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Sample records for condition indicators forecasting

  1. Seasonal streamflow forecasting by conditioning climatology with precipitation indices

    Science.gov (United States)

    Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian; Perrin, Charles

    2017-03-01

    Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

  2. Application of Polar Cap (PC) indices in analyses and forecasts of geophysical conditions

    Science.gov (United States)

    Stauning, Peter

    2016-07-01

    The Polar Cap (PC) indices could be considered to represent the input of power from the solar wind to the Earth's magnetosphere. The indices have been used to analyse interplanetary electric fields, effects of solar wind pressure pulses, cross polar cap voltages and polar cap diameter, ionospheric Joule heating, and other issues of polar cap dynamics. The PC indices have also been used to predict auroral electrojet intensities and global auroral power as well as ring current intensities. For specific space weather purposes the PC indices could be used to forecast substorm development and predict associated power line disturbances in the subauroral regions. The presentation shall outline the general background for applying the PC indices in analyses or forecasts of solar wind-magnetosphere-ionosphere interactions and provide illustrative examples of the use of the Polar Cap indices in specific cases

  3. Solar Indices Forecasting Tool

    Science.gov (United States)

    Henney, Carl John; Shurkin, Kathleen; Arge, Charles; Hill, Frank

    2016-05-01

    Progress to forecast key space weather parameters using SIFT (Solar Indices Forecasting Tool) with the ADAPT (Air Force Data Assimilative Photospheric flux Transport) model is highlighted in this presentation. Using a magnetic flux transport model, ADAPT, we estimate the solar near-side field distribution that is used as input into empirical models for predicting F10.7(solar 10.7 cm, 2.8 GHz, radio flux), the Mg II core-to-wing ratio, and selected bands of solar far ultraviolet (FUV) and extreme ultraviolet (EUV) irradiance. Input to the ADAPT model includes the inferred photospheric magnetic field from the NISP ground-based instruments, GONG & VSM. Besides a status update regarding ADAPT and SIFT models, we will summarize the findings that: 1) the sum of the absolute value of strong magnetic fields, associated with sunspots, is shown to correlate well with the observed daily F10.7 variability (Henney et al. 2012); and 2) the sum of the absolute value of weak magnetic fields, associated with plage regions, is shown to correlate well with EUV and FUV irradiance variability (Henney et al. 2015). This work utilizes data produced collaboratively between Air Force Research Laboratory (AFRL) and the National Solar Observatory (NSO). The ADAPT model development is supported by AFRL. The input data utilized by ADAPT is obtained by NISP (NSO Integrated Synoptic Program). NSO is operated by the Association of Universities for Research in Astronomy (AURA), Inc., under a cooperative agreement with the National Science Foundation (NSF). The 10.7 cm solar radio flux data service, utilized by the ADAPT/SIFT F10.7 forecasting model, is operated by the National Research Council of Canada and National Resources Canada, with the support of the Canadian Space Agency.

  4. Social Indicators and Social Forecasting.

    Science.gov (United States)

    Johnston, Denis F.

    The paper identifies major types of social indicators and explains how they can be used in social forecasting. Social indicators are defined as statistical measures relating to major areas of social concern and/or individual well being. Examples of social indicators are projections, forecasts, outlook statements, time-series statistics, and…

  5. Econometric Models for Forecasting of Macroeconomic Indices

    Science.gov (United States)

    Sukhanova, Elena I.; Shirnaeva, Svetlana Y.; Mokronosov, Aleksandr G.

    2016-01-01

    The urgency of the research topic was stipulated by the necessity to carry out an effective controlled process by the economic system which can hardly be imagined without indices forecasting characteristic of this system. An econometric model is a safe tool of forecasting which makes it possible to take into consideration the trend of indices…

  6. Drought forecasting in Luanhe River basin involving climatic indices

    Science.gov (United States)

    Ren, Weinan; Wang, Yixuan; Li, Jianzhu; Feng, Ping; Smith, Ronald J.

    2016-09-01

    Drought is regarded as one of the most severe natural disasters globally. This is especially the case in Tianjin City, Northern China, where drought can affect economic development and people's livelihoods. Drought forecasting, the basis of drought management, is an important mitigation strategy. In this paper, we evolve a probabilistic forecasting model, which forecasts transition probabilities from a current Standardized Precipitation Index (SPI) value to a future SPI class, based on conditional distribution of multivariate normal distribution to involve two large-scale climatic indices at the same time, and apply the forecasting model to 26 rain gauges in the Luanhe River basin in North China. The establishment of the model and the derivation of the SPI are based on the hypothesis of aggregated monthly precipitation that is normally distributed. Pearson correlation and Shapiro-Wilk normality tests are used to select appropriate SPI time scale and large-scale climatic indices. Findings indicated that longer-term aggregated monthly precipitation, in general, was more likely to be considered normally distributed and forecasting models should be applied to each gauge, respectively, rather than to the whole basin. Taking Liying Gauge as an example, we illustrate the impact of the SPI time scale and lead time on transition probabilities. Then, the controlled climatic indices of every gauge are selected by Pearson correlation test and the multivariate normality of SPI, corresponding climatic indices for current month and SPI 1, 2, and 3 months later are demonstrated using Shapiro-Wilk normality test. Subsequently, we illustrate the impact of large-scale oceanic-atmospheric circulation patterns on transition probabilities. Finally, we use a score method to evaluate and compare the performance of the three forecasting models and compare them with two traditional models which forecast transition probabilities from a current to a future SPI class. The results show that the

  7. Seasonal Forecasts of Climate Indices: Impact of Definition and Spatial Aggregation on Predictive Skill

    Science.gov (United States)

    Bhend, Jonas; Mahlstein, Irina; Liniger, Mark

    2016-04-01

    Seasonal forecasting models are increasingly being used to forecast application-relevant aspects. A simple way to make such user-oriented predictions are application-specific climate indices. Little is known, however, on how the predictive skill of forecasts of such climate indices relates to the predictive skill in forecasting seasonal mean conditions. Here we analyse forecasts of two types of indices derived from daily precipitation and temperature: counts of events such as the number of dry days and accumulated threshold exceedances such as degree days. We find that the predictive skill of forecasts of heating and cooling degree days and of consecutive dry days is generally lower than the skill of seasonal mean temperature and rainfall forecasts respectively. By use of a toy model we demonstrate that this reduction in skill is more pronounced for skilful forecasts and climate indices with a threshold in the tail of the statistical distribution. We further analyse the impact of spatial aggregation and find that aggregation generally improves the predictive skill. Using appropriate covariates for weighting - for example population density to derive a proxy for the national energy demand for heating - the usefulness of forecasts of climate indices can be further enhanced while retaining predictive skill. We conclude that processing of direct model output to derive climate indices in combination with spatial aggregation can be used to render still skilful and even more useful seasonal forecasts of user-relevant quantities.

  8. Forecasting Investment Risks in Conditions of Uncertainty

    Directory of Open Access Journals (Sweden)

    Andrenko Elena A.

    2017-04-01

    Full Text Available The article is aimed at studying the topical problem of evaluation and forecasting risks of investment activity of enterprises in conditions of uncertainty. Generalizing the researches on qualitative and quantitative methods for evaluating investment risks has helped to reveal certain shortcomings of the proposed approaches, to note in most of the publications there are no results as to any practical application, and to allocate promising directions. On the basis of the theory of fuzzy sets, a model of forecasting the expected risk has been proposed, making use of the Gauss membership function, which has certain advantages over the multi-angular membership functions. Dependences of investment risk from the parameters characterizing the investment project have been obtained. Using the formulas obtained, the total risk of investing in innovation project depending on the boundary conditions has been defined. As the researched target, index of profitability has been selected. The model provides the potential investors and developers with forecasting possible scenarios of investment process to make informed managerial decisions about the appropriateness of introduction and implementation of a project.

  9. Condition Indicators for Gearbox Condition Monitoring Systems

    OpenAIRE

    P. Večeř; M. Kreidl; R. Šmíd

    2005-01-01

    Condition monitoring systems for manual transmissions based on vibration diagnostics are widely applied in industry. The systems deal with various condition indicators, most of which are focused on a specific type of gearbox fault. Frequently used condition indicators (CIs) are described in this paper. The ability of a selected condition indicator to describe the degree of gearing wear was tested using vibration signals acquired during durability testing of manual transmission with helical ge...

  10. Condition Indicators for Gearbox Condition Monitoring Systems

    Directory of Open Access Journals (Sweden)

    P. Večeř

    2005-01-01

    Full Text Available Condition monitoring systems for manual transmissions based on vibration diagnostics are widely applied in industry. The systems deal with various condition indicators, most of which are focused on a specific type of gearbox fault. Frequently used condition indicators (CIs are described in this paper. The ability of a selected condition indicator to describe the degree of gearing wear was tested using vibration signals acquired during durability testing of manual transmission with helical gears. 

  11. Forecasting of enterprise’s financial indicators as the basis for financial stability formation

    Directory of Open Access Journals (Sweden)

    D.Е. Ponomariv

    2016-05-01

    Full Text Available The issue of company’s financial sustainability ensuring in modern business conditions is very important for enterprises of any size. This article is dedicated to the research of forecasting tools and their role for the formation of company’s stable financial development in Ukrainian realities. The functions and methods of forecasting, the main stages of the research and analysis of predictive values are considered. The recommendations for the financial indicator forecasting development are presented. These forecasts enable modern Ukrainian companies to provide their financial situation ahead. They also allow companies to develop an action plan according to each potential situation in the future and analyze the occurrence of possible threats from both external economic environment and internal environment of the company. The advanced analysis and forecast of the financial condition allow to minimize the impact of adverse factors, and in some cases result in avoiding them generally.

  12. On the impact of bias correcting and conditioning precipitation inputs on seasonal streamflow forecast quality

    Science.gov (United States)

    Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian; Perrin, Charles

    2017-04-01

    Skillful seasonal streamflow forecasts are increasingly requested for decision-making in areas such as drought risk assessment or reservoir management. Meteorological forcing can be the major source of uncertainty in seasonal forecasts as early as in the first month of the forecast period. The choice of the hydrological model inputs thus has a major impact on the quality of generated streamflow forecasts. In this study, we assess the impact of two types of precipitation forecast post-treatment: 1) bias correction and 2) conditioning, on streamflow forecast quality. We first evaluated several bias correction approaches and conditioned precipitation scenarios in sixteen catchments in France, with the help of ECMWF System 4 seasonal precipitation forecasts and the GR6J hydrological model. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often sharper than the conventional ESP method. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. The empirical distribution mapping of daily values was successful in improving forecast reliability, but sometimes at the expense of forecast sharpness. We also evaluated several conditioning methods based on ECMWF System 4 precipitation forecasts to generate seasonal streamflow forecasts in the same sixteen catchments. Four precipitation indices based on System 4 precipitation were used to condition historical streamflow or historical precipitations to be used as input to the GR6J model. Our results evaluate how the conditioning impacts the reliability and sharpness of streamflow forecasts, as well as forecasts of drought indices. We show that conditioning past observations based on the three-month Standardized Precipitation Index (SPI3) can improve the sharpness of ensemble forecasts based on historical data, but also often decrease reliability. References: Crochemore, L., Ramos, M.-H., and Pappenberger

  13. Bioclimatic indices as a tool in pollen forecasting.

    Science.gov (United States)

    Valencia-Barrera, Rosa María; Comtois, Paul; Fernández-González, Delia

    2002-09-01

    The use of bioclimatic indices could be a major step forward in the methodology of pollen forecasting. The basis for this proposal is that simple meteorological parameters do not reflect the global status of the atmosphere, but merely some static measurements. However, pollen dispersal is, above all, a dynamic phenomenon, and this fact should be reflected in the variables we used to explain it. Here, we test the two methodologies for routine pollen forecasting by comparing correlation coefficients using the same daily Poaceae airborne pollen data base from León (6 years, from 1994 to 1999) as the dependent variable and either simple daily meteorological variables or compound daily bioclimatic indices as independent variables. Both simple and compound indices reproduced the same profile of evolution of plant eco-physiological requirements, as the length of the study period during the pollen season increased. However, for time frames larger than the main pollen period, bioclimatic indices gave superior coefficients, which seems to indicate that these could be more valuable for pre-season pollen forecasting. The continentality index produced the highest mean coefficient, higher than those generated by any meteorological variable. Furthermore, at least for a Mediterranean climate, site location and evapotranspiration in relation to precipitation seem to be the most promising factors for increasing success when forecasting Poaceae airborne pollen concentration.

  14. Global Wildfire Forecasts Using Large Scale Climate Indices

    Science.gov (United States)

    Shen, Huizhong; Tao, Shu

    2016-04-01

    Using weather readings, fire early warning can provided forecast 4-6 hour in advance to minimize fire loss. The benefit would be dramatically enhanced if relatively accurate long-term projection can be also provided. Here we present a novel method for predicting global fire season severity (FSS) at least three months in advance using multiple large-scale climate indices (CIs). The predictive ability is proven effective for various geographic locations and resolution. Globally, as well as in most continents, the El Niño Southern Oscillation (ENSO) is the dominant driving force controlling interannual FSS variability, whereas other CIs also play indispensable roles. We found that a moderate El Niño event is responsible for 465 (272-658 as interquartile range) Tg carbon release and an annual increase of 29,500 (24,500-34,800) deaths from inhalation exposure to air pollutants. Southeast Asia accounts for half of the deaths. Both intercorrelation and interaction of WPs and CIs are revealed, suggesting possible climate-induced modification of fire responses to weather conditions. Our models can benefit fire management in response to climate change.

  15. Seasonal forecasts of impact-relevant climate information indices developed as part of the EUPORIAS project

    Science.gov (United States)

    Spirig, Christoph; Bhend, Jonas

    2015-04-01

    Climate information indices (CIIs) represent a way to communicate climate conditions to specific sectors and the public. As such, CIIs provide actionable information to stakeholders in an efficient way. Due to their non-linear nature, such CIIs can behave differently than the underlying variables, such as temperature. At the same time, CIIs do not involve impact models with different sources of uncertainties. As part of the EU project EUPORIAS (EUropean Provision Of Regional Impact Assessment on a Seasonal-to-decadal timescale) we have developed examples of seasonal forecasts of CIIs. We present forecasts and analyses of the skill of seasonal forecasts for CIIs that are relevant to a variety of economic sectors and a range of stakeholders: heating and cooling degree days as proxies for energy demand, various precipitation and drought-related measures relevant to agriculture and hydrology, a wild fire index, a climate-driven mortality index and wind-related indices tailored to renewable energy producers. Common to all examples is the finding of limited forecast skill over Europe, highlighting the challenge for providing added-value services to stakeholders operating in Europe. The reasons for the lack of forecast skill vary: often we find little skill in the underlying variable(s) precisely in those areas that are relevant for the CII, in other cases the nature of the CII is particularly demanding for predictions, as seen in the case of counting measures such as frost days or cool nights. On the other hand, several results suggest there may be some predictability in sub-regions for certain indices. Several of the exemplary analyses show potential for skillful forecasts and prospect for improvements by investing in post-processing. Furthermore, those cases for which CII forecasts showed similar skill values as those of the underlying meteorological variables, forecasts of CIIs provide added value from a user perspective.

  16. Can confidence indicators forecast the probability of expansion in Croatia?

    Directory of Open Access Journals (Sweden)

    Mirjana Čižmešija

    2016-04-01

    Full Text Available The aim of this paper is to investigate how reliable are confidence indicators in forecasting the probability of expansion. We consider three Croatian Business Survey indicators: the Industrial Confidence Indicator (ICI, the Construction Confidence Indicator (BCI and the Retail Trade Confidence Indicator (RTCI. The quarterly data, used in the research, covered the periods from 1999/Q1 to 2014/Q1. Empirical analysis consists of two parts. The non-parametric Bry-Boschan algorithm is used for distinguishing periods of expansion from the period of recession in the Croatian economy. Then, various nonlinear probit models were estimated. The models differ with respect to the regressors (confidence indicators and the time lags. The positive signs of estimated parameters suggest that the probability of expansion increases with an increase in Confidence Indicators. Based on the obtained results, the conclusion is that ICI is the most powerful predictor of the probability of expansion in Croatia.

  17. Exploiting teleconnection indices for probabilistic forecasting of drought class transitions in Sicily region (Italy)

    Science.gov (United States)

    Bonaccorso, Brunella; Cancelliere, Antonino

    2015-04-01

    In the present study two probabilistic models for short-medium term drought forecasting able to include information provided by teleconnection indices are proposed and applied to Sicily region (Italy). Drought conditions are expressed in terms of the Standardized Precipitation-Evapotranspiration Index (SPEI) at different aggregation time scales. More specifically, a multivariate approach based on normal distribution is developed in order to estimate: 1) on the one hand transition probabilities to future SPEI drought classes and 2) on the other hand, SPEI forecasts at a generic time horizon M, as functions of past values of SPEI and the selected teleconnection index. To this end, SPEI series at 3, 4 and 6 aggregation time scales for Sicily region are extracted from the Global SPEI database, SPEIbase , available at Web repository of the Spanish National Research Council (http://sac.csic.es/spei/database.html), and averaged over the study area. In particular, SPEIbase v2.3 with spatial resolution of 0.5° lat/lon and temporal coverage between January 1901 and December 2013 is used. A preliminary correlation analysis is carried out to investigate the link between the drought index and different teleconnection patterns, namely: the North Atlantic Oscillation (NAO), the Scandinavian (SCA) and the East Atlantic-West Russia (EA-WR) patterns. Results of such analysis indicate a strongest influence of NAO on drought conditions in Sicily with respect to other teleconnection indices. Then, the proposed forecasting methodology is applied and the skill in forecasting of the proposed models is quantitatively assessed through the application of a simple score approach and of performance indices. Results indicate that inclusion of NAO index generally enhance model performance thus confirming the suitability of the models for short- medium term forecast of drought conditions.

  18. Performance of Vegetation Indices for Wheat Yield Forecasting for Punjab, Pakistan

    Science.gov (United States)

    Dempewolf, J.; Becker-Reshef, I.; Adusei, B.; Barker, B.

    2013-12-01

    Forecasting wheat yield in major producer countries early in the growing season allows better planning for harvest deficits and surplus with implications for food security, world market transactions, sustaining adequate grain stocks, policy making and other matters. Remote sensing imagery is well suited for yield forecasting over large areas. The Normalized Difference Vegetation Index (NDVI) has been the most-used spectral index derived from remote sensing imagery for assessing crop condition of major crops and forecasting crop yield. Many authors have found that the highest correlation between NDVI and yield of wheat crops occurs at the height of the growing season when NDVI values and photosynthetic activity of the wheat plants are at their relative maximum. At the same time NDVI saturates in very dense and vigorous (healthy, green) canopies such as wheat fields during the seasonal peak and shows significantly reduced sensitivity to further increases in photosynthetic activity. In this study we compare the performance of different vegetation indices derived from space-borne red and near-infrared spectral reflectance measurements for wheat yield forecasting in the Punjab Province, Pakistan. Areas covered by wheat crop each year were determined using a time series of MODIS 8-day composites at 250 m resolution converted to temporal metrics and classified using a bagged decision tree approach, driven by classified multi-temporal Landsat scenes. Within the wheat areas we analyze and compare wheat yield forecasts derived from three different satellite-based vegetation indices at the peak of the growing season. We regressed in turn NDVI, Wide Dynamic Range Vegetation Index (WDRVI) and the Vegetation Condition Index (VCI) from the four years preceding the wheat growing season 2011/12 against reported yield values and applied the regression equations to forecast wheat yield for the 2011/12 season per district for each of 36 Punjab districts. Yield forecasts overall

  19. Improved forecasting with leading indicators: the principal covariate index

    NARCIS (Netherlands)

    C. Heij (Christiaan)

    2007-01-01

    textabstractWe propose a new method of leading index construction that combines the need for data compression with the objective of forecasting. This so-called principal covariate index is constructed to forecast growth rates of the Composite Coincident Index. The forecast performance is compared

  20. Forecasting Ionospheric Conditions with 4DVAR Assimilation Model

    Science.gov (United States)

    Wang, C.; Akopian, V.; Pi, X.; Mannucci, A. J.; Usc/Jpl Gaim Team

    2010-12-01

    optimization criterion. The same approach also allows us to simultaneously estimate the driving forces and the electron density. The 4DVAR implementation relies on an intermittent assimilation cycle to process measurement data and to produce forecasts. Sensitivity studies of the ionospheric observables to the driving forces are performed by the USC/JPL team. We have also investigated the feasibility of the estimation of the ionospheric drivers through a series of Observation System Simulation Experiments (OSSE). Our simulation results indicate that when persistence in time in the sun fixed frame is a valid assumption, the 4DVAR approach can be effectively used to forecast the ionospheric conditions. In this presentation, we present our implementation of the 4DVAR model and the scheduling of data assimulation cycles. We shall also present the results of our sensitivity study, as well as, the results of the OSSEs.

  1. Role of climate forecasts and initial land-surface conditions in developing operational streamflow and soil moisture forecasts in a rainfall-runoff regime: skill assessment

    Directory of Open Access Journals (Sweden)

    T. Sinha

    2012-04-01

    Full Text Available Skillful seasonal streamflow forecasts obtained from climate and land surface conditions could significantly improve water and energy management. Since climate forecasts are updated on monthly basis, we evaluate the potential in developing operational monthly streamflow forecasts on a continuous basis throughout the year. Further, basins in the rainfall-runoff regime critically depend on the forecasted precipitation in the upcoming months as opposed to snowmelt regimes where initial hydrological conditions (IHC play a critical role. The goal of this study is to quantify the role of monthly updated precipitation forecasts and IHC in forecasting 6-month lead monthly streamflow for a rainfall-runoff mechanism dominated basin – Apalachicola River at Chattahoochee, FL. The Variable Infiltration Capacity (VIC land surface model is implemented with two forcings: (a monthly updated precipitation forecasts from ECHAM4.5 Atmospheric General Circulation Model (AGCM forced with sea surface temperature forecasts and (b daily climatological ensemble. The difference in skill between the above two quantifies the improvements that could be attainable using the AGCM forecasts. Monthly retrospective streamflow forecasts are developed from 1981 to 2010 and streamflow forecasts estimated from the VIC model are also compared with those predicted by using the principal component regression (PCR model. Mean square error (MSE in predicting monthly streamflow using the above VIC model are compared with the MSE of streamflow climatology under ENSO conditions as well as under normal years. Results indicate that VIC forecasts, at 1–2 month lead time, obtained using ECHAM4.5 are significantly better than VIC forecasts obtained using climatological ensemble over all the seasons except forecasts issued in fall and the PCR models perform better during the fall months. Over longer lead times (3–6 months, VIC forecasts derived using ECHAM4.5 forcings alone performed better

  2. Seasonal forecasts for the agricultural sector in Peru through user-tailored indices

    Science.gov (United States)

    Sedlmeier, Katrin; Gubler, Stefanie; Spierig, Christoph; Quevedo, Karim; Escajadillo, Yury; Avalos, Griña; Liniger, Mark A.; Schwierz, Cornelia

    2017-04-01

    In the agricultural sector, the demand for seasonal forecast information is high since agriculture depends strongly on climatic conditions during the growing season. Unfavorable weather and climate events, such as droughts or frost events, can lead to crop losses and thereby to large economic damages or life-threatening conditions in case of subsistence farming. The generally used presentation form of tercile probabilities of seasonally averaged meteorological quantities are not specific enough for end users. More user-tailored seasonal information is necessary. For example, warmer than average temperatures might be favorable for a crop as long as they remain below a plant-specific critical threshold. If, on the other hand, too many days show temperatures above this critical threshold, a mitigation action such as e.g. changing the crop type would be required. In the framework of the CLIMANDES project (a pilot project of the Global Framework for Climate Services led by WMO [http://www.wmo.int/gfcs/climandes]), user-tailored seasonal forecast products are developed for the agricultural sector in the Peruvian Andes. Such products include indices such as e.g. the frost risk, the occurrence of long dry periods, or the start of the rainy season which is crucial to schedule sowing. Furthermore, more specific indices derived from crop requirement studies are elaborated such as the number of days exceeding or falling below plant specific temperature thresholds for given phenological stages. The applicability of these products highly depends on forecast skill. In this study, the potential predictability and the skill of selected indicators are presented using seasonal hindcast data of the ECMWF system 4 for Peru during the time period 1981-2010. Furthermore, the influence of ENSO on the prediction skill is investigated. In this study, reanalysis data, ground measurements, and a gridded precipitation dataset are used for verification. The results indicate that temperature

  3. Improving Forecasts of Generalized Autoregressive Conditional Heteroskedasticity with Wavelet Transform

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    Yu Zhao

    2013-01-01

    Full Text Available In the study, we discussed the generalized autoregressive conditional heteroskedasticity model and enhanced it with wavelet transform to evaluate the daily returns for 1/4/2002-30/12/2011 period in Brent oil market. We proposed discrete wavelet transform generalized autoregressive conditional heteroskedasticity model to increase the forecasting performance of the generalized autoregressive conditional heteroskedasticity model. Our new approach can overcome the defect of generalized autoregressive conditional heteroskedasticity family models which can’t describe the detail and partial features of times series and retain the advantages of them at the same time. Comparing with the generalized autoregressive conditional heteroskedasticity model, the new approach significantly improved forecast results and greatly reduces conditional variances.

  4. Forecasting Chinese GDP Growth with Mixed Frequency Data: Which Indicators to Look at?

    OpenAIRE

    Mikosch, Heiner; Zhang, Ying

    2014-01-01

    Building on a mixed data sampling (MIDAS) model we evaluate the predictive power of a variety of monthly macroeconomic indicators for forecasting quarterly Chinese GDP growth. We iterate the evaluation over forecast horizons from 370 days to 1 day prior to GDP release and track the release days of the indicators so as to only use information which is actually available at the respective day of forecast. This procedure allows us to detect how useful a specific indicator is at a specific foreca...

  5. Seasonal forecasts of drought indices in African basins

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    E. Dutra

    2012-09-01

    Full Text Available Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas which often have a very low resilience and limited capabilities to mitigate their effects. This paper tries to assess the predictive capabilities of an integrated drought monitoring and forecasting system based on the Standard precipitation index (SPI. The system is firstly constructed by temporally extending near real-time precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System-Outgoing Longwave Radiation Precipitation Index, CAMS-OPI with forecasted fields as provided by the ECMWF seasonal forecasting system and then is evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. All the datasets show similar patterns in the South and North West Africa, while there is a low correlation in the tropical region which makes it difficult to define ground truth and choose an adequate product for monitoring. The Seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depends strongly on the SPI time-scale, and more skill is observed at larger time-scales. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast. Furthermore, poor quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in two to four months lead time.

  6. Bayesian Sampling using Condition Indicators

    DEFF Research Database (Denmark)

    Faber, Michael H.; Sørensen, John Dalsgaard

    2002-01-01

    . This allows for a Bayesian formulation of the indicators whereby the experience and expertise of the inspection personnel may be fully utilized and consistently updated as frequentistic information is collected. The approach is illustrated on an example considering a concrete structure subject to corrosion....... It is shown how half-cell potential measurements may be utilized to update the probability of excessive repair after 50 years....

