An explicit four-dimensional variational data assimilation method

A new data assimilation method called the explicit four-dimensional variational (4DVAR) method is proposed. In this method, the singular value decomposition (SVD) is used to construct the orthogonal basis vectors from a forecast ensemble in a 4D space. The basis vectors represent not only the spatial structure of the analysis variables but also the temporal evolution. After the analysis variables are ex-pressed by a truncated expansion of the basis vectors in the 4D space, the control variables in the cost function appear explicitly, so that the adjoint model, which is used to derive the gradient of cost func-tion with respect to the control variables, is no longer needed. The new technique significantly simpli-fies the data assimilation process. The advantage of the proposed method is demonstrated by several experiments using a shallow water numerical model and the results are compared with those of the conventional 4DVAR. It is shown that when the observation points are very dense, the conventional 4DVAR is better than the proposed method. However, when the observation points are sparse, the proposed method performs better. The sensitivity of the proposed method with respect to errors in the observations and the numerical model is lower than that of the conventional method.     

Prediction and predictability of a catastrophic local extreme precipitation event through cloud-resolving ensemble analysis and forecasting with Doppler radar observations

Local extreme rain usually resulted in disasters such as flash floods and landslides. Upon today, it is still one of the most difficult tasks for operational weather forecast centers to predict those events accurately. In this paper, we simulate an extreme precipitation event with ensemble Kalman filter (EnKF) assimilation of Doppler radial-velocity observations, and analyze the uncertainties of the assimilation. The results demonstrate that, without assimilation radar data, neither a single initialization of deterministic forecast nor an ensemble forecast with adding perturbations or multiple physical parameterizations can predict the location of strong precipitation. However, forecast was significantly improved with assimilation of radar data, especially the location of the precipitation. The direct cause of the improvement is the buildup of a deep mesoscale convection system with EnKF assimilation of radar data. Under a large scale background favorable for mesoscale convection, efficient perturbations of upstream mid-low level meridional wind and moisture are key factors for the assimilation and forecast. Uncertainty still exists for the forecast of this case due to its limited predictability. Both the difference of large scale initial fields and the difference of analysis obtained from EnKF assimilation due to small amplitude of initial perturbations could have critical influences to the event's prediction. Forecast could be improved through more cycles of EnKF assimilation. Sensitivity tests also support that more accurate forecasts are expected through improving numerical models and observations.     

基于修正K_p指数的中纬区单站电离层暴预报方法研究

对电离层暴进行有效预报具有很重要的实际应用价值,但目前这一工作还存在着许多方面有待改进.本文假设电离层扰动的季节性规律占主导地位,借鉴Kutiev and Muhtarov构建修正K_p指数表示地磁活动的方法,建立了一个适用于中纬地区Yakutsk单台站电离层暴预报的线性经验模型.检验表明,该模型对Yakutsk站夏季和春秋季电离层扰动的预测效果明显优于IRI中的STORM模型,模型改善度平均可达35%左右;能很好地反映磁暴连续发生的情况下δfoF₂分阶段下降的形态;能反映出一定的电离层正相扰动及其持续时间,但是正扰动的预报精度有待进一步提高.     

Evolutionary computation-based approach for model error correction and calibration

Calibration is typically used for improving the predictability of mechanistic simulation models by adjusting a set of model parameters and fitting model predictions to observations. Calibration does not, however, account for or correct potential misspecifications in the model structure, limiting the accuracy of modeled predictions. This paper presents a new approach that addresses both parameter error and model structural error to improve the predictive capabilities of a model. The new approach simultaneously conducts a numeric search for model parameter estimation and a symbolic (regression) search to determine a function to correct misspecifications in model equations. It is based on an evolutionary computation approach that integrates genetic algorithm and genetic programming operators. While this new approach is designed generically and can be applied to a broad array of mechanistic models, it is demonstrated for an illustrative case study involving water quality modeling and prediction. Results based on extensive testing and evaluation, show that the new procedure performs consistently well in fitting a set of training data as well as predicting a set of validation data, and outperforms a calibration procedure and an empirical model fitting procedure.     

