三峡库区陆气耦合研究及应用

以延长洪水预见期、提高预报精度为目标,研究气象水文耦合机制,利用数值天气预报模式WRF(Weather Research and Forecasting)驱动分布式VIC(Variable Infiltration Capacity)水文模型,构建三峡库区陆气耦合洪水预报系统,并对2007~2008年期间四场暴雨洪水进行日滚动预报试验。结果表明,WRF模式在三峡库区内有着良好的短期降水预报精度,基于数值天气预报模式和分布式水文模型的陆气耦合洪水预报系统能有效延长三峡入库洪水预见期、提高洪水预报精度,具有较大的应用潜力。     

贝叶斯概率水文预报系统在中长期径流预报中的应用

采用贝叶斯概率水文预报理论制订水电站水库中长期径流预报模型,以概率分布的形式定量地描述水文预报的不确定度,探索概率水文预报理论及其应用价值。采用气象因子灰关联预报模型处理输入因子的不确定度,将实时气象信息和历史水文资料有效结合,突破传统确定性预报方法在信息利用和样本学习方面的局限性,以提高水文预报的精确度。以丰满水电厂水库为例对所建模型进行检验,模拟计算结果表明,该模型与确定性径流预报方法相比,不仅有利于决策人员定量考虑不确定性,而且在期望意义上提高了径流预报精度,具有较高的应用价值。     

耦合SWAT与RIEMS模拟黑河干流山区径流

以黑河干流山区为研究区,采用1：100 000植被类型图、1：1 000 000土壤类型图和气象水文观测数据,耦合SWAT水文模型与RIEMS高分辨率区域气候模式,模拟1995-2010年月径流变化过程,探讨水文模型气象驱动数据的优化方法和气候水文模型耦合的区域适宜性。RIEMS气候模式输出精度较高,降水、温度、湿度、风速的相关系数均在0.80以上,均通过了0.01显著性水平检验,时空分辨率达到6 h和3 km。构建虚拟气象站点,弥补气象观测站点稀少且分布不均匀的不足,对水文模型气象驱动数据进行优化;遵循多时间尺度、多变量和多站点的原则来校准模型。结果表明,径流模拟值与观测值的过程趋势拟合程度较好,NSE均在0.60以上,PBIAS介于±20%之间,R²达到0.70以上。径流模拟在枯水期表现较好,在丰水期存在一定的误差,主要是受降水驱动数据偏高的影响,气候模式模拟能力需要提高,水文模型空间插值方法和气候水文模型耦合方案需进一步完善。总体来看,耦合SWAT模型与RIEMS模式能够较好地模拟黑河干流山区水文过程,可为流域水资源的预测和管理提供科学依据。     

耦合水库群参数化方案的区域陆面水文模拟

中国水库数目众多,密集的水库运行和调蓄影响着陆地水循环和陆气间的水分能量交换过程,给高强度开发地区水文规律认知和水文模拟及预报带来了挑战。围绕这一问题,本研究以鄱阳湖流域及流域内千余座水库为研究对象,从水库水量平衡方程、水库蓄泄规则、多阻断扩散波汇流等方面构建了水库群参数化方案,并从地表水、地下水、蒸散发、能量通量等角度实现了其与陆面水文双向耦合模式（CLHMS）的动态耦合。结果表明:所构建的水库群参数化方案可以较好地模拟水库的蓄泄过程,提高了耦合模式在鄱阳湖流域的径流模拟精度;水库群可以使赣江、信江、抚河丰水期径流减少3.7%~6.0%,枯水期径流增加5.9%~12.6%,多年平均径流减少0.6%~1.5%;在空间分布上,水库群对流域中部和北部径流的调蓄作用相对显著。本研究所改进的陆面水文模式可以为高强度人类活动影响下气象、水文、水资源交叉领域的研究提供分析工具和模型基础,从而支撑变化环境下区域水资源的可持续利用。     

