On-line economic optimization of energy systems using weather forecast information

We establish an on-line optimization framework to exploit weather forecast information in the operation of energy systems. We argue that anticipating the weather conditions can lead to more proactive and cost-effective operations. The framework is based on the solution of a stochastic dynamic real-time optimization (D-RTO) problem incorporating forecasts generated from a state-of-the-art weather prediction model. The necessary uncertainty information is extracted from the weather model using an ensemble approach. The accuracy of the forecast trends and uncertainty bounds are validated using real meteorological data. We present a numerical simulation study in a building system to demonstrate the developments.     

Parallelizing the ECMWF's weather forecast program: the 2D case

GMD and ECMWF (European Centre for Medium-Range Weather Forecasts) joined forces some months ago in order to parallelize ECMWF's production code for medium-range weather forecasts, the Integrated Forecasting System (IFS). Meanwhile, the first milestone of this cooperation has been reached: The 2D model of the IFS, which contains already all relevant data structures and algorithmic components of the corresponding 3D models, has been parallelized and run successfully on quite a large variety of differential parallel machines. Performance measurements confirm the expected parallel efficiencies of up to 80% and more. This paper discusses the parallelization strategy employed and gives a survey on the performance results obtained on the parallel systems.     

Estimation of the evapotranspiration using surface and satellite data

Abstract

A method to derive evapotranspiration from a combination of satellite and conventional data is investigated. For this purpose NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) infrared images on clear days of various seasons are used to derive surface temperatures over France. These temperatures are then compared to the shelter-height temperatures collected at the WMO (World Meteorological Organization) standard meteorological stations at the time of satellite overpass. The difference between the two temperatures varies both with season and latitude. To analyse those results we use a model of the soil-vegetation interface, forced by a reconstruction of the surface fluxes derived from the WMO data. The model simulates reasonably well the diurnal and seasonal variations in the difference between satellite surface temperature and surface-air temperature. The corresponding latitudinal variations which occur in summer may be interpreted in terms of evapotranspiration. The limitations of this method are determined by a model sensitivity study; in particular they are due to the role played by tall vegetation.     

On the feasibility to deploy mobile industrial applications using wireless communications

Wireless communications can facilitate the deployment of novel industrial applications to improve productivity or health and safety conditions. Health and safety applications require mobile solutions capable to operate under harsh propagation conditions at low cost and energy consumption. This paper presents the results of an extensive measurement campaign that demonstrate the feasibility to deploy industrial mobile sensing applications with reliable wireless connectivity levels using short-range IEEE 802.15.4. The campaign also analyses the capability of various wireless technologies to provide the throughput levels necessary for wireless local data distribution and backhaul connectivity.     

Combining weather prediction and remote sensing data for the calculation of evapotranspiration rates: application to Denmark

Evapotranspiration rates in Denmark were estimated using Advanced Very High Resolution Radiometer (AVHRR) satellite data and weather conditions predicted by a high-resolution weather forecast model (HIRLAM). The predictions were used both for atmospheric correction of satellite data and for remote sensing based calculation of net radiation, sensible heat fluxes and evapotranspiration rates. Climate predictions at 12 GMT were used as proxies for the atmospheric conditions at the time of the afternoon satellite passage (12.30–14.30 GMT). The air temperature at the time of the satellite passage was retrieved with a rms error of 1.9°C, and the rms error of the retrieved air humidity was 204 Pa. The evapotranspiration results were significantly influenced by the spatial distribution of weather conditions. Due to the encirclement of Denmark by sea shorelines, sea breezes extending more than 30?km inland were responsible for the intrusion of cooler air temperatures which increased the sensible heat fluxes and suppressed the evapotranspiration rates. The predictions were linearly related to eddy-covariance flux measurements representing agricultural land, beech forest and conifer forest, but the relationships were also characterized by a large degree of scattering. The results are discussed in relation to inaccuracies and future perspectives.     

