caribbean, largely manifest in rainfall activity, have important consequences for the large-scale water budget, natural vegetation, and land use in the region. The wet and dry seasons will be defined, and the important roles played by the El Ni?o-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) in modulating the rainfall during these seasons will be discussed. The seasonal climate forecasts in this paper are made by 13 state of the art coupled atmosphere-ocean general circulation models (CGCMs) and by the Florida State University Synthetic Superensemble (FSUSSE), whose forecasts are obtained by a weighted combination of the individual CGCM forecasts based on a training period. The success of the models in simulating the observed 1989–2001 climatology of the various forecast parameters will be examined and linked to the models’ success in predicting the seasonal climate for individual years. Seasonal forecasts will be examined for precipitation, sea-surface temperature (SST), 2-meter air temperature, and 850 hPa u- and v-wind components during the period 1989–2001. Evaluation metrics include root mean square (RMS) error and Brier skill score. It will be shown that the FSUSSE is superior to the individual CGCMs and their ensemble mean both in simulating the 1989–2001 climatology for the various parameters and in predicting the seasonal climate of the various parameters for individual years. The seasonal climate forecasts of the FSUSSE and of the ensemble mean of the 13 state of the art CGCMs will be evaluated for years (during the period 1989–2001) that have particular ENSO and NAO signals that are known to influence Caribbean weather, particularly the rainfall. It will be shown that the FSUSSE provides superior forecasts of rainfall, SST, 2-meter air temperature, and 850 hPa u- and v-wind components during dry summers that are modulated by negative SOI and/or positive NAO indices. Such summers have become a feature of a twenty-year pattern of drought in the Caribbean region. The results presented in this paper will show that the FSUSSE is a valuable tool for forecasting rainfall and other atmospheric and oceanic variables during such periods of drought.     

基于卡尔曼滤波的中国区域气温和降水的多模式集成预报

利用TIGGE资料集下欧洲中期天气预报中心(ECMWF)、日本气象厅(JMA)、美国国家环境预报中心(NCEP)、中国气象局(CMA)和英国气象局(UKMO)5个模式预报的结果,对基于卡尔曼滤波的气温和降水的多模式集成预报进行研究。结果表明,卡尔曼滤波方法的预报效果优于消除偏差集合平均(BREM)和单模式的预报,但是对于地面气温和降水,其预报效果也存在一定的差异。在中国区域2 m气温的预报中,卡尔曼滤波的预报结果最优。而对于24 h累积降水预报,尽管卡尔曼滤波在所有量级下的TS评分均优于BREM,但随着预报时效增加,其在大雨及以上量级的TS评分跟最佳单模式UKMO预报相当,改进效果不明显。卡尔曼滤波在地面气温和24 h累积降水每个预报时效下的均方根误差均最优,预报效果更佳且稳定。     

GRAPES_MESO模式预报降水相态诊断及应用研究

当地面气温在0℃附近时,降水相态通常比较复杂,降水相态预报一直是天气预报的难点之一。本文根据降水粒子在下落过程中的热力结构,利用BTC算法、修订的BTC算法、Ramer算法、Bourgouin算法4种计算方案,通过GRAPES_MESO中尺度模式提供所需参数,对落到地面的降水相态进行诊断,诊断的降水相态包括雨、雪、冻雨和冰粒4种,然后利用集成方法获得大概率且破坏程度大的降水相态预报产品。基于2个冬季降水个例,研究发现4种计算方案都能较合理地得到雨雪分界线,以及降雨、降雪落区范围。其中,修订后的BTC算法可以修正原始BTC算法对降雪的诊断偏差,并且预测的冰粒比原始冰粒更少。Ramer算法会比其他几种算法获得更多的冻雨事件,而Bourgouin算法最接近合成算法的结果。对于不确定性较大的中间态的冻雨和冰粒预报,鉴于其高影响特性,方案倾向于过高估计冻雨和冰粒的预报落区。集成的最终降水相态产品反映其发生的概率大或影响程度大,能为防灾减灾提供有效的警示信息。     