  7. Seasonal forecasts of drought indices in African basins

    Science.gov (United States)

    Dutra, Emanuel; Di Giuseppe, Francesca; Wetterhall, Fredrik; Pappenberge, Florian

    2013-04-01

    Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas which often have a very low resilience and limited capabilities to mitigate drought impacts. In this work we assess the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near real-time monthly precipitation fields (ERA-Interim reanalysis and the Climate Anomaly Monitoring System-Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system. This new seamless approach to merge monitoring and forecasting fields of precipitation to generate SPI at different time-scales is evaluated within the framework of the EU project DEWFORA. In particular, the evaluation was preformed over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. All the datasets show similar spatial and temporal patterns in the South and North West Africa, while there is a low correlation in the equatorial area which makes it difficult to define ground truth and choose an adequate product for monitoring. The Seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depends strongly on the SPI time-scale, and more skill is observed at longer time-scales. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near real-time data is available, the seasonal forecast can be used for monitoring (first

  8. Forecasting conditional climate-change using a hybrid approach

    Science.gov (United States)

    Esfahani, Akbar Akbari; Friedel, Michael J.

    2014-01-01

    A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009–2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.

  9. Use of Multivariate Relevance Vector Machines in forecasting multiple geomagnetic indices

    Science.gov (United States)

    Andriyas, T.; Andriyas, S.

    2017-02-01

    The forecasting ability of Multivariate Relevance Vector Machines (MVRVM), used previously to generate forecasts for the Dst index, is extended to forecast the Dst, AL, and PC indices during the years 1975-2007. Such learning machines are used in forecasting because of their robustness, efficiency, and sparseness. The MVRVM model was trained on solar wind and geomagnetic activity data sampled every hour with activity periods of various intensities, durations, and features. It was found that during the training phase, for a given error threshold, 14.60% of the training data was needed to explain the features of the data. The trained model was then tested on 177 different storm intervals, at various levels of geomagnetic activity, to generate simultaneous forecasts of the three indices at a lead time of one hour (1-h). The focus of the modeling was to assess the forecasts during main storm (MS) time periods when the indices show enhanced activity above quiet time values. The forecasts obtained by the MVRVM model reported in this paper returned a MS time average prediction efficiency, PE bar of 82.42%, 84.40%, and 76.00% and RMSE bar of 13.70 nT, 97.00 nT, and -0.77 mV/m, for the Dst, AL, and PC indices, respectively. The qualitative numbers indicated that the model underestimated the peak amplitude of the indices during the geomagnetic activity, but the peaks were forecasted on time by the model, on average. The forecasting results indicate a robust model generalization and the MVRVM's ability to learn the input-output relationship through a sparse model framework. A qualitative comparison with the previous univariate RVM forecast of Dst indicates that the model goodness of fit numbers improved in the present study.

  10. Ensemble dispersion forecasting - Part 1. Concept, approach and indicators

    DEFF Research Database (Denmark)

    Galmarini, S.; Bianconi, R.; Klug, W.;

    2004-01-01

    The paper presents an approach to the treatment and analysis of long-range transport and dispersion model forecasts. Long-range is intended here as the space scale of the order of few thousands of kilometers known also as continental scale. The method is called multi-model ensemble dispersion...... proposed are particularly suited for long-range transport and dispersion models although they can also be applied to short-range dispersion and weather fields. (C) 2004 Elsevier Ltd. All rights reserved....

  11. Forecasting Instability Indicators in the Horn of Africa

    Science.gov (United States)

    2008-03-01

    Introduction……………………………………………………………………...4-1 4.2. Principal Component and Cannonical Correlation Loadings……………………4-1 4.3. The Forecasting Models... Cannonical Correlation The idea behind canonical correlation is simple. Given two sets of multivariate data X1 and X2, find 1’a Xη = and 2’b Xφ...battle deaths, refugees, and genocide were not part of the final recommended models. Country Year Battle Deaths OLS Can Corr polreg2/ gauss polreg3

  12. Drought Forecasting with Vegetation Temperature Condition Index Using ARIMA Models in the Guanzhong Plain

    Directory of Open Access Journals (Sweden)

    Miao Tian

    2016-08-01

    Full Text Available This paper works on the agricultural drought forecasting in the Guanzhong Plain of China using Autoregressive Integrated Moving Average (ARIMA models based on the time series of drought monitoring results of Vegetation Temperature Condition Index (VTCI. About 90 VTCI images derived from Advanced Very High Resolution Radiometer (AVHRR data were selected to develop the ARIMA models from the erecting stage to the maturity stage of winter wheat (early March to late May in each year at a ten-day interval of the years from 2000 to 2009. We take the study area overlying on the administration map around the study area, and divide the study area into 17 parts where at least one weather station is located in each part. The pixels where the 17 weather stations are located are firstly chosen and studied for their fitting models, and then the best models for all pixels of the whole area are determined. According to the procedures for the models’ development, the selected best models for the 17 pixels are identified and the forecast is done with three steps. The forecasting results of the ARIMA models were compared with the monitoring ones. The results show that with reference to the categorized VTCI drought monitoring results, the categorized forecasting results of the ARIMA models are in good agreement with the monitoring ones. The categorized drought forecasting results of the ARIMA models are more severity in the northeast of the Plain in April 2009, which are in good agreements with the monitoring ones. The absolute errors of the AR(1 models are lower than the SARIMA models, both in the frequency distributions and in the statistic results. However, the ability of SARIMA models to detect the changes of the drought situation is better than the AR(1 models. These results indicate that the ARIMA models can better forecast the category and extent of droughts and can be applied to forecast droughts in the Plain.

  13. Advanced, Cost-Based Indices for Forecasting the Generation of Photovoltaic Power

    Science.gov (United States)

    Bracale, Antonio; Carpinelli, Guido; Di Fazio, Annarita; Khormali, Shahab

    2014-01-01

    Distribution systems are undergoing significant changes as they evolve toward the grids of the future, which are known as smart grids (SGs). The perspective of SGs is to facilitate large-scale penetration of distributed generation using renewable energy sources (RESs), encourage the efficient use of energy, reduce systems' losses, and improve the quality of power. Photovoltaic (PV) systems have become one of the most promising RESs due to the expected cost reduction and the increased efficiency of PV panels and interfacing converters. The ability to forecast power-production information accurately and reliably is of primary importance for the appropriate management of an SG and for making decisions relative to the energy market. Several forecasting methods have been proposed, and many indices have been used to quantify the accuracy of the forecasts of PV power production. Unfortunately, the indices that have been used have deficiencies and usually do not directly account for the economic consequences of forecasting errors in the framework of liberalized electricity markets. In this paper, advanced, more accurate indices are proposed that account directly for the economic consequences of forecasting errors. The proposed indices also were compared to the most frequently used indices in order to demonstrate their different, improved capability. The comparisons were based on the results obtained using a forecasting method based on an artificial neural network. This method was chosen because it was deemed to be one of the most promising methods available due to its capability for forecasting PV power. Numerical applications also are presented that considered an actual PV plant to provide evidence of the forecasting performances of all of the indices that were considered.

  14. Two-step forecast of geomagnetic storm using coronal mass ejection and solar wind condition

    Science.gov (United States)

    Kim, R-S; Moon, Y-J; Gopalswamy, N; Park, Y-D; Kim, Y-H

    2014-01-01

    To forecast geomagnetic storms, we had examined initially observed parameters of coronal mass ejections (CMEs) and introduced an empirical storm forecast model in a previous study. Now we suggest a two-step forecast considering not only CME parameters observed in the solar vicinity but also solar wind conditions near Earth to improve the forecast capability. We consider the empirical solar wind criteria derived in this study (Bz ≤ −5 nT or Ey ≥ 3 mV/m for t≥ 2 h for moderate storms with minimum Dst less than −50 nT) and a Dst model developed by Temerin and Li (2002, 2006) (TL model). Using 55 CME-Dst pairs during 1997 to 2003, our solar wind criteria produce slightly better forecasts for 31 storm events (90%) than the forecasts based on the TL model (87%). However, the latter produces better forecasts for 24 nonstorm events (88%), while the former correctly forecasts only 71% of them. We then performed the two-step forecast. The results are as follows: (i) for 15 events that are incorrectly forecasted using CME parameters, 12 cases (80%) can be properly predicted based on solar wind conditions; (ii) if we forecast a storm when both CME and solar wind conditions are satisfied (∩), the critical success index becomes higher than that from the forecast using CME parameters alone, however, only 25 storm events (81%) are correctly forecasted; and (iii) if we forecast a storm when either set of these conditions is satisfied (∪), all geomagnetic storms are correctly forecasted. PMID:26213515

  15. Economic indicators selection for crime rates forecasting using cooperative feature selection

    Science.gov (United States)

    Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Salleh Sallehuddin, Roselina

    2013-04-01

    Features selection in multivariate forecasting model is very important to ensure that the model is accurate. The purpose of this study is to apply the Cooperative Feature Selection method for features selection. The features are economic indicators that will be used in crime rate forecasting model. The Cooperative Feature Selection combines grey relational analysis and artificial neural network to establish a cooperative model that can rank and select the significant economic indicators. Grey relational analysis is used to select the best data series to represent each economic indicator and is also used to rank the economic indicators according to its importance to the crime rate. After that, the artificial neural network is used to select the significant economic indicators for forecasting the crime rates. In this study, we used economic indicators of unemployment rate, consumer price index, gross domestic product and consumer sentiment index, as well as data rates of property crime and violent crime for the United States. Levenberg-Marquardt neural network is used in this study. From our experiments, we found that consumer price index is an important economic indicator that has a significant influence on the violent crime rate. While for property crime rate, the gross domestic product, unemployment rate and consumer price index are the influential economic indicators. The Cooperative Feature Selection is also found to produce smaller errors as compared to Multiple Linear Regression in forecasting property and violent crime rates.

  16. Survey-based indicators vs. hard data: What improves export forecasts in Europe?

    OpenAIRE

    2015-01-01

    In this study, we evaluate whether survey-based indicators produce lower forecast errorsfor export growth than indicators obtained from hard data such as price and costcompetitiveness measures. Our pseudo out-of-sample analyses and forecastencompassingtests reveal that survey-based indicators outperform the benchmarkmodel as well as the indicators from hard data for most of the twenty European statesfocused on in our study and the aggregates EA-18 and EU-28. The most accurate forecastsare on ...

  17. Experiments with Seasonal Forecasts of ocean conditions for the Northern region of the California Current upwelling system

    Science.gov (United States)

    Siedlecki, Samantha A.; Kaplan, Isaac C.; Hermann, Albert J.; Nguyen, Thanh Tam; Bond, Nicholas A.; Newton, Jan A.; Williams, Gregory D.; Peterson, William T.; Alin, Simone R.; Feely, Richard A.

    2016-06-01

    Resource managers at the state, federal, and tribal levels make decisions on a weekly to quarterly basis, and fishers operate on a similar timeframe. To determine the potential of a support tool for these efforts, a seasonal forecast system is experimented with here. JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE) features dynamical downscaling of regional ocean conditions in Washington and Oregon waters using a combination of a high-resolution regional model with biogeochemistry and forecasts from NOAA’s Climate Forecast System (CFS). Model performance and predictability were examined for sea surface temperature (SST), bottom temperature, bottom oxygen, pH, and aragonite saturation state through model hindcasts, reforecast, and forecast comparisons with observations. Results indicate J-SCOPE forecasts have measurable skill on seasonal timescales. Experiments suggest that seasonal forecasting of ocean conditions important for fisheries is possible with the right combination of components. Those components include regional predictability on seasonal timescales of the physical environment from a large-scale model, a high-resolution regional model with biogeochemistry that simulates seasonal conditions in hindcasts, a relationship with local stakeholders, and a real-time observational network. Multiple efforts and approaches in different regions would advance knowledge to provide additional tools to fishers and other stakeholders.

  18. Performance of technical indicators in forecasting high-frequency foreign exchange rates

    Directory of Open Access Journals (Sweden)

    Václav Mastný

    2004-01-01

    Full Text Available This paper deals with technical analysis and its forecasting ability in the intradaily foreign exchange market. The objective of this study is to investigate whether technical indicators are able to provide prediction superior to „buy and hold“ strategy. Each indicator is tested with series of parameters in time series of different frequency (5, 15, 30, 60 min. The profitability of each indicator is examined in simple trading modell.

  19. Forecasting with Leading Indicators by means of the Principal Covariate Index

    NARCIS (Netherlands)

    P.J.F. Groenen (Patrick); C. Heij (Christiaan); D.J.C. van Dijk (Dick)

    2011-01-01

    textabstractA new method of leading index construction is proposed, which explicitly takes into account the purpose of using the index for forecasting a coincident economic indicator. This so-called principal covariate index combines the need for compressing the information in a large number of

  20. Weather Forecast Based Conditional Pest Management: A Stochastic Optimal Control Investigation

    OpenAIRE

    Lu, Liang; Elbakidze, Levan

    2011-01-01

    In this paper, we examine conditional, forecast-based dynamic pest management in agricultural crop production given stochastic pest infestations and stochastic climate dynamics throughout the growing season. Using stochastic optimal control we show that correlation between forecast error for climate prediction and forecast error for pest outbreaks can be used to improve pesticide application efficiency. In the general setting, we apply modified Hamiltonian approach to discuss the steady state...

  1. Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks.

    Science.gov (United States)

    Maca, Petr; Pech, Pavel

    2016-01-01

    The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948-2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.

  2. Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Petr Maca

    2016-01-01

    Full Text Available The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI and the standardized precipitation evaporation index (SPEI and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.

  3. Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing

    NARCIS (Netherlands)

    Candogan Yossef, N.; Winsemius, H.C.; Weerts, A.; Van Beek, R.; Bierkens, M.F.P.

    2013-01-01

    We investigate the relative contributions of initial conditions (ICs) and meteorological forcing (MF) to the skill of the global seasonal streamflow forecasting system FEWS-World, using the global hydrological model PCRaster Global Water Balance. Potential improvement in forecasting skill through be

  4. Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing

    NARCIS (Netherlands)

    Candogan Yossef, N.; Winsemius, H.C.; Weerts, A.; Van Beek, R.; Bierkens, M.F.P.

    2013-01-01

    We investigate the relative contributions of initial conditions (ICs) and meteorological forcing (MF) to the skill of the global seasonal streamflow forecasting system FEWS-World, using the global hydrological model PCRaster Global Water Balance. Potential improvement in forecasting skill through be

  5. Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing

    NARCIS (Netherlands)

    Candogan Yossef, N.; Winsemius, H.C.; Weerts, A.; Van Beek, R.; Bierkens, M.F.P.

    2013-01-01

    We investigate the relative contributions of initial conditions (ICs) and meteorological forcing (MF) to the skill of the global seasonal streamflow forecasting system FEWS-World, using the global hydrological model PCRaster Global Water Balance. Potential improvement in forecasting skill through

  6. GARCH based artificial neural networks in forecasting conditional variance of stock returns

    Directory of Open Access Journals (Sweden)

    Josip Arnerić

    2014-12-01

    Full Text Available Portfolio managers, option traders and market makers are all interested in volatility forecasting in order to get higher profits or less risky positions. Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most popular models in modelling volatility are GARCH type models because they can account excess kurtosis and asymmetric effects of financial time series. A standard GARCH(1,1 model usually indicates high persistence in the conditional variance, which may originate from structural changes. The first objective of this paper is to develop a parsimonious neural networks (NN model, which can capture the nonlinear relationship between past return innovations and conditional variance. Therefore, the goal is to develop a neural network with an appropriate recurrent connection in the context of nonlinear ARMA models, i.e., the Jordan neural network (JNN. The second objective of this paper is to determine if JNN outperforms the standard GARCH model. Out-of-sample forecasts of the JNN and the GARCH model will be compared to determine their predictive accuracy. The data set consists of returns of the CROBEX index daily closing prices obtained from the Zagreb Stock Exchange. The results indicate that the selected JNN(1,1,1 model has superior performances compared to the standard GARCH(1,1 model. The contribution of this paper can be seen in determining the appropriate NN that is comparable to the standard GARCH(1,1 model and its application in forecasting conditional variance of stock returns. Moreover, from the econometric perspective, NN models are used as a semi-parametric method that combines flexibility of nonparametric methods and the interpretability of parameters of parametric methods.

  7. Two-Step Forecast of Geomagnetic Storm Using Coronal Mass Ejection and Solar Wind Condition

    Science.gov (United States)

    Kim, R.-S.; Moon, Y.-J.; Gopalswamy, N.; Park, Y.-D.; Kim, Y.-H.

    2014-01-01

    To forecast geomagnetic storms, we had examined initially observed parameters of coronal mass ejections (CMEs) and introduced an empirical storm forecast model in a previous study. Now we suggest a two-step forecast considering not only CME parameters observed in the solar vicinity but also solar wind conditions near Earth to improve the forecast capability. We consider the empirical solar wind criteria derived in this study (Bz = -5 nT or Ey = 3 mV/m for t = 2 h for moderate storms with minimum Dst less than -50 nT) (i.e. Magnetic Field Magnitude, B (sub z) less than or equal to -5 nanoTeslas or duskward Electrical Field, E (sub y) greater than or equal to 3 millivolts per meter for time greater than or equal to 2 hours for moderate storms with Minimum Disturbance Storm Time, Dst less than -50 nanoTeslas) and a Dst model developed by Temerin and Li (2002, 2006) (TL [i.e. Temerin Li] model). Using 55 CME-Dst pairs during 1997 to 2003, our solar wind criteria produce slightly better forecasts for 31 storm events (90 percent) than the forecasts based on the TL model (87 percent). However, the latter produces better forecasts for 24 nonstorm events (88 percent), while the former correctly forecasts only 71 percent of them. We then performed the two-step forecast. The results are as follows: (i) for 15 events that are incorrectly forecasted using CME parameters, 12 cases (80 percent) can be properly predicted based on solar wind conditions; (ii) if we forecast a storm when both CME and solar wind conditions are satisfied (n, i.e. cap operator - the intersection set that is comprised of all the elements that are common to both), the critical success index becomes higher than that from the forecast using CME parameters alone, however, only 25 storm events (81 percent) are correctly forecasted; and (iii) if we forecast a storm when either set of these conditions is satisfied (?, i.e. cup operator - the union set that is comprised of all the elements of either or both

  8. Assessment of forecast indices over Sriharikota using ground-based microwave radiometer

    Science.gov (United States)

    Pushpa Saroja, R.; Rajasekhar, M.; Papa Rao, G.; Rajeevan, M.; Bharathi, G.

    2016-05-01

    Continuous measurements of vertical profiles of thermodynamic variables are important for severe weather nowcasting & forecasting over a region instead of radiosonde observations which are available once or twice daily. Microwave Radiometer (MWR) provides high quality of thermodynamic (temperature, water vapor, and cloud liquid) soundings up to an altitude of 10 Kms in the clear and cloudy weather conditions except during heavy rainfall. Retrievals of MWR profiles are based on the intensity of the atmospheric radiation at selected frequencies (22-30 GHz) & (51-59 GHz) with high temporal and vertical resolution in the troposphere. The MWR used in the present study is TP/WVP-3166A, measures the intensity of radiation at 8 water vapor channels and 14 oxygen channels which is installed at Sriharikota in June. In this paper we analyzed the thermodynamic indices derived from MWR profiles during severe convective thunderstorms for Sriharikota region. MWR derived thermodynamic profiles are compared with radiosonde observations during rainy & non rainy days. MWR temperature profiles and vapor density profiles are well correlated with the observations with a cold bias of 1.5°C & 2.5°C and with a dry bias of 0.37 g/m3 & 0.04 g/m3respectively. For this we considered 10 thunderstorm cases from June to November 2014 analysed with indices K index, MDPI, CAPE, Windex, KO index, L index, S index, Showalter index, Total totals index, Vertical totals along with integrated liquid water and vapour density. MDPI, CAP index, Windex, Kindex, Lindex and convective temperature were best performed indices two hours prior to thunderstorm over SHAR region.

  9. Forecasting, Forecasting

    Science.gov (United States)

    Michael A. Fosberg

    1987-01-01

    Future improvements in the meteorological forecasts used in fire management will come from improvements in three areas: observational systems, forecast techniques, and postprocessing of forecasts and better integration of this information into the fire management process.

  10. Quantifying uncertainty in Gulf of Mexico forecasts stemming from uncertain initial conditions

    KAUST Repository

    Iskandarani, Mohamed

    2016-06-09

    Polynomial Chaos (PC) methods are used to quantify the impacts of initial conditions uncertainties on oceanic forecasts of the Gulf of Mexico circulation. Empirical Orthogonal Functions are used as initial conditions perturbations with their modal amplitudes considered as uniformly distributed uncertain random variables. These perturbations impact primarily the Loop Current system and several frontal eddies located in its vicinity. A small ensemble is used to sample the space of the modal amplitudes and to construct a surrogate for the evolution of the model predictions via a nonintrusive Galerkin projection. The analysis of the surrogate yields verification measures for the surrogate\\'s reliability and statistical information for the model output. A variance analysis indicates that the sea surface height predictability in the vicinity of the Loop Current is limited to about 20 days. © 2016. American Geophysical Union. All Rights Reserved.

  11. Quantifying uncertainty in Gulf of Mexico forecasts stemming from uncertain initial conditions

    Science.gov (United States)

    Iskandarani, Mohamed; Le Hénaff, Matthieu; Thacker, William Carlisle; Srinivasan, Ashwanth; Knio, Omar M.

    2016-07-01

    Polynomial Chaos (PC) methods are used to quantify the impacts of initial conditions uncertainties on oceanic forecasts of the Gulf of Mexico circulation. Empirical Orthogonal Functions are used as initial conditions perturbations with their modal amplitudes considered as uniformly distributed uncertain random variables. These perturbations impact primarily the Loop Current system and several frontal eddies located in its vicinity. A small ensemble is used to sample the space of the modal amplitudes and to construct a surrogate for the evolution of the model predictions via a nonintrusive Galerkin projection. The analysis of the surrogate yields verification measures for the surrogate's reliability and statistical information for the model output. A variance analysis indicates that the sea surface height predictability in the vicinity of the Loop Current is limited to about 20 days.

  12. Forecast indices from ground-based microwave radiometer for operational meteorology

    Science.gov (United States)

    Cimini, D.; Nelson, M.; Güldner, J.; Ware, R.

    2014-07-01

    Today, commercial microwave radiometers profilers (MWRP) are robust and unattended instruments providing real time accurate atmospheric observations at ~ 1 min temporal resolution under nearly all-weather conditions. Common commercial units operate in the 20-60 GHz frequency range and are able to retrieve profiles of temperature, vapour density, and relative humidity. Temperature and humidity profiles retrieved from MWRP data are used here to feed tools developed for processing radiosonde observations to obtain values of forecast indices (FI) commonly used in operational meteorology. The FI considered here include K index, Total Totals, KO index, Showalter index, T1 Gust, Fog Threat, Lifted Index, S Index (STT), Jefferson Index, MDPI, Thompson Index, TQ Index, and CAPE. Values of FI computed from radiosonde and MWRP-retrieved temperature and humidity profiles are compared in order to quantitatively demonstrate the level of agreement and the value of continuous FI updates. This analysis is repeated for two sites at midlatitude, the first one located at low altitude in Central Europe (Lindenberg, Germany), while the second one located at high altitude in North America (Whistler, Canada). It is demonstrated that FI computed from MWRP well correlate with those computed from radiosondes, with the additional advantage of nearly continuous update. The accuracy of MWRP-derived FI is tested against radiosondes, taken as a reference, showing different performances depending upon index and environmental situation. Overall, FI computed from MWRP retrievals agree well with radiosonde values, with correlation coefficients usually above 0.8 (with few exceptions). We conclude that MWRP retrievals can be used to produce meaningful FI, with the advantage (with respect to radiosondes) of nearly continuous update.

  13. Forecast indices from a ground-based microwave radiometer for operational meteorology

    Science.gov (United States)

    Cimini, D.; Nelson, M.; Güldner, J.; Ware, R.

    2015-01-01

    Today, commercial microwave radiometer profilers (MWRPs) are robust and unattended instruments providing real-time, accurate atmospheric observations at ~ 1 min temporal resolution under nearly all weather conditions. Common commercial units operate in the 20-60 GHz frequency range and are able to retrieve profiles of temperature, vapour density, and relative humidity. Temperature and humidity profiles retrieved from MWRP data are used here to feed tools developed for processing radiosonde observations to obtain values of forecast indices (FIs) commonly used in operational meteorology. The FIs considered here include K index, total totals, KO index, Showalter index, T1 gust, fog threat, lifted index, S index (STT), Jefferson index, microburst day potential index (MDPI), Thompson index, TQ index, and CAPE (convective available potential energy). Values of FIs computed from radiosonde and MWRP-retrieved temperature and humidity profiles are compared in order to quantitatively demonstrate the level of agreement and the value of continuous FI updates. This analysis is repeated for two sites at midlatitude, the first one located at low altitude in central Europe (Lindenberg, Germany) and the second one located at high altitude in North America (Whistler, Canada). It is demonstrated that FIs computed from MWRPs well correlate with those computed from radiosondes, with the additional advantage of nearly continuous updates. The accuracy of MWRP-derived FIs is tested against radiosondes, taken as a reference, showing different performances depending upon index and environmental situation. Overall, FIs computed from MWRP retrievals agree well with radiosonde values, with correlation coefficients usually above 0.8 (with few exceptions). We conclude that MWRP retrievals can be used to produce meaningful FIs, with the advantage (with respect to radiosondes) of nearly continuous updates.

  14. Forecast indices from ground-based microwave radiometer for operational meteorology

    Directory of Open Access Journals (Sweden)

    D. Cimini

    2014-07-01

    Full Text Available Today, commercial microwave radiometers profilers (MWRP are robust and unattended instruments providing real time accurate atmospheric observations at ~ 1 min temporal resolution under nearly all-weather conditions. Common commercial units operate in the 20–60 GHz frequency range and are able to retrieve profiles of temperature, vapour density, and relative humidity. Temperature and humidity profiles retrieved from MWRP data are used here to feed tools developed for processing radiosonde observations to obtain values of forecast indices (FI commonly used in operational meteorology. The FI considered here include K index, Total Totals, KO index, Showalter index, T1 Gust, Fog Threat, Lifted Index, S Index (STT, Jefferson Index, MDPI, Thompson Index, TQ Index, and CAPE. Values of FI computed from radiosonde and MWRP-retrieved temperature and humidity profiles are compared in order to quantitatively demonstrate the level of agreement and the value of continuous FI updates. This analysis is repeated for two sites at midlatitude, the first one located at low altitude in Central Europe (Lindenberg, Germany, while the second one located at high altitude in North America (Whistler, Canada. It is demonstrated that FI computed from MWRP well correlate with those computed from radiosondes, with the additional advantage of nearly continuous update. The accuracy of MWRP-derived FI is tested against radiosondes, taken as a reference, showing different performances depending upon index and environmental situation. Overall, FI computed from MWRP retrievals agree well with radiosonde values, with correlation coefficients usually above 0.8 (with few exceptions. We conclude that MWRP retrievals can be used to produce meaningful FI, with the advantage (with respect to radiosondes of nearly continuous update.