On the complexity of concurrency control using semantic information

In the presence of semantic information, serializability is too strong a correctness criterion and unnecessarily restricts concurrency. Many researchers have investigated the use of semantic information to allow interleaving among transactions which are non-serializable, but which nonetheless preserves the consistency of the database and is acceptable to the users. In this paper we consider a class of schedules, calledconflict-correct schedules, first proposed by Farrag and Özsu, which enlarges upon the class of serializable schedules by taking semantic information of transactions into account. In this paper we show that the problem of recognizing schedules in this class is NP-complete. Thus it is unlikely that there exists an efficient scheduler which accepts the entire class of conflict-correct schedules.This research was partially supported by MICRO grants with IBM and XEROX Corporations.     

基于粗糙集的CBR检索在天气预测中的应用

针对灾害性天气预报和警报的迫切需要,研究了从气象数据中挖掘出知识、积累预报员的经验建立范例库,并利用CBR技术实现相似范例的检索,从而提高天气预报的水平。提出了快速检索的思路,先利用粗糙集理论去除冗余的范例决策表的特征属性,再利用BP神经网络来实现相似范例的检索。该检索方法速度快,效率高。给出了该方法在天气情况预测上的实例,验证了其有效性和准确性。     

Prediction of space weather by adaptive information processing

Space weather can be predicted using data from satellites. For example, the condition of high-energy electrons at geostationary orbit is vital in providing warnings for spacecraft operations. We investigate an adaptive predictor based on intelligent information processing. The predictor forecasts the condition of high-energy electrons at geostationary orbit 24 h ahead. This article focuses on adaptation and performance of the predictor. Our proposed model succeeded in forecasting the high-energy electron flux at geostationary orbit 24 h ahead. Furthermore, we also consider the relationship between the prediction performance and tuning parameters.     

On initial instantaneous jumps of singular systems

This paper is concerned with instantaneous jumps in the state trajectory of a singular system. It is shown that such jumps generically exist at the initial time due to inconsistent initial states. The formula for the jumps is derived, which indicates that the jumps linearly depend on the initial state. Moreover, the set of all initial states which do not cause any state jumps for any output feedback is characterized and is revealed to be a linear subspace. In the meantime, the necessary and sufficient conditions are given for the existence of an output feedback to suppress completely the jumps for a given initial state. Finally, we formulate the problem of designing an output feedback not only to stabilize the plant but also to achieve minimum initial jumps as a minimization problem     

嵌入式公路气象信息分布式采集系统设计

影响交通安全的最主要自然因素是气象.针对公路气象分散采样的特点,介绍了一种嵌入式公路气象信息分布式采集系统.该系统采用模块化设计,运用了嵌入式技术,用CAN现场总线构建传感采集网络,以GPRS作为远程无线通信方案,并给出了软件设计程序流程.经过对系统的软硬件实验测试,验证了该系统设计的正确性和可靠性.     

On modeling of linguistic information using random sets

This paper discusses the formal connection between possibility distributions (Zadeh [21]) and the theory of random sets via Choquet's theorem. Based upon these relationships, it is suggested that plausible inferences and modeling of common sense can be derived from the statistics of random sets. The analysis of subjectivity in meaning representation of natural languages can be carried out by taking account of covariates of individuals as in the statistical analysis of survival data.     

Development of real-time reference evapotranspiration at the regional scale using satellite-based observations

