日最高温度统计降尺度方法的比较研究

针对日最高温度的降尺度问题,发展了一种统计降尺度的新方法——优选格点回归法(OPR)。利用该方法与双线性插值法(BI)对平原(山东)和高原(云南)的日最高温度进行降尺度对比分析,结果表明:无论对于平原(山东)还是高原(云南)地区以及夏季(7月)还是冬季(1月),OPR方法都明显优于BI方法,特别是从高原地区的均方根误差来看,降尺度效果优势更加明显。进一步对OPR方法降尺度过程中所做的方差放大对比分析显示,方差放大后对日最高温度的降尺度效果不但没有改进,在某些方面如均方根误差和极端误差等还有变差的表现。     

Precipitation and temperature space–time variability and extremes in the Mediterranean region: evaluation of dynamical and statistical downscaling methods

This study evaluates how statistical and dynamical downscaling models as well as combined approach perform in retrieving the space–time variability of near-surface temperature and rainfall, as well as their extremes, over the whole Mediterranean region. The dynamical downscaling model used in this study is the Weather Research and Forecasting (WRF) model with varying land-surface models and resolutions (20 and 50 km) and the statistical tool is the Cumulative Distribution Function-transform (CDF-t). To achieve a spatially resolved downscaling over the Mediterranean basin, the European Climate Assessment and Dataset (ECA&D) gridded dataset is used for calibration and evaluation of the downscaling models. In the frame of HyMeX and MED-CORDEX international programs, the downscaling is performed on ERA-I reanalysis over the 1989–2008 period. The results show that despite local calibration, CDF-t produces more accurate spatial variability of near-surface temperature and rainfall with respect to ECA&D than WRF which solves the three-dimensional equation of conservation. This first suggests that at 20–50 km resolutions, these three-dimensional processes only weakly contribute to the local value of temperature and precipitation with respect to local one-dimensional processes. Calibration of CDF-t at each individual grid point is thus sufficient to reproduce accurately the spatial pattern. A second explanation is the use of gridded data such as ECA&D which smoothes in part the horizontal variability after data interpolation and damps the added value of dynamical downscaling. This explains partly the absence of added-value of the 2-stage downscaling approach which combines statistical and dynamical downscaling models. The temporal variability of statistically downscaled temperature and rainfall is finally strongly driven by the temporal variability of its forcing (here ERA-Interim or WRF simulations). CDF-t is thus efficient as a bias correction tool but does not show any added-value regarding the time variability of the downscaled field. Finally, the quality of the reference observation dataset is a key issue. Comparison of CDF-t calibrated with ECA&D dataset and WRF simulations to local measurements from weather stations not assimilated in ECA&D, shows that the temporal variability of the downscaled data with respect to the local observations is closer to the local measurements than to ECA&D data. This highlights the strong added-value of dynamical downscaling which improves the temporal variability of the atmospheric dynamics with regard to the driving model. This article highlights the benefits and inconveniences emerging from the use of both downscaling techniques for climate research. Our goal is to contribute to the discussion on the use of downscaling tools to assess the impact of climate change on regional scales.     

我国地面降水的分级回归统计降尺度预报研究

利用TIGGE资料中欧洲中期天气预报中心(ECMWF,the European Centre for Medium-Range Weather Forecasts)、日本气象厅(JMA,the Japan Meteorological Agency)、美国国家环境预报中心(NCEP,the National Centers for Environmental Prediction)以及英国气象局(UKMO,the UK Met Office)4个中心1~7 d预报的日降水量集合预报资料,并以中国降水融合产品作为"观测值",对我国地面降水量预报进行统计降尺度处理。采用空间滑动窗口增加中雨和大雨雨量样本,建立分级雨量的回归方程,并与未分级雨量的统计降尺度预报进行对比。结果表明,对于不同模式、不同预报时效以及不同降水量级,统计降尺度的预报技巧改进程度不尽相同。统计降尺度的预报技巧依赖于模式本身的预报效果。相比雨量未分级回归,雨量分级回归的统计降尺度预报与观测值的距平相关系数更高,均方根误差更小,不同量级降水的ETS评分明显提高。对雨量分级回归统计降尺度预报结果进行二次订正,可大大减少小雨的空报。     