  15. Forecasting of the electrical actuators condition using stator’s current signals

    Science.gov (United States)

    Kruglova, T. N.; Yaroshenko, I. V.; Rabotalov, N. N.; Melnikov, M. A.

    2017-02-01

    This article describes a forecasting method for electrical actuators realized through the combination of Fourier transformation and neural network techniques. The method allows finding the value of diagnostic functions in the iterating operating cycle and the number of operational cycles in time before the BLDC actuator fails. For forecasting of the condition of the actuator, we propose a hierarchical structure of the neural network aiming to reduce the training time of the neural network and improve estimation accuracy.

  16. Influence of Met-Ocean Condition Forecasting Uncertainties on Weather Window Predictions for Offshore Operations

    DEFF Research Database (Denmark)

    Gintautas, Tomas; Sørensen, John Dalsgaard

    2017-01-01

    The article briefly presents a novel methodology of weather window estimation for offshore operations and mainly focuses on effects of met-ocean condition forecasting uncertainties on weather window predictions when using the proposed methodology. It is demonstrated that the proposed methodology...... to include stochastic variables, representing met-ocean forecasting uncertainties and the results of such modification are given in terms of predicted weather windows for a selected test case....

  17. Seasonal Forecasts of Extreme Conditions for Wildland Fire Management in Alaska using NMME

    Science.gov (United States)

    Bhatt, U. S.; Bieniek, P.; Thoman, R.; York, A.; Ziel, R.

    2016-12-01

    The summer of 2015 was the second largest Alaska fire season since 1950 where approximately the land area of Massachusetts burned. The record fire year of 2004 resulted in 6.5 million acres burned and was costly from property loss (> 35M) and emergency personnel (> 17M). In addition to requiring significant resources, wildfire smoke impacts air quality in Alaska and downstream into North America. Fires in Alaska result from lightning strikes coupled with persistent (extreme) dry warm conditions in remote areas with limited fire management and the seasonal climate/weather determine the extent of the fire season in Alaska. Fire managers rely on weather/climate outlooks for allocating staff and resources from days to a season in advance. Though currently few tested products are available at the seasonal scale. Probabilistic forecasts of the expected seasonal climate/weather would aid tremendously in the planning process. Advanced knowledge of both lightning and fuel conditions would assist managers in planning resource allocation for the upcoming season. For fuel conditions, the Canadian Forest Fire Weather Index System (CFFWIS) has been used since 1992 because it better suits the Alaska fire regime than the standard US National Fire Danger Rating System (NFDRS). This CFFWIS is based on early afternoon values of 2-m air temperature, relative humidity, and 10-m winds and daily total precipitation. Extremes of these indices and the variables are used to calculate these indices will be defined in reference to fire weather for the boreal forest. The CFFWIS will be applied and evaluated for the NMME hindcasts. This study will evaluate the quality of the forecasts comparing the hindcast NMME CFFWIS to acres burned in Alaska. Spatial synoptic patterns in the NMME related to fire weather extremes will be constructed using self-organized maps and probabilities of occurrence will be evaluated against acres burned.

  18. Overtaking as Indicator of Road Traffic Conditions

    Directory of Open Access Journals (Sweden)

    Dražen Topolnik

    2012-10-01

    Full Text Available Overtaking is presemed as one of the indicators of roadtraffic flow. The possibility of overtaking depends on the existenceof an intetval in the opposing traffic flow sufficient to performovertaking. It also analyses the probability of overtakingby applying adequate equations and graphical presentations

  19. Spatiotemporal monthly rainfall forecasts for south-eastern and eastern Australia using climatic indices

    Science.gov (United States)

    Montazerolghaem, Maryam; Vervoort, Willem; Minasny, Budiman; McBratney, Alex

    2016-05-01

    Knowledge about future rainfall is important for agriculture management and planning in arid and semi-arid regions. Australia has complex variations in rainfall patterns in time and space, arising from the combination of the geographic structure and the dual effects of Indian and Pacific Ocean. This study aims to develop a forecasting model of spatiotemporal monthly rainfall totals using lagged climate indices and historical rainfall data from 1950-2011 for south-eastern and eastern Australia. Data were obtained from the Australian Bureau of Meteorology (BoM) from 136 high-quality weather stations. To reduce spatial complexity, climate regionalization was used to divide the stations in homogenous sub-regions based on similarity of rainfall patterns and intensity using principal component analysis (PCA) and K-means clustering. Subsequently, a fuzzy ranking algorithm (FRA) was applied to the lagged climatic predictors and monthly rainfall in each sub-region to identify the best predictors. Selected predictors by FRA were found to vary by sub-region. After these two stages of pre-processing, an artificial neural network (ANN) model was developed and optimized separately for each sub-region and the entire area. The results indicate that climate regionalization can improve a monthly spatiotemporal rainfall forecast model. The location and number of sub-regions were important for ranking predictors and modeling. This further suggests that the impact of climate variables on Australian rainfall is more variable in both time and space than indicated thus far.

  20. Survey of Condition Indicators for Condition Monitoring Systems (Open Access)

    Science.gov (United States)

    2014-09-29

    Renewable Energy Laboratory (NREL) published a document named ‘Wind Turbine Gearbox Condition Monitoring Round Robin Study – Vibration Analysis’ in 2012... Mean Square (RMS) RMS describes the energy content of the signal. RMS is used to evaluate the overall condition of the components. Therefore, it...13) ̅ is the mean value of signal N is the number of data point in the dataset x Energy

  1. Big Data Analytics for Modelling and Forecasting of Geomagnetic Field Indices

    Science.gov (United States)

    Wei, H. L.

    2016-12-01

    A massive amount of data are produced and stored in research areas of space weather and space climate. However, the value of a vast majority of the data acquired every day may not be effectively or efficiently exploited in our daily practice when we try to forecast solar wind parameters and geomagnetic field indices using these recorded measurements or digital signals, probably due to the challenges stemming from the dealing with big data which are characterized by the 4V futures: volume (a massively large amount of data), variety (a great number of different types of data), velocity (a requirement of quick processing of the data), and veracity (the trustworthiness and usability of the data). In order to obtain more reliable and accurate predictive models for geomagnetic field indices, it requires that models should be developed from the big data analytics perspective (or it at least benefits from such a perspective). This study proposes a few data-based modelling frameworks which aim to produce more efficient predictive models for space weather parameters forecasting by means of system identification and big data analytics. More specifically, it aims to build more reliable mathematical models that characterise the relationship between solar wind parameters and geomagnetic filed indices, for example the dependent relationship of Dst and Kp indices on a few solar wind parameters and magnetic field indices, namely, solar wind velocity (V), southward interplanetary magnetic field (Bs), solar wind rectified electric field (VBs), and dynamic flow pressure (P). Examples are provided to illustrate how the proposed modelling approaches are applied to Dst and Kp index prediction.

  2. Multiple indices method for real-time tsunami inundation forecast using a dense offshore observation network

    Science.gov (United States)

    Yamamoto, N.; Aoi, S.; Hirata, K.; Suzuki, W.; Kunugi, T.; Nakamura, H.

    2015-12-01

    We started to develop a new methodology for real-time tsunami inundation forecast system (Aoi et al., 2015, this meeting) using densely offshore tsunami observations of the Seafloor Observation Network for Earthquakes and Tsunamis (S-net), which is under construction along the Japan Trench (Kanazawa et al., 2012, JpGU; Uehira et al., 2015, IUGG). In our method, the most important concept is involving any type and/or form uncertainties in the tsunami forecast, which cannot be dealt with any of standard linear/nonlinear least square approaches. We first prepare a Tsunami Scenario Bank (TSB), which contains offshore tsunami waveforms at the S-net stations and tsunami inundation information calculated from any possible tsunami source. We then quickly select several acceptable tsunami scenarios that can explain offshore observations by using multiple indices and appropriate thresholds, after a tsunami occurrence. At that time, possible tsunami inundations coupled with selected scenarios are forecasted (Yamamoto et al., 2014, AGU). Currently, we define three indices: correlation coefficient and two variance reductions, whose L2-norm part is normalized either by observations or calculations (Suzuki et al., 2015, JpGU; Yamamoto et al., 2015, IUGG). In this study, we construct the TSB, which contains various tsunami source models prepared for the probabilistic tsunami hazard assessment in the Japan Trench region (Hirata et al., 2014, AGU). To evaluate the propriety of our method, we adopt the fault model based on the 2011 Tohoku earthquake as a pseudo "observation". We also calculate three indices using coastal maximum tsunami height distributions between observation and calculation. We then obtain the correlation between coastal and offshore indices. We notice that the index value of coastal maximum tsunami heights is closer to 1 than the index value of offshore waveforms, i.e., the coastal maximum tsunami height may be predictable within appropriate thresholds defined for

  3. Assimilation and High Resolution Forecasts of Surface and Near Surface Conditions for the 2010 Vancouver Winter Olympic and Paralympic Games

    Science.gov (United States)

    Bernier, Natacha B.; Bélair, Stéphane; Bilodeau, Bernard; Tong, Linying

    2014-01-01

    A dynamical model was experimentally implemented to provide high resolution forecasts at points of interests in the 2010 Vancouver Olympics and Paralympics Region. In a first experiment, GEM-Surf, the near surface and land surface modeling system, is driven by operational atmospheric forecasts and used to refine the surface forecasts according to local surface conditions such as elevation and vegetation type. In this simple form, temperature and snow depth forecasts are improved mainly as a result of the better representation of real elevation. In a second experiment, screen level observations and operational atmospheric forecasts are blended to drive a continuous cycle of near surface and land surface hindcasts. Hindcasts of the previous day conditions are then regarded as today's optimized initial conditions. Hence, in this experiment, given observations are available, observation driven hindcasts continuously ensure that daily forecasts are issued from improved initial conditions. GEM-Surf forecasts obtained from improved short-range hindcasts produced using these better conditions result in improved snow depth forecasts. In a third experiment, assimilation of snow depth data is applied to further optimize GEM-Surf's initial conditions, in addition to the use of blended observations and forecasts for forcing. Results show that snow depth and summer temperature forecasts are further improved by the addition of snow depth data assimilation.

  4. An application and verification of ensemble forecasting on wind power to assess operational risk indicators in power grids

    Energy Technology Data Exchange (ETDEWEB)

    Alessandrini, S.; Ciapessoni, E.; Cirio, D.; Pitto, A.; Sperati, S. [Ricerca sul Sistema Energetico RSE S.p.A., Milan (Italy). Power System Development Dept. and Environment and Sustainable Development Dept.; Pinson, P. [Technical University of Denmark, Lyngby (Denmark). DTU Informatics

    2012-07-01

    Wind energy is part of the so-called not schedulable renewable sources, i.e. it must be exploited when it is available, otherwise it is lost. In European regulation it has priority of dispatch over conventional generation, to maximize green energy production. However, being variable and uncertain, wind (and solar) generation raises several issues for the security of the power grids operation. In particular, Transmission System Operators (TSOs) need as accurate as possible forecasts. Nowadays a deterministic approach in wind power forecasting (WPF) could easily be considered insufficient to face the uncertainty associated to wind energy. In order to obtain information about the accuracy of a forecast and a reliable estimation of its uncertainty, probabilistic forecasting is becoming increasingly widespread. In this paper we investigate the performances of the COnsortium for Small-scale MOdelling Limited area Ensemble Prediction System (COSMO-LEPS). First the ensemble application is followed by assessment of its properties (i.e. consistency, reliability) using different verification indices and diagrams calculated on wind power. Then we provide examples of how EPS based wind power forecast can be used in power system security analyses. Quantifying the forecast uncertainty allows to determine more accurately the regulation reserve requirements, hence improving security of operation and reducing system costs. In particular, the paper also presents a probabilistic power flow (PPF) technique developed at RSE and aimed to evaluate the impact of wind power forecast accuracy on the probability of security violations in power systems. (orig.)

  5. Improving Energy Use Forecast for Campus Micro-grids using Indirect Indicators

    Energy Technology Data Exchange (ETDEWEB)

    Aman, Saima [Univ. of Southern California, Los Angeles, CA (United States). Dept. of Computer Science; Simmhan, Yogesh [Univ. of Southern California, Los Angeles, CA (United States). Dept. of Electrical Engineering; Prasanna, Viktor K. [Univ. of Southern California, Los Angeles, CA (United States). Dept. of Electrical Engineering

    2011-12-11

    The rising global demand for energy is best addressed by adopting and promoting sustainable methods of power consumption. We employ an informatics approach towards forecasting the energy consumption patterns in a university campus micro-grid which can be used for energy use planning and conservation. We use novel indirect indicators of energy that are commonly available to train regression tree models that can predict campus and building energy use for coarse (daily) and fine (15-min) time intervals, utilizing 3 years of sensor data collected at 15min intervals from 170 smart power meters. We analyze the impact of individual features used in the models to identify the ones best suited for the application. Our models show a high degree of accuracy with CV-RMSE errors ranging from 7.45% to 19.32%, and a reduction in error from baseline models by up to 53%.

  6. Improved forecasting of global vegetation conditions using remotely-sensed surface soil moisture

    Science.gov (United States)

    Timely and accurate monitoring of anomalies in root-zone soil water availability is essential for assessing global agricultural crop conditions. Root-zone soil moisture estimates are particularly important for obtaining forecasts of end-of-season crop yield fluctuations provided by the United States...

  7. Forecasting Low-Visibility Conditions at Vienna Airport with Tree-Based Statistical Models

    Science.gov (United States)

    Dietz, Sebastian; Kneringer, Philipp; Mayr, Georg J.; Zeileis, Achim

    2016-04-01

    Low visibility conditions at airports can lead to capacity problems and therefore to delays or cancelation of arriving and departing airplanes. To keep the capacity as high as possible, accurate visibility forecasts are required. Therefore tree-based statistical nowcasting models were developed, which split the data in the sense of decision rules by recursive partitioning. Benefits of this models are fast update cycles and low computation times. Highly-resolved meteorological observation data at the airport form the large pool of input variables for the models. In this study we identify the most important predictors for different lead times to create the most accurate forecasts.

  8. Demand Forecasting at Low Aggregation Levels using Factored Conditional Restricted Boltzmann Machine

    DEFF Research Database (Denmark)

    Mocanu, Elena; Nguyen, Phuong H.; Gibescu, Madeleine

    2016-01-01

    The electrical demand forecasting problem can be regarded as a nonlinear time series prediction problem depending on many complex factors since it is required at various aggregation levels and at high temporal resolution. To solve this challenging problem, various time series and machine learning...... developed deep learning model for time series prediction, namely Factored Conditional Restricted Boltzmann Machine (FCRBM), and extend it for electrical demand forecasting. The assessment is made on the EcoGrid dataset, originating from the Bornholm island experiment in Denmark, consisting of aggregated...

  9. Forecasting changes of arid geosystems under ecological destabilizing conditions in the Aral Sea region

    Directory of Open Access Journals (Sweden)

    V.A. Rifikov

    2014-05-01

    Full Text Available We discuss the main natural and anthropogenic factors of forecasting and establish the basic tendencies to change natural complexes. We conclude that the Aral Sea and the Aral Sea region are genetically uniform and paragenetically dynamical macro geosystems. By considering properties and features of structural and dynamic conditions of superaqual, subequal, and eluvial geosystems of the Aral Sea region and the Aral Sea, a forecast of its transformation by 2020 year is developed. We develop a practical plan of action for cardinal improvement of the environment in the Amu Darya Delta and the dried bottom of the Aral Sea.

  10. Forecast and restoration of geomagnetic activity indices by using the software-computational neural network complex

    Science.gov (United States)

    Barkhatov, Nikolay; Revunov, Sergey

    2010-05-01

    It is known that currently used indices of geomagnetic activity to some extent reflect the physical processes occurring in the interaction of the perturbed solar wind with Earth's magnetosphere. Therefore, they are connected to each other and with the parameters of near-Earth space. The establishment of such nonlinear connections is interest. For such purposes when the physical problem is complex or has many parameters the technology of artificial neural networks is applied. Such approach for development of the automated forecast and restoration method of geomagnetic activity indices with the establishment of creative software-computational neural network complex is used. Each neural network experiments were carried out at this complex aims to search for a specific nonlinear relation between the analyzed indices and parameters. At the core of the algorithm work program a complex scheme of the functioning of artificial neural networks (ANN) of different types is contained: back propagation Elman network, feed forward network, fuzzy logic network and Kohonen layer classification network. Tools of the main window of the complex (the application) the settings used by neural networks allow you to change: the number of hidden layers, the number of neurons in the layer, the input and target data, the number of cycles of training. Process and the quality of training the ANN is a dynamic plot of changing training error. Plot of comparison of network response with the test sequence is result of the network training. The last-trained neural network with established nonlinear connection for repeated numerical experiments can be run. At the same time additional training is not executed and the previously trained network as a filter input parameters get through and output parameters with the test event are compared. At statement of the large number of different experiments provided the ability to run the program in a "batch" mode is stipulated. For this purpose the user a

  11. Shared investment projects and forecasting errors: setting framework conditions for coordination and sequencing data quality activities.

    Directory of Open Access Journals (Sweden)

    Stephan Leitner

    Full Text Available In this paper, we investigate the impact of inaccurate forecasting on the coordination of distributed investment decisions. In particular, by setting up a computational multi-agent model of a stylized firm, we investigate the case of investment opportunities that are mutually carried out by organizational departments. The forecasts of concern pertain to the initial amount of money necessary to launch and operate an investment opportunity, to the expected intertemporal distribution of cash flows, and the departments' efficiency in operating the investment opportunity at hand. We propose a budget allocation mechanism for coordinating such distributed decisions The paper provides guidance on how to set framework conditions, in terms of the number of investment opportunities considered in one round of funding and the number of departments operating one investment opportunity, so that the coordination mechanism is highly robust to forecasting errors. Furthermore, we show that-in some setups-a certain extent of misforecasting is desirable from the firm's point of view as it supports the achievement of the corporate objective of value maximization. We then address the question of how to improve forecasting quality in the best possible way, and provide policy advice on how to sequence activities for improving forecasting quality so that the robustness of the coordination mechanism to errors increases in the best possible way. At the same time, we show that wrong decisions regarding the sequencing can lead to a decrease in robustness. Finally, we conduct a comprehensive sensitivity analysis and prove that-in particular for relatively good forecasters-most of our results are robust to changes in setting the parameters of our multi-agent simulation model.

  12. Shared investment projects and forecasting errors: setting framework conditions for coordination and sequencing data quality activities.

    Science.gov (United States)

    Leitner, Stephan; Brauneis, Alexander; Rausch, Alexandra

    2015-01-01

    In this paper, we investigate the impact of inaccurate forecasting on the coordination of distributed investment decisions. In particular, by setting up a computational multi-agent model of a stylized firm, we investigate the case of investment opportunities that are mutually carried out by organizational departments. The forecasts of concern pertain to the initial amount of money necessary to launch and operate an investment opportunity, to the expected intertemporal distribution of cash flows, and the departments' efficiency in operating the investment opportunity at hand. We propose a budget allocation mechanism for coordinating such distributed decisions The paper provides guidance on how to set framework conditions, in terms of the number of investment opportunities considered in one round of funding and the number of departments operating one investment opportunity, so that the coordination mechanism is highly robust to forecasting errors. Furthermore, we show that-in some setups-a certain extent of misforecasting is desirable from the firm's point of view as it supports the achievement of the corporate objective of value maximization. We then address the question of how to improve forecasting quality in the best possible way, and provide policy advice on how to sequence activities for improving forecasting quality so that the robustness of the coordination mechanism to errors increases in the best possible way. At the same time, we show that wrong decisions regarding the sequencing can lead to a decrease in robustness. Finally, we conduct a comprehensive sensitivity analysis and prove that-in particular for relatively good forecasters-most of our results are robust to changes in setting the parameters of our multi-agent simulation model.

  13. Continued Evaluation of Gear Condition Indicator Performance on Rotorcraft Fleet

    Science.gov (United States)

    Delgado, Irebert R.; Dempsey, Paula J.; Antolick, Lance J.; Wade, Daniel R.

    2013-01-01

    This paper details analyses of condition indicator performance for the helicopter nose gearbox within the U.S. Army's Condition-Based Maintenance Program. Ten nose gearbox data sets underwent two specific analyses. A mean condition indicator level analysis was performed where condition indicator performance was based on a 'batting average' measured before and after part replacement. Two specific condition indicators, Diagnostic Algorithm 1 and Sideband Index, were found to perform well for the data sets studied. A condition indicator versus gear wear analysis was also performed, where gear wear photographs and descriptions from Army tear-down analyses were categorized based on ANSI/AGMA 1010-E95 standards. Seven nose gearbox data sets were analyzed and correlated with condition indicators Diagnostic Algorithm 1 and Sideband Index. Both were found to be most responsive to gear wear cases of micropitting and spalling. Input pinion nose gear box condition indicators were found to be more responsive to part replacement during overhaul than their corresponding output gear nose gear box condition indicators.

  14. Regional air-quality forecasting for the Pacific Northwest using MOPITT/TERRA assimilated carbon monoxide MOZART-4 forecasts as a near real-time boundary condition

    Directory of Open Access Journals (Sweden)

    F. L. Herron-Thorpe

    2012-06-01

    Full Text Available Results from a regional air quality forecast model, AIRPACT-3, were compared to AIRS carbon monoxide column densities for the spring of 2010 over the Pacific Northwest. AIRPACT-3 column densities showed high correlation (R > 0.9 but were significantly biased (~25% with consistent under-predictions for spring months when there is significant transport from Asia. The AIRPACT-3 CO bias relative to AIRS was eliminated by incorporating dynamic boundary conditions derived from NCAR's MOZART forecasts with assimilated MOPITT carbon monoxide. Changes in ozone-related boundary conditions derived from MOZART forecasts are also discussed and found to affect background levels by ± 10 ppb but not found to significantly affect peak ozone surface concentrations.

  15. Regional air-quality forecasting for the Pacific Northwest using MOPITT/TERRA assimilated carbon monoxide MOZART-4 forecasts as a near real-time boundary condition

    Directory of Open Access Journals (Sweden)

    F. L. Herron-Thorpe

    2012-02-01

    Full Text Available Results from a regional air quality forecast model, AIRPACT-3, were compared to AIRS carbon monoxide column densities for the spring of 2010 over the Pacific Northwest. AIRPACT-3 column densities showed high correlation (R>0.9 but were significantly biased (~25 % with significant under-predictions for spring months with significant transport from Asia. The AIRPACT-3 CO bias relative to AIRS was eliminated by incorporating dynamic boundary conditions derived from NCAR's MOZART forecasts with assimilated MOPITT carbon monoxide. Changes in ozone-related boundary conditions derived from MOZART forecasts are also discussed and found to affect background levels by ±10 ppb but not found to significantly affect peak ozone surface concentrations.

  16. PRECURSOR WEATHER CONDITIONS FOR HAIL-EVENT FORECASTING IN THE MOLDAVIA

    Directory of Open Access Journals (Sweden)

    VASILICĂ ISTRATE

    2016-03-01

    Full Text Available he present work analysed a statistically representative number of severe convective events which caused hail and material damage during 1990-2015. For these episodes heights of 0oC, -6oC, -10oC and -20oC isotherms were analyzed. In the layers between these four isotherms, the formation and growth of hailstones occur. The height of these isotherms in the period preceding hail is essential in weather forecast of this phenomenon. Lifted index values recorded before hail events are also correlated with height isotherms. The altitudinal differences between the height of -6oC and -20oC isotherm and freezing level which represents intervals of initiation and formation of hail are identified. Not at last the values of the meteorological indices used to forecast the potential formation and hail falling in extracarpathian Moldavia are presented in details.

  17. Improving forecast skill by assimilation of quality-controlled AIRS temperature retrievals under partially cloudy conditions

    Science.gov (United States)

    Reale, O.; Susskind, J.; Rosenberg, R.; Brin, E.; Liu, E.; Riishojgaard, L. P.; Terry, J.; Jusem, J. C.

    2008-04-01

    The National Aeronautics and Space Administration (NASA) Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite is now recognized as an important contributor towards the improvement of weather forecasts. At this time only a small fraction of the total data produced by AIRS is being used by operational weather systems. In fact, in addition to effects of thinning and quality control, the only AIRS data assimilated are radiance observations of channels unaffected by clouds. Observations in mid-lower tropospheric sounding AIRS channels are assimilated primarily under completely clear-sky conditions, thus imposing a very severe limitation on the horizontal distribution of the AIRS-derived information. In this work it is shown that the ability to derive accurate temperature profiles from AIRS observations in partially cloud-contaminated areas can be utilized to further improve the impact of AIRS observations in a global model and forecasting system. The analyses produced by assimilating AIRS temperature profiles obtained under partial cloud cover result in a substantially colder representation of the northern hemisphere lower midtroposphere at higher latitudes. This temperature difference has a strong impact, through hydrostatic adjustment, in the midtropospheric geopotential heights, which causes a different representation of the polar vortex especially over northeastern Siberia and Alaska. The AIRS-induced anomaly propagates through the model's dynamics producing improved 5-day forecasts.

  18. Improving Forecast Skill by Assimilation of Quality-controlled AIRS Temperature Retrievals under Partially Cloudy Conditions

    Science.gov (United States)

    Reale, O.; Susskind, J.; Rosenberg, R.; Brin, E.; Riishojgaard, L.; Liu, E.; Terry, J.; Jusem, J. C.

    2007-01-01

    The National Aeronautics and Space Administration (NASA) Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite has been long recognized as an important contributor towards the improvement of weather forecasts. At this time only a small fraction of the total data produced by AIRS is being used by operational weather systems. In fact, in addition to effects of thinning and quality control, the only AIRS data assimilated are radiance observations of channels unaffected by clouds. Observations in mid-lower tropospheric sounding AIRS channels are assimilated primarily under completely clear-sky conditions, thus imposing a very severe limitation on the horizontal distribution of the AIRS-derived information. In this work it is shown that the ability to derive accurate temperature profiles from AIRS observations in partially cloud-contaminated areas can be utilized to further improve the impact of AIRS observations in a global model and forecasting system. The analyses produced by assimilating AIRS temperature profiles obtained under partial cloud cover result in a substantially colder representation of the northern hemisphere lower midtroposphere at higher latitudes. This temperature difference has a strong impact, through hydrostatic adjustment, in the midtropospheric geopotential heights, which causes a different representation of the polar vortex especially over northeastern Siberia and Alaska. The AIRS-induced anomaly propagates through the model's dynamics producing improved 5-day forecasts.