Real-time data of reference evapotranspiration (ET₀) at different space-time scales are essential to regional agricultural drought assessment, water accounting at the watershed to basin scale, and provide irrigation advisory to farmers. Here, we present a data-fusion approach that integrates satellite-based insolation product (8 km) from an Indian geostationary satellite (Kalpana-1) sensor (VHRR; Very High Resolution Radiometer) and high-resolution (~ 5 km) short-range weather forecast into an FAO56 model based on the classical Penman–Monteith (P-M) formulation. Five year (2009–2013) mean monthly estimates from the daily ET₀ product over the Indian landmass were found to vary between 10 and 350 mm. It increased from January to May (70–350 mm), followed by a decrease to reach the lowest in November (10–140 mm), thus typically showing unimodal distribution. The comparison of daily space-based and station-based estimates (at six ground stations) produced a root mean square deviation (RMSD) ranging from 21% to 38% for 977 paired data sets with the correlation coefficient (r) varying from 0.32 to 0.82. The error was reduced from 25% to 10% with an increase in ‘r’ from 0.43 to 0.98 for daily to 10 day summation period. Spatial grid-to-grid comparison of monthly ET₀ estimates with Global Data Assimilation System (GDAS) potential evapotranspiration (PET) showed RMSD within a range of 1.4–18.4% for most of the months, except for two. Further ET₀ analysis over normal and drought years showed that it could be used for comprehensive drought assessment with other existing indicators.     

大洋X-CG字幕机在电视天气预报节目制作中的应用

以应用大洋X-CG字幕机制作辽宁气象影视中心天气预报为例,阐述了大洋字幕机在天气预报生成系统中所体现出的特点和优势.     

大洋X-CG字幕机在电视天气预报节目制作中的应用

以应用大洋X-CG字幕机制作辽宁气象影视中心天气预报为例,阐述了大洋字幕机在天气预报生成系统中所体现出的特点和优势。     

Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors

There is considerable interest in using remote sensing to characterize the hydrologic behavior of the land surface on a routine basis. Information on moisture fluxes between the surface and lower atmosphere reveals linkages and land-atmosphere feedback mechanisms, aiding our understanding of energy and water balance cycles. Techniques that combine information on land and atmospheric properties with remotely sensed variables would allow improved prediction for a number of hydrological variables. Over the last few decades, there has been a focus on better determining evapotranspiration and its spatial variability, but for many regions routine prediction is not generally available at a spatial resolution appropriate to the underlying surface heterogeneity. Over agricultural regions, this is particularly critical, since the spatial extent of typical field scales is not regularly resolved within the pixel resolution of satellite sensors. Understanding the role of landscape heterogeneity and its influence on the scaling behavior of surface fluxes as observed by satellite sensors with different spatial resolutions is a critical research need. To attend this task, data from Landsat-ETM (60 m), ASTER (90 m), and MODIS (1020 m) satellite platforms are employed to independently estimate evapotranspiration. The range of the satellite sensor resolutions allows analyses that span scales from (point-scale) in-situ tower measurements to the MODIS kilometer-scale. Evapotranspiration estimates derived at these multiple resolutions were assessed against eddy covariance flux measurements collected during the 2002 Soil Moisture Atmospheric Coupling Experiment (SMACEX) over the Walnut Creek watershed in Iowa. Together, these data allow a comprehensive scale intercomparison of remotely sensed predictions, which include intercomparisons of the evapotranspiration products from the various sensors as well as a statistical analysis for the retrievals at the watershed scale. A high degree of consistency was observed between the retrievals from the higher-resolution satellite platforms (Landsat-ETM and ASTER). The MODIS-based estimates, while unable to discriminate the influence of land surface heterogeneity at the field scale, effectively reproduced the watershed average response, illustrating the utility of this sensor for regional-scale evapotranspiration estimation.     

Estimation of instantaneous air temperature using remote sensing data

In arid areas, the variation of air temperature can be considerable, so instantaneous air temperature (T_ai) estimation is needed in different environmental researches. In this research, two different remote sensing data are used for estimating T_ai for clear sky days in 2009 in Fars Province, Iran, including atmospheric temperature profile and land surface temperature (LST) data from Moderate Resolution Imaging Spectroradiometer. The T_ai from a number of surface weather sites is used to judge the best T_ai estimation. Stations’ elevation, latitude, and land cover type are considered to show their effect on T_ai estimation. The estimated T_ai evaluation focuses on daily and seasonal timescales in the daytime and night time separately. Both LST and vertical temperature profile data produced relatively high coefficient of determination values and small root mean square error value for T_ai estimation, especially during the night time. Land cover and elevation vary the error values in T_ai estimation more, when LST data is used. In comparison atmospheric temperature profile indicates a smaller error in T_ai estimation in spring and summer and in urban land cover type, while using LST data presents a better result in fall and winter especially at night time.     

