Contesting ‘Local’ Commemoration of the Second World War: the case of the Changi Chapel and Museum in Singapore

This paper concerns the performance of national war memory at the Changi Chapel and Museum, a site honouring the many prisoners interned in Japanese-Occupied Singapore—especially at Changi—during the Second World War. In the light of the global nature of ‘the Changi story’, and the predominantly transnational nature of its present in situ memorialisation, we first examine how the Singapore state has sought to ‘localise’ the site to make it equally appealing to Singaporeans, as a place where a sense of their ‘shared history’ may be invoked. We then explore the Singaporeans' views about the site and its reconfiguration as a national icon, arguing how the state's task of ‘localising’ the site has been a vexed process due to myriad factors such as ethnic and religious plurality, and the already foreign-centric bias of prevailing knowledge about Changi. Following that, we show how this performance of national memory is also inflected by Australia's national remembrance of the same event. More broadly, we highlight the contested process of ‘localising’ such war memoryscapes as national iconography, suggesting how it may be more fruitful to conceive them as ‘international memoryscapes’, or places to which all individuals can relate regardless of race or national affiliations.     

Stochastic Modelling of the Impact of Flood Protection Measures Along the River Waal in the Netherlands

River flooding is a problem of international interest. In the past few years many countries suffered from severe floods. A large part of the Netherlands is below sea level and river levels. The Dutch flood defences along the river Rhine are designed for water levels with a probability of exceedance of 1/1250 per year. These water levels are computed with a hydrodynamic model using a deterministic bed level and a deterministic design discharge. Traditionally, the safety against flooding in the Netherlands is obtained by building and reinforcing dikes. Recently, a new policy was proposed to cope with increasing design discharges in the Rhine and Meuse rivers. This policy is known as the Room for the River (RfR) policy, in which a reduction of flood levels is achieved by measures creating space for the river, such as dike replacement, side channels and floodplain lowering. As compared with dike reinforcement, these measures may have a stronger impact on flow and sediment transport fields, probably leading to stronger morphological effects. As a result of the latter the flood conveyance capacity may decrease over time. An a priori judgement of safety against flooding on the basis of an increased conveyance capacity of the river can be quite misleading. Therefore, the determination of design water levels using a fixed-bed hydrodynamic model may not be justified and the use of a mobile-bed approach may be more appropriate. This problem is addressed in this paper, using a case study of the river Waal (one of the Rhine branches in the Netherlands). The morphological response of the river Waal to a flood protection measure (floodplain lowering in combination with summer levee removal) is analysed. The effect of this measure is subject to various sources of uncertainty. Monte Carlo simulations are applied to calculate the impact of uncertainties in the river discharge on the bed levels. The impact of the “uncertain” morphological response on design flood level predictions is analysed for three phenomena, viz. the impact of the spatial morphological variation over years, the impact of the seasonal morphological variation and the impact of the morphological variability around bifurcation points. The impact of seasonal morphological variations turns out to be negligible, but the other two phenomena appear to have each an appreciable impact (order of magnitude 0.05–0.1 m) on the computed design water levels. We have to note however, that other sources of uncertainty (e.g. uncertainty in hydraulic roughness predictor), which may be of influence, are not taken into consideration. In fact, the present investigation is limited to the sensitivity of the design water levels to uncertainties in the predicted bed level.     

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.     

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.     

高性能计算技术在气象领域的应用

高性能计算通过应用超级计算机与并行处理技术解决复杂的计算问题,是信息技术发展比较迅猛的领域之一。气象应用始终是高性能计算的重要领域,高性能计算技术有效地解决了高分辨率、高精度气象数值预报模式发展限制,在气象预报预测业务中发挥着核心支撑作用。数十年以来,由于数值天气模式研究和业务运行对计算资源的强烈需求,国内外气象领域高性能计算机及应用迅速发展起来。气象领域对高性能计算能力及系统的可靠性需求日益提升。高性能计算技术将与气象预报应用日益融合,相互影响促进,不断创新发展。为满足气象预报预测业务和科研工作需求,中国气象部门将进一步提升高性能计算能力,并致力于优化集约高性能计算系统布局,高效管理计算资源,发挥最大效益。     

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。     

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。     

Estimation of transit trip production using Factorial Kriging with External Drift: an aggregated data case study

Studies in transportation planning routinely use data in which location attributes are an important source of information. Thus, using spatial attributes in urban travel forecasting models seems reasonable. The main objective of this paper is to estimate transit trip production using Factorial Kriging with External Drift (FKED) through an aggregated data case study of Traffic Analysis Zones in São Paulo city, Brazil. The method consists of a sequential application of Principal Components Analysis (PCA) and Kriging with External Drift (KED). The traditional Linear Regression (LR) model was adopted with the aim of validating the proposed method. The results show that PCA summarizes and combines 23 socioeconomic variables using 4 components. The first component is introduced in KED, as secondary information, to estimate transit trip production by public transport in geographic coordinates where there is no prior knowledge of the values. Cross-validation for the FKED model presented high values of the correlation coefficient between estimated and observed values. Moreover, low error values were observed. The accuracy of the LR model was similar to FKED. However, the proposed method is able to map the transit trip production in several geographical coordinates of non-sampled values.