南宁各种下垫面温度特征及预报方法探讨

介绍下垫面温度观测的原理,并用南宁2002年12月至2003年11月资料,分析了沥青、水泥、泥土温度的特征,包括这3种下垫面的日平均温度、日最高温度、日最低温度、温度日较差特征以及它们与气温的差值。指出气温是影响各种下垫面温度的最主要因素,云对下垫面日最高温度影响显著,大气环流变换对各种下垫面温度的波动有明显影响。对各种下垫面日最高温度的预报方法进行了初步探讨。建立了基于日最高气温预报之上的各种下垫面日最高温度预报模型,并对预报与实况值进行了对比分析,指出提高次日最高气温的预报准确率和预报是否出现阴天有中到大雨天气的预报准确率,可显著改善各种下垫面日最高温度的预报效果。     

降尺度方法在东亚气候预测中的应用

东亚气候变异十分复杂,全球动力预测系统对该地气候异常的预测能力偏低,如何进一步提高东亚地区气候异常的预测水平是一个非常重要的科学和现实需求问题。为此,近些年一系列的动力和统计降尺度方法得以发展。本文主要回顾了这些降尺度方法在东亚气候预测研究和实时预测中的应用。首先,文中简要介绍了我国目前应用于实时预测的全球动力预测系统及其性能,这是开展降尺度的科学和技术基础;在此基础上,从区域模式物理过程参数化方案的评估与遴选、区域模式在东亚气候预测中的应用两个方面,对于动力降尺度方法的发展和应用做了回顾;在统计降尺度的综述中,本文主要关注了东亚夏季汛期和冬季气候异常的预测,特别是针对东亚冬季气候异常,本文中提出了新的高效的统计与动力相结合的预测方法。最后,展望了短期气候预测需要进一步深入研究的科学和技术问题。     

基于TIGGE资料的北半球地面气温预报的统计降尺度研究

基于TIGGE(THORPEX Interactive Grand Global Ensemble,全球交互式大集合)资料中欧洲中期天气预报中心(European Centre for Medium-Range Weather,ECMWF)、日本气象厅(Japan Meteorological Agency,JMA)、美国国家环境预报中心(National Centers for Environmental Prediction,NCEP)和英国气象局(United Kingdom Met Office,UKMO)4个中心的北半球地面2 m气温集合平均预报资料,利用插值技术与回归分析,并引入了消除偏差集合平均(bias-removed ensemble mean,BREM)和多模式超级集合(superensemble,SUP)方法进行统计降尺度预报研究。结果表明,在2007年夏季3个月中,4个单中心的降尺度预报明显地改善了预报效果。引入SUP和BREM两种集成预报方法后,预报误差得到进一步减小。对比综合表现最好的单中心ECMWF的预报,1~7 d的降尺度预报误差改进率均达20%以上。研究还发现,引入SUP方法的降尺度预报效果优于引入BREM方法的降尺度预报,利用双线性插值方法在上述两方案中的预报效果优于其他3种插值方法。     

基于时空统计降尺度的淮河流域夏季分月降水概率预测

针对淮河流域水资源短缺、洪涝、干旱并存的问题,基于国家气候中心第二代季节气候模式的集合回报数据集(1991—2014年),建立时空相结合的统计降尺度模型,提前1—3个月预测该流域夏季分月降水,应用ROC(relative operating characteristics)评分评估比较了不同集合预测方案的预测技巧。交叉检验结果表明,样本数取18、20、22、28时,集合预测方案对3、4、5月三个起报时次预测的夏季各月降水技巧预测均高于模式预测技巧。2015—2017年的独立样本检验进一步表明该统计降尺度模型能够明显降低3月、5月起报的6月和8月的降水预测偏差。认为可尝试将该降尺度方法应用于淮河流域夏季降水预测及进一步的流域水文预测。     