Research on runoff forecast approaches to the Aksu River basin

The Aksu River (the international river between China and Kirghiz) has become the main water source for the Tarim River. It significantly influences the Tarim River’s formation, development and evolution. Along with the western region development strategy and the Tarim River basin comprehensive development and implementation, the research is now focused on the Aksu River basin hydrologic characteristic and hydrologic forecast. Moreover, the Aksu River is representative of rivers supplied with glacier and snow melt in middle-high altitude arid district. As a result, the research on predicting the river flow of the Aksu River basin has theoretical and practical significance. In this paper, considering the limited hydrometeorological data for the Aksu River basin, we have constructed four hydrologic forecast approaches using the daily scale to simulate and forecast daily runoff of two big branches of the Aksu River basin. The four approaches are the upper air temperature and the daily runoff correlation method, AR(p) runoff forecast model, temperature and precipitation revised AR(p) model and the NAM rainfall-runoff model. After comparatively analyzing the simulation results of the four approaches, we discovered that the temperature and precipitation revised AR(p) model, which needs less hydrological and meteorological data and is more predictive, is suitable for the short-term runoff forecast of the Aksu River basin. This research not only offers a foundation for the Aksu River and Tarim Rivers’ hydrologic forecast, flood prevention, control and the entire basin water collocation, but also provides the hydrologic forecast reference approach for other arid ungauged basins.     

Research on runoff forecast approaches to the Aksu River basin

The Aksu River (the international river between China and Kirghiz) has become the main water source for the Tarim River. It significantly influences the Tarim River's formation, development and evolution. Along with the western region development strategy and the Tarim River basin comprehensive devel-opment and implementation, the research is now focused on the Aksu River basin hydrologic charac-teristic and hydrologic forecast. Moreover, the Aksu River is representative of rivers supplied with gla-cier and snow melt in middle-high altitude arid district. As a result, the research on predicting the river flow of the Aksu River basin has theoretical and practical significance. In this paper, considering the limited hydrometeorological data for the Aksu River basin, we have constructed four hydrologic forecast approaches using the daily scale to simulate and forecast daily runoff of two big branches of the Aksu River basin. The four approaches are the upper air temperature and the daily runoff correlation method, AR(p) runoff forecast model, temperature and precipitation revised AR(p) model and the NAM rainfall-runoff model. After comparatively analyzing the simulation results of the four approaches, we discovered that the temperature and precipitation revised AR(p) model, which needs less hydrological and meteorological data and is more predictive, is suitable for the short-term runoff forecast of the Aksu River basin. This research not only offers a foundation for the Aksu River and Tarim Rivers' hydrologic forecast, flood prevention, control and the entire basin water collocation, but also provides the hydrologic forecast reference approach for other arid ungauged basins.     

基于面平均雨量误差修正的实时洪水预报修正方法

空间集总式水文模型的洪水预报精度会受到面平均雨量估计误差的严重影响.点雨量监测值的误差类型、误差大小以及流域的雨量站点密度和站点的空间分布都会影响到面平均雨量的计算.为提高实时洪水预报精度,本文提出了一种基于降雨系统响应曲线洪水预报误差修正方法.通过此方法估计降雨输入项的误差,从而提高洪水预报精度.此方法将水文模型做为输入和输出之间的响应系统,用实测流量和计算流量之间的差值做为信息,通过降雨系统响应曲线,使用最小二乘估计原理,对面平均雨量进行修正,再用修正后的面平均雨量重新计算出流过程.将此修正方法结合新安江模型使用理想案例进行检验,并应用于王家坝流域的16场历史洪水以及此流域不同雨量站密度的情况下,结果证明均有明显修正效果,且在雨量站密度较低时修正效果更加明显.该方法是一种结构简单且不增加模型参数和复杂度的实时洪水修正的新方法.     

A simple technique for measuring rainfall interception by small shrub: “interception flow collection box”

In this paper a simple technique for field measurement of rain water loss arising from interception and water flows associated with species of small Mediterranean shrub is described: the ‘interception flow collection box’. This technique solves the problem of installing devices to control stemflow in species with a multiple trunk and demonstrates its efficiency through the results obtained from the data observed for three species of semi-arid Mediterranean shrub: Juniperus oxycedrus, Rosmarinus officinalis and Thymus vulgaris. Finally, the empirical equations for the prediction of throughfall, stemflow and rain water loss through interception are presented for the three selected species and the validity of the technique employed is established. © 1998 John Wiley & Sons, Ltd.     

