Analysis of the environmental sensitivities of a typical dynamic epikarst system at the Nongla monitoring site, Guangxi, China

Nongla, a typical karst dynamic system (KDS) monitoring site, is located at Nongla Village, Mashan County, Guangxi, China. The data from a Greenspan CTDP300 multichannel data logger indicates that the KDS is highly sensitive to environmental changes. Multi-day and diurnal physico-chemical composition of epikarst spring water is quite different under different climatic conditions. During a day with no rainfall, water temperature and air temperature have similar variations. Electrical conductivity (EC) has good positive correlation with pH value and water temperature. During rainstorms, the physico-chemical composition of the spring water is initially strongly effected by dilution, pH and EC drop rapidly. However, half to one hour later, EC returns to normal and the CO₂ effects will be the dominant physical effect. This is due to the high fissure rates and high permeability in the epikarst zone. Dilution effects were observed during the entire rainstorm event,whereas, it only acts during the earliest period of light rain. Therefore, it is necessary to examine the water–rock–CO₂ combination as a whole system to explain the hydrochemical behavior of epikarst processes.     

Ocean current estimation using a Multi-Model Ensemble Kalman Filter during the Grand Lagrangian Deployment experiment (GLAD)

In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3 km resolution (AMSEAS), and two versions of the Hybrid Coordinates Ocean Model (HYCOM) set at 4 km were running daily and delivering 72-h range forecasts. They all assimilated remote sensing and local profile data but they were not assimilating the drifter’s observations. This work presents a non-intrusive methodology named Multi-Model Ensemble Kalman Filter that allows assimilating the local drifter data into such a set of models, to produce improved ocean currents forecasts. The filter is to be used when several modeling systems or ensembles are available and/or observations are not entirely handled by the operational data assimilation process. It allows using generic in situ measurements over short time windows to improve the predictability of local ocean dynamics and associated high-resolution parameters of interest for which a forward model exists (e.g. oil spill plumes). Results can be used for operational applications or to derive enhanced background fields for other data assimilation systems, thus providing an expedite method to non-intrusively assimilate local observations of variables with complex operators. Results for the GLAD experiment show the method can improve water velocity predictions along the observed drifter trajectories, hence enhancing the skills of the models to predict individual trajectories.     

Efficient estimation of flood forecast prediction intervals via single‐ and multi‐objective versions of the LUBE method

Prediction intervals (PIs) are commonly used to quantify the accuracy and precision of a forecast. However, traditional ways to construct PIs typically require strong assumptions about data distribution and involve a large computational burden. Here, we improve upon the recent proposed Lower Upper Bound Estimation method and extend it to a multi‐objective framework. The proposed methods are demonstrated using a real‐world flood forecasting case study for the upper Yangtze River Watershed. Results indicate that the proposed methods are able to efficiently construct appropriate PIs, while outperforming other methods including the widely used Generalized Likelihood Uncertainty Estimation approach. Copyright © 2016 John Wiley & Sons, Ltd.     

The data‐driven approach as an operational real‐time flood forecasting model

Accurate water level forecasts are essential for flood warning. This study adopts a data‐driven approach based on the adaptive network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People's Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four‐ and five‐lead‐day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto‐ and cross‐correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi‐step‐ahead error prediction was superior to the fully recursive procedure. The RAR‐based partial recursive updating procedure significantly improved three‐, four‐ and five‐lead‐day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR‐based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest. Copyright © 2011 John Wiley & Sons, Ltd.     

全球地面降水月值历史数据集研制

全球降水历史数据是开展气候、水循环等研究的基础。收集整理全球12个数据源降水历史月值资料,通过站号、站名甄别不同数据源中相同台站,对344个通过相关系数、一致率、均值t检验、方差F检验的台站多源资料进行拼接,尽可能多地融合各套数据产品优势,最终形成全球降水历史月值数据集(CMA Global PrecipitationV1.0, CGP)。数据集重点解决当前国际数据产品在东亚地区站点稀少、同时应用多套数据应用门槛较高等问题。数据集收录3.1万个台站共计1.87×107组月降水记录, 4152个台站序列长度达百年。与美国大气海洋局(NOAA)的全球降水数据集(GHCN-M V2.0)对比,CGP新增1万个站点、0.5×107组有效观测记录和1030条百年序列,其中141条百年序列通过多源整合技术获取。CGP的站点和数据量优势主要体现在东亚、东欧、西伯利亚等站点稀疏地区。基于CGP分析的全球降水时空特征与国际同类产品的结果较一致。新增的数据虽然没有改变全球降水分布的总体特征,但对区域性的百年降水变化检测有一定影响。基于CGP的全球降水百年序列结果显示,20世纪前半叶全球降水量偏小,近20年是1900年以来全球降水量最大的时期,各纬度带、各个国家或地区的降水长期变化趋势呈现显著的差异。      

A timescale decomposed threshold regression downscaling approach to forecasting South China early summer rainfall

A timescale decomposed threshold regression(TSDTR) downscaling approach to forecasting South China early summer rainfall(SCESR) is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR.The two models are developed based on the partial least squares(PLS) regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915–84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation(PDO). On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985–2006, compared to other simpler approaches. This study suggests that the TSDTR approach,considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.     