黄河源区水文预报的关键科学问题

黄河源区水文预报对龙羊峡、刘家峡等黄河上游梯级水库群的洪水资源化调度乃至整个黄河流域的水旱灾害防御都起着举足轻重的作用。然而,由于降水的地面观测严重匮乏、且缺少适用于高寒山区的专用水文模型,源区水文预报成为长期困扰黄河洪水/径流业务预报的难题。在回顾国内外相关研究的基础上,从高海拔缺资料地区的降水观测与降水预报、寒区水文模型构建与气象水文耦合系统集成、高原降水发生的气象成因与形成机制3个方面,评价了黄河源区水文预报的当前现状与技术水平,提出了黄河源区水文预报面临的关键科学问题;指出新一代多源降水信息融合与同化、高寒区特殊产汇流模型构建、无缝隙气象水文集合预报、强降水与连阴雨的多影响天气系统解析等,是当前黄河源区水文预报的研究重点与发展方向。     

融合数据同化与机器学习的流域径流模拟方法

环境变化影响下流域径流的精确模拟对洪涝灾害防治与区域水资源管理都具有重要意义。在径流模拟研究中，现有机器学习模型未能充分考虑水文中间状态变量对降雨-径流过程的影响，本研究基于集合卡尔曼滤波(EnKF)更新水文状态变量，结合主成分分析(PCA)提取预报因子的主要特征，采用长短时记忆神经网络(LSTM)构建考虑水文中间变量的机器学习水文模型EnKF-PCA-LSTM。以赣江流域为例，评估EnKF-PCA-LSTM模型的径流模拟效果，同时将模拟结果与LSTM模型、物理水文模型HYMOD做对比分析。结果表明，EnKF-PCA-LSTM模型模拟径流的纳什效率系数、Kling-Gupta效率系数和对数纳什效率系数分别为0.954、0.971和0.972,比LSTM模型和HYMOD模型具有更好的模拟性能，说明考虑水文状态变量可有效提高机器学习模型的径流模拟精度及稳定性。研究成果可为流域径流模拟提供技术参考。     

丹江口水库秋汛期长期径流预报

针对目前长期径流预报中物理成因考虑较少的问题,以丹江口水库为例,在分析影响径流物理背景的基础上,研究前期气象因子与水库秋汛期入库径流过程的相关关系,识别影响径流的大气环流与海温等物理因子,利用主成分分析法提取主要预报信息,建立了包含大气环流因子、海温因子等气象物理信息以及前期降雨、径流等水文信息作为预报因子集的三层BP神经网络预报模型.利用1956~2008年秋汛期9、10月入库径流量进行模拟与试报,并与仅采用前期降雨径流的预测模型进行了比较,结果显示基于物理成因分析的预测模型稳定性良好,模拟及试报精度较高,9、10月试报精度平均提高约30%,分别达到87.5%和75%,并对预报年份中的丰枯特征有较好的体现.     

降水临近预报及其在水文预报中的应用研究进展

随着全球气候变化、自然变迁及陆表生境改变,极端天气频发且呈现出多尺度时空变异特征,对其进行预报和预警一直是气象水文领域关注的焦点。临近预报可较准确地预报未来短时间天气显著变化,是当前预报强降水等极端事件的主要手段。从基于天气雷达0~3 h外推临近预报、融合数值模式0~6 h临近预报的发展历程梳理了临近预报的研究进展,阐述了雷达外推算法的发展进程、雷达外推预报与数值模式预报融合技术进展,指出"取长补短"的0~6 h融合预报在提高降水预报精度、延长降水预见期等多方面有较大的发展潜力,进一步探寻及提升融合技术是未来融合预报发展的核心。将临近预报以气象水文耦合的方式引入水文预报是从源头提高水文预报精度、保障水文预报效果的主要途径,总结了现阶段主流耦合方式、空间尺度匹配技术、水文模型不确定等陆气耦合中的关键问题,阐述了外推临近预报、融合临近预报作为水文预报输入的研究进展,明确了融合临近预报在延长洪水预见期、提高洪水预报精度中存在优势,并讨论了未来的研究重点及发展方向。     