  19. An experimental seasonal hydrological forecasting system over the Yellow River basin - Part 1: Understanding the role of initial hydrological conditions

    Science.gov (United States)

    Yuan, Xing; Ma, Feng; Wang, Linying; Zheng, Ziyan; Ma, Zhuguo; Ye, Aizhong; Peng, Shaoming

    2016-06-01

    The hydrological cycle over the Yellow River has been altered by the climate change and human interventions greatly during past decades, with a decadal drying trend mixed with a large variation of seasonal hydrological extremes. To provide support for the adaptation to a changing environment, an experimental seasonal hydrological forecasting system is established over the Yellow River basin. The system draws from a legacy of a global hydrological forecasting system that is able to make use of real-time seasonal climate predictions from North American Multimodel Ensemble (NMME) climate models through a statistical downscaling approach but with a higher resolution and a spatially disaggregated calibration procedure that is based on a newly compiled hydrological observation dataset with 5 decades of naturalized streamflow at 12 mainstream gauges and a newly released meteorological observation dataset including 324 meteorological stations over the Yellow River basin. While the evaluation of the NMME-based seasonal hydrological forecasting will be presented in a companion paper to explore the added values from climate forecast models, this paper investigates the role of initial hydrological conditions (ICs) by carrying out 6-month Ensemble Streamflow Prediction (ESP) and reverse ESP-type simulations for each calendar month during 1982-2010 with the hydrological models in the forecasting system, i.e., a large-scale land surface hydrological model and a global routing model that is regionalized over the Yellow River. In terms of streamflow predictability, the ICs outweigh the meteorological forcings up to 2-5 months during the cold and dry seasons, but the latter prevails over the former in the predictability after the first month during the warm and wet seasons. For the streamflow forecasts initialized at the end of the rainy season, the influence of ICs for lower reaches of the Yellow River can be 5 months longer than that for the upper reaches, while such a difference

  20. Forecasting Skill

    Science.gov (United States)

    1981-01-01

    indicated that forecasting experience has little relationship to forecasting performance. In the latter three studies, neophyte forecasters became... Europe . Within a few months after a new commander was assigned, this unit’s performance rose to first place in the theater and remained there

  1. L band microwave remote sensing and land data assimilation improve the representation of prestorm soil moisture conditions for hydrologic forecasting

    Science.gov (United States)

    Crow, W. T.; Chen, F.; Reichle, R. H.; Liu, Q.

    2017-06-01

    Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total streamflow divided by total rainfall accumulation in depth units) and prestorm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting streamflow response to future rainfall events.type="synopsis">type="main">Plain Language SummaryForecasting streamflow conditions is important for minimizing loss of life and property during flooding and adequately planning for low streamflow conditions accompanying drought. One way to improve these forecasts is measuring the amount of water in the soil—since soil moisture conditions determine what fraction of rainfall will run off horizontally into stream channels (versus vertically infiltrate into the soil column). Within the past 5 years, there have been important advances in our ability to monitor soil moisture over large scales using both satellite-based sensors and the application of new land data assimilation techniques. This paper illustrates that these advances have significantly improved our capacity to forecast how much streamflow will be generated by future precipitation events. These results may eventually be used by operational forecasters to improve flash flood forecasting and agricultural water use management.

  2. FORECASTING AND DECISION-MAKING VIA USE OF STATISTICAL LAWS OF RANDOM ECONOMIC INDICATORS

    Directory of Open Access Journals (Sweden)

    Victor A. Vlasov

    2013-01-01

    Full Text Available The article shows that the estimation (forecasting of the values of random variables, according to the minimum variance estimation error is not always effective, and sometimes not even possible. The authors give examples to prove it. The estimation is described with a functional. The task of the estimation is treated as a search for an optimal solution and is illustrated with formation of the optimal playing strategy.

  3. Using initial and boundary condition perturbations in medium-range regional ensemble forecasting with two nested domains

    Science.gov (United States)

    Jiang, J.; Koracin, D.; Vellore, R.; Xiao, M.; Lewis, J. M.

    2010-12-01

    Simulated evolution of climate and weather is sensitive to the specification of their initial state. Small errors in the initial state could lead the forecast into a different direction. It is essential to estimate the impact of the uncertainty in initial conditions on the forecast accuracy. For limited-area or regional forecasting, lateral boundary conditions also have considerable influence on the development of mesoscale or local-scale phenomena. Strong lateral boundary conditions derived from a larger scale environment could significantly alter or even remove local-scale components. This study investigates the impact of uncertainty in initial and lateral boundary conditions on medium-range regional forecasting using the Advanced Weather Research and Forecasting (WRF) model. The WRF model was configured with two nested domains: the parent domain has a 108 km horizontal resolution, and a nested domain with 36 km resolution covers the western U.S. The ensemble forecasting was conducted with 50 ensemble members using random perturbations in the initial conditions (ICs) and lateral boundary conditions (LBCs). A case period of 15 days in December 2008 is chosen, during which two intense frontal passages occurred in the western U.S. Results show that, applying only IC perturbations, the contribution from the IC perturbations to the ensemble spread decreases with time. Using both randomly perturbed LBCs and ICs from the coarser domain, the inner nested domain shows a wider ensemble spread. The resulting ensemble forecasting can be interpreted as a probabilistic prediction for wind energy, especially for wind gust and wind turbine operational cut-off. The analysis also includes an efficiency comparison of using coarser ensemble forecasting vs. a higher resolution single control run.

  4. Forecast daily indices of solar activity, F10.7, using support vector regression method

    Institute of Scientific and Technical Information of China (English)

    Cong Huang; Dan-Dan Liu; Jing-Song Wang

    2009-01-01

    The 10.7cm solar radio flux (F10.7), the value of the solar radio emission flux density at a wavelength of 10.7cm, is a useful index of solar activity as a proxy for solar extreme ultraviolet radiation. It is meaningful and important to predict F10.7 values accurately for both long-term (months-years) and short-term (days) forecasting, which are often used as inputs in space weather models. This study applies a novel neural network technique, support vector regression (SVR), to forecasting daily values of F10.7. The aim of this study is to examine the feasibility of SVR in short-term F10.7 forecasting. The approach, based on SVR, reduces the dimension of feature space in the training process by using a kernel-based learning algorithm. Thus, the complexity of the calculation becomes lower and a small amount of training data will be sufficient. The time series of F10.7 from 2002 to 2006 are employed as the data sets. The performance of the approach is estimated by calculating the norm mean square error and mean absolute percentage error. It is shown that our approach can perform well by using fewer training data points than the traditional neural network.

  5. Multi-initial-conditions and Multi-physics Ensembles in the Weather Research and Forecasting Model to Improve Coastal Stratocumulus Forecasts for Solar Power Integration

    Science.gov (United States)

    Yang, H.

    2015-12-01

    In coastal Southern California, variation in solar energy production is predominantly due to the presence of stratocumulus clouds (Sc), as they greatly attenuate surface solar irradiance and cover most distributed photovoltaic systems on summer mornings. Correct prediction of the spatial coverage and lifetime of coastal Sc is therefore vital to the accuracy of solar energy forecasts in California. In Weather Research and Forecasting (WRF) model simulations, underprediction of Sc inherent in the initial conditions directly leads to an underprediction of Sc in the resulting forecasts. Hence, preprocessing methods were developed to create initial conditions more consistent with observational data and reduce spin-up time requirements. Mathiesen et al. (2014) previously developed a cloud data assimilation system to force WRF initial conditions to contain cloud liquid water based on CIMSS GOES Sounder cloud cover. The Well-mixed Preprocessor and Cloud Data Assimilation (WEMPPDA) package merges an initial guess of cloud liquid water content obtained from mixed-layer theory with assimilated CIMSS GOES Sounder cloud cover to more accurately represent the spatial coverage of Sc at initialization. The extent of Sc inland penetration is often constrained topographically; therefore, the low inversion base height (IBH) bias in NAM initial conditions decreases Sc inland penetration. The Inversion Base Height (IBH) package perturbs the initial IBH by the difference between model IBH and the 12Z radiosonde measurement. The performance of these multi-initial-condition configurations was evaluated over June, 2013 against SolarAnywhere satellite-derived surface irradiance data. Four configurations were run: 1) NAM initial conditions, 2) RAP initial conditions, 3) WEMPPDA applied to NAM, and 4) IBH applied to NAM. Both preprocessing methods showed significant improvement in the prediction of both spatial coverage and lifetime of coastal Sc. The best performing configuration was then

  6. Empirical regional models for the short-term forecast of M3000F2 during not quiet geomagnetic conditions over Europe

    Directory of Open Access Journals (Sweden)

    M. Pietrella

    2013-10-01

    Full Text Available Twelve empirical local models have been developed for the long-term prediction of the ionospheric characteristic M3000F2, and then used as starting point for the development of a short-term forecasting empirical regional model of M3000F2 under not quiet geomagnetic conditions. Under the assumption that the monthly median measurements of M3000F2 are linearly correlated to the solar activity, a set of regression coefficients were calculated over 12 months and 24 h for each of 12 ionospheric observatories located in the European area, and then used for the long-term prediction of M3000F2 at each station under consideration. Based on the 12 long-term prediction empirical local models of M3000F2, an empirical regional model for the prediction of the monthly median field of M3000F2 over Europe (indicated as RM_M3000F2 was developed. Thanks to the IFELM_foF2 models, which are able to provide short-term forecasts of the critical frequency of the F2 layer (foF2STF up to three hours in advance, it was possible to considerer the Brudley–Dudeney algorithm as a function of foF2STF to correct RM_M3000F2 and thus obtain an empirical regional model for the short-term forecasting of M3000F2 (indicated as RM_M3000F2_BD up to three hours in advance under not quiet geomagnetic conditions. From the long-term predictions of M3000F2 provided by the IRI model, an empirical regional model for the forecast of the monthly median field of M3000F2 over Europe (indicated as IRI_RM_M3000F2 was derived. IRI_RM_M3000F2 predictions were modified with the Bradley–Dudeney correction factor, and another empirical regional model for the short-term forecasting of M3000F2 (indicated as IRI_RM_M3000F2_BD up to three hours ahead under not quiet geomagnetic conditions was obtained. The main results achieved comparing the performance of RM_M3000F2, RM_M3000F2_BD, IRI_RM_M3000F2, and IRI_RM_M3000F2_BD are (1 in the case of moderate geomagnetic activity, the Bradley–Dudeney correction

  7. Forecasting Urban Water Demand via Machine Learning Methods Coupled with a Bootstrap Rank-Ordered Conditional Mutual Information Input Variable Selection Method

    Science.gov (United States)

    Adamowski, J. F.; Quilty, J.; Khalil, B.; Rathinasamy, M.

    2014-12-01

    This paper explores forecasting short-term urban water demand (UWD) (using only historical records) through a variety of machine learning techniques coupled with a novel input variable selection (IVS) procedure. The proposed IVS technique termed, bootstrap rank-ordered conditional mutual information for real-valued signals (brCMIr), is multivariate, nonlinear, nonparametric, and probabilistic. The brCMIr method was tested in a case study using water demand time series for two urban water supply system pressure zones in Ottawa, Canada to select the most important historical records for use with each machine learning technique in order to generate forecasts of average and peak UWD for the respective pressure zones at lead times of 1, 3, and 7 days ahead. All lead time forecasts are computed using Artificial Neural Networks (ANN) as the base model, and are compared with Least Squares Support Vector Regression (LSSVR), as well as a novel machine learning method for UWD forecasting: the Extreme Learning Machine (ELM). Results from one-way analysis of variance (ANOVA) and Tukey Honesty Significance Difference (HSD) tests indicate that the LSSVR and ELM models are the best machine learning techniques to pair with brCMIr. However, ELM has significant computational advantages over LSSVR (and ANN) and provides a new and promising technique to explore in UWD forecasting.

  8. THE ANALYSIS OF THE COMMODITY PRICE FORECASTING SUCCESS CONSIDERING DIFFERENT LENGTHS OF THE INITIAL CONDITION DRIFT

    Directory of Open Access Journals (Sweden)

    Marcela Lascsáková

    2015-09-01

    Full Text Available In the paper the numerical model based on the exponential approximation of commodity stock exchanges was derived. The price prognoses of aluminium on the London Metal Exchange were determined as numerical solution of the Cauchy initial problem for the 1st order ordinary differential equation. To make the numerical model more accurate the idea of the modification of the initial condition value by the stock exchange was realized. By having analyzed the forecasting success of the chosen initial condition drift types, the initial condition drift providing the most accurate prognoses for the commodity price movements was determined. The suggested modification of the original model made the commodity price prognoses more accurate.

  9. Climate indices over the last three decades in Tunisia using Weather Research and Forecasting Model:WRF

    Science.gov (United States)

    Deli, Meriem; Mkhinini, Nadia; Sadok Guellouz, Mohamed; Benjabrallah, Sadok

    2016-04-01

    Tunisia is a country situated in the south of the mediterannen basin. This region undergoes direct and indirect effects of climate change. Actually, we notice that summer temperatures have risen during the last decades. Nevertheless research on the tunisian climate are not well developed and are mainly based on observations; short and mid term forecast are not available for the tunisian case. In this context we have studied the climate properties of Tunisia over the last 30 years using Weather Research and Forecasting model WRF. Afterwards we compared our results to the observations that we have obteined on behalf of the National Institute of Meteorology. Results were then used to calculate different climate indices related to the air temperature such as extreme values during a specific period exceeding specific limits (Percentile), warm and cold spell duration and growing season length. We admit that we have created a reliable database for the Tunisian climate.

  10. Dynamic Forecasting Conditional Probability of Bombing Attacks Based on Time-Series and Intervention Analysis.

    Science.gov (United States)

    Li, Shuying; Zhuang, Jun; Shen, Shifei

    2017-07-01

    In recent years, various types of terrorist attacks occurred, causing worldwide catastrophes. According to the Global Terrorism Database (GTD), among all attack tactics, bombing attacks happened most frequently, followed by armed assaults. In this article, a model for analyzing and forecasting the conditional probability of bombing attacks (CPBAs) based on time-series methods is developed. In addition, intervention analysis is used to analyze the sudden increase in the time-series process. The results show that the CPBA increased dramatically at the end of 2011. During that time, the CPBA increased by 16.0% in a two-month period to reach the peak value, but still stays 9.0% greater than the predicted level after the temporary effect gradually decays. By contrast, no significant fluctuation can be found in the conditional probability process of armed assault. It can be inferred that some social unrest, such as America's troop withdrawal from Afghanistan and Iraq, could have led to the increase of the CPBA in Afghanistan, Iraq, and Pakistan. The integrated time-series and intervention model is used to forecast the monthly CPBA in 2014 and through 2064. The average relative error compared with the real data in 2014 is 3.5%. The model is also applied to the total number of attacks recorded by the GTD between 2004 and 2014. © 2016 Society for Risk Analysis.

  11. Spatiotemporal monthly rainfall forecasting for south-eastern and eastern Australia using climatic indices

    Science.gov (United States)

    Montazerolghaem, Maryam; Vervoort, Willem; Minasny, Budiman; McBratney, Alex

    2014-05-01

    Knowledge about future rainfall would significantly benefit land, water resources and agriculture management, as this assists with planning and management decisions. Forecasting spatiotemporal monthly rainfall is difficult, especially in Australia where there is a complex interaction between topography and the effect of Indian and Pacific Ocean. This study describes a method for spatiotemporal monthly rainfall forecasting in south-eastern and eastern part of Australia using climatic and non-climatic variables. Rainfall data were obtained from Bureau of Meteorology (BoM) from 136 high quality weather stations from the south-eastern and eastern part of Australia with monthly rainfall records from 1879 to 2012. To reduce spatial complexity of the area and improve model accuracy, spatial classification (regionalization) was considered as first step. Significant predictors for each sub-region among lagged climatic input variables were selected using Fuzzy Ranking Algorithm (FRA). Climate classification: 1) discovered homogenous sub-regions with a similar rainfall patterns and investigated spatiotemporal rainfall variations in the area, 2) allowed selection of significant predictors with a fine resolution for each area, 3) improved the prediction model and increased model accuracy. PCA was used to reduce the dimensions of the dataset and to remove the rainfall time series correlation. K-means clustering was used on the loadings of PCs describing 93% of long-term monthly rainfall variations. The analysis was repeated for different numbers of sub-regions (3 - 8) to identify the best number of clusters to improve the forecast model performance. Subsequently, a Fuzzy Ranking Algorithm (FRA) was applied to the lagged climatic predictors and monthly rainfall in each sub-region to identify the best predictors. After these two stages of pre-processing, a Neural Network model was developed and optimized for each of the sub-regions as well as for the entire area. It is concluded

  12. Can Agrometeorological Indices of Adverse Weather Conditions Help to Improve Yield Prediction by Crop Models?

    Directory of Open Access Journals (Sweden)

    Branislava Lalić

    2014-12-01

    Full Text Available The impact of adverse weather conditions (AWCs on crop production is random in both time and space and depends on factors such as severity, previous agrometeorological conditions, and plant vulnerability at a specific crop development stage. Any exclusion or improper treatment of any of these factors can cause crop models to produce significant under- or overestimates of yield. The analysis presented in this paper focuses on a range of agrometeorological indices (AMI related to AWCs that might affect real yield as well as simulated yield. For this purpose, the analysis addressed four indicators of extreme temperatures and three indicators of dry conditions during the growth period of maize and winter wheat in Austria, Croatia, Serbia, Slovakia, and Sweden. It is shown that increases in the number and intensity of AWCs cannot be unambiguously associated with increased deviations in simulated yields. The identified correlations indicate an increase in modeling uncertainty. This finding represents important information for the crop modeling community. Additionally, it opens a window of opportunity for a statistical (“event scenario” approach based on correlations between agrometeorological indices of AWCs and crop yield data series. This approach can provide scenarios for certain locations, crop types, and AWC patterns and, therefore, improve yield forecasting in the presence of AWCs.

  13. Condition Indicators for Inspection Planning of Concrete Structures

    DEFF Research Database (Denmark)

    Faber, M.H.; Sørensen, John Dalsgaard

    2002-01-01

    Based on previous work by the authors a Bayesian formulation of condition indicators is developed further whereby in conjunction with a systems modelling of concrete structures the experience and expertise of the inspection personnel may be fully utilized. It is shown how the predicted evolution...

  14. Priming effect in indicative and subjunctive exceptive conditionals.

    Science.gov (United States)

    Espino, Orlando; Sánchez-Curbelo, Isana

    2016-03-01

    We report the results of three experiments that examine the mental representations underlying the comprehension stages of negative exceptive conditionals using subjunctive mood ('B a menos que A', 'B a no ser que A'; 'B excepto que A'='B unless A') and indicative mood ('B excepto si A' and 'B salvo si A'='B except if A'). The mental representations during the comprehension stage were analyzed using a priming methodology. All experiments showed that participants read the true possibility 'not-B & A' faster when it was primed by exceptive conditionals requiring the subjunctive mood than when it was primed by exceptive conditionals requiring the indicative mood; other possibilities ('B & A', 'B & not-A', 'not-B & not-A') were primed equally by both connectives. The experiments showed that (a) when people understand negative exceptive conditionals using the subjunctive mood, such as 'B a menos que A'/'B a no ser que A'/'B excepto que A', they access the true possibilities 'not-B & A' and 'B & not-A', and (b) when they understand negative exceptive conditionals using the indicative mood, such as 'B excepto si A'/'B salvo si A', they access 'B & not-A', but not 'not-B & A'. We discuss the implications of this for current theories of reasoning.

  15. The Role of Model and Initial Condition Error in Numerical Weather Forecasting Investigated with an Observing System Simulation Experiment

    Science.gov (United States)

    Prive, Nikki C.; Errico, Ronald M.

    2013-01-01

    A series of experiments that explore the roles of model and initial condition error in numerical weather prediction are performed using an observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO). The use of an OSSE allows the analysis and forecast errors to be explicitly calculated, and different hypothetical observing networks can be tested with ease. In these experiments, both a full global OSSE framework and an 'identical twin' OSSE setup are utilized to compare the behavior of the data assimilation system and evolution of forecast skill with and without model error. The initial condition error is manipulated by varying the distribution and quality of the observing network and the magnitude of observation errors. The results show that model error has a strong impact on both the quality of the analysis field and the evolution of forecast skill, including both systematic and unsystematic model error components. With a realistic observing network, the analysis state retains a significant quantity of error due to systematic model error. If errors of the analysis state are minimized, model error acts to rapidly degrade forecast skill during the first 24-48 hours of forward integration. In the presence of model error, the impact of observation errors on forecast skill is small, but in the absence of model error, observation errors cause a substantial degradation of the skill of medium range forecasts.

  16. [LIFE CONDITIONS: NON-SPECIFIC STRESS INDICATORS AND DENTOALVEOLAR PATHOLOGIES].

    Science.gov (United States)

    Mosticone, Romina; Pescucci, Lisa; Porreca, Flavia

    2015-01-01

    Trauma, diseases, diet, daily work and environmental factors shape bodies. From birth to death, these processes leave on the skeleton markers that can be recognized and studied, thus providing an overview of the health conditions of past populations. The present work analyzes data collected in seven necropolises. During our study, we exploited nonspecific stress and dental pathologies as key indicators of health conditions. In particula; we analyzed the three most common indicators of stress: porotic hyperostosis; enamel hypoplasia; and Harris lines on shins. Additionally, we examined the most important dental alveolar pathologies, including carious lesions, periodontal diseases, antemortem tooth loss, abscesses, and calculi. The data we analyzed suggest that, despite the different urban and suburban origins, all the samples belong to a middle-range or low social class, whose living conditions were modest. The only necropolis which stands out is Casal Bertone Mausoleo, where the samples present the lowest frequencies with respect to both the stress indicators and the oral pathologies, suggesting better living conditions.

  17. Analysis and forecast of the economic indicators of S.C DEDEMAN.SRL

    Directory of Open Access Journals (Sweden)

    Mihai FÂNARU

    2016-07-01

    Full Text Available The increasing pace of change characteristic to the contemporary era requires anticipating them on larger period of time. Researching the future becomes a constant concern of both individuals and professionals as well as some national and international bodies and institutions. As John Naisbitt states, "a man can survive only by its ability to act in the present, based on past experience, with consequences in the future. Assuming ones future, the man makes his present bearable and its past significant. Past, present and future alternatives are intertwined in anticipation and forecasting of future actions. "The bricolage market is estimated at a value of 2 billion euro, being currently dominated by Romanian players like Dedeman, Arabesque and Ambient. The approximate knowledge of the future is a way through which the bricolage company Dedeman is preparing to face the unexpected.

  18. Data Fusion Tool for Spiral Bevel Gear Condition Indicator Data

    Science.gov (United States)

    Dempsey, Paula J.; Antolick, Lance J.; Branning, Jeremy S.; Thomas, Josiah

    2014-01-01

    Tests were performed on two spiral bevel gear sets in the NASA Glenn Spiral Bevel Gear Fatigue Test Rig to simulate the fielded failures of spiral bevel gears installed in a helicopter. Gear sets were tested until damage initiated and progressed on two or more gear or pinion teeth. During testing, gear health monitoring data was collected with two different health monitoring systems. Operational parameters were measured with a third data acquisition system. Tooth damage progression was documented with photographs taken at inspection intervals throughout the test. A software tool was developed for fusing the operational data and the vibration based gear condition indicator (CI) data collected from the two health monitoring systems. Results of this study illustrate the benefits of combining the data from all three systems to indicate progression of damage for spiral bevel gears. The tool also enabled evaluation of the effectiveness of each CI with respect to operational conditions and fault mode.

  19. Evaluation of Gear Condition Indicator Performance on Rotorcraft Fleet

    Science.gov (United States)

    Antolick, Lance J.; Branning, Jeremy S.; Wade, Daniel R.; Dempsey, Paula J.

    2010-01-01

    The U.S. Army is currently expanding its fleet of Health Usage Monitoring Systems (HUMS) equipped aircraft at significant rates, to now include over 1,000 rotorcraft. Two different on-board HUMS, the Honeywell Modern Signal Processing Unit (MSPU) and the Goodrich Integrated Vehicle Health Management System (IVHMS), are collecting vibration health data on aircraft that include the Apache, Blackhawk, Chinook, and Kiowa Warrior. The objective of this paper is to recommend the most effective gear condition indicators for fleet use based on both a theoretical foundation and field data. Gear diagnostics with better performance will be recommended based on both a theoretical foundation and results of in-fleet use. In order to evaluate the gear condition indicator performance on rotorcraft fleets, results of more than five years of health monitoring for gear faults in the entire HUMS equipped Army helicopter fleet will be presented. More than ten examples of gear faults indicated by the gear CI have been compiled and each reviewed for accuracy. False alarms indications will also be discussed. Performance data from test rigs and seeded fault tests will also be presented. The results of the fleet analysis will be discussed, and a performance metric assigned to each of the competing algorithms. Gear fault diagnostic algorithms that are compliant with ADS-79A will be recommended for future use and development. The performance of gear algorithms used in the commercial units and the effectiveness of the gear CI as a fault identifier will be assessed using the criteria outlined in the standards in ADS-79A-HDBK, an Army handbook that outlines the conversion from Reliability Centered Maintenance to the On-Condition status of Condition Based Maintenance.

  20. The case for indicator condition-guided HIV screening

    DEFF Research Database (Denmark)

    Lazarus, J V; Hoekstra, M; Raben, D

    2013-01-01

    One-half of the estimated 2.5 million people who now live with HIV in the World Health Organization (WHO) European Region are still diagnosed late. A central question is which clinical scenarios should trigger an HIV test recommendation in order to avoid late presentation. Drawing on the work...... of the HIV Indicator Diseases across Europe Study (HIDES), new guidance brings together in one place a list of the conditions that should result in an HIV screening recommendation....

  1. Errors in Moral Forecasting: Perceptions of Affect Shape the Gap Between Moral Behaviors and Moral Forecasts.

    Science.gov (United States)

    Teper, Rimma; Tullett, Alexa M; Page-Gould, Elizabeth; Inzlicht, Michael

    2015-07-01

    Research in moral decision making has shown that there may not be a one-to-one relationship between peoples' moral forecasts and behaviors. Although past work suggests that physiological arousal may account for part of the behavior-forecasting discrepancy, whether or not perceptions of affect play an important determinant remains unclear. Here, we investigate whether this discrepancy may arise because people fail to anticipate how they will feel in morally significant situations. In Study 1, forecasters predicted cheating significantly more on a test than participants in a behavior condition actually cheated. Importantly, forecasters who received false somatic feedback, indicative of high arousal, produced forecasts that aligned more closely with behaviors. In Study 2, forecasters who misattributed their arousal to an extraneous source forecasted cheating significantly more. In Study 3, higher dispositional emotional awareness was related to less forecasted cheating. These findings suggest that perceptions of affect play a key role in the behavior-forecasting dissociation.

  2. Condition Indicators for Inspection Planning of Concrete Structures

    DEFF Research Database (Denmark)

    Faber, M.H.; Sørensen, John Dalsgaard

    2002-01-01

    Based on previous work by the authors a Bayesian formulation of condition indicators is developed further whereby in conjunction with a systems modelling of concrete structures the experience and expertise of the inspection personnel may be fully utilized. It is shown how the predicted evolution...... of the deterioration of the structure may be consistently updated based on inspection results. This facilitates that inspection results may be used in the long term planning of inspection and maintenance of structures. The approach is illustrated on an example considering half-cell measurement inspections...