江苏—南黄海地区M≥6强震有序网络结构及其预测研究

基于TIGGE（THORPEX Interactive Grand Global Ensemble,全球交互式大集合）资料中欧洲中期天气预报中心（European Centre for Medium-Range Weather,ECMWF）、日本气象厅（Japan Meteorological Agency,JMA）、美国国家环境预报中心（National Centers for Environmental Prediction,NCEP）和英国气象局（United Kingdom Met Office,UKMO）4个中心的北半球地面2m气温集合平均预报资料,利用插值技术与回归分析,并引入了消除偏差集合平均（bias-removed ensemble mean,BREM）和多模式超级集合（superensemble,SUP）方法进行统计降尺度预报研究.结果表明,在2007年夏季3个月中,4个单中心的降尺度预报明显地改善了预报效果.引入SUP和BREM两种集成预报方法后,预报误差得到进一步减小.对比综合表现最好的单中心ECMWF的预报,1～7d的降尺度预报误差改进率均达20％以上.研究还发现,引入SUP方法的降尺度预报效果优于引入BREM方法的降尺度预报,利用双线性插值方法在上述两方案中的预报效果优于其他3种插值方法.     

Impact of the lateral boundary conditions resolution on dynamical downscaling of precipitation in mediterranean spain

Conclusions on the General Circulation Models (GCMs) horizontal and temporal optimum resolution for dynamical downscaling of rainfall in Mediterranean Spain are derived based on the statistical analysis of mesoscale simulations of past events. These events correspond to the 165 heavy rainfall days during 1984–1993, which are simulated with the HIRLAM mesoscale model. The model is nested within the European Centre for Medium-Range Weather Forecasts atmospheric grid analyses. We represent the spectrum of GCMs resolutions currently applied in climate change research by using varying horizontal and temporal resolutions of these analyses. Three sets of simulations are designed using input data with 1°, 2° and 3° horizontal resolutions (available at 6 h intervals), and three additional sets are designed using 1° horizontal resolution with less frequent boundary conditions updated every 12, 24 and 48 h. The quality of the daily rainfall forecasts is verified against rain-gauge observations using correlation and root mean square error analysis as well as Relative Operating Characteristic curves. Spatial distribution of average precipitation fields are also computed and verified against observations. For the whole Mediterranean Spain, model skill is not appreciably improved when using enhanced spatial input data, suggesting that there is no clear benefit in using high resolution data from General Circulation Model for the regional downscaling of precipitation under the conditions tested. However, significant differences are found in verification scores when boundary conditions are interpolated less frequently than 12 h apart. The analysis is particularized for six major rain bearing flow regimes that affect the region, and differences in model performance are found among the flow types, with slightly better forecasts for Atlantic and cold front passage flows. A remarkable spatial variability in forecast quality is found in the domain, with an overall tendency for higher Relative Operating Characteristic scores in the west and north of the region and over highlands, where the two previous flow regimes are quite influential. The findings of this study could be of help for dynamical downscaling design applied to future precipitation scenarios in the region, as well as to better establish confidence intervals on its results.     

北京地区中尺度非静力数值预报产品释用技术研究

在国家气象中心与北京市气象局联合开发的“北京地区中尺度数值预报业务系统”基础上，充分利用高分辨率中尺度数值预报产品和地面加密气象观测资料，分别使用统计和动力释用方法进行局地温度、风和北京市区空气污染状况的预报。试预报结果表明，用卡尔曼滤波方法建立的温度和风的预报效果高于中尺度数值预报直接输出结果；使用中尺度数值预报结果作为一个简单的污染预报动力模式的气象背景场，进行了污染预报试验，结果表明该预报方法具有一定的预报能力，其中SO₂、NO₂、CO浓度的预报准确率在夏、秋、冬三季中均可达到65%以上。上述研究成果已用于业务预报。     

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。      

Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling

Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling.     