Data assimilation for precipitation nowcasting using Bayesian inference

This work introduces a new variational Bayes data assimilation method for the stochastic estimation of precipitation dynamics using radar observations for short term probabilistic forecasting (nowcasting). A previously developed spatial rainfall model based on the decomposition of the observed precipitation field using a basis function expansion captures the precipitation intensity from radar images as a set of ‘rain cells’. The prior distributions for the basis function parameters are carefully chosen to have a conjugate structure for the precipitation field model to allow a novel variational Bayes method to be applied to estimate the posterior distributions in closed form, based on solving an optimisation problem, in a spirit similar to 3D VAR analysis, but seeking approximations to the posterior distribution rather than simply the most probable state. A hierarchical Kalman filter is used to estimate the advection field based on the assimilated precipitation fields at two times. The model is applied to tracking precipitation dynamics in a realistic setting, using UK Met Office radar data from both a summer convective event and a winter frontal event. The performance of the model is assessed both traditionally and using probabilistic measures of fit based on ROC curves. The model is shown to provide very good assimilation characteristics, and promising forecast skill. Improvements to the forecasting scheme are discussed.     

初始误差和参数误差对混沌系统可预报性影响的比较

利用非线性误差增长理论,以Lorenz系统为例比较研究了初始误差和参数误差对混沌系统可预报性的影响．结果表明：在初始误差和参数误差单独存在时,系统的可预报期限随误差大小的变化规律基本上相同;对于相同的误差大小,初始误差和参数误差对系统可预报期限的影响几乎相同,这一结果基本上不随参数范围的变化而变化．当初始误差和参数误差同时存在时,两者对可预报期限影响所起的作用大小主要取决于初始误差和参数误差的相对大小．当初始误差远大于参数误差时,Lorenz系统的可预报期限主要由初始误差决定,可以不用考虑参数误差对预报模式可预报性的影响;反之,当参数误差远大于初始误差时,Lorenz系统的可预报期限主要由参数误差决定;当初始误差和参数误差大小相当时,两者都对系统的可预报期限起重要作用．在后两种情况下,在考虑初始误差对可预报性影响的同时还必须考虑参数误差的作用．这提醒我们在作实际数值天气预报的时候,不仅要重视初值的确定,也要重视数值模式控制参数的确定．     

Surface water input from snowmelt and rain throughfall in western juniper: potential impacts of climate change and shifts in semi‐arid vegetation

In the western USA, shifts from snow to rain precipitation regimes and increases in western juniper cover in shrub‐dominated landscapes can alter surface water input via changes in snowmelt and throughfall. To better understand how shifts in both precipitation and semi‐arid vegetation cover alter above‐ground hydrological processes, we assessed how rain interception differs between snow and rain surface water input; how western juniper alters snowpack dynamics; and how these above‐ground processes differ across western juniper, mountain big sagebrush and low sagebrush plant communities. We collected continuous surface water input with four large lysimeters, interspace and below‐canopy snow depth data and conducted periodic snow surveys for two consecutive water years (2013 and 2014). The ratio of interspace to below‐canopy surface water input was greater for snow relative to rain events, averaging 79.4% and 54.8%, respectively. The greater surface water input ratio for snow is in part due to increased deposition of redistributed snow under the canopy. We simulated above‐ground energy and water fluxes in western juniper, low sagebrush and mountain big sagebrush for two 8‐year periods under current and projected mid‐21st century warmer temperatures with the Simultaneous Heat and Water (SHAW) model. Juniper compared with low and mountain sagebrush reduced surface water input by an average of 138 mm or 24% of the total site water budget. Conversely, warming temperatures reduced surface water input by only an average of 14 mm across the three vegetation types. The future (warmer) simulations resulted in earlier snow disappearance and surface water input by 51 and 45 days, respectively, across juniper, low sagebrush and mountain sagebrush. Information from this study can help land managers in the sagebrush steppe understand how both shifts in climate and semi‐arid vegetation will alter fundamental hydrological processes. Copyright © 2016 John Wiley & Sons, Ltd.     