开都河流域山区径流模拟及降雨输入的不确定性分析

选取塔里木河源区的开都河流域为研究区,将流域内气象水文站点数据与遥感数据相结合,利用气象、土壤类型、土地利用和地表覆盖、数字高程(DEM)和降雨等资料,模拟流域水文过程,并在出山口实测径流数据的基础上对模型进行率定和验证;对降雨输入所带来的径流模拟不确定性进行分析,探讨降雨输入的空间异质性对水文预报结果的影响机制.结果表明:MIKE-SHE模型能在水文、气象站点稀少,土壤及水文地质数据缺乏的条件下,模拟开都河流域的日径流过程,但精度上仍有待提高;降雨输入的空间分布程度对径流模拟有重要影响.FY-2C遥感估算降雨资料能够更好地表达降雨时间的空间异质性,相应地对径流模拟精度也有一定程度的提高.     

水文预报加法模型在甘肃省主要河流年径流预测中的应用研究

加法模型是长期及超长期水文预测预报常用方法之一。本文阐述了水文预报加法模型原理,以甘肃省主要河流年径流超长期预报为例,建立起水文预报加法模型,并对各主要河流2018年径流预报情况进行对比研究,预报精度满足规范要求。并提出水文预报加法模型应用中的具体建议,以提高模型的预报精度。     

长江水文发展及展望

长江水文监测的水文、水质、河道信息,水文气象预报、水文水资源分析评价成果是维护河流健康的基础支撑信息。加强水文水资源监测站网的规划,加快水文测报现代化建设,提升水文水资源预报能力,大力开展长江水文水资源变化规律研究,促进水文事业发展,是长江水文的发展方向。     

Multi-model ensemble schemes for predicting northeast monsoon rainfall over peninsular India

The northeast (NE) monsoon season (October, November and December) is the major period of rainfall activity over south peninsular India. This study is mainly focused on the prediction of northeast monsoon rainfall using lead-1 products (forecasts for the season issued in beginning of September) of seven general circulation models (GCMs). An examination of the performances of these GCMs during hindcast runs (1982–2008) indicates that these models are not able to simulate the observed interannual variability of rainfall. Inaccurate response of the models to sea surface temperatures may be one of the probable reasons for the poor performance of these models to predict seasonal mean rainfall anomalies over the study domain. An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among these three schemes, SVD based MME has more skill than other MME schemes as well as member models.     

Evaluations and Ensemble Approaches of Western-Pacific Subtropical High and South-Asian High Ensemble Forecasting in GRAPES-GEPS

Based on the operational standard indices, the prediction skills of the Western-Pacific Subtropical High (WPSH) and South-Asian High (SAH) using 2019 real-time forecasts derived from the Global Ensemble Prediction System of GRAPES (GRAPES-GEPS) in China Meteorological Administration (CMA) Numerical Prediction Center were evaluated and the effects of different ensemble approaches on the prediction skills of WPSH and SAH indices were further investigated in this study. The results show that for WPSH, the GRAPES-GEPS has its highest prediction skill for the ridge line index, considerably high skill for the intensity and area indices, but relatively low skill for the western boundary index, and for SAH, it has comparatively high skill for the intensity and center latitude indices, but relatively lower skill for the center longitude index. Prediction errors of the GRAPES-GEPS for the WPSH forecasts are featured by the weaker intensity and area and the more eastward center position, compared with the observation, which can be effectively reduced by employing the maximum/minimum approach from ensemble members, relative to the ensemble mean approach. By direct comparison, prediction errors of the GRAPES-GEPS for the SAH forecasts are featured by the weaker intensity and the more southward center position, which tends to be slightly reduced using the ensemble mean approach. Finally, for the extreme forecast, the maximum approach provides superior performance for both WPSH and SAH than the ensemble mean approach, which can be validated in terms of the two extreme cases. These results clearly indicate that the maximum approach could better improve the GRAPES-GEPS performance for the extreme forecasting of the two primary circulation patterns than the traditional ensemble mean approach.     

Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts

The continuous ranked probability score (CRPS) is a much used measure of performance for probabilistic forecasts of a scalar observation. It is a quadratic measure of the difference between the forecast cumulative distribution function (CDF) and the empirical CDF of the observation. Analytic formulations of the CRPS can be derived for most classical parametric distributions, and be used to assess the efficiency of different CRPS estimators. When the true forecast CDF is not fully known, but represented as an ensemble of values, the CRPS is estimated with some error. Thus, using the CRPS to compare parametric probabilistic forecasts with ensemble forecasts may be misleading due to the unknown error of the estimated CRPS for the ensemble. With simulated data, the impact of the type of the verified ensemble (a random sample or a set of quantiles) on the CRPS estimation is studied. Based on these simulations, recommendations are issued to choose the most accurate CRPS estimator according to the type of ensemble. The interest of these recommendations is illustrated with real ensemble weather forecasts. Also, relationships between several estimators of the CRPS are demonstrated and used to explain the differences of accuracy between the estimators.     

基于EMD组合模型的径流多尺度预测

受全球气候变化与人类活动影响,径流序列愈发呈现出非稳态与非线性特征,为降低由此而引发的预报误差,充分发挥不同模型对提高径流预测精度的优势,针对传统径流预报模型的单一性,以干旱区典型内陆河玛纳斯河为例,采用经验模态分解(EMD)提取径流序列中具有物理含义的信号,得到不同时间尺度的多个固有模态函数(IMF)及1个趋势项,利用 ARIMA模型与GRNN模型分别对不同时间尺度的IMF分量进行模拟,分析径流未来变化趋势。运用多元线性回归法、Spearman相关系数法、平均影响值法筛选大气环流因子作为神经网络模型的输入项,根据子序列的局部频率特点构建组合模型。最后将各IMF分量的预测结果重构,得到径流的最终预测值。单一评价指标无法全面评价模型精度,本文通过构建TOPSIS评价模型对径流预测模型进行定量评估,客观评价模型优度。结果表明:EMD分解能有效提取径流序列中隐含的多时间尺度信号,由趋势项可知玛纳斯河径流量总体呈上升趋势;EMD分解可提高ARIMA模型25%的合格率,但对于高频率分量IMF1、IMF2、IMF3,ARIMA模型的相对误差达到70%以上,预测结果不理想;经过筛选预报因子可有效提高GRNN模型精度,其中MIV法筛选的预报因子最适合玛纳斯河,与EMD-ARIMA组合后的GRNN模型的合格率最高,TOPSIS模型得分也最高。预测结果可作为水资源规划与调度的科学依据,建模思路也可为优化径流预测模型提供新途径。     

A comparison between a Monte Carlo implementation of retrospective optimal interpolation and an ensemble Kalman filter in nonlinear dynamics

To more correctly estimate the error covariance of an evolved state of a nonlinear dynamical system, the second and higher-order moments of the prior error need to be known. Retrospective optimal interpolation (ROI) may require relatively less information on the higher-order moments of the prior errors than an ensemble Kalman filter (EnKF) because it uses the initial conditions as the background states instead of forecasts. Analogous to the extension of a Kalman filter into an EnKF, an ensemble retrospective optimal interpolation (EnROI) technique was derived using the Monte Carlo method from ROI. In contrast to the deterministic version of ROI, the background error covariance is represented by a background ensemble in EnROI. By sequentially applying EnROI to a moving limited analysis window and exploiting the forecast from the average of the background ensemble of EnROI as a guess field, the computation costs for EnROI can be reduced. In the numerical experiment using a Lorenz-96 model and a Model-III of Lorenz with a perfect-model assumption, the cost-effectiveness of the suboptimal version of EnROI is demonstrated to be superior to that of EnKF using perturbed observations.     