Statistical decadal predictions for sea surface temperatures: a benchmark for dynamical GCM predictions

Accurate decadal climate predictions could be used to inform adaptation actions to a changing climate. The skill of such predictions from initialised dynamical global climate models (GCMs) may be assessed by comparing with predictions from statistical models which are based solely on historical observations. This paper presents two benchmark statistical models for predicting both the radiatively forced trend and internal variability of annual mean sea surface temperatures (SSTs) on a decadal timescale based on the gridded observation data set HadISST. For both statistical models, the trend related to radiative forcing is modelled using a linear regression of SST time series at each grid box on the time series of equivalent global mean atmospheric CO₂ concentration. The residual internal variability is then modelled by (1) a first-order autoregressive model (AR1) and (2) a constructed analogue model (CA). From the verification of 46 retrospective forecasts with start years from 1960 to 2005, the correlation coefficient for anomaly forecasts using trend with AR1 is greater than 0.7 over parts of extra-tropical North Atlantic, the Indian Ocean and western Pacific. This is primarily related to the prediction of the forced trend. More importantly, both CA and AR1 give skillful predictions of the internal variability of SSTs in the subpolar gyre region over the far North Atlantic for lead time of 2–5 years, with correlation coefficients greater than 0.5. For the subpolar gyre and parts of the South Atlantic, CA is superior to AR1 for lead time of 6–9 years. These statistical forecasts are also compared with ensemble mean retrospective forecasts by DePreSys, an initialised GCM. DePreSys is found to outperform the statistical models over large parts of North Atlantic for lead times of 2–5 years and 6–9 years, however trend with AR1 is generally superior to DePreSys in the North Atlantic Current region, while trend with CA is superior to DePreSys in parts of South Atlantic for lead time of 6–9 years. These findings encourage further development of benchmark statistical decadal prediction models, and methods to combine different predictions.     

An Experiment of a Statistical Downscaling Forecast Model for Summer Precipitation over China

A combination of the optimal subset regression (OSR) approach, the coupled general circulation model of the National Climate Center (NCC-CGCM) and precipitation observations from 160 stations over China is used to construct a statistical downscaling forecast model for precipitation in summer. Retroactive forecasts are performed to assess the skill of statistical downscaling during the period from 2003 to 2009. The results show a poor simulation for summer precipitation by the NCC- CGCM for China, and the average spatial anomaly correlation coefficient (ACC) is 0.01 in the forecast period. The forecast skill can be improved by OSR statistical downscaling, and the OSR forecast performs better than the NCC-CGCM in most years except 2003. The spatial ACC is more than 0.2 in the years 2008 and 2009, which proves to be relatively skillful. Moreover, the statistical downscaling forecast performs relatively well for the main rain belt of the summer precipitation in some years, including 2005, 2006, 2008, and 2009. However, the forecast skill of statistical downscaling is restricted to some extent by the relatively low skill of the NCC- CGCM.     

Performance comparison of three predictor selection methods for statistical downscaling of daily precipitation

Predictor selection is a critical factor affecting the statistical downscaling of daily precipitation. This study provides a general comparison between uncertainties in downscaled results from three commonly used predictor selection methods (correlation analysis, partial correlation analysis, and stepwise regression analysis). Uncertainty is analyzed by comparing statistical indices, including the mean, variance, and the distribution of monthly mean daily precipitation, wet spell length, and the number of wet days. The downscaled results are produced by the artificial neural network (ANN) statistical downscaling model and 50 years (1961–2010) of observed daily precipitation together with reanalysis predictors. Although results show little difference between downscaling methods, stepwise regression analysis is generally the best method for selecting predictors for the ANN statistical downscaling model of daily precipitation, followed by partial correlation analysis and then correlation analysis.     

一种基于全球动力模式和SMART原理结合的统计降尺度区域季节气候预测方法

针对江苏夏季旱涝和高温热浪等异常气候的预测难题，以江苏夏季站点降水和气温为预测目标，建立了一种基于全球动力模式BCC_CSM1.1（m）和最优可预测气候模态和异常相对倾向（SMART）原理结合的统计降尺度季节气候预测方法。利用历史观测资料和SVD方法提取决定中国夏季降水异常相对倾向的同期热带地区向外长波辐射（Outgoing Longwave Radiation，OLR）和北半球中高纬500 hPa位势高度场异常相对倾向的最优可预测气候模态，并利用逐步回归法构建其与同期江苏站点降水和气温异常相对倾向同期关系的统计降尺度模型；将动力模式对最优可预测气候模态的预测带入统计降尺度模型，实现对区域降水和气温异常相对倾向的预测；最后通过引入观测的近期背景异常实现对降尺度的降水和气温总距平的预测。通过对1991—2019年江苏夏季降水和气温的回报检验表明，本文建立的统计降尺度模型效果较BCC_CSM1.1（m）动力模式的直接预测效果有显著提高，为区域精细化季节气候预测提供了一种有效的手段。     