基于多模式集成技术的地面气温精细化预报

基于ECMWF、JMA、T639、WRF四个数值模式2012年6月1日—9月30日地面气温3—60 h预报资料和郑州加密自动站资料,利用多模式集合平均(EMN)、消除偏差集合平均(BREM)、加权消除偏差集合(WBREM)及多模式超级集合(SUP)4种方法,对2012年8月29日—9月27日郑州城区11个站点地面逐3 h气温进行多模式集成预报试验,采用绝对误差对预报结果进行检验评估,结果表明:在30天的预报期内,BREM、WBREM及SUP对于大多数站气温预报效果有明显改善,而EMN方案对11个站预报效果改善则不太明显;4种方案中,BREM和WBREM预报效果相对较好且稳定,各个站上3—60 h预报的绝对误差均在2℃附近或以下;SUP方案虽然对个别站预报误差较低,但是其预报效果并不稳定,一些站点的个别预报时效误差大于2℃。对于郑州观测站的气温预报而言,4种集成方案20时起报的气温误差明显小于08时起报的误差,并且20时起报的SUP集成方案绝对误差明显小于其他方案的绝对误差。总体而言,BREM、WBREM及SUP三种集成方案能够给郑州精细化预报业务提供较好的参考。     

冬季东亚高空副热带急流和温带急流协同变化与中国南方地区降水的关系

利用NCEP/NCAR再分析数据和中国台站降水资料研究冬季东亚高空副热带急流和温带急流协同变化特征及其与中国南方地区降水的关系,发现冬季东亚高原急流与温带急流同期反向协同变化特征最为显著。即高原急流增强,同时温带急流减弱(SW型)和高原急流减弱,同时温带急流增强(WS型)。当高原急流增强(减弱)而温带急流减弱(增强)时,中国南方地区降水显著增加(减少)。合成分析表明,不同急流协同变化型态下冷暖空气活动特征存在较大差异,高原急流与温带急流的反向协同变化可以真实反映与冬季中国南方地区降水相关联的冷暖空气活动特征,进而导致不同降水形态的产生。     

中国东部夏季暴雨的年代际跃变及其大尺度环流背景

本文利用1960~2011年中国东部地面测站的逐日降水资料和JRA-55再分析资料探讨了夏季暴雨分布的年代际跃变及其相关联的大尺度环流异常特征。基于暴雨频数和占比(夏季暴雨占比是指5~8月暴雨降水量对总降水量的贡献百分比)的分析结果表明:中国东部夏季暴雨分布在20世纪70年代末和90年代初经历两次反相的经向"三极子"跃变。中国东部夏季暴雨的年代际演变过程可分为三个时段:1960~1979年为华南和华北暴雨偏多、江淮流域暴雨偏少的经向"三极子"分布;1980~1991年为南方和华北暴雨偏少、江淮流域暴雨偏多的经向三极子"分布;1992~2011年为南方暴雨显著偏多、华北暴雨持续偏少,逐渐形成经向"偶极子"分布,并导致近十多年我国夏季"南涝北旱"的整体格局。1970年代末(1990年代初)跃变相关联的大尺度环流异常配置:东亚夏季风的减弱(增强),西太平洋副高的增强西伸但南撤(北抬),南亚高压的减弱南缩(增强东扩),以及蒙古高原中低层的气旋式(反气旋式)环流异常。与此同时,低层局地环流也发生调整:华北和黄淮地区以及华南和江南地区均为反气旋式(气旋式)环流异常,而江淮流域和四川盆地受控于风场切变式辐合(辐散)异常;涡度场发生相应变化,南北方大部分地区的负(正)涡度异常不(有)利于低涡的发展,而江淮流域和四川盆地的正(负)涡度异常有(不)利于低涡的发展,进而引发江南和华南暴雨减少(增加)、江淮流域和四川盆地暴雨增加(减少)、黄淮和华北暴雨减少(增加)的经向"三极子"跃变。     

中国地面气温统计降尺度预报方法研究

利用中国752个基本、基准地面气象观测站2000—2010年地面温度日值数据,采用具有自适应特征的Kalman滤波类型的递减平均统计降尺度技术,对中国地面温度进行精细化预报研究。分析该方案的降尺度效果,并与常用插值降尺度方法进行比较。结果表明:1)递减平均统计降尺度技术相比插值方法有较大的提高,显著减小东西部预报效果差异,1~3 d预报的均方根误差减小了1.4℃;2)该方案1~3 d预报的均方根误差为1.5℃,预报误差从东南地区(均方根误差为1.4℃)向西北地区(均方根误差为1.8℃)逐渐增大,并且预报效果夏季优于冬季。因此,递减平均统计降尺度技术对中国地面温度进行精细化预报是可行的。