地震可预测性研究的CSEP计划及其启示

地震可预测性是当前地震预测预报研究关注的前沿问题,对此问题的关注也代表了地震预测预报研究的现状,即采用更务实的态度、“循序渐进”地研究地震的可预测属性。本文介绍了国际上目前在地震可预测性研究中影响较大的“地震可预测性合作研究”（Collaboratory for the Study of Earthquake Predictability,CSEP）计划,通过对CSEP计划的历史背景、研究现状和技术特点的介绍,讨论了其对中国地震预测预报研究的可能的启发意义。     

An explicit four-dimensional variational data assimilation method based on the proper orthogonal decomposition: Theoretics and evaluation

The proper orthogonal decomposition (POD) method is used to construct a set of basis functions for spanning the ensemble of data in a certain least squares optimal sense. Compared with the singular value decomposition (SVD), the POD basis functions can capture more energy in the forecast ensemble space and can represent its spatial structure and temporal evolution more effectively. After the analysis variables are expressed by a truncated expansion of the POD basis vectors in the ensemble space, the control variables appear explicitly in the cost function, so that the adjoint model, which is used to derive the gradient of the cost function with respect to the control variables, is no longer needed. The application of this new technique significantly simplifies the data assimilation process. Several assimilation experiments show that this POD-based explicit four-dimensional variational data assimilation method performs much better than the usual ensemble Kalman filter method on both enhancing the assimilation precision and reducing the computation cost. It is also better than the SVD-based explicit four-dimensional assimilation method, especially when the forecast model is not perfect and the forecast error comes from both the noise of the initial filed and the uncertainty of the forecast model. Supported by the National Natural Science Foundation of China (Grant No. 40705035), National High Technology Research and Development Program of China (Grant No. 2007AA12Z144), Knowledge Innovation Project of Chinese Academy of Sciences (Grant Nos. KZCX2-YW-217 and KZCX2-YW-126-2), and National Basic Research Program of China (Grant No. 2005CB321704)     

The predictability of near-coastal currents using a baroclinic unstructured grid model

A limited domain, coastal ocean forecast system consisting of an unstructured grid model, a meteorological model, a regional ocean model, and a global tidal database is designed to be globally relocatable. For such a system to be viable, the predictability of coastal currents must be well understood with error sources clearly identified. To this end, the coastal forecast system is applied at the mouth of Chesapeake Bay in response to a Navy exercise. Two-day forecasts are produced for a 10-day period from 4 to 14 June 2010 and compared to real-time observations. Interplay between the temporal frequency of the regional model boundary forcing and the application of external tides to the coastal model impacts the tidal characteristics of the coastal current, even contributing a small phase error. Frequencies of at least 3 h are needed to resolve the tidal signal within the regional model; otherwise, externally applied tides from a database are needed to capture the tidal variability. Spatial resolution of the regional model (3 vs 1 km) does not impact skill of the current prediction. Tidal response of the system indicates excellent representation of the dominant M ₂ tide for water level and currents. Diurnal tides, especially K ₁, are amplified unrealistically with the application of coarse 27-km winds. Higher-resolution winds reduce current forecast error with the exception of wind originating from the SSW, SSE, and E. These winds run shore parallel and are subject to strong interaction with the shoreline that is poorly represented even by the 3-km wind fields. The vertical distribution of currents is also well predicted by the coastal model. Spatial and temporal resolution of the wind forcing including areas close to the shoreline is the most critical component for accurate current forecasts. Additionally, it is demonstrated that wind resolution plays a large role in establishing realistic thermal and density structures in upwelling prone regions.     

利用风云三号微波成像仪资料遥感“桑达”台风降雨云结构

本文利用搭载于我国风云三号B星上的微波成像仪(MWRI)观测亮温数据,结合戈达德廓线反演算法,对1102号"桑达"台风地面雨强和降雨云结构进行反演试验.利用AMSR-E业务降水产品对地面雨强反演结果进行了检验,结果表明,MWRI和AMSR-E反演的地面雨强在空间分布上非常吻合,相关性达76%,均方根误差约2.8 mm/h,二者的观测亮温及地面雨强反演结果具有较好的一致性.提取洋面台风雨区的平均水凝物廓线,其垂直结构显示,雨水和可降冰含量丰富,随高度变化明显,且具有明显峰值高度,云水和云冰含量则较少,且随高度变化不明显;当降水增强时,雨水和可降冰各层含量稳定增加,且峰值高度基本保持不变,云水和云冰含量则增幅不稳,且峰值高度有所改变.地面雨强随距台风中心距离的变化阐释了台风的螺旋结构及降水特点,距台风中心距离0.3°和0.6°附近分别出现了地面雨强峰值和次峰值,且66%的降水集中在距台风中心距离1°的空间范围内.MWRI提供的台风地面雨强和降雨云垂直信息具有较高的可信度,对于我们监测台风降水、分析台风降水结构的时空演变特征以及数值预报模式应用等具有重要的参考价值.     