  3. Combining Satellite-Based Precipitation and Vegetation Indices to Achieve a Mid-Summer Agricultural Forecast in Jamaica

    Science.gov (United States)

    Curtis, S.; Allen, T. L.; Gamble, D.

    2009-12-01

    In this study global Earth observations of precipitation and Normalized Difference Vegetation Indices (NDVI) are used to assess the mid-summer dry spell’s (MSD) strength and subsequent impact on agriculture in the St. Elizabeth parish of Jamaica. St. Elizabeth is known as the ‘bread basket’ of Jamaica and has been the top or second highest producer of domestic food crops in the last twenty years. Yet, St. Elizabeth sits in the Jamaican rain shadow and is highly affected by drought. In addition, the summer rainy season is regularly interrupted by an MSD, which often occurs in July, has strong interannual variability, and greatly affects cropping strategies and yields. The steps undertaken to achieve a mid-summer agricultural forecast are: 1) use relationships between Global Precipitation Climatology Project v2.1 data over western Jamaica and predictive climate modes from 1979 to present to develop a forecast of July rainfall 2) downscale the rainfall variability in time to sub-monthly and space to the St. Elizabeth parish using the Tropical Rainfall Measuring Mission 3) link rainfall variability to vegetation vigor with the MODIS NDVI data 4) communicate with St. Elizabeth farmers via the University of West Indies, Mona. An important finding from this study is a decrease in vegetative vigor follows the MSD by two to four weeks in St. Elizabeth and the vegetation in the southern portion of the parish appears to be more sensitive to the MSD than vegetation elsewhere in the country.

  4. Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models

    Science.gov (United States)

    2016-01-01

    The motivation behind this research is to innovatively combine new methods like wavelet, principal component analysis (PCA), and artificial neural network (ANN) approaches to analyze trade in today’s increasingly difficult and volatile financial futures markets. The main focus of this study is to facilitate forecasting by using an enhanced denoising process on market data, taken as a multivariate signal, in order to deduct the same noise from the open-high-low-close signal of a market. This research offers evidence on the predictive ability and the profitability of abnormal returns of a new hybrid forecasting model using Wavelet-PCA denoising and ANN (named WPCA-NN) on futures contracts of Hong Kong’s Hang Seng futures, Japan’s NIKKEI 225 futures, Singapore’s MSCI futures, South Korea’s KOSPI 200 futures, and Taiwan’s TAIEX futures from 2005 to 2014. Using a host of technical analysis indicators consisting of RSI, MACD, MACD Signal, Stochastic Fast %K, Stochastic Slow %K, Stochastic %D, and Ultimate Oscillator, empirical results show that the annual mean returns of WPCA-NN are more than the threshold buy-and-hold for the validation, test, and evaluation periods; this is inconsistent with the traditional random walk hypothesis, which insists that mechanical rules cannot outperform the threshold buy-and-hold. The findings, however, are consistent with literature that advocates technical analysis. PMID:27248692

  5. Response of ecosystem productivity to dry/wet conditions indicated by different drought indices.

    Science.gov (United States)

    Wang, Haiyan; He, Bin; Zhang, Yafeng; Huang, Ling; Chen, Ziyue; Liu, Junjie

    2017-08-28

    Various climatic and hydrological variables such as precipitation, soil moisture, stream flow, and water level can be used to assess drought conditions, however, the response of ecosystem productivity to such metrics is not very clear. In this study, we examined the sensitivity of GPP anomalies to five drought indicators: the Standardized Precipitation Index (SPI), the Standardized Precipitation-Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), deficit of soil moisture (DSM), and the difference between precipitation (P) and evapotranspiration (ET) (D(P-ET)). The global spatial distributions of drying and wetting trends from 2000 to 2014 determined by these five indices were similar. Additionally, the percent of drought-impacted areas decreased over the study period, indicating a reduction in drought conditions. GPP increased over the study period in the Northern Hemisphere (NH) but decreased in the Southern Hemisphere (SH), resulting in a net increase in global GPP. GPP anomalies were more sensitive to drought indices in the SH than in the NH. Among the five indices, GPP anomalies were most closely correlated with SPI in the NH (R=0.60, P<0.05) and SPEI in the SH (R=0.93, P<0.01). Regionally speaking, annual and seasonal GPP anomalies were most sensitive to DSM and PDSI, highlighting the importance of soil moisture observations to regional drought monitoring and assessment. The results of this study are important for evaluating the impacts of drought on ecosystem production and the global carbon cycle. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Improving PM2. 5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter

    Science.gov (United States)

    Peng, Zhen; Liu, Zhiquan; Chen, Dan; Ban, Junmei

    2017-04-01

    In an attempt to improve the forecasting of atmospheric aerosols, the ensemble square root filter algorithm was extended to simultaneously optimize the chemical initial conditions (ICs) and emission input. The forecast model, which was expanded by combining the Weather Research and Forecasting with Chemistry (WRF-Chem) model and a forecast model of emission scaling factors, generated both chemical concentration fields and emission scaling factors. The forecast model of emission scaling factors was developed by using the ensemble concentration ratios of the WRF-Chem forecast chemical concentrations and also the time smoothing operator. Hourly surface fine particulate matter (PM2. 5) observations were assimilated in this system over China from 5 to 16 October 2014. A series of 48 h forecasts was then carried out with the optimized initial conditions and emissions on each day at 00:00 UTC and a control experiment was performed without data assimilation. In addition, we also performed an experiment of pure assimilation chemical ICs and the corresponding 48 h forecasts experiment for comparison. The results showed that the forecasts with the optimized initial conditions and emissions typically outperformed those from the control experiment. In the Yangtze River delta (YRD) and the Pearl River delta (PRD) regions, large reduction of the root-mean-square errors (RMSEs) was obtained for almost the entire 48 h forecast range attributed to assimilation. In particular, the relative reduction in RMSE due to assimilation was about 37.5 % at nighttime when WRF-Chem performed comparatively worse. In the Beijing-Tianjin-Hebei (JJJ) region, relatively smaller improvements were achieved in the first 24 h forecast but then no improvements were achieved afterwards. Comparing to the forecasts with only the optimized ICs, the forecasts with the joint adjustment were always much better during the night in the PRD and YRD regions. However, they were very similar during daytime in both

  7. Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM).

    Science.gov (United States)

    Nadiri, Ata Allah; Gharekhani, Maryam; Khatibi, Rahman; Sadeghfam, Sina; Moghaddam, Asghar Asghari

    2017-01-01

    This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), Neuro-Fuzzy (NF) and Gene Expression Programming (GEP). Each model uses the DRASTIC index, the acronym of 7 geological, hydrological and hydrogeological parameters, which collectively represents intrinsic (or natural) vulnerability and gives a sense of contaminants, such as nitrate-N, penetrating aquifers from the surface. These models are trained to modify or condition their DRASTIC index values by measured nitrate-N concentration. The three AI-techniques often perform similarly but have differences as well and therefore SICM exploits the situation to improve the modeled values by producing a hybrid modeling results through selecting better performing SVM, NF and GEP components. The models of the study area at Ardabil aquifer show that the vulnerability indices by the DRASTIC framework produces sharp fronts but AI models smoothen the fronts and reflect a better correlation with observed nitrate values; SICM improves on the performances of three AI models and cope well with heterogeneity and uncertain parameters. Copyright © 2016 Elsevier B.V. All rights reserved.

  8. Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning, volume 2

    Science.gov (United States)

    Keitz, J. F.

    1982-01-01

    The impact of more timely and accurate weather data on airline flight planning with the emphasis on fuel savings is studied. This volume of the report discusses the results of Task 2 of the four major tasks included in the study. Task 2 compares various catagories of flight plans and flight tracking data produced by a simulation system developed for the Federal Aviation Administrations by SRI International. (Flight tracking data simulate actual flight tracks of all aircraft operating at a given time and provide for rerouting of flights as necessary to resolve traffic conflicts.) The comparisons of flight plans on the forecast to flight plans on the verifying analysis confirm Task 1 findings that wind speeds are generally underestimated. Comparisons involving flight tracking data indicate that actual fuel burn is always higher than planned, in either direction, and even when the same weather data set is used. Since the flight tracking model output results in more diversions than is known to be the case, it was concluded that there is an error in the flight tracking algorithm.

  9. Improving Weather Research and Forecasting Model Initial Conditions via Surface Pressure Analysis

    Science.gov (United States)

    2015-09-01

    Obsgrid) that creates input data for the Advanced Research version of the Weather Research and Forecasting model (WRF-ARW) is modified to perform a...Configuration  The Advanced Research version of the Weather Research and Forecasting model (WRF-ARW) V3.6.1 (Skamarock et al. 2008) is applied with 56 vertical...those with more benign weather. On 7 February a trough moved onshore and led to widespread precipitation in the region . More quiescent weather was in

  10. Predicting favorable conditions for early leaf spot of peanut using output from the Weather Research and Forecasting (WRF) model.

    Science.gov (United States)

    Olatinwo, Rabiu O; Prabha, Thara V; Paz, Joel O; Hoogenboom, Gerrit

    2012-03-01

    Early leaf spot of peanut (Arachis hypogaea L.), a disease caused by Cercospora arachidicola S. Hori, is responsible for an annual crop loss of several million dollars in the southeastern United States alone. The development of early leaf spot on peanut and subsequent spread of the spores of C. arachidicola relies on favorable weather conditions. Accurate spatio-temporal weather information is crucial for monitoring the progression of favorable conditions and determining the potential threat of the disease. Therefore, the development of a prediction model for mitigating the risk of early leaf spot in peanut production is important. The specific objective of this study was to demonstrate the application of the high-resolution Weather Research and Forecasting (WRF) model for management of early leaf spot in peanut. We coupled high-resolution weather output of the WRF, i.e. relative humidity and temperature, with the Oklahoma peanut leaf spot advisory model in predicting favorable conditions for early leaf spot infection over Georgia in 2007. Results showed a more favorable infection condition in the southeastern coastline of Georgia where the infection threshold were met sooner compared to the southwestern and central part of Georgia where the disease risk was lower. A newly introduced infection threat index indicates that the leaf spot threat threshold was met sooner at Alma, GA, compared to Tifton and Cordele, GA. The short-term prediction of weather parameters and their use in the management of peanut diseases is a viable and promising technique, which could help growers make accurate management decisions, and lower disease impact through optimum timing of fungicide applications.

  11. Predicting favorable conditions for early leaf spot of peanut using output from the Weather Research and Forecasting (WRF) model

    Science.gov (United States)

    Olatinwo, Rabiu O.; Prabha, Thara V.; Paz, Joel O.; Hoogenboom, Gerrit

    2012-03-01

    Early leaf spot of peanut ( Arachis hypogaea L.), a disease caused by Cercospora arachidicola S. Hori, is responsible for an annual crop loss of several million dollars in the southeastern United States alone. The development of early leaf spot on peanut and subsequent spread of the spores of C. arachidicola relies on favorable weather conditions. Accurate spatio-temporal weather information is crucial for monitoring the progression of favorable conditions and determining the potential threat of the disease. Therefore, the development of a prediction model for mitigating the risk of early leaf spot in peanut production is important. The specific objective of this study was to demonstrate the application of the high-resolution Weather Research and Forecasting (WRF) model for management of early leaf spot in peanut. We coupled high-resolution weather output of the WRF, i.e. relative humidity and temperature, with the Oklahoma peanut leaf spot advisory model in predicting favorable conditions for early leaf spot infection over Georgia in 2007. Results showed a more favorable infection condition in the southeastern coastline of Georgia where the infection threshold were met sooner compared to the southwestern and central part of Georgia where the disease risk was lower. A newly introduced infection threat index indicates that the leaf spot threat threshold was met sooner at Alma, GA, compared to Tifton and Cordele, GA. The short-term prediction of weather parameters and their use in the management of peanut diseases is a viable and promising technique, which could help growers make accurate management decisions, and lower disease impact through optimum timing of fungicide applications.

  12. Instability indices and forecasting thunderstorms: the case of 30 April 2009

    Directory of Open Access Journals (Sweden)

    S. Tajbakhsh

    2012-02-01

    Full Text Available In this paper, one meteorological case study for two Iranian airports are presented. Attempts have been made to study the predefined threshold amounts of some instability indices such as vertical velocity and relative humidity. Two important output variables from a numerical weather prediction model have been used to survey thunderstorms. The climatological state of thunder days in Iran has been determined to aid in choosing the airports for the case studies. The synoptic pattern, atmospheric thermodynamics and output from a numerical weather prediction model have been studied to evaluate the occurrence of storms and to verify the threshold instability indices that are based on Gordon and Albert (2000 and Miller (1972.

    Using data from the Statistics and Data Center of the Iran Meteorological Organization, 195 synoptic stations were used to study the climatological pattern of thunderstorm days in Iran during a 15-yr period (1991–2005. Synoptic weather maps and thermodynamic diagrams have been drawn using data from synoptic stations and radiosonde data. A 15-km resolution version of the WRF numerical model has been implemented for the Middle East region with the assistance of global data from University Corporation for Atmospheric Research (UCAR.

    The Tabriz airport weather station has been selected for further study due to its high frequency of thunderstorms (more than 35 thunderstorm days per year and the existence of an upper air station. Despite the fact that storms occur less often at the Tehran weather station, the station has been chosen as the second case study site due to its large amount of air traffic. Using these two case studies (Tehran at 00:00 UTC, 31 April 2009 and Tabriz at 12:00 UTC, 31 April 2009, the results of this research show that the threshold amounts of 30 °C for KI, −2 °C for LI and −3 °C for SI suggests the occurrence and non-occurrence of thunderstorms at the Tehran and Tabriz stations

  13. Combining Diffusion Models and Macroeconomic Indicators with a Modified Genetic Programming Method: Implementation in Forecasting the Number of Mobile Telecommunications Subscribers in OECD Countries

    Directory of Open Access Journals (Sweden)

    Konstantinos Salpasaranis

    2014-01-01

    Full Text Available This paper proposes a modified Genetic Programming method for forecasting the mobile telecommunications subscribers’ population. The method constitutes an expansion of the hybrid Genetic Programming (hGP method improved by the introduction of diffusion models for technological forecasting purposes in the initial population, such as the Logistic, Gompertz, and Bass, as well as the Bi-Logistic and LogInLog. In addition, the aforementioned functions and models expand the function set of hGP. The application of the method in combination with macroeconomic indicators such as Gross Domestic Product per Capita (GDPpC and Consumer Prices Index (CPI leads to the creation of forecasting models and scenarios for medium- and long-term level of predictability. The forecasting module of the program has also been improved with the multi-levelled use of the statistical indices as fitness functions and model selection indices. The implementation of the modified-hGP in the datasets of mobile subscribers in the Organisation for Economic Cooperation and Development (OECD countries shows very satisfactory forecasting performance.

  14. FORECASTING OF ECONOMIC GROWTH OF REGION IN CONDITIONS OF DEFICIENCY OF THE INFORMATION

    Directory of Open Access Journals (Sweden)

    S.L. Sadov

    2007-12-01

    Full Text Available The new approach to forecasting economic growth of the region, showing minimal requirements to a supply with information, is offered in clause. It is based on combinatory likelihood modelling of dependence of a parameter of economic growth from its reliability. The method we shall apply to regions with the expressed branch specialization for the period before realization of structural reorganization of economy. In the conclusion the forecast of growth of Republic Komi VRP up to 2020 is given − is shown, that it will make 4 − 6% a year at preservation of energy raw specializations.

  15. Identification of appropriate lags and temporal resolutions for low flow indicators in the River Rhine to forecast low flows with different lead times

    NARCIS (Netherlands)

    Demirel, M.C.; Booij, Martijn J.; Hoekstra, Arjen Ysbert

    2013-01-01

    The aim of this paper is to assess the relative importance of low flow indicators for the River Rhine and to identify their appropriate temporal lag and resolution. This is done in the context of low flow forecasting with lead times of 14 and 90 days. First, the Rhine basin is subdivided into seven

  16. Forecasting of the industrial power consumption in the conditions of volatility price signals

    Directory of Open Access Journals (Sweden)

    Igor Aleksandrovich Baev

    2012-12-01

    Full Text Available Article is devoted to problems of purchase of the electric power in the wholesale market for the industry of Russia. Authors considered the mechanism of pricing and various combinations between the prices of the market for days forward and the prices of the balancing market. Favorable and adverseratios between the prices of the balancing market and submitted plans for power consumption are revealed. The urgency of forecasting of the industrial power consumption, allowing providing a sustainable development not only power supply systems and the power companies, but also region economy as a whole is proved. Recommendations about improvement of forecasting of the power consumption, based on the account not only the factors defining requirement for the electric power, but also factors considering tendencies of the balancing market are offered. As methods of forecasting sharing of methods of the regression analysis and method of expert evaluations is offered. Results of research will allow to increase accuracy of forecasting and to reduce financial losses not only at level of the concrete enterprises, but also at region level as a whole.

  17. Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators.

    Science.gov (United States)

    Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina

    2013-01-01

    Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.

  18. Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators

    Directory of Open Access Journals (Sweden)

    Razana Alwee

    2013-01-01

    Full Text Available Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR and autoregressive integrated moving average (ARIMA to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.

  19. Long Range River Discharge Forecasting Using the Gravity Recovery and Climate Experiment (GRACE) Satellite to Predict Conditions for Endemic Cholera

    Science.gov (United States)

    Jutla, A.; Akanda, A. S.; Colwell, R. R.

    2014-12-01

    Prediction of conditions of an impending disease outbreak remains a challenge but is achievable if the associated and appropriate large scale hydroclimatic process can be estimated in advance. Outbreaks of diarrheal diseases such as cholera, are related to episodic seasonal variability in river discharge in the regions where water and sanitation infrastructure are inadequate and insufficient. However, forecasting river discharge, few months in advance, remains elusive where cholera outbreaks are frequent, probably due to non-availability of geophysical data as well as transboundary water stresses. Here, we show that satellite derived water storage from Gravity Recovery and Climate Experiment Forecasting (GRACE) sensors can provide reliable estimates on river discharge atleast two months in advance over regional scales. Bayesian regression models predicted flooding and drought conditions, a prerequisite for cholera outbreaks, in Bengal Delta with an overall accuracy of 70% for upto 60 days in advance without using any other ancillary ground based data. Forecasting of river discharge will have significant impacts on planning and designing intervention strategies for potential cholera outbreaks in the coastal regions where the disease remain endemic and often fatal.

  20. Biosimilars and the extrapolation of indications for inflammatory conditions

    Science.gov (United States)

    Tesser, John RP; Furst, Daniel E; Jacobs, Ira

    2017-01-01

    Extrapolation is the approval of a biosimilar for use in an indication held by the originator biologic not directly studied in a comparative clinical trial with the biosimilar. Extrapolation is a scientific rationale that bridges all the data collected (ie, totality of the evidence) from one indication for the biosimilar product to all the indications originally approved for the originator. Regulatory approval and marketing authorization of biosimilars in inflammatory indications are made on a case-by-case and agency-by-agency basis after evaluating the totality of evidence from the entire development program. This totality of the evidence comprises extensive comparative analytical, functional, nonclinical, and clinical pharmacokinetic/pharmacodynamic, efficacy, safety, and immunogenicity studies used by regulators when evaluating whether a product can be considered a biosimilar. Extrapolation reduces or eliminates the need for duplicative clinical studies of the biosimilar but must be justified scientifically with appropriate data. Understanding the concept, application, and regulatory decisions based on the extrapolation of data is important since biosimilars have the potential to significantly impact patient care in inflammatory diseases. PMID:28255229

  1. Conditions, Criteria, Indicators and Levels of Forming Communicative Competence

    Science.gov (United States)

    Zascerinska, Jelena

    2010-01-01

    Individuals need communicative competence for personal fulfillment and development, active citizenship, social inclusion and employment. However, the success of communicative competence within a multicultural environment requires that a system of criteria, indicators and levels of forming communicative competence have to be considered. Aim of the…

  2. Stock Indices as Generalizing Indicators of the Stock Markets Condition in the European Union Countries

    Directory of Open Access Journals (Sweden)

    Shuba M. V.

    2015-03-01

    Full Text Available The aim of the article is to determine the degree of interdependence of stock markets in separate countries of the European Union, namely: France, Germany, Great Britain, Poland, the Czech Republic and Hungary on the basis of studying the changes in stock indexes, as well as determining the existence of tendencies of approximating the dynamics of the national stock index «PFTS Index» to the corresponding dynamics of stock indexes in surveyed countries. The article analyzes the dynamics of changes in stock indices in the UK (FTSE, Germany (DAX 30, France (CAC 40 and pan-European ones (EURO STOXX 50, as well as changes in stock indices in Poland (WIG 20, Czech Republic (PX, Hungary (BUX. Calculations of the coefficients of pair correlation between changes in stock indices in the studied countries have been performed. The calculation results show a substantial connection between the indicators of changes in stock indices and allow to make a conclusion that in the dynamics of stock indices of national stock markets of the studied EU countries some common trends are observed, moreover, in the behavior of the considered indices common local trends are noticed as well. The author calculated the coefficient of pair correlation between the indicators of changes in the national stock index «PFTS Index» and the stock indices of the «old» and «new» EU countries. The calculations showed that the PFTS Index does not demonstrate a high level of correlation with stock indices of the «old» EU countries and has a tendency of approaching the corresponding dynamics of stock indices of the «new» EU countries.

  3. Extragalactic interstellar extinction curves: Indicators of local physical conditions

    Energy Technology Data Exchange (ETDEWEB)

    Cecchi-Pestellini, Cesare [INAF-Osservatorio Astronomico di Palermo, P.zza Parlamento 1, I-90134 Palermo (Italy); Viti, Serena; Williams, David A., E-mail: cecchi-pestellini@astropa.unipa.it, E-mail: sv@star.ucl.ac.uk, E-mail: daw@star.ucl.ac.uk [Department of Physics and Astronomy, University College London Gower Street, London WC1E 6BT (United Kingdom)

    2014-06-20

    Normalized interstellar extinction curves (ISECs) in the Milky Way and other galaxies show a variety of shapes. This variety is attributed to differences along different sight lines in the abundances of the several dust and gas components contributing to extinction. In this paper we propose that these abundance differences are not arbitrary but are a specific consequence of the physical conditions on those sight lines. If this proposal is correct, then it implies that ISECs contain information about physical conditions in the regions generating extinction. This may be particularly important for high redshift galaxies where information on the conditions may be difficult to obtain. We adopt a model of extinction carriers in which the solid and gaseous components are not immutable but respond time-dependently to the local physics. We validate this model by fitting extinction curves measured on sight lines in the Magellanic Clouds and obtained for the gamma-ray burst afterglow GRB 080605. We present results for this model as follows: (1) we show that computed ISECs are controlled by a small number of physical parameters, (2) we demonstrate the sensitivity of computed ISECs to these parameters, (3) we compute as examples ISECs for particular galaxy types, and (4) we note that different galaxy types have different shapes of ISEC.

  4. Validation of Helicopter Gear Condition Indicators Using Seeded Fault Tests

    Science.gov (United States)

    Dempsey, Paula; Brandon, E. Bruce

    2013-01-01

    A "seeded fault test" in support of a rotorcraft condition based maintenance program (CBM), is an experiment in which a component is tested with a known fault while health monitoring data is collected. These tests are performed at operating conditions comparable to operating conditions the component would be exposed to while installed on the aircraft. Performance of seeded fault tests is one method used to provide evidence that a Health Usage Monitoring System (HUMS) can replace current maintenance practices required for aircraft airworthiness. Actual in-service experience of the HUMS detecting a component fault is another validation method. This paper will discuss a hybrid validation approach that combines in service-data with seeded fault tests. For this approach, existing in-service HUMS flight data from a naturally occurring component fault will be used to define a component seeded fault test. An example, using spiral bevel gears as the targeted component, will be presented. Since the U.S. Army has begun to develop standards for using seeded fault tests for HUMS validation, the hybrid approach will be mapped to the steps defined within their Aeronautical Design Standard Handbook for CBM. This paper will step through their defined processes, and identify additional steps that may be required when using component test rig fault tests to demonstrate helicopter CI performance. The discussion within this paper will provide the reader with a better appreciation for the challenges faced when defining a seeded fault test for HUMS validation.

  5. Study on Model of Indoor Air Pollution Forecast for Decoration Under Natural Ventilation Condition

    Institute of Scientific and Technical Information of China (English)

    YAN-FENG HONG; XUN CHEN; NING XU

    2005-01-01

    Objective To establish the model of indoor air pollution forecast for decoration. Methods The model was based on the balance model for diffusing mass. Results The data between testing concentration and estimating concentration were compared. The maximal error was less than 30% and average error was 14.6%. Conclusion The model can easily predict whether the pollution for decoration exceeds the standard and how long the room is decorated.

  6. Assessment of Sea Surface Temperature and Sea Ice Initial Conditions on Coupled Model Forecasts

    Science.gov (United States)

    Intrieri, J. M.; Solomon, A.; Persson, O. P. G.; Capotondi, A.; LaFontaine, F.; Jedlovec, G.

    2016-12-01

    We present weather-scale (0-10 day) sea ice forecast validation and skill results from an experimental coupled ice-ocean-atmosphere model during the fall freeze-up periods for 2015 and 2016. The model is a mesoscale, coupled atmosphere-ice-ocean mixed-layer model, termed RASM-ESRL, that was developed from the larger-scale Regional Arctic System Model (RASM) architecture. The atmospheric component of RASM-ESRL consists of the Weather Research and Forecasting (WRF) model, the sea-ice component is the Los Alamos CICE model, and the ocean model is POP. Experimental 5-day forecasts were run daily with RASM-ESRL from July through mid-November in 2015 and 2016. Our project focuses on how the modeled sea ice evolution compares to observed physical processes including atmospheric forcing of sea ice movement, melt, and freeze-up through energy fluxes. Model hindcast output is validated against buoy observations, satellite measurements, and concurrent in situ flux observations made from the R/V Sikuliaq in the fall of 2015. Model skill in predicting atmospheric state variables, wind and boundary layer structures, synoptic features, cloud microphysical and ocean properties will be discussed. We will show results of using different initializations of ocean sea surface temperature and sea ice extent and the impacts on sea ice edge prediction.

  7. Biochemical indicators of condition, nutrition and nitrogen excretion in caribou

    Directory of Open Access Journals (Sweden)

    Ray Case

    1996-01-01

    Full Text Available Urinary urea nitrogen to creatinine ratios, urinary Nt-methylhistidine to creatinine ratios, serum urea nitrogen concentrations (SUN mg/dl, and serum Nt-methylhistidine concentrations were compared with physical measures of body composition in adult female barren-ground caribou (Rangifer tarandus groenlandicus from the Bathurst and Southampton Island herds during late winter. Body weight and UUC were used to estimate urinary urea nitrogen (urea-N excretion in free ranging caribou. Only mean UUC reflected differences in fat reserves between populations. None of the biochemical indicators were directly related to body composition. However, elevated UUC were only observed in caribou with depleted fat reserves as demonstrated by low kidney fat index (KFK40 and/or reduced femur marrow fat (FMF<80. UUC greater than 0.25 were indicative of undernourished animals with depleted fat reserves. SUN and UN -MHC showed no clear relationship with fat reserves. The mean estimated daily urea-N excretion for adult female caribou in late winter was extremely low (0.11+0.01SE g urea-N/day, n=76, range=0.011-0.510. The results of my study suggest that UUC can be used to detect nutritionally stressed caribou with depleted fat reserves on lichen winter ranges.