神经网络预测远程控制系统中信息延时的研究

针对网络控制系统中前向通道和反馈通道同时存在随机延迟及延时不确定的问题,通过实例分析了随机延时对闭环控制系统影响的根源,提出了通过基于神经网络的预测控制方法解决存在网络随机延时的控制系统闭环控制的可行性。仿真结果表明该方法能够反映并预测该测量数据所代表的网络路径之间的时延特性,并能在基于Internet的闭环控制系统的设计中有效地替代实际网络进行研究;而所用的方法具有快速、准确的特点,能用于在线学习网络模型并对网络的时延值进行预测,为基于Internet的远程闭环控制提供了新的思路。     

On using the stochastic superindicator for information security evaluation in automated systems

This paper presents an approach for the information security analysis of automated systems. The random behavior of new vulnerabilities that occur in the automated system in the process of operation is investigated. The formalization required to evaluate information security, which is based on the methods of the stochastic indication theory, is presented.     

Estimation of evapotranspiration using remotely sensed land surface temperature and the revised three-temperature model

Evapotranspiration (ET) is one of the most important land surface processes for terrestrial ecosystems because it strongly relates to both the energy balance and the mass balance of these ecosystems. Although there are many ground based approaches to estimate ET, it is still a challenge to estimate ET by remote sensing for most applications. The three-temperature model (3T model) is a recently developed approach used to estimate ET based on ground measurement. Compared with other conventional ET estimation algorithms, fewer input parameters are required in the 3T model. Therefore, it shows good potential for remote sensing application. In this study, we aimed to extend the 3T model for remote sensing application and to validate its accuracy. First, algorithms to extend the 3T model for estimating the regional ET of mixed landscapes were proposed. Then, two case studies were presented using Landsat Thematic Mapper (TM)/Landsat Enhanced Thematic Mapper Plus (ETM+) images. Thereafter, the Bowen ratio energy balance method (BREBM) and the residual method of the energy balance equation (RMEBE) were adopted to validate the performance of the revised 3T model. Results showed that the absolute error of the mean daily ET estimated by the BREBM and the revised 3T model was 0.23 mm d^?1, whereas between the RMEBE and the revised 3T model it was 0.07 mm d^?1. This accuracy is acceptable for most application purposes. The revised 3T model demonstrates the ability to remotely sense actual regional evapotranspiration.     

On the estimation of independent binomial random variables using occurrence and sequential information

We re-visit² the age-old problem of estimating the parameters of a distribution from its observations. Traditionally, scientists and statisticians have attempted to obtain strong estimates by ‘extracting’ the information contained in the observations taken as a set. However, generally speaking, the information contained in the sequence in which the observations have appeared, has been ignored—i.e., except to consider dependence information as in the case of Markov models and n-gram statistics. In this paper, we present results which, to the best of our knowledge, are the first reported results, which consider how estimation can be enhanced by utilizing both the information in the observations and in their sequence of appearance. The strategy, known as sequence based estimation (SBE) works as follows. We first quickly allude to the results pertaining to computing the maximum likelihood estimates (MLE) of the data when the samples are taken individually. We then derive the corresponding MLE results when the samples are taken two-at-a-time, and then extend these for the cases when they are processed three-at-a-time, four-at-a-time etc. In each case, we also experimentally demonstrate the convergence of the corresponding estimates. We then suggest various avenues for future research, including those by which these estimates can be fused to yield a superior overall cumulative estimate of the parameter of the distribution, in pattern recognition (PR), and in other internet and compression applications. We believe that our new estimates have great potential for practitioners, especially when the cardinality of the observation set is small.