Predictability of Indian Monsoon Circulation with High Resolution ECMWF Model in the Perspective of Tropical Forecast During the Tropical Convection Year 2008

To address some of the issues of project Year of Tropical Convection (YOTC) and the project ATHENA as ongoing international activities, an endeavor has been made for the first time to study the predictability of Indian summer monsoon in the backdrop of tropical predictability using 850 hPa atmospheric circulations with the high resolution (T1279) ECMWF model during the boreal summer of 2008 as one of the focus years of YOTC. The major findings obtained from the statistical forecast have been substantiated by the dynamical prediction in terms of the systematic error energy, its growth rate and the attribution of the dominant nonlinear dynamical processes to error growth. The systematic error energy of T1279 (16 km resolution) ECMWF model are generated in African landmass, India and its adjoining oceanic region, in near equatorial west Pacific and around the Madagascar region where the root mean square errors are observed and the zonal wind anomaly shows poor forecast skill. As far as the inadequate predictability of Indian summer monsoon by T1279 ECMWF model (revealed from the results of project ATHENA) is concerned, the systematic error energy and the error growth over Arabian Sea, in the eastern and western India due to the nonlinear convergence and divergence of error flux along with the erroneous Mascarene high may possibly be the determining factors for not showing any discernable improvement in Indian monsoon during the medium range forecast up to 240 h. This work suggests that the higher resolution of ECMWF model may not necessarily lead to the better forecast of Indian monsoon circulations during 2008 unless a methodology can be devised to isolate the errors due to the nonlinear processes that are inherent within the system.     

Assessing rainfall–runoff models for the management of Lake Verbano

Growing human pressure and potential change in precipitation pattern induced by climate change require a more efficient and sustainable use of water resources. Hydrological models can provide a fundamental contribution to this purpose, especially as increasing availability of meteorological data and forecast allows for more accurate runoff predictions. In this article, two models are presented for describing the flow formation process in a sub‐alpine catchment: a distributed parameter, physically based model, and a lumped parameter, empirical model. The scope is to compare the two modelling approaches and to assess the impact of hydrometeorological information, either observations or forecast, on water resources management. This is carried out by simulating the real‐time management of the regulated lake that drains the catchment, using the inflow predictions provided by the two models. Copyright © 2010 John Wiley & Sons, Ltd.     

Improved normalization of time‐lapse seismic data using normalized root mean square repeatability data to improve automatic production and seismic history matching in the Nelson field

Updating of reservoir models by history matching of 4D seismic data along with production data gives us a better understanding of changes to the reservoir, reduces risk in forecasting and leads to better management decisions. This process of seismic history matching requires an accurate representation of predicted and observed data so that they can be compared quantitatively when using automated inversion. Observed seismic data is often obtained as a relative measure of the reservoir state or its change, however. The data, usually attribute maps, need to be calibrated to be compared to predictions. In this paper we describe an alternative approach where we normalize the data by scaling to the model data in regions where predictions are good. To remove measurements of high uncertainty and make normalization more effective, we use a measure of repeatability of the monitor surveys to filter the observed time‐lapse data. We apply this approach to the Nelson field. We normalize the 4D signature based on deriving a least squares regression equation between the observed and synthetic data which consist of attributes representing measured acoustic impedances and predictions from the model. Two regression equations are derived as part of the analysis. For one, the whole 4D signature map of the reservoir is used while in the second, 4D seismic data is used from the vicinity of wells with a good production match. The repeatability of time‐lapse seismic data is assessed using the normalized root mean square of measurements outside of the reservoir. Where normalized root mean square is high, observations and predictions are ignored. Net: gross and permeability are modified to improve the match. The best results are obtained by using the normalized root mean square filtered maps of the 4D signature which better constrain normalization. The misfit of the first six years of history data is reduced by 55 per cent while the forecast of the following three years is reduced by 29 per cent. The well based normalization uses fewer data when repeatability is used as a filter and the result is poorer. The value of seismic data is demonstrated from production matching only where the history and forecast misfit reductions are 45% and 20% respectively while the seismic misfit increases by 5%. In the best case using seismic data, it dropped by 6%. We conclude that normalization with repeatability based filtering is a useful approach in the absence of full calibration and improves the reliability of seismic data.