  8. Disposable indicators for monitoring lighting conditions in museums.

    Science.gov (United States)

    Bacci, Mauro; Cucci, Costanza; Dupont, Anne-Laurence; Lavédrine, Bertrand; Picollo, Marcello; Porcinai, Simone

    2003-12-15

    Photoinduced alterations of light-sensitive artifacts represent one of the main problems that conservators and curators have to face for environmental control in museums and galleries. Therefore, increasing attention has been recently devoted to developing strategies of indoor light monitoring, especially aimed at minimizing the cumulated light exposure for the objects on exhibit. In this work a prototype of a light dosimeter, constituted by a photosensitive dyes/polymer mixture applied on a paper substrate, is presented. This indicator, specially designed for a preventive assessment of the risk of damage for highly light-sensitive objects, undergoes a progressive color variation as its exposure to the light increases. Different, easily distinguishable color steps are exhibited depending on the light dose received, so that the dosimeter can be used straightforwardly to have a first, instrumentation-free estimation of the total light exposure. A reflectance spectroscopy study in the 350-860 nm range was carried out on prototype dosimeters exposed to light emitted from a tungsten-halogen lamp to investigate the response of the dosimeter to the light and to study the fading mechanism. Two different approaches were evaluated for the calibration of the prototype: colorimetry and principal component analysis of the reflectance spectra. The usefulness of the two methods in providing a quantitative indication of the light dose received was evaluated.

  9. Economic Indicators of Condition and Tendencies of Serbian Economy

    Directory of Open Access Journals (Sweden)

    Zorica SREDOJEVIĆ

    2011-12-01

    Full Text Available Global economic crisis has, following financial crisis, hit real sector, and as after effect, large number, mostly developed countries in the world are in recession. Serbian industry is also influenced by global economic crisis. Outer debt is significantly and constantly increasing since beginning of transition process. Main cause to it is rather large disproportion between import and export. Trends in structure of outer debt indicate on notable decrease of national debt on account to private one, during whole transition period. On short term there is no significant risk for country on account of outer debt, but for long term elimination of this risk, it is necessary to considerably increase total export. Former policies should be linked to unconventional employment initiatives, as for new labour, as for redundant ones from restructuring economy branches. State has prominent role in transition process, by helping market exhibit its functions through physical and institutional infrastructure, as well trough public sector, removing most of the market obstacles, and stimulating technical-technological development and education.

  10. Analysis and Indications on Long-term Forecasting of the Oceanic Niño Index with Wavelet-Induced Components

    Science.gov (United States)

    Deliège, Adrien; Nicolay, Samuel

    2017-02-01

    The present paper provides an analysis and a long-term forecasting scheme of the Oceanic Niño Index (ONI) using the continuous wavelet transform. First, it appears that oscillatory components with main periods of about 17, 31, 43, 61 and 140 months govern most of the variability of the signal, which is consistent with previous works. Then, this information enables us to derive a simple algorithm to model and forecast ONI. The model is based on the observation that the modes extracted from the signal are generally phased with positive or negative anomalies of ONI (El Niño and La Niña events). Such a feature is exploited to generate locally stationary curves that mimic this behavior and which can be easily extrapolated to form a basic forecast. The wavelet transform is then used again to smooth out the process and finalize the predictions. The skills of the technique described in this paper are assessed through retroactive forecasts of past El Niño and La Niña events and via classic indicators computed as functions of the lead time. The main asset of the proposed model resides in its long-lead prediction skills. Consequently, this approach should prove helpful as a complement to other models for estimating the long-term trends of ONI.

  11. Hydrological modelling for flood forecasting: Calibrating the post-fire initial conditions

    Science.gov (United States)

    Papathanasiou, C.; Makropoulos, C.; Mimikou, M.

    2015-10-01

    Floods and forest fires are two of the most devastating natural hazards with severe socioeconomic, environmental as well as aesthetic impacts on the affected areas. Traditionally, these hazards are examined from different perspectives and are thus investigated through different, independent systems, overlooking the fact that they are tightly interrelated phenomena. In fact, the same flood event is more severe, i.e. associated with increased runoff discharge and peak flow and decreased time to peak, if it occurs over a burnt area than that occurring over a land not affected by fire. Mediterranean periurban areas, where forests covered with flammable vegetation coexist with agricultural land and urban zones, are typical areas particularly prone to the combined impact of floods and forest fires. Hence, the accurate assessment and effective management of post-fire flood risk becomes an issue of priority. The research presented in this paper aims to develop a robust methodological framework, using state of art tools and modern technologies to support the estimation of the change in time of five representative hydrological parameters for post-fire conditions. The proposed methodology considers both longer- and short-term initial conditions in order to assess the dynamic evolution of the selected parameters. The research focuses on typical Mediterranean periurban areas that are subjected to both hazards and concludes with a set of equations that associate post-fire and pre-fire conditions for five Fire Severity (FS) classes and three soil moisture states. The methodology has been tested for several flood events on the Rafina catchment, a periurban catchment in Eastern Attica (Greece). In order to validate the methodology, simulated hydrographs were produced and compared against available observed data. Results indicate a close convergence of observed and simulated flows. The proposed methodology is particularly flexible and thus easily adaptable to catchments with similar

  12. Forecasting of photovoltaic power at hourly intervals with artificial neural networks under fluctuating weather conditions

    Directory of Open Access Journals (Sweden)

    Stamatia Dimopoulou, Alice Oppermann, Ekkehard Boggasch, Andreas Rausch

    2017-01-01

    Full Text Available The requirement of the in advance knowledge of the future photovoltaic (PV production in the domestic field for a better allocation of the on-site PV generation to the local load demand and the available storage facilities is more and more emerging. In this study two different methods were applied so as to forecast the next hour PV power using artificial neural networks (ANN. In the first case the weather parameters of solar irradiance and ambient temperature were predicted, the output was fed to the developed model of the PV installation and the next hour PV power was computed. In the second case it was attempted to predict directly the PV power. The performance of the applied ANNs was compared with the respective outcomes from the persistence models. In each case the applied ANN outperforms the persistence model. In addition, during the evaluation phase the extracted annual energy results were compared with the respective registered data from the installed meters. Again in both cases the results approximated the reality, though in the first case the difficulty in identification and representation of malfunctions in operation of the PV plants due to snow accumulation on the panels caused minor deviations.

  13. 基于Web的神经网络指标预测系统的实现%Web-based Indicators of Neural Network Forecasting System Implementation

    Institute of Scientific and Technical Information of China (English)

    谭伟娟

    2011-01-01

    本文结合指标预测软件的发展现状及趋势,介绍了采用神经网络和主成分分析方法的指标预测系统,根据网络技术的发展情况,重点提出了基于Web的神经网络指标预测系统解决方法。建立了建设工程指标预测的基本模型。系统采用了实际工程项目的资料作为学习内容,运行结果表明,采用该方法建立的建设工程指标预测模型是正确可行的。%In this paper, the status indicators and predict trends in software development,describes the use of neural networks and principal component analysis of indicators forecasting system,based on network technology development and focuses on Web-based indicators of neural network prediction system solutions.Construction of indicators to forecast the establishment of the basic model.System uses the actual project as a learning content information,the results show that this method of construction oroiects to establish indicators of t~rediction model is correct and feasible.

  14. Gastro-intestinal nematode infection in lambs — A model based on climatic indices for forecasting peak pasture larval contamination

    Science.gov (United States)

    Paton, G.

    1987-06-01

    The parasite Ostertagia circumcincta is the primary cause of parasitic gastro-enteritis in lambs during their first season at grass. The life-cycle of this nematode parasite involves the development and survival of the free-living stages on pasture. Accordingly the pasture is the site of deposition, development and transmission of infection and meteorological factors affecting the pasture will affect the parasites. In this paper two empirical models for forecasting the timing of the “summer wave” of infective larvae on pasture are presented. These models are similar in form to that described by Starr and Thomas (1980) but involve different approaches to assessing the temperature and moisture components of the daily index value. Further, using the prediction model described by Paton, Thomas and Waller (1984) as an investigative tool, certain tentative suggestions are made as to a general fundamental weakness of empirical index methods.

  15. Impact of AIRS Thermodynamic Profile on Regional Weather Forecast

    Science.gov (United States)

    Chou, Shih-Hung; Zavodsky, Brad; Jedlovee, Gary

    2010-01-01

    Prudent assimilation of AIRS thermodynamic profiles and quality indicators can improve initial conditions for regional weather models. AIRS-enhanced analysis has warmer and moister PBL. Forecasts with AIRS profiles are generally closer to NAM analyses than CNTL. Assimilation of AIRS leads to an overall QPF improvement in 6-h accumulated precipitation forecasts. Including AIRS profiles in assimilation process enhances the moist instability and produces stronger updrafts and a better precipitation forecast than the CNTL run.

  16. Forecast salt regime unwatered mine reclaimed dump in unstable conditions in Western Donbass

    Directory of Open Access Journals (Sweden)

    Yevhrashkina H.P.

    2016-02-01

    Full Text Available On the based theory of physical-chemical hydrodynamic of porous media was executed prognosis salt rate no suppying with water mine dumps of minimum sense transhiration in conditions no established rate.

  17. On reliability analysis of multi-categorical forecasts

    Directory of Open Access Journals (Sweden)

    J. Bröcker

    2008-08-01

    Full Text Available Reliability analysis of probabilistic forecasts, in particular through the rank histogram or Talagrand diagram, is revisited. Two shortcomings are pointed out: Firstly, a uniform rank histogram is but a necessary condition for reliability. Secondly, if the forecast is assumed to be reliable, an indication is needed how far a histogram is expected to deviate from uniformity merely due to randomness. Concerning the first shortcoming, it is suggested that forecasts be grouped or stratified along suitable criteria, and that reliability is analyzed individually for each forecast stratum. A reliable forecast should have uniform histograms for all individual forecast strata, not only for all forecasts as a whole. As to the second shortcoming, instead of the observed frequencies, the probability of the observed frequency is plotted, providing and indication of the likelihood of the result under the hypothesis that the forecast is reliable. Furthermore, a Goodness-Of-Fit statistic is discussed which is essentially the reliability term of the Ignorance score. The discussed tools are applied to medium range forecasts for 2 m-temperature anomalies at several locations and lead times. The forecasts are stratified along the expected ranked probability score. Those forecasts which feature a high expected score turn out to be particularly unreliable.

  18. BIRD COMMUNITIES AND HABITAT AS ECOLOGICAL INDICATORS OF FOREST CONDITION IN REGIONAL MONITORING

    Science.gov (United States)

    Ecological indicators for long-term monitoring programs are needed to detect and assess changing environmental conditions, We developed and tested community-level environmental indicators for monitoring forest bird populations and associated habitat. We surveyed 197 sampling plo...

  19. Urban fine-scale forecasting reveals weather conditions with unprecedented detail

    NARCIS (Netherlands)

    Ronda, R.J.; Steeneveld, G.J.; Heusinkveld, B.G.; Attema, Jisk; Holtslag, A.A.M.

    2017-01-01

    Feasibility of Numerical Weather Prediction at urban neighborhood and street scales demonstrated for summer conditions in the Amsterdam metropolitan region (Netherlands). As the number of urban dwellers increases from an estimated 4 billion in 2014 to an expected 6.5 billion by 2050 (UN 2014),

  20. Do morphological condition indices predict locomotor performance in the lizard Podarcis sicula?

    Science.gov (United States)

    Vervust, Bart; Lailvaux, Simon P.; Grbac, Irena; Van Damme, Raoul

    2008-09-01

    Biologists have developed a number of simple metrics to assess the health and energetic status of individual organisms and populations. While these condition indices have been widely used to address questions in evolutionary ecology and conservation biology, the ability of such indices to predict ecologically relevant locomotor performance abilities remains unknown. We show here that the functional links between six commonly used morphological condition indices and locomotor performance in two populations of Adriatic lizards ( Podarcis sicula) are weak at best. Indeed, no indices consistently predict either maximum sprint speed or maximum exertion across sexes, seasons or populations. These results cast doubt on the ecological relevance of morphological condition indices in terms of locomotor performance, measured in laboratory conditions, at least in this species. We urge caution in using condition indices as proxies for individual physiological or phenotypic quality in ecological and evolutionary studies.

  1. Forecasts of forest conditions in regions of the United States under future scenarios: a technical document supporting the Forest Service 2012 RPA Assessment

    Science.gov (United States)

    David N. Wear; Robert Huggett; Ruhong Li; Benjamin Perryman; Shan Liu

    2013-01-01

    The 626 million acres of forests in the conterminous United States represent significant reserves of biodiversity and terrestrial carbon and provide substantial flows of highly valued ecosystem services, including timber products, watershed protection benefits, and recreation. This report describes forecasts of forest conditions for the conterminous United States in...

  2. Numerical Simulation and Forecast of Equatorial Spread F Under Realistic Postsunset Conditions

    Science.gov (United States)

    2012-01-30

    a pedagogical review can be found in Trac and Pen [2003]) directly applicable to the ion continuity problem (once the recombination term has been...represented in this diagnostic figure. D R A F T November 14, 2011, 2:27pm D R A F T Figure 3: Initial conditions for ESF model run. Top: Plasma number...currents are not represented in this diagnostic figure. 8 AVEIRO ET AL.: 3-D ESF SIMULATIONS AND OBSERVATIONS X - 25 Figure 4. Simulated plasma

  3. INFORMATION-ANALYTICAL SYSTEM OF FORECAST VEGETATION FIRES IN NATURAL CONDITIONS

    Directory of Open Access Journals (Sweden)

    R. M. Kogan

    2015-01-01

    Full Text Available A system for spatial prediction for fire danger as function of weather and pyrological vegetation characteristics was constructed. The method of calculating the time conducted vegetable combustible materials in fire condition of each month of the season was suggested. Calculate the probability of fires and danger periods of plant formations in a monsoon climate. The geographic information system was developed, it was tested in the Middle Amur region in the Russian Far East.

  4. Towards leprosy elimination by 2020: forecasts of epidemiological indicators of leprosy in Corrientes, a province of northeastern Argentina that is a pioneer in leprosy elimination.

    Science.gov (United States)

    Odriozola, Elisa Petri de; Quintana, Ana María; González, Victor; Pasetto, Roque Antonio; Utgés, María Eugenia; Bruzzone, Octavio Augusto; Arnaiz, María Rosa

    2017-06-01

    Corrientes, a province of northeastern Argentina with endemic leprosy, has improved its epidemiological indicators, however, a study of the dynamics over time is lacking. We analysed data of 1308 leprosy patients between 1991 to 2014, and the forecast for 2020. Descriptive statistics and stepwise Bayesian model selection were performed. Forecasts were made using the median of 100,000 projections using the parameters calculated via Monte Carlo methods. We found a decreasing number of new leprosy cases (-2.04 cases/year); this decrease is expected to continue by an estimated 20.28 +/- 10.00 cases by 2020, evidenced by a sustained decline in detection rate (from 11 to 2.9/100,000 inhabitants). Age groups that were most affected were 15-44 (40.13%) and 45-64 (38.83%) year olds. Multibacillary forms (MB) predominated (70.35%) and while gradually declining, between 10 and 30% developed disability grade 2 (DG2) (0.175 (0.110 - 0.337) DG2/MB cases), with a time delay between 0 to 15 years (median = 0). The proportion of MB clinic forms and DG2 increased and will continuously increase in the short term (0.036 +/- 0.018 logit (MB/total of cases). Corrientes is on the way to eliminating leprosy by 2020, however the increased proportion of MB clinical forms and DG2 signals a warning for disease control efforts.

  5. An overview of health forecasting.

    Science.gov (United States)

    Soyiri, Ireneous N; Reidpath, Daniel D

    2013-01-01

    Health forecasting is a novel area of forecasting, and a valuable tool for predicting future health events or situations such as demands for health services and healthcare needs. It facilitates preventive medicine and health care intervention strategies, by pre-informing health service providers to take appropriate mitigating actions to minimize risks and manage demand. Health forecasting requires reliable data, information and appropriate analytical tools for the prediction of specific health conditions or situations. There is no single approach to health forecasting, and so various methods have often been adopted to forecast aggregate or specific health conditions. Meanwhile, there are no defined health forecasting horizons (time frames) to match the choices of health forecasting methods/approaches that are often applied. The key principles of health forecasting have not also been adequately described to guide the process. This paper provides a brief introduction and theoretical analysis of health forecasting. It describes the key issues that are important for health forecasting, including: definitions, principles of health forecasting, and the properties of health data, which influence the choices of health forecasting methods. Other matters related to the value of health forecasting, and the general challenges associated with developing and using health forecasting services are discussed. This overview is a stimulus for further discussions on standardizing health forecasting approaches and methods that will facilitate health care and health services delivery.

  6. Dynamic Adjustment of Climatological Ozone Boundary Conditions for Air-Quality Forecasts

    Directory of Open Access Journals (Sweden)

    P. A. Makar

    2010-06-01

    Full Text Available Ten different approaches for applying lateral and top climatological boundary conditions for ozone have been evaluated using the off-line regional air-quality model AURAMS. All ten approaches employ the same climatological ozone profiles, but differ in the manner in which they are applied, via the inclusion or exclusion of (i a dynamic adjustment of the climatological ozone profile in response to the model-predicted tropopause height, (ii a sponge zone for ozone on the model top, (iii upward extrapolation of the climatological ozone profile, and (iv different mass consistency corrections. The model performance for each approach was evaluated against North American surface ozone and ozonesonde observations from the BAQS-Met field study period in the summer of 2007. The original daily one-hour maximum surface ozone biases of about +15 ppbv were greatly reduced (halved in some simulations using alternative methodologies. However, comparisons to ozonesonde observations showed that the reduction in surface ozone bias sometimes came at the cost of significant positive biases in ozone concentrations in the free troposphere and upper troposphere. The best overall performance throughout the troposphere was achieved using a methodology that included dynamic tropopause height adjustment, no sponge zone at the model top, extrapolation of ozone when required above the limit of the climatology, and no mass consistency corrections (global mass conservation was still enforced. The simulation using this model version had a one-hour daily maximum surface ozone bias of +8.6 ppbv, with small reductions in model correlation, and the best comparison to ozonesonde profiles. This recommended and original methodologies were compared for two further case studies: a high-resolution simulation of the BAQS-Met measurement intensive, and a study of the downwind region of the Canadian Rockies. Significant improvements were noted for the high resolution simulations during the

  7. Practical implementation of a particle filter data assimilation approach to estimate initial hydrologic conditions and initialize medium-range streamflow forecasts

    Science.gov (United States)

    Clark, E.; Wood, A.; Nijssen, B.; Newman, A. J.; Mendoza, P. A.

    2016-12-01

    The System for Hydrometeorological Applications, Research and Prediction (SHARP), developed at the National Center for Atmospheric Research (NCAR), University of Washington, U.S. Army Corps of Engineers, and U.S. Bureau of Reclamation, is a fully automated ensemble prediction system for short-term to seasonal applications. It incorporates uncertainty in initial hydrologic conditions (IHCs) and in hydrometeorological predictions. In this implementation, IHC uncertainty is estimated by propagating an ensemble of 100 plausible temperature and precipitation time series through the Sacramento/Snow-17 model. The forcing ensemble explicitly accounts for measurement and interpolation uncertainties in the development of gridded meteorological forcing time series. The resulting ensemble of derived IHCs exhibits a broad range of possible soil moisture and snow water equivalent (SWE) states. To select the IHCs that are most consistent with the observations, we employ a particle filter (PF) that weights IHC ensemble members based on observations of streamflow and SWE. These particles are then used to initialize ensemble precipitation and temperature forecasts downscaled from the Global Ensemble Forecast System (GEFS), generating a streamflow forecast ensemble. We test this method in two basins in the Pacific Northwest that are important for water resources management: 1) the Green River upstream of Howard Hanson Dam, and 2) the South Fork Flathead River upstream of Hungry Horse Dam. The first of these is characterized by mixed snow and rain, while the second is snow-dominated. The PF-based forecasts are compared to forecasts based on a single IHC (corresponding to median streamflow) paired with the full GEFS ensemble, and 2) the full IHC ensemble, without filtering, paired with the full GEFS ensemble. In addition to assessing improvements in the spread of IHCs, we perform a hindcast experiment to evaluate the utility of PF-based data assimilation on streamflow forecasts at 1

  8. Forecasting of DST index from auroral electrojet indices using time-delay neural network + particle swarm optimization

    Science.gov (United States)

    Lazzús, J. A.; López-Caraballo, C. H.; Rojas, P.; Salfate, I.; Rivera, M.; Palma-Chilla, L.

    2016-05-01

    In this study, an artificial neural network was optimized with particle swarm algorithm and trained to predict the geomagmetic DST index one hour ahead using the past values of DST and auroral electrojet indices. The results show that the proposed neural network model can be properly trained for predicting of DST(t + 1) with acceptable accuracy, and that the geomagnetic indices used have influential effects on the good training and predicting capabilities of the chosen network.

  9. Forecast and Analysis of the Main Indicators for the Development of Ocean Fishery in China during the Twelfth Five-Year Plan Period

    Institute of Scientific and Technical Information of China (English)

    Xianghong; LIN; Zhankun; WANG; Weiling; SONG; Congchun; XU

    2013-01-01

    The ocean fishery is an important part of the marine economy and an important source of human food.The leaders at all levels attach great importance to the development and utilization of ocean fishery resources.Using the regression analysis method,we forecast the main indicators for the development of ocean fishery during the Twelfth Five-Year Plan period.Through the study,it is found that in 2015,the value added of ocean fishery in China is expected to exceed 360 billion yuan,and the output of marine products will exceed 30 million tons.The marine products make stable contribution to the marine economy and the residents’nutrient composition,conducive to maintaining the stable supply of the market and meeting the residents’daily needs for marine products.

  10. Precipitation and temperature ensemble forecasts from single-value forecasts

    Directory of Open Access Journals (Sweden)

    J. Schaake

    2007-04-01

    Full Text Available A procedure is presented to construct ensemble forecasts from single-value forecasts of precipitation and temperature. This involves dividing the spatial forecast domain and total forecast period into a number of parts that are treated as separate forecast events. The spatial domain is divided into hydrologic sub-basins. The total forecast period is divided into time periods, one for each model time step. For each event archived values of forecasts and corresponding observations are used to model the joint distribution of forecasts and observations. The conditional distribution of observations for a given single-value forecast is used to represent the corresponding probability distribution of events that may occur for that forecast. This conditional forecast distribution subsequently is used to create ensemble members that vary in space and time using the "Schaake Shuffle" (Clark et al, 2004. The resulting ensemble members have the same space-time patterns as historical observations so that space-time joint relationships between events that have a significant effect on hydrological response tend to be preserved.

    Forecast uncertainty is space and time-scale dependent. For a given lead time to the beginning of the valid period of an event, forecast uncertainty depends on the length of the forecast valid time period and the spatial area to which the forecast applies. Although the "Schaake Shuffle" procedure, when applied to construct ensemble members from a time-series of single value forecasts, may preserve some of this scale dependency, it may not be sufficient without additional constraint. To account more fully for the time-dependent structure of forecast uncertainty, events for additional "aggregate" forecast periods are defined as accumulations of different "base" forecast periods.

    The generated ensemble members can be ingested by an Ensemble Streamflow Prediction system to produce ensemble forecasts of streamflow and other

  11. Examples of landscape indicators for assessing environmental conditions and problems in urban and suburban areas

    Science.gov (United States)

    Martin-Duque, J. F.; Godfrey, A.; Diez, A.; Cleaves, E.; Pedraza, J.; Sanz, M.A.; Carrasco, R.M.; Bodoque, J.; Brebbia, C.A.; Martin-Duque, J.F.; Wadhwa, L.C.

    2002-01-01

    Geo-indicators can help to assess environmental conditions in city urban and suburban areas. Those indicators should be meaningful for understanding environmental changes. From examples of Spanish and American cities, geo-indicators for assessing environmental conditions and changes in urban and suburban areas are proposed. The paper explore two types of geo-indicators. The first type presents general information that can be used to indicate the presence of a broad array of geologic conditions, either favouring or limiting various kinds of uses of the land. The second type of geo-indicator is the one most commonly used, and as a group most easily understood; these are site and problem specific and they are generally used after a problem is identified. Among them, watershed processes, seismicity and physiographic diversity are explained in more detail. A second dimension that is considered when discussing geo-indicators is the issue of scale. Broad scale investigations, covering extensive areas are only efficient at cataloguing general conditions common to much of the area or some outstanding feature within the area. This type of information is best used for policy type decisions. Detailed scale investigations can provide information about local conditions, but are not efficient at cataloguing vast areas. Information gathered at the detailed level is necessary for project design and construction.

  12. A Financial Condition Indicator System for School Districts: A Case Study of New York

    Science.gov (United States)

    Ammar, Salwa; Duncombe, William; Jump, Bernard; Wright, Ronald

    2005-01-01

    State governments are in the midst of one of the most severe fiscal crises of the last half century. The magnitude of the fiscal challenges facing state and local governments highlights the importance of sound fiscal planning and access to key financial indicators. The objective of this article is to develop a financial condition indicator system…

  13. Determination of the indicators for working with area flooding under nonisothermic filtering conditions

    Energy Technology Data Exchange (ETDEWEB)

    Ushakov, V.V.; Borisov, Yu.P.; Rozenberg, M.D.; Teslyuk, Ye.V.

    1983-01-01

    Engineering methods were developed for computing the nonisothermic displacement of oil by water under conditions of area systems of flooding for layered-heterogeneous beds. It is indicated that the mathematical model realized on a computer which takes into consideration the most important physical phenomena provides the possibility of fully performing the studies and analyzing the technological indicators.

  14. Summer Fish Communities in Northern Gulf of Mexico Estuaries: Indices of Ecological Condition

    Science.gov (United States)

    We used fish community data from trawl samples in >100 estuaries, bayous, and coastal lagoons of the Louisianan Biogeographic Province (Gulf of Mexico) to develop indicators of ecological condition. One data set, from which we derived reference values for fish community indicator...