龙川县气温预报方法的建立与应用

利用龙川站2008-2012年地面观测资料、欧洲中心数值预报产品,采用常规统计预报方法（逐步回归）,将最高（低）气温实况作为预报对象,把可能影响气温变化的气象要素作为预报因子,分月建立未来24～ 72 h最高（低）气温的MOS预报方程,通过对2013年全年的检验预报表明：未来24～72 h最高（低）气温预报平均绝对误差均在2.0℃以内.     

An Improved Statistical-Dynamical Downscaling Scheme and its Application to the Alpine Precipitation Climatology

Summary An improved statistical-dynamical downscaling method for the regionalization of large-scale climate analyses or simulations is introduced. The method is based on the disaggregation of a multi-year time-series of large-scale meteorological data into multi-day episodes of quasi-stationary circulation. The episodes are subsequently grouped into a defined number of classes. A regional model is used to simulate the evolution of weather during the most typical episode of each class. These simulations consider the effects of the regional topography. Finally, the regional model results are statistically weighted with the climatological frequencies of the respective circulation classes in order to provide regional climate patterns. The statistical-dynamical downscaling procedure is applied to large-scale analyses for a 12-year climate period 1981–1992. The performance of the new method is demonstrated for winter precipitation in the Alpine region. With the help of daily precipitation analyses it was possible to validate the results and to assess the different sources of errors. It appeared that the main error originates from the regional model, whereas the error of the procedure itself was relatively unimportant. This new statistical-dynamical downscaling method turned out to be an efficient alternative to the commonly used method of nesting a regional model continuously within a general circulation model (dynamical downscaling). Received April 8, 1999 Revised July 30, 1999     

Statistical Downscaling of Pattern Projection Using Multi-Model Output Variables as Predictors

A pattern projection downscaling method is employed to predict monthly station precipitation. The predictand is the monthly precipitation at 1 station in China, 60 stations in Korea, and 8 stations in Thailand. The predictors are multiple variables from the output of operational dynamical models. The hindcast datasets span a period of 21 yr from 1983 to 2003. A downscaled prediction is made for each model separately within a leave-one-out cross-validation framework. The pattern projection method uses a moving window, which scans globally, in order to seek the most optimal predictor for each station. The final forecast is the average of the model downscaled precipitation forecasts using the best predictors and is referred to as DMME. It is found that DMME significantly improves the prediction skill by correcting the erroneous signs of the rainfall anomalies in coarse resolution predictions of general circulation models. The correlation coefficient between the prediction of DMME and the observation in Beijing of China reaches 0.71; the skill is improved to 0.75 for Korea and 0.61 for Thailand. The improvement of the prediction skills for the first two cases is attributed to three steps: coupled pattern selection, optimal predictor selection, and multi-model downscaled precipitation ensemble. For Thailand, we use the single-predictor prediction, which results in a lower prediction skill than the other two cases. This study indicates that the large-scale circulation variables, which are predicted by the current operational dynamical models, if selected well, can be used to make skillful predictions of local precipitation by means of appropriate statistical downscaling.     

中国夏季降水的组合统计降尺度模型预测研究

现阶段的动力气候模式尚不能满足东亚区域气候预测的实际需求,这就需要动力和统计相结合的方法,将动力模式中具有较高预测技巧的大尺度环流信息应用到降水等气象要素的统计预测模型当中,以改善后者预测效果。本文中所介绍的组合统计降尺度模型,可将动力气候模式预测的大尺度环流变量和前期观测的外强迫信号作为预测因子来预测中国夏季降水异常。交叉检验结果显示,组合统计降尺度预测模型的距平相关系数较原始模式结果有较大提高。在实时夏季降水预测中,2013～2018年平均的预测技巧相对较高,趋势异常综合检验（PS）评分平均为71.5分,特别是2015～2018年平均的PS评分预测技巧达到72.7分,总体上高于业务模式原始预测和业务发布预测的技巧。该组合统计降尺度模型预测性能稳定,为我国季节预测业务提供了一种有效参考。