利用深度学习开展偏振雷达定量降水估测研究

利用2018—2020年经偏振升级改造后的广州S波段双偏振雷达（CINRAD/SAD）82892个体扫的0.5°仰角数据,以及雷达100 km探测范围内1109个雨量站共计538560个分钟雨量数据,分别构建了单参量、三参量雷达定量降水估测（QPE）深度学习网络架构（Z-Rnet、K_DP-Rnet、Pol-Rnet）,并以K_DP=0.5°/km为阈值分别训练得到大雨、小雨、总体等9个定量降水估测模型。在常用的均方误差作为损失函数的基础上,对不同降水强度采用不同权重提出了自定义损失函数,并利用比率偏差、相对偏差、均方差、平均绝对误差和平均相对误差作为评价指标对模型进行评估。通过对以积层混合云为主、以对流云为主和以层状云为主的3次降水过程的模型验证结果表明,利用深度学习训练的模型有较好的定量降水估测效果,区分雨强的小雨、大雨模型比不区分雨强的总体模型的效果要好。采用自定义损失函数模型效果更好,其均方差、平均绝对误差和平均相对误差分别较采用传统均方误差损失函数提升了8.62%、12.52%、16.34%。自定义损失函数中,采用Z_H-Z_DR-K_DP三参量网络架构训练得到的定量降水估测模型效果最好,其均方差、平均绝对误差和平均相对误差分别较采用Z_H的单参量Z-Rnet架构提升6.82%、8.43%、7.22%;较采用K_DP的单参量K_DP-Rnet架构提升12.33%、17.61%、17.26%。      

A short-range quantitative precipitation forecast algorithm using back-propagation neural network approach

A back-propagation neural network （BPNN） was used to establish relationships between the shortrange （0-3-h） rainfall and the predictors ranging from extrapolative forecasts of radar reflectivity, satelliteestimated cloud-top temperature, lightning strike rates, and Nested Grid Model （NGM） outputs. Quan- titative precipitation forecasts （QPF） and the probabilities of categorical precipitation were obtained. Results of the BPNN algorithm were compared to the results obtained from the multiple linear regression algorithm for an independent dataset from the 1999 warm season over the continental United States. A sample forecast was made over the southeastern United States. Results showed that the BPNN categorical rainfall forecasts agreed well with Stage Ⅲ observations in terms of the size and shape of the area of rainfall. The BPNN tended to over-forecast the spatial extent of heavier rainfall amounts, but the positioning of the areas with rainfall ≥25.4 mm was still generally accurate. It appeared that the BPNN and linear regression approaches produce forecasts of very similar quality, although in some respects BPNN slightly outperformed the regression.     

Improving long-lead seasonal forecasts of precipitation over Southern China based on statistical downscaling using BCC_CSM1.1m

Long-lead precipitation forecasts for 1–4 seasons ahead are usually difficult in dynamical climate models due to the model deficiencies and the limited persistence of initial signals. But, these forecasts could be empirically improved by statistical approaches. In this study, to improve the seasonal precipitation forecast over the southern China (SC), the statistical downscaling (SD) models are built by using the predictors of atmospheric circulation and sea surface temperature (SST) simulated by the Beijing Climate Center Climate System Model version 1.1 m (BCC_CSM1.1 m). The different predictors involved in each SD model is selected based on both its close relationship with the target seasonal precipitation and its reasonable prediction skill in the BCC_CSM1.1 m. Cross and independent validations show the superior performance of the SD models, relative to the BCC_CSM1.1 m. The temporal correlation coefficient of SD models could reach > 0.4, exceeding the 95 % confidence level. The SC precipitation index can be much better forecasted by the SD models than by the BCC_CSM1.1 m in terms of the interannual variability. In addition, the errors of the precipitation forecast in all four seasons are significantly reduced over most of SC in the SD models. For the 2015/2016 strong El Niño event, the SD models outperform the dynamical BCC_CSM1.1 m model on the spatial and regional-average precipitation anomalies, mostly due to the effective SST predictor in the SD models and the weak response of the SC precipitation to El Niño-related SST anomalies in the BCC_CSM1.1 m.     