  15. Evaluation of weather research and forecasting model parameterizations under sea-breeze conditions in a North Sea coastal environment

    Science.gov (United States)

    Salvador, Nadir; Reis, Neyval Costa; Santos, Jane Meri; Albuquerque, Taciana Toledo de Almeida; Loriato, Ayres Geraldo; Delbarre, Hervé; Augustin, Patrick; Sokolov, Anton; Moreira, Davidson Martins

    2016-12-01

    Three atmospheric boundary layer (ABL) schemes and two land surface models that are used in the Weather Research and Forecasting (WRF) model, version 3.4.1, were evaluated with numerical simulations by using data from the north coast of France (Dunkerque). The ABL schemes YSU (Yonsei University), ACM2 (Asymmetric Convective Model version 2), and MYJ (Mellor-Yamada-Janjic) were combined with two land surface models, Noah and RUC (Rapid Update Cycle), in order to determine the performances under sea-breeze conditions. Particular attention is given in the determination of the thermal internal boundary layer (TIBL), which is very important in air pollution scenarios. The other physics parameterizations used in the model were consistent for all simulations. The predictions of the sea-breeze dynamics output from the WRF model were compared with observations taken from sonic detection and ranging, light detection and ranging systems and a meteorological surface station to verify that the model had reasonable accuracy in predicting the behavior of local circulations. The temporal comparisons of the vertical and horizontal wind speeds and wind directions predicted by the WRF model showed that all runs detected the passage of the sea-breeze front. However, except for the combination of MYJ and Noah, all runs had a time delay compared with the frontal passage measured by the instruments. The proposed study shows that the synoptic wind attenuated the intensity and penetration of the sea breeze. This provided changes in the vertical mixing in a short period of time and on soil temperature that could not be detected by the WRF model simulations with the computational grid used. Additionally, among the tested schemes, the combination of the localclosure MYJ scheme with the land surface Noah scheme was able to produce the most accurate ABL height compared with observations, and it was also able to capture the TIBL.

  16. On density forecast evaluation

    NARCIS (Netherlands)

    Diks, C.

    2008-01-01

    Traditionally, probability integral transforms (PITs) have been popular means for evaluating density forecasts. For an ideal density forecast, the PITs should be uniformly distributed on the unit interval and independent. However, this is only a necessary condition, and not a sufficient one, as

  17. Forecast Forecasts the Trend

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    The latest release of "2009 China Luxury Forecast" shows that while the financial crisis is leading a general decline in demand for luxury brands in Europe,America and Japan,the global economic downturn has had limited impact on Chinese luxury consumption and that there is widespread confidence in the future among Chinese luxury consumers.

  18. Forecast Forecasts the Trend

    Institute of Scientific and Technical Information of China (English)

    Wang Ting

    2009-01-01

    @@ The latest release of "2009 China Luxury Forecast" shows that while the financial crisis is leading a general decline in demand for luxury brands in Europe,America and Japan,the global economic downturn has had limited impact on Chinese luxury consumption and that there is widespread confidence in the future among Chinese luxury consumers.

  19. Study on Statistical Forecast Method for O3 Concentration near the Ground in Pudong District of Shanghai Based on Meteorological Condition Analysis

    Institute of Scientific and Technical Information of China (English)

    MA; Jing-hui; MA; Lei-ming; GENG; Fu-hai; TAN; Jian-guo; GAO; Wei; ZHOU; Wei-dong

    2012-01-01

    [Objective]The research aimed to study statistical forecast method for O3 concentration near the ground in Pudong District of Shanghai based on meteorological condition analysis. [Method] Via observation and statistical analysis of the O3 concentration near the ground in Pudong District of Shanghai from 2006 to 2008, by considering meteorological condition, a kind of simple and practical new method suiting for forecast of the O3 concentration and pre-warning of the high-concentration O3 pollution event in whole year was established. [Result]Meteorological condition had obvious influence on O3 concentration near the ground. O3 concentration was the biggest in sunny day, followed by cloudy day. O3 concentration near the ground had typical seasonal change characteristics, and high value mainly happened in summer. Meteorological condition generating high-concentration O3 included sunny day, strong UV radiation, low relative humidity, high temperature and small wind speed, etc. By surveying historical weather chart, 10 kinds of main weather situations affecting Shanghai were summed. Under each weather situation, occurrence probability of the high-concentration O3 near the ground and average O3 concentration were conducted statistics. We found that occurrence probability of the high-concentration O3 was the biggest under northwest side of the subtropical high type, followed by internal type of the subtropical high. By introducing HPPI and WDI and comprehensively considering various meteorological factors, forecasting equation of the O3 concentration was established based on stepwise regression. The equation had good fitting effect and predictability on the daily maximum O3 concentration. [Conclusion]The method also could provide reference for O3 forecast in other areas.

  20. Study on Statistical Forecast Method for O_3 Concentration near the Ground in Pudong District of Shanghai Based on Meteorological Condition Analysis

    Institute of Scientific and Technical Information of China (English)

    MA; Jing-hui; MA; Lei-ming; GENG; Fu-hai; TAN; Jian-guo; GAO; Wei; ZHOU; Wei-dong

    2012-01-01

    [Objective]The research aimed to study statistical forecast method for O3 concentration near the ground in Pudong District of Shanghai based on meteorological condition analysis. [Method] Via observation and statistical analysis of the O3 concentration near the ground in Pudong District of Shanghai from 2006 to 2008, by considering meteorological condition, a kind of simple and practical new method suiting for forecast of the O3 concentration and pre-warning of the high-concentration O3 pollution event in whole year was established. [Result]Meteorological condition had obvious influence on O3 concentration near the ground. O3 concentration was the biggest in sunny day, followed by cloudy day. O3 concentration near the ground had typical seasonal change characteristics, and high value mainly happened in summer. Meteorological condition generating high-concentration O3 included sunny day, strong UV radiation, low relative humidity, high temperature and small wind speed, etc. By surveying historical weather chart, 10 kinds of main weather situations affecting Shanghai were summed. Under each weather situation, occurrence probability of the high-concentration O3 near the ground and average O3 concentration were conducted statistics. We found that occurrence probability of the high-concentration O3 was the biggest under northwest side of the subtropical high type, followed by internal type of the subtropical high. By introducing HPPI and WDI and comprehensively considering various meteorological factors, forecasting equation of the O3 concentration was established based on stepwise regression. The equation had good fitting effect and predictability on the daily maximum O3 concentration. [Conclusion]The method also could provide reference for O3 forecast in other areas.

  1. Methods and Indicators for Assessment of Regional Ground-Water Conditions in the Southwestern United States

    Science.gov (United States)

    Tillman, Fred D; Leake, Stanley A.; Flynn, Marilyn E.; Cordova, Jeffrey T.; Schonauer, Kurt T.; Dickinson, Jesse E.

    2008-01-01

    Monitoring the status and trends in the availability of the Nation's ground-water supplies is important to scientists, planners, water managers, and the general public. This is especially true in the semiarid to arid southwestern United States where rapid population growth and limited surface-water resources have led to increased use of ground-water supplies and water-level declines of several hundred feet in many aquifers. Individual well observations may only represent aquifer conditions in a limited area, and wells may be screened over single or multiple aquifers, further complicating single-well interpretations. Additionally, changes in ground-water conditions may involve time scales ranging from days to many decades, depending on the timing of recharge, soil and aquifer properties, and depth to the water table. The lack of an easily identifiable ground-water property indicative of current conditions, combined with differing time scales of water-level changes, makes the presentation of ground-water conditions a difficult task, particularly on a regional basis. One approach is to spatially present several indicators of ground-water conditions that address different time scales and attributes of the aquifer systems. This report describes several methods and indicators for presenting differing aspects of ground-water conditions using water-level observations in existing data-sets. The indicators of ground-water conditions developed in this study include areas experiencing water-level decline and water-level rise, recent trends in ground-water levels, and current depth to ground water. The computer programs written to create these indicators of ground-water conditions and display them in an interactive geographic information systems (GIS) format are explained and results illustrated through analyses of ground-water conditions for selected alluvial basins in the Lower Colorado River Basin in Arizona.

  2. A conditional extreme value theory approach in value-at-risk forecasting: Evidence from Southeastern Europe and USA market

    Directory of Open Access Journals (Sweden)

    Totić Selena

    2015-01-01

    Full Text Available As a consequence of the recent financial crisis, the adequacy of different Value-at-Risk (VaR methodologies was heavily questioned. Current practice in VaR assessment relies on modeling the whole distribution of returns. As an alternative, in this paper we model tail behavior of returns, and thus VaR, using conditional Extreme Value Theory (EVT, which combines EVT and GARCH methodology. Moreover, we examine the performance of conditional EVT with the daily returns of seven stock market indices, of which six are from Southeastern Europe (BelexLine, BET, BUX, CROBEX, SBITOP, SOFIX from the period of September 2004 - April 2013, and one from USA market (Standard&Poors 500 Index from the period January 1998 - April 2013. Backtesting of historical daily returns proves that conditional EVT model gives good predictions for all indices and for all confidence levels.

  3. Useful model organisms, indicators, or both? Ground beetles (Coleoptera, Carabidae reflecting environmental conditions

    Directory of Open Access Journals (Sweden)

    Matti Koivula

    2011-05-01

    Full Text Available Classic studies have successfully linked single-species abundances, life-history traits, assemblage structures and biomass of carabid beetles to past and present, human-caused environmental impacts and variation in ‘natural’ conditions. This evidence has led many to suggest carabids to function as ‘indicators’ − a term that bears multiple meanings. Here, a conservation-oriented definition for an indicator is used, carabid indicator potential from seven views is evaluated, and ways to proceed in indicator research are discussed. (1 Carabid species richness poorly indicates the richness and abundance of other taxa, which underlines the importance of using multiple taxa in environmental assessments. The ability of assemblage indices and specialist or functional-group abundances to reflect rare species and habitats should be examined in detail. (2 Experimental evidence suggests that carabids may potentially serve as keystone indicators. (3 Carabids are sensitive to human-altered abiotic conditions, such as pesticide use in agro-ecosystems and heavy metal contamination of soils. Carabids might thus reflect ecological sustainability and ‘ecosystem health’. (4 Carabid assemblages host abundant species characteristic of particular habitat types or successional stages, which makes them promising dominance indicators. (5 Carabids reflect variation in ‘natural’ conditions, but vegetation and structural features are more commonly adopted as condition indicators. Carabids nevertheless provide yet another, equally accurate, view on the structure of the environment. (6 Carabids may function as early-warning signalers, as suggested by recent studies linking climate and carabid distributions. (7 Carabids reflect natural and human-caused disturbances and management, but the usefulness of these responses for conservation purposes requires further research. In summary, European carabids appear useful model organisms and possibly indicators because

  4. Using Gambusia affinis growth and condition to assess estuarine habitat quality: A comparison of indices

    Science.gov (United States)

    Piazza, Bryan P.; La Peyre, M.K.

    2010-01-01

    Numerous indices have been used to estimate fish growth and condition however, differences in sensitivity and reliability of the methods have hampered efforts to identify appropriate indicators for routine evaluation of habitat quality in the field. We compared common morphometric (length, weight, somatic growth, length-weight condition) and biochemical (RNA:DNA ratio, relative DNA content, energy density) growth indices on the same wild-caught mosquitofish Gambusia affinis to examine their usefulness as indicators of habitat quality. A laboratory experiment was used to quantify growth rates of wild-caught G. affinis under different feeding treatments. Field studies consisted of both a short-term enclosure experiment (10 d) and weekly (7 wk) fish collections to compare growth indices in managed inflow and reference marshes during a winter/spring freshwater pulse event in upper Breton Sound, Louisiana, USA. Marshes flooded by restored freshwater pulses were capable of producing optimum growth (0.001 g DW d-1 DW = dry weight) and energetically valuable habitat (>6000 cal g-1 DW) for trophic transport. Because of differences in timing of response, morphometric and biochemical indices were generally not directly correlated, but there was clear agreement in direction and magnitude of response. The most striking difference in timing was that biochemical indices (RNA:DNA) responded more slowly to treatments than did morphometric growth indices. While gross patterns are comparable between indicators, differences in sensitivity and response time between indicators suggest that choice of indicator needs to be accounted for in interpretation and analysis of effects. ?? Inter-Research 2010, www.int-res.com.

  5. NYHOPS Forecast Model Results

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — 3D Marine Nowcast/Forecast System for the New York Bight NYHOPS subdomain. Currents, waves, surface meteorology, and water conditions.

  6. HEMATOLOGICAL INDICES OF RAT ORGANISMS UNDER CONDITIONS OF OXIDATIVE STRESS AND LIPOSOMAL PREPARATION ACTION

    Directory of Open Access Journals (Sweden)

    M. Khariv

    2016-04-01

    Full Text Available The article deals with the results of search of the influence of developed complex liposomal drug on dynamics of hematological parameters of rat organisms under conditions of simulated oxidative stress caused by the use of carbon tetrachloride. Intramuscular injection of 50% tetrachloromethane to rats at a dose of 0.25 ml per 100 g of body weight causes antigenic load on the body and leads to disruption of physiologic level of hematological indices of experimental animal organisms. This indicates the number reduce of red blood cells, hemoglobin content, hemoglobin concentration in erythrocyte, increasing the number of leukocytes, mass of hemoglobin in erythrocyte and increased of color index. To normalize the hematological indices of rat organisms for the development of oxidative stress it is advisable to apply the liposomal drug that incorporates contains butafosfan, interferon, milk thistle and vitamins. When using liposomal drug to rats, under conditions of oxidative stress, the normalization of hematological indices comes in blood, namely on 14th day within physiological variables were indicators of the number of erythrocytes of hemoglobin content, white blood cell count and indices of red blood cells compared to controls, indicating a recovery of hematopoietic function of marrow.

  7. Comparison of Test Stand and Helicopter Oil Cooler Bearing Condition Indicators

    Science.gov (United States)

    Dempsey, Paula J.; Branning, Jeremy; Wade, Damiel R.; Bolander, Nathan

    2010-01-01

    The focus of this paper was to compare the performance of HUMS condition indicators (CI) when detecting a bearing fault in a test stand or on a helicopter. This study compared data from two sources: first, CI data collected from accelerometers installed on two UH-60 Black Hawk helicopters when oil cooler bearing faults occurred, along with data from helicopters with no bearing faults; and second, CI data that was collected from ten cooler bearings, healthy and faulted, that were removed from fielded helicopters and installed in a test stand. A method using Receiver Operating Characteristic (ROC) curves to compare CI performance was demonstrated. Results indicated the bearing energy CI responded differently for the helicopter and the test stand. Future research is required if test stand data is to be used validate condition indicator performance on a helicopter.

  8. Comparison of Test Stand and Helicopter Oil Cooler Bearing Condition Indicators

    Science.gov (United States)

    Dempsey, Paula J.; Branning, Jeremy; Wade, Damiel R.; Bolander, Nathan

    2010-01-01

    The focus of this paper was to compare the performance of HUMS condition indicators (CI) when detecting a bearing fault in a test stand or on a helicopter. This study compared data from two sources: first, CI data collected from accelerometers installed on two UH-60 Black Hawk helicopters when oil cooler bearing faults occurred, along with data from helicopters with no bearing faults; and second, CI data that was collected from ten cooler bearings, healthy and faulted, that were removed from fielded helicopters and installed in a test stand. A method using Receiver Operating Characteristic (ROC) curves to compare CI performance was demonstrated. Results indicated the bearing energy CI responded differently for the helicopter and the test stand. Future research is required if test stand data is to be used validate condition indicator performance on a helicopter.

  9. Evaluation of redox indicators for determining sulfate-reducing and dechlorinating conditions.

    Science.gov (United States)

    Jones, Brian D; Ingle, James D

    2005-11-01

    An in situ methodology based on covalently bonded redox indicators has been developed for determining when sulfate-reducing conditions exist in environmental samples. Three immobilized redox indicators [thionine (Thi, formal potential at pH 7 (E(0')7) equals 52 mV), cresyl violet (CV, E(0')7 = -81 mV), and phenosafranine (PSaf, E(0')7 = -267 mV)] were tested for their response to sulfide in synthetic solutions and under sulfate-reducing conditions in wastewater slurries. The byproduct of the sulfate-reducing process, sulfide, was found to couple well to CV in the concentration range of 1-100 microM total sulfide ([S(-II)]) and the pH range of 6-8. Thi, the indicator with the highest formal potential, reacts rapidly with sulfide at levels well below 1 microM while PSaf, the indicator with the lowest formal potential, does not couple to sulfide at levels in excess of 100 microM [S(-II)]. The degree of reduction of the indicators (i.e., the fraction of cresyl violet oxidized) in contact with a given level of sulfide can be modeled qualitatively with an equilibrium expression for [S(-II)]-indicator based on the Nernst equation assuming that rhombic sulfur is the product of sulfide oxidation. In a groundwater sample with dechlorinating microbes, reduction of Thi and partial reduction of CV correlated with dechlorination of TCE to cis-DCE.

  10. Mathematical forecasting methods for predicting lead contents in animal organs on the basis of the environmental conditions.

    Science.gov (United States)

    Czech, Tomasz; Gambuś, Florian; Wieczorek, Jerzy

    2014-12-01

    The main objective of this study was to determine and describe the lead transfer in the soil-plant-animal system in areas polluted with this metal at varying degrees, with the use of mathematical forecasting methods and data mining tools contained in the Statistica 9.0 software programme. The starting point for the forecasting models comprised results derived from an analysis of different features of soil and plants, collected from 139 locations in an area covering 100km(2) around a lead-zinc ore mining and processing plant ('Boleslaw'), at Bukowno in southern Poland. In addition, the lead content was determined in the tissues and organs of 110 small rodents (mainly mice) caught in the same area. The prediction models, elaborated with the use of classification algorithms, forecasted with high probability the class (range) of pollution in animal tissues and organs with lead, based on various soil and plant properties of the study area. However, prediction models which use multilayer neural networks made it possible to calculate the content of lead (predicted versus measured) in animal tissues and organs with an excellent correlation coefficient.

  11. How crucial is it to account for the antecedent moisture conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments

    Science.gov (United States)

    Berthet, L.; Andréassian, V.; Perrin, C.; Javelle, P.

    2009-06-01

    This paper compares event-based and continuous hydrological modelling approaches for real-time forecasting of river flows. Both approaches are compared using a lumped hydrologic model (whose structure includes a soil moisture accounting (SMA) store and a routing store) on a data set of 178 French catchments. The main focus of this study was to investigate the actual impact of soil moisture initial conditions on the performance of flood forecasting models and the possible compensations with updating techniques. The rainfall-runoff model assimilation technique we used does not impact the SMA component of the model but only its routing part. Tests were made by running the SMA store continuously or on event basis, everything else being equal. The results show that the continuous approach remains the reference to ensure good forecasting performances. We show, however, that the possibility to assimilate the last observed flow considerably reduces the differences in performance. Last, we present a robust alternative to initialize the SMA store where continuous approaches are impossible because of data availability problems.

  12. How crucial is it to account for the antecedent moisture conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments

    Directory of Open Access Journals (Sweden)

    L. Berthet

    2009-06-01

    Full Text Available This paper compares event-based and continuous hydrological modelling approaches for real-time forecasting of river flows. Both approaches are compared using a lumped hydrologic model (whose structure includes a soil moisture accounting (SMA store and a routing store on a data set of 178 French catchments. The main focus of this study was to investigate the actual impact of soil moisture initial conditions on the performance of flood forecasting models and the possible compensations with updating techniques. The rainfall-runoff model assimilation technique we used does not impact the SMA component of the model but only its routing part. Tests were made by running the SMA store continuously or on event basis, everything else being equal. The results show that the continuous approach remains the reference to ensure good forecasting performances. We show, however, that the possibility to assimilate the last observed flow considerably reduces the differences in performance. Last, we present a robust alternative to initialize the SMA store where continuous approaches are impossible because of data availability problems.

  13. Early forecasting of crop condition using an integrative remote sensing method for corn and soybeans in Iowa and Illinois, USA

    Science.gov (United States)

    Seo, Bumsuk; Lee, Jihye; Kang, Sinkyu

    2017-04-01

    The weather-related risks in crop production is not only crucial for farmers but also for market participants and policy makers since securing food supply is an important issue for society. While crop growth condition and phenology are essential information about such risks, the extensive observations on those are often non-existent in many parts of the world. In this study, we have developed a novel integrative approach to remotely sense crop growth condition and phenology at a large scale. For corn and soybeans in Iowa and Illinois of USA (2003-2014), we assessed crop growth condition and crop phenology by EO data and validated it against the United States Department of Agriculture (USDA) National Agriculture Statistics System (NASS) crop statistics. For growth condition, we used two distinguished approaches to acquire crop condition indicators: a process-based crop growth modelling and a satellite NDVI based method. Based on their pixel-wise historic distributions, we determined relative growth conditions and scaled-down to the state-level. For crop phenology, we calculated three crop phenology metrics [i.e., start of season (SOS), end of season (EOS), and peak of season (POS)] at the pixel level from MODIS 8-day Normalized Difference Vegetation Index (NDVI). The estimates were compared with the Crop Progress and Condition (CPC) data of NASS. For the condition, the state-level 10-day estimates showed a moderate agreement (RMSE 70%). Notably, the condition estimates corresponded to the severe soybeans disease in 2003 and the drought in 2012 for both crops. For the phenology, the average RMSE of the estimates was 8.6 day for the all three metrics. The average |ME| was smaller than 1.0 day after bias correction. The proposed method enables us to evaluate crop growth at any given period and place. Global climate changes are increasing the risk in agricultural production such as long-term drought. We hope that the presented remote sensing method for crop condition

  14. A Weekly Indicator of Surface Moisture Status from Satellite Data for Operational Monitoring of Crop Conditions

    Directory of Open Access Journals (Sweden)

    Francesco Nutini

    2017-06-01

    Full Text Available The triangle method has been applied to derive a weekly indicator of evaporative fraction on vegetated areas in a temperate region in Northern Italy. Daily MODIS Aqua Land Surface Temperature (MYD11A1 data has been combined with air temperature maps and 8-day composite MODIS NDVI (MOD13Q1/MYD13Q1 data to estimate the Evaporative Fraction (EF at 1 km resolution, on a daily basis. Measurements at two eddy covariance towers located within the study area have been exploited to assess the reliability of satellite based EF estimations as well as the robustness of input data. Weekly syntheses of the daily EF indicator (EFw were then derived at regional scale for the years 2010, 2011 and 2012 as a proxy of overall surface moisture condition. EFw showed a temporal behavior consistent with growing cycles and agro-practices of the main crops cultivated in the study area (rice, forages and corn. Comparison with official regional corn yield data showed that variations in EFw cumulated over summer are related with crop production shortages induced by water scarcity. These results suggest that weekly-averaged EF estimated from MODIS data is sensible to water stress conditions and can be used as an indicator of crops’ moisture conditions at agronomical district level. Advantages and disadvantages of the proposed approach to provide information useful to issue operational near real time bulletins on crop conditions at regional scale are discussed.

  15. High incidence of cervical ribs indicates vulnerable condition in Late Pleistocene woolly rhinoceroses

    Directory of Open Access Journals (Sweden)

    Alexandra A.E. van der Geer

    2017-08-01

    Full Text Available Mammals as a rule have seven cervical vertebrae, a number that remains remarkably constant. Changes of this number are associated with major congenital abnormalities (pleiotropic effects that are, at least in humans, strongly selected against. Recently, it was found that Late Pleistocene mammoths (Mammuthus primigenius from the North Sea have an unusually high incidence of abnormal cervical vertebral numbers, approximately ten times higher than that of extant elephants. Abnormal numbers were due to the presence of large cervical ribs on the seventh vertebra, indicating a homeotic change from a cervical rib-less vertebra into a thoracic rib-bearing vertebra. The high incidence of cervical ribs indicates a vulnerable condition and is thought to be due to inbreeding and adverse conditions that may have impacted early pregnancies in declining populations. In this study we investigated the incidence of cervical ribs in another extinct Late Pleistocene megaherbivore from the North Sea and the Netherlands, the woolly rhinoceros (Coelodonta antiquitatis. We show that the incidence of abnormal cervical vertebral numbers in the woolly rhinoceros is unusually high for mammals (15,6%, n = 32 and much higher than in extant Rhinoceratidae (0%, n = 56. This indicates that woolly rhinoceros lived under vulnerable conditions, just like woolly mammoths. The vulnerable condition may well have contributed to their eventual extinction.

  16. Studies on Tomato Seedling Quality Indices Under Simulated Shipping and Storage Conditions

    Institute of Scientific and Technical Information of China (English)

    NING Wei; GE Xiao-guang; LI Tian-lai

    2004-01-01

    Indices of the tomato seedling quality maintenance level after production before field planting were studied through simulated experiments, small--scale operation, indoor analyses and measurements, and field observation. The results showed that under simulated shipping and storage conditions, seedling quality change following different durations (days) of shipping and storage was correlated significantly or even very significantly with certain physiological and morphological indices. With various measured indices following different periods of shipping and storage treatment subjected to multinomial successive regressive correlation analysis, the principal factors influencing seedling quality maintenance level are identified to be chlorophyll content→dry short weight→ leaf freshness index in order of their importance. Significance analysis with multinomial fitted equation indicated that correlations between any one of above three factors and the growth index after field planting reached very significant difference level.

  17. Evolving forecasting classifications and applications in health forecasting

    Science.gov (United States)

    Soyiri, Ireneous N; Reidpath, Daniel D

    2012-01-01

    Health forecasting forewarns the health community about future health situations and disease episodes so that health systems can better allocate resources and manage demand. The tools used for developing and measuring the accuracy and validity of health forecasts commonly are not defined although they are usually adapted forms of statistical procedures. This review identifies previous typologies used in classifying the forecasting methods commonly used in forecasting health conditions or situations. It then discusses the strengths and weaknesses of these methods and presents the choices available for measuring the accuracy of health-forecasting models, including a note on the discrepancies in the modes of validation. PMID:22615533

  18. Evaluation of HIV testing recommendations in specialty guidelines for the management of HIV indicator conditions

    DEFF Research Database (Denmark)

    Lord, E; Stockdale, A J; Malek, R

    2017-01-01

    OBJECTIVES: European guidelines recommend HIV testing for individuals presenting with indicator conditions (ICs) including AIDS-defining conditions (ADCs). The extent to which non-HIV specialty guidelines recommend HIV testing in ICs and ADCs is unknown. Our aim was to pilot a methodology in the UK......%). There was no association between recommendation to test and publication year (P = 0.62). CONCLUSIONS: The majority of guidelines for ICs do not recommend testing. Clinicians managing ICs may be unaware of recommendations produced by HIV societies or the prevalence of undiagnosed HIV infection among these patients. We...

  19. Myocardial capillary permeability for small hydrophilic indicators during normal physiological conditions and after ischemia and reperfusion

    DEFF Research Database (Denmark)

    Svendsen, Jesper Hastrup

    1991-01-01

    of the injected indicator molecules in an extracted and a transmitted fraction of molecules. In open chest dog hearts measurements performed during normal physiological conditions gave mean capillary extraction values of 43.5-47.5% and the corresponding calculated PdS values were 47.1 - 57.5 ml.(100g.min)-1. From......Myocardial capillary permeability for small hydrophilic solutes (51Cr-EDTA or 99mTc-DTPA) has been measured using intracoronary indicator bolus injection and external radioactivity registration (the single injection, residue detection method). The method is based on kinetic separation...

  20. Multi-GCM Projections of Global Drought Conditions With Use of the Palmer Drought Indices

    Science.gov (United States)

    Dubrovsky, M.; Svoboda, M.; Trnka, M.; Hayes, M.; Wilhite, D.; Zalud, Z.