产流误差比例系数的系统响应修正方法

为提高洪水预报的精度以及修正的稳定性,在产流误差动态系统响应曲线修正方法的基础上,提出了产流比例系数的系统响应修正方法.将产流系列按照一定原则分成若干组,假定每组存在系统误差,通过引入一个比例系数来表示,应用系统响应理论,选择适当的参数率定方法确定最优比例系数,进而对时段产流量分组进行修正.将产流误差比例系数的系统响应修正方法应用于滩坑流域,并与产流误差动态系统响应曲线修正方法相比较,结果显示,对于流域的17场历史洪水,二者均能提高洪水预报的精度,但前者的修正效果更好,修正稳定性更强,适用范围更广.     

Skills of different mesoscale models over Indian region during monsoon season: Forecast errors

Performance of four mesoscale models namely, the MM5, ETA, RSM and WRF, run at NCMRWF for short range weather forecasting has been examined during monsoon-2006. Evaluation is carried out based upon comparisons between observations and day-1 and day-3 forecasts of wind, temperature, specific humidity, geopotential height, rainfall, systematic errors, root mean square errors and specific events like the monsoon depressions.It is very difficult to address the question of which model performs best over the Indian region? An honest answer is ‘none’. Perhaps an ensemble approach would be the best. However, if we must make a final verdict, it can be stated that in general, (i) the WRF is able to produce best All India rainfall prediction compared to observations in the day-1 forecast and, the MM5 is able to produce best All India rainfall forecasts in day-3, but ETA and RSM are able to depict the best distribution of rainfall maxima along the west coast of India, (ii) the MM5 is able to produce least RMSE of wind and geopotential fields at most of the time, and (iii) the RSM is able to produce least errors in the day-1 forecasts of the tracks, while the ETA model produces least errors in the day-3 forecasts.     

洪水预报产流误差的动态系统响应曲线修正方法

为提高实时洪水预报精度,提出了一种基于动态系统响应曲线洪水预报误差修正新方法。该方法将动态系统响应曲线引入洪水预报误差修正中,建立一种向误差源头追溯的动态反馈修正模型。此修正方法将新安江模型产流以下的部分作为响应系统, 用线性差分近似代替非线性系统响应函数的偏导数值,得到时段产流量所对应的系统响应曲线。用实测流量和计算流量之间的差值作为信息,使用最小二乘估计原理,对产流量进行修正, 再用修正后的产流量重新计算出流过程。该修正方法分别用理想案例和王家坝流域进行检验,结果证明此方法效果比传统二阶自回归模型有明显提高。     

Fast linearized forecasts for subsurface flow data assimilation with ensemble Kalman filter

Ensemble methods present a practical framework for parameter estimation, performance prediction, and uncertainty quantification in subsurface flow and transport modeling. In particular, the ensemble Kalman filter (EnKF) has received significant attention for its promising performance in calibrating heterogeneous subsurface flow models. Since an ensemble of model realizations is used to compute the statistical moments needed to perform the EnKF updates, large ensemble sizes are needed to provide accurate updates and uncertainty assessment. However, for realistic problems that involve large-scale models with computationally demanding flow simulation runs, the EnKF implementation is limited to small-sized ensembles. As a result, spurious numerical correlations can develop and lead to inaccurate EnKF updates, which tend to underestimate or even eliminate the ensemble spread. Ad hoc practical remedies, such as localization, local analysis, and covariance inflation schemes, have been developed and applied to reduce the effect of sampling errors due to small ensemble sizes. In this paper, a fast linear approximate forecast method is proposed as an alternative approach to enable the use of large ensemble sizes in operational settings to obtain more improved sample statistics and EnKF updates. The proposed method first clusters a large number of initial geologic model realizations into a small number of groups. A representative member from each group is used to run a full forward flow simulation. The flow predictions for the remaining realizations in each group are approximated by a linearization around the full simulation results of the representative model (centroid) of the respective cluster. The linearization can be performed using either adjoint-based or ensemble-based gradients. Results from several numerical experiments with two-phase and three-phase flow systems in this paper suggest that the proposed method can be applied to improve the EnKF performance in large-scale problems where the number of full simulation is constrained.