Short-range ensemble forecasting of quantitative precipitation

为了研究不同集合预报释用产品对重庆地区气温和降水预报的性能差异,基于重庆市气象局业务运行的对流尺度集合预报系统,对2020年全年的24 h累计降水和2 m气温的控制预报、集合平均预报和集合分位数预报及24 h累计降水的概率匹配平均预报等地面要素集合预报释用产品预报性能的差异及其时空分布特征进行了综合对比分析。结果表明：(1) 各集合预报释用产品对降水的预报性能随预报时效的增加而降低;当预报时效一致时,集合平均预报、概率匹配平均预报、60%及以上分位数产品对降水的预报皆优于控制预报,90%分位数对降水的预报能力优于其他分位数产品。(2) 在夏秋两季,各产品的预报结果差异较大,90%分位数预报、概率匹配平均预报和集合平均预报效果较好。(3) 控制预报、集合平均预报、概率匹配平均预报和90%分位数预报的SEEPS评分都呈现出川东一带较高、渝东北和渝东南较低的空间分布特征,说明预报能力可能与地形有一定关系。(4) 对于不同时效的气温预报,总体上看,集合平均预报效果最佳,集合分位数产品中70%分位数预报效果最佳。

     

不同校准方法检验雷达定量估测降水的效果对比

应用雷达低仰角基本反射率资料和地面加密自动站降水量资料,采用最优化方法,根据天津地区降水特点和不同降水类型,建立适用本地的雷达Z-I关系。经实际应用检验,积混降水类型Z-I关系具实用性。在天津本地化Z-I关系基础上,通过了对比分析6种不同校准方法在天津夏季降水估测中的检验效果。结果表明：Z-I关系校准法和最大集成法对降水的估测偏高,误差较大;最优插值法的估测精度最高,平均绝对误差和均方根误差最小;但计算不同校准方法与实况相关性表明,变分校准法的估测效果与雨量计降水量的相关性最好。同时,所有估测校准法对小雨量级的降水均出现了不同程度的偏高估测。     

国家气象中心集合数值预报业务系统的发展及应用

利用ECMWF集合预报对2016年6月11—12日发生在长三角地区的一次暴雨过程进行了分析,并对集合“好” “坏”两类成员的预报结果进行了对比。分析表明：集合预报对本次暴雨过程具有比较好的预报能力,集合平均预报效果要优于确定性预报,其雨量预报的增大趋势对暴雨的预报具有一定的指示意义;高分位数集合成员对于暴雨预报有比较好的参考价值,尤其是在预报时效还较长的时候,如果连续多起报时次高分位数集合成员都预报出暴雨,以及低分位数集合成员的雨量预报呈现逐渐增大趋势,预示着暴雨的可能性在增大,有助于暴雨预报的决策;对天气系统和气象要素的预报差异是造成“好”“坏”两类集合成员对本次暴雨过程模拟效果差异的主要原因,对500 hPa高空槽、850 hPa低涡及其切变线、西南气流和偏东气流的模拟是决定“好”“坏”两类集合成员模拟效果的关键因素。

     

暴雨集合预报-观测概率匹配订正法在四川盆地的应用研究

利用2015年6月1日至8月31日ECMWF集合预报系统（EPS）51个成员的降水预报结果,基于安徽省大别山区12个代表站点1~3 d的逐12 h降水观测资料,采用TS评分、箱须图、泰勒图等方法,研究ECMWF集合预报降水结果在安徽省大别山区降水分级预报中的应用效果。主要结论如下：ECMWF集合降水预报中,08时起报的24 h降水量和20时起报的48 h降水量可参考性较大;降水分级检验中,晴雨预报正确率在70%以上,小雨和中雨的空报率较高,对大雨以上量级的预报能力相对较差;大型降水过程中,暴雨以上量级的预报需关注不同预报时效下降水量预报的变化趋势,个别成员对极端降水的表现也值得关注。

     

全国248站中—大雨以上降水概率MOS预报及其因子设计

本文给出了全国中—大雨以上降水概率MOS预报的制作方法、样本过滤条件、预报因子的设计与推导、预报值后处理以及检验与误差原因分析。还论述了MOS预报中采用σ面上的数值预报产品作因子、两维二次插值和预报值变量因子的作用与求取方法。