    2007-12-01

    We use two Palmer Drought Indices (the PDSI and Z-index) to assess the drought conditions in future climates as projected by seven Global Climate Models (GCMs). Both indices are based on precipitation and temperature data (this makes them more suitable for climate change impact studies compared to the Standardized Precipitation Index, which is based only on precipitation) and the available water content of the soil. In contrast to the PDSI, the Z-index does not account for any persistence within the climate; rather, it characterizes the immediate (for a given week or month) conditions. The indices are calculated by computer programs available from the National Drought Mitigation Center and the Computer Science and Engineering Department, both located at the University of Nebraska-Lincoln. To allow for the assessment of climate change impacts, we modified the original computer code: the indices (which we named "relative" drought indices) are now calibrated using the present climate weather series and then applied to the future climate weather series. The resultant time series thus displays the drought conditions in terms of the present climate. The relative drought indices are applied to gridded (whole globe) GCM-simulated surface monthly weather series (available from the IPCC database), and the available water content is based on soil- texture-based water holding capacity global data developed by Webb et al. (1993, Global Biogeochem. Cycles 7: 97-108). The indices are calibrated with 1991-2020 period (considered to be the present climate) and then applied to two future periods: 2031-2060 and 2060-2099. To quantify impacts of climate change on the drought conditions, we analyze shifts in the grid-specific means of the drought indices and in the frequency of months belonging to drought spells. The drought spell is defined here as continuous periods in which the index does not exceed -1, and falls below -3 for at least one month. Results obtained by single GCMs

  1. Conditioned Fear Associated Phenotypes as Robust, Translational Indices of Trauma-, Stressor-, and Anxiety-related Behaviors

    Directory of Open Access Journals (Sweden)

    Maria Anne Briscione

    2014-07-01

    Full Text Available Posttraumatic stress disorder (PTSD is a heterogeneous disorder that affects individuals exposed to trauma (e.g., combat, interpersonal violence, and natural disasters. It is characterized by hyperarousal, intrusive reminders of the trauma, avoidance of trauma-related cues, and negative cognition and mood. This heterogeneity indicates the presence of multiple neurobiological mechanisms underlying the development and maintenance of PTSD. Fear conditioning is a robust, translational experimental paradigm that can be employed to elucidate these mechanisms by allowing for the study of fear-related dimensions of PTSD (e.g., fear extinction, fear inhibition, and generalization of fear across multiple units of analysis. Fear conditioning experiments have identified varying trajectories of the dimensions described, highlighting exciting new avenues of targeted, focused study. Additionally, fear conditioning studies provide a translational platform to develop novel interventions. The current review highlights the versatility of fear conditioning paradigms, the implications for pharmacological and non-pharmacological treatments, the robustness of these paradigms to span an array of neuroscientific measures (e.g., genetic studies, and finally the need to understand the boundary conditions under which these paradigms are effective. Further understanding these paradigms will ultimately allow for optimization of fear conditioning paradigms, a necessary step towards the advancement of PTSD treatment methods.

  2. Conditioned fear associated phenotypes as robust, translational indices of trauma-, stressor-, and anxiety-related behaviors.

    Science.gov (United States)

    Briscione, Maria Anne; Jovanovic, Tanja; Norrholm, Seth Davin

    2014-01-01

    Post-traumatic stress disorder (PTSD) is a heterogeneous disorder that affects individuals exposed to trauma (e.g., combat, interpersonal violence, and natural disasters). It is characterized by hyperarousal, intrusive reminders of the trauma, avoidance of trauma-related cues, and negative cognition and mood. This heterogeneity indicates the presence of multiple neurobiological mechanisms underlying the development and maintenance of PTSD. Fear conditioning is a robust, translational experimental paradigm that can be employed to elucidate these mechanisms by allowing for the study of fear-related dimensions of PTSD (e.g., fear extinction, fear inhibition, and generalization of fear) across multiple units of analysis. Fear conditioning experiments have identified varying trajectories of the dimensions described, highlighting exciting new avenues of targeted, focused study. Additionally, fear conditioning studies provide a translational platform to develop novel interventions. The current review highlights the versatility of fear conditioning paradigms, the implications for pharmacological and non-pharmacological treatments, the robustness of these paradigms to span an array of neuroscientific measures (e.g., genetic studies), and finally the need to understand the boundary conditions under which these paradigms are effective. Further understanding these paradigms will ultimately allow for optimization of fear conditioning paradigms, a necessary step towards the advancement of PTSD treatment methods.

  3. Numeric score-based conditional and overall change-in-status indices for ordered categorical data.

    Science.gov (United States)

    Lyles, Robert H; Kupper, Lawrence L; Barnhart, Huiman X; Martin, Sandra L

    2015-11-30

    Planned interventions and/or natural conditions often effect change on an ordinal categorical outcome (e.g., symptom severity). In such scenarios, it is sometimes desirable to assign a priori scores to observed changes in status, typically giving higher weight to changes of greater magnitude. We define change indices for such data based upon a multinomial model for each row of a c × c table, where the rows represent the baseline status categories. We distinguish an index designed to assess conditional changes within each baseline category from two others designed to capture overall change. One of these overall indices measures expected change across a target population. The other is scaled to capture the proportion of total possible change in the direction indicated by the data, so that it ranges from -1 (when all subjects finish in the least favorable category) to +1 (when all finish in the most favorable category). The conditional assessment of change can be informative regardless of how subjects are sampled into the baseline categories. In contrast, the overall indices become relevant when subjects are randomly sampled at baseline from the target population of interest, or when the investigator is able to make certain assumptions about the baseline status distribution in that population. We use a Dirichlet-multinomial model to obtain Bayesian credible intervals for the conditional change index that exhibit favorable small-sample frequentist properties. Simulation studies illustrate the methods, and we apply them to examples involving changes in ordinal responses for studies of sleep deprivation and activities of daily living.

  4. Investigation of Gearbox Vibration Transmission Paths on Gear Condition Indicator Performance

    Science.gov (United States)

    Dempsey, Paula J.; Islam, AKM Anwarul; Feldman, Jason; Larsen, Chris

    2013-01-01

    Helicopter health monitoring systems use vibration signatures generated from damaged components to identify transmission faults. For damaged gears, these signatures relate to changes in dynamics due to the meshing of the damaged tooth. These signatures, referred to as condition indicators (CI), can perform differently when measured on different systems, such as a component test rig, or a full-scale transmission test stand, or an aircraft. These differences can result from dissimilarities in systems design and environment under dynamic operating conditions. The static structure can also filter the response between the vibration source and the accelerometer, when the accelerometer is installed on the housing. To assess the utility of static vibration transfer paths for predicting gear CI performance, measurements were taken on the NASA Glenn Spiral Bevel Gear Fatigue Test Rig. The vibration measurements were taken to determine the effect of torque, accelerometer location and gearbox design on accelerometer response. Measurements were taken at the housing and compared while impacting the gear set near mesh. These impacts were made at gear mesh to simulate gear meshing dynamics. Data measured on a helicopter gearbox installed in a static fixture were also compared to the test rig. The behavior of the structure under static conditions was also compared to CI values calculated under dynamic conditions. Results indicate that static vibration transfer path measurements can provide some insight into spiral bevel gear CI performance by identifying structural characteristics unique to each system that can affect specific CI response.

  5. A national-scale seasonal hydrological forecast system: development and evaluation over Britain

    Science.gov (United States)

    Bell, Victoria A.; Davies, Helen N.; Kay, Alison L.; Brookshaw, Anca; Scaife, Adam A.

    2017-09-01

    Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better) rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009) are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean) rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region). Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 %) in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows), whereas for the 3-month ahead lead time, GloSea5 forecasts account for ˜ 70 % of the forecast

  6. A national-scale seasonal hydrological forecast system: development and evaluation over Britain

    Directory of Open Access Journals (Sweden)

    V. A. Bell

    2017-09-01

    Full Text Available Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts from the GloSea5 model (1996 to 2009 are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region. Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 % in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows, whereas for the 3-month ahead lead time, GloSea5 forecasts account for  ∼ 70

  7. Impact of parboiling conditions on Maillard precursors and indicators in long-grain rice cultivars.

    Science.gov (United States)

    Lamberts, Lieve; Rombouts, Ine; Brijs, Kristof; Gebruers, Kurt; Delcour, Jan A

    2008-10-15

    The effect of steaming conditions (mild, intermediate and severe) during parboiling of five different long-grain rice cultivars (brown rice cultivars Puntal, Cocodrie, XL8 and Jacinto, and a red rice) on rice colour, and Maillard precursors and indicators was investigated. Rice colour increased with severity of parboiling conditions. Redness increased more than yellowness when parboiling brown rice. Parboiling turned red rice black. It changed the levels of glucose, fructose, sucrose, and maltose. Losses of the non-reducing sugar, sucrose were caused by both leaching into the soaking water and enzymic conversion, rather than by thermal degradation during steaming. Concentrations of the reducing sugars, glucose and fructose, in intermediately parboiled rice were higher than those of mildly parboiled rice. After severe parboiling, glucose levels were lower than those of intermediately parboiled rice, while fructose levels were higher. These changes were ascribed to the sum of losses in the Maillard reaction (MR), formations as a result of starch degradation and isomerisation of glucose into fructose. It was clear that the ε-amino group of protein-bound lysine was more affected by parboiling conditions and loss in MRs, than that of free lysine. Low values of the MR indicators furosine and free 5-hydroxymethyl-2-furaldehyde (HMF) in processed brown and red rices were related to mild parboiling, whereas high furosine and low free HMF levels were indicative of rices being subjected to intermediate processing conditions. High furosine and high free HMF contents corresponded to severe hydrothermal treatments. The strong correlation (r=0.89) between the free HMF levels and the increase in redness of parboiled brown rices suggested that Maillard browning was reflected more in the red than in the yellow colour.

  8. Some implications of time series analysis for describing climatologic conditions and for forecasting. An illustrative case: Veracruz, Mexico

    Energy Technology Data Exchange (ETDEWEB)

    Gay, C.; Estrada, F.; Conde, C. [Centro de Ciencias de la Atmosfera, Universidad Nacional Autonoma de Mexico, Mexico, D.F. (Mexico)]. E:mail: feporrua@atmosfera.unam.mx

    2007-04-15

    The common practice of using 30-year sub-samples of climatological data for describing past, present and future conditions has been widely applied, in many cases without considering the properties of the time series analyzed. This paper shows that this practice can lead to an inefficient use of the information contained in the data and to an inaccurate characterization of present, and especially future, climatological conditions because parameters are time and sub-sample size dependent. Furthermore, this approach can lead to the detection of spurious changes in distribution parameters. The time series analysis of observed monthly temperature in Veracruz, Mexico, is used to illustrate the fact that these techniques permit to make a better description of the mean and variability of the series, which in turn allows (depending on the class of process) to restrain uncertainty of forecasts, and therefore provides a better estimation of present and future risk of observing values outside a given coping range. Results presented in this paper show that, although a significant trend is found in the temperatures, giving possible evidence of observed climate change in the region, there is no evidence to support changes in the variability of the series and therefore there is neither observed evidence to support that monthly temperature variability will increase (or decrease) in the future. That is, if climate change is already occurring, it has manifested itself as a change-in-the-mean of these processes and has not affected other moments of their distributions (homogeneous non-stationary processes). The Magicc-Scengen, a software useful for constructing climate change scenarios, uses 20-year sub-samples to estimate future climate variability. For comparison purposes, possible future probability density functions are constructed following two different approaches: one, using solely the Magicc-Scengen output, and another one using a combination of this information and the time

  9. Demand forecasting

    OpenAIRE

    Gregor, Belčec

    2011-01-01

    Companies operate in an increasingly challenging environment that requires them to continuously improve all areas of the business process. Demand forecasting is one area in manufacturing companies where we can hope to gain great advantages. Improvements in forecasting can result in cost savings throughout the supply chain, improve the reliability of information and the quality of the service for our customers. In the company Danfoss Trata, d. o. o. we did not have a system for demand forecast...

  10. Using fish larvae as indicators of estuarine ecosystem condition in Rio de Janeiro State, Brazil

    Directory of Open Access Journals (Sweden)

    Regis Vinícius Souza Santos

    2015-11-01

    Full Text Available Estuaries enhance growth and maximize the survivorship of initial development stages of fish species, functioning as nursery grounds for many species. However, estuaries throughout the world host a wide variety of human activities, and fish communities can be severely impacted. Protection of aquatic biodiversity and proper management of these coastal systems require robust tools to assess habitat integrity and ecological quality status. Thus, the present study investigated the use of fish larvae as indicators of estuarine ecosystem condition in Brazil, testing the hypothesis that estuaries with different human impacts and environmental conditions carry distinct larval fish assemblages. For this, four estuaries were analysed with: similar environmental conditions (the same water mass surveyed, similar pool of species (the same geographical region and no seasonal influences (different periods analysed separately. Surveys were conducted in Macaé, São João, Bracuí and Perequê-Açu estuaries located in Rio de Janeiro State, Brazil. Sampling surveys were conducted every two months between May 2013 and March 2015. All samples were taken in the estuary middle region (salinity 15-25 during nightly ebb tides. At each estuary, ichthyoplankton subsurface tows were perfomed using a Bongo net. Water parameters were measured by a multiparameter probe and surface water samples collected for further analytical analyses. Fish larvae were identified and species were assigned into functional guilds. The water conditions were assessed based on water temperature, pH, chlorophyll a, dissolved oxygen and total particulate matter. Faecal coliforms, nutrient load (NO3, NO2, NH3, PO4, SiO3 and presence/absence of dams, dredging and mangroves were used as anthropogenic pressure descriptors. The species composition and ecological guilds of Macaé and Perequê-Açu differed significantly of São João and Bracuí, separating the impacted versus non-impacted estuaries

  11. MANAGEMENT SYSTEM OF THE ORGANIZATION PERFORMANCE INDICATORS IN THE CONDITIONS OF BUSINESS PROCESSES REALIZATION

    Directory of Open Access Journals (Sweden)

    Aleksandr Vladimirovich Gagarinskii

    2016-12-01

    Full Text Available This article discusses the problem of the effective work of managers of industrial enterprises, which is the basis of economic development in modern Russia. The authors suggest that the management of the system of performance indicators in managers in the conditions of realization of various business processes can reduce the risk of crises in the enterprise, and improve the efficiency of labour management and productivity in the company in a whole. According to the authors, improving the efficiency of management in the conditions of implementation of the various business processes of industrial enterprises is an integral element of the overall strategic development of the company. The article presents the results of work performance assessment of managers in the implementation of business process management. In this article there is developed performance business process management on the example of the metal cutting enterprise management levels: the corporate level, the first operational level, the second operational level, and the line level. For these indicators the performers are defined and criteria are given.

  12. MR cholangiopancreatography: technique, potential indications, and diagnostic features of benign, postoperative, and malignant conditions

    Energy Technology Data Exchange (ETDEWEB)

    Becker, C.D. [Department of Radiology, Division of Diagnostic and Interventional Radiology, University Hospital of Geneva, CH-1211 Geneva (Switzerland); Grossholz, M. [Department of Radiology, Division of Diagnostic and Interventional Radiology, University Hospital of Geneva, CH-1211 Geneva (Switzerland); Mentha, G. [Department of Surgery, University Hospital of Geneva, CH-1211 Geneva (Switzerland); Peyer, R. de [Division of Gastroenterology, University Hospital of Geneva, CH-1211 Geneva (Switzerland); Terrier, F. [Department of Radiology, Division of Diagnostic and Interventional Radiology, University Hospital of Geneva, CH-1211 Geneva (Switzerland)

    1997-08-01

    The objective of this article is to review technical aspects, discuss potential clinical indications for MR cholangiopancreatography (MRCP) and demonstrate the spectrum of diagnostic findings in benign, postoperative, and malignant conditions. We describe our current imaging protocol in comparison with other available techniques. Using a non-breath-hold, heavily T2-weighted fast-spin-echo (FSE) sequence with or without respiratory gating we obtained coronal and axial source images and maximum intensity projections (MIPs) in 102 patients with suspected abnormalities of the biliary or pancreatic ducts. Based on this series we demonstrate the diagnostic appearance of a variety of benign, postoperative, and malignant conditions of the biliary and pancreatic ducts and discuss potential clinical indications for MRCP. The non-breath-hold FSE technique enables a consistent image quality even in patients who cannot cooperate well. Respiratory gating increased the rate of diagnostic examinations from 79 to 95 %. Acquisition of coronal and axial source images enables detection of bile duct stones as small as 2 mm, although calculi that are impacted and not surrounded by hyperintense bile may sometimes be difficult to detect. The MIP reconstructions help to determine the level of obstruction in malignant jaundice, delineate anatomical variants and malformations, and to diagnose inflammatory conditions, e. g., sclerosing cholangitis, the Mirizzi syndrome and inflammatory changes in the main pancreatic duct. The MRCP technique also correctly demonstrates the morphology of bilio-enteric or bilio-biliary anastomoses. Because MRCP provides sufficient diagnostic information in a wide range of benign and malignant biliary and pancreatic disorders, it could obviate diagnostic endoscopic retrograde cholangiopancreatography (ERCP) in many clinical settings. The ERCP technique may be increasingly reserved for patients in whom nonsurgical interventional procedures are anticipated. (orig

  13. Spectral Indices to Improve Crop Residue Cover Estimation under Varying Moisture Conditions

    Directory of Open Access Journals (Sweden)

    Miguel Quemada

    2016-08-01

    Full Text Available Crop residues on the soil surface protect the soil against erosion, increase water infiltration and reduce agrochemicals in runoff water. Crop residues and soils are spectrally different in the absorption features associated with cellulose and lignin. Our objectives were to: (1 assess the impact of water on the spectral indices for estimating crop residue cover (fR; (2 evaluate spectral water indices for estimating the relative water content (RWC of crop residues and soils; and (3 propose methods that mitigate the uncertainty caused by variable moisture conditions on estimates of fR. Reflectance spectra of diverse crops and soils were acquired in the laboratory over the 400–2400-nm wavelength region. Using the laboratory data, a linear mixture model simulated the reflectance of scenes with various fR and levels of RWC. Additional reflectance spectra were acquired over agricultural fields with a wide range of crop residue covers and scene moisture conditions. Spectral indices for estimating crop residue cover that were evaluated in this study included the Normalized Difference Tillage Index (NDTI, the Shortwave Infrared Normalized Difference Residue Index (SINDRI and the Cellulose Absorption Index (CAI. Multivariate linear models that used pairs of spectral indices—one for RWC and one for fR—significantly improved estimates of fR using CAI and SINDRI. For NDTI to reliably assess fR, scene RWC should be relatively dry (RWC < 0.25. These techniques provide the tools needed to monitor the spatial and temporal changes in crop residue cover and help determine where additional conservation practices may be required.

  14. Monitoring the condition of the Canadian forest environment: The relevance of the concept of 'ecological indicators'.

    Science.gov (United States)

    Kimmins, J P

    1990-11-01

    The Canadian forest environment is characterized by high spatial and temporal variability, especially in the west. Our forests vary according to climate, landform, and surficial geology, and according to the type, intensity, extent of, and the time since the last disturbance. Most Canadian forests have had a history of repeated acute, episodic disturbance from fire, insects, wind, diseases and/or logging, with a frequency of disturbance varying from a few decades to many centuries. These sources of variability have resulted in a complex and continually changing mosaic of forest conditions and stages of successional development.Monitoring the 'quality' of this dynamic forested landscape mosaic is extremely difficult, and in most cases the concept of a relatively simple index of forest ecosystem quality or condition (i.e. an 'ecological indicator') is probably inappropriate. Such ecological indicators are better suited for monitoring chronic anthropogenically induced disturbances that are continuous in their effect (e.g. 'acid rain', heavy metal pollution, air pollution, and the 'greenhouse effect') in ecosystems that, in the absence of such chronic disturbance, exhibit very slow directional change (e.g. lakes, higher order streams and rivers). Monitoring the effects of a chronic anthropogenic disturbance to forest ecosystems to determine if it is resulting in a sustained, directional alteration of environmental 'quality' will require a definition of the expected pattern of episodic disturbance and recovery therefrom (i.e. patterns of secondary succession in the absence of the chronic disturbance). Only when we have such a 'temporal fingerprint' of forest ecosystem condition for 'normal' patterns of disturbance and recovery can we determine if the ecosystem condition is being degraded by chronic human-induced alteration of the environment. Thus, degradation is assessed in terms of deviations from the expected temporal pattern of conditions rather than in terms of an

  15. EFFECT OF MATERNAL SOCIOECONOMIC CONDITIONS ON MATERNAL HEALTH INDICATORS AND NEONATAL PARAMETERS IN PAKISTAN

    Directory of Open Access Journals (Sweden)

    Muhammad Usman

    2015-12-01

    Full Text Available Infant mortality rate is a serious issue worldwide. Pakistan being a developing country comprises of diverse socioeconomic classes. Various studies have suggested some association of maternal anthropometric parameters with neonatal outcomes. The aim behind this research is to determine the impact of socioeconomic conditions on maternal health indicators and neonatal parameters in the population of Pakistan. This study included 90 pregnant females belonging to different socioeconomic conditions and were grouped according to their socioeconomic classes. Data was collected from the case histories of participants admitted in different hospitals of Karachi. The data of maternal and neonatal parameters was assessed statistically and their associations with the socioeconomic conditions were assessed. Maternal hemoglobin and maternal gravidity have shown a strong association with socioeconomic conditions with high significance (p < 0.05. However, neonatal parameters have shown diverse results among the three classes. Neonatal gestational age was found to be significant in comparison between upper versus lower (p = 0.001 and upper versus middle classes (p = 0.006, but it was insignificant in case of middle versus lower class (p = 0.88. Likewise, neonatal birth weight is significant between upper versus lower (p = 0.001 and upper versus middle classes (p = 0.019, but it was insignificant in case of middle versus lower class (p = 0.258. Neonatal apgar score is found to be significant in upper versus lower (p = 0.001 and middle versus lower classes (p = 0.001 and insignificant between middle and lower class (p = 0.125. This study concludes that socioeconomic factors play a vital role in determining the maternal health characteristics which in turn affects the neonatal outcomes. It is therefore recommended that antenatal care should be provided to all pregnant females. The whole community should work hand in hand to establish good health care centers, create

  16. The Effect of Body Weight on Heat Strain Indices in Hot and Dry Climatic Conditions

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    Habibi

    2016-03-01

    Full Text Available Background Being overweight is a characteristic that may influence a person’s heat exchange. Objectives The purpose of this study was to assess the effect of body weight on heat strain indices in hot and dry climatic conditions. Materials and Methods This study was completed with a sample of 30 participants with normal weights, as well as 25 participants who were overweight. The participants were physically inactive for a period of 120 minutes in a climatic chamber with hot and dry conditions (22 - 32°C and with 40% relative humidity (RH.The physiological strain index (PSI and heat strain score index (HSSI questionnaires were used. Simultaneous measurements were completed during heat exposure for periods of five minutes. The resting periods acted as the initial measurements for 15 minutes. Results In both groups, oral temperature, heart rate, and thermal perceptual responses increased during heat exposure. The means and standard deviations of heart rate and oral temperature were gathered when participants were in hot and dry climatic conditions and were not physically active. The heart rates and oral temperatures were 79.21 ± 5.93 bpm and 36.70 ± 0.45°C, respectively, for those with normal weights. For overweight individuals, the measurements for heart rate and oral temperature reached 82.21 ± 8.9 bpm and 37.84 ± 0.37°C, respectively. Conclusions The results showed that, compared to participants with normal weights, physiological and thermal perceptual responses were higher in overweight participants. Therefore, overweight individuals should avoid hot/dry weather conditions to decrease the amount of heat strain.

  17. Adaptive Weather Forecasting using Local Meteorological Information

    NARCIS (Netherlands)

    Doeswijk, T.G.; Keesman, K.J.

    2005-01-01

    In general, meteorological parameters such as temperature, rain and global radiation are important for agricultural systems. Anticipating on future conditions is most often needed in these systems. Weather forecasts then become of substantial importance. As weather forecasts are subject to

  18. Novel Indications for Benzodiazepine Antagonist Flumazenil in GABA Mediated Pathological Conditions of the Central Nervous System.

    Science.gov (United States)

    Hulse, Gary; Kelty, Erin; Hood, Sean; Norman, Amanda; Basso, Maria Rita; Reece, Albert Stuart

    2015-01-01

    This review paper discusses the central role of gamma-aminobutyric acid (GABA) in diverse physiological systems and functions and the therapeutic potential of the benzodiazepine antagonist flumazenil (Ro 15- 1788) for a wide range of disorders of the central nervous system (CNS). Our group and others have studied the potential of flumazenil as a treatment for benzodiazepine dependence. A small but growing body of research has indicated that flumazenil may also have clinical application in CNS disorders such as Parkinson's disease, idiopathic hypersomnia and amyotrophic lateral sclerosis. Despite this body of research the therapeutic potential of flumazenil remains poorly understood and largely unrealized. The purpose of this paper is not to provide an exhaustive review of all possible therapeutic applications for flumazenil but rather to stimulate research interest, and discussion of the exciting therapeutic potential of this drug for a range of chronic debilitating conditions.

  19. Estimation of gear tooth transverse crack size from vibration by fusing selected gear condition indices

    Science.gov (United States)

    Choi, Sukhwan; Li, C. James

    2006-09-01

    Gears are common power transmission elements and are frequently responsible for transmission failures. Since a tooth crack is not directly measurable while a gear is in operation, one has to develop an indirect method to estimate its size from some measurables. This study developed such a method to estimate the size of a tooth transverse crack for a spur gear in operation. Using gear vibrations measured from an actual gear accelerated test, this study examined existing gear condition indices to identify those correlated well to crack size and established their utility for crack size estimation through index fusion using a neural network. When tested with vibrations measured from another accelerated test, the method had an averaged estimation error of about 5%.

  20. Extraordinary incidence of cervical ribs indicates vulnerable condition in Late Pleistocene mammoths

    Directory of Open Access Journals (Sweden)

    Jelle W.F. Reumer

    2014-03-01

    Full Text Available The number of cervical vertebrae in mammals is highly conserved at seven. We have shown that changes of this number are selected against due to a coupling with major congenital abnormalities (pleiotropic effects. Here we show that the incidence of abnormal cervical vertebral numbers in Late Pleistocene mammoths from the North Sea is high (33.3% and approximately 10 times higher than that of extant elephants (3.6%. Abnormal numbers were due to the presence of large cervical ribs on the seventh vertebra, which we deduced from the presence of rib articulation facets on sixth (posterior side and seventh (anterior side cervical vertebrae. The incidence of abnormal cervical vertebral numbers in mammoths appears to be much higher than in other mammalian species, apart from exceptional sloths, manatees and dugongs and indicates a vulnerable condition. We argue that the increased incidence of cervical ribs in mammoths is probably caused by inbreeding and adverse conditions that impact early pregnancies in declining populations close to extinction in the Late Pleistocene.

  1. Comparative Study of Vibration Condition Indicators for Detecting Cracks in Spur Gears

    Science.gov (United States)

    Nanadic, Nenad; Ardis, Paul; Hood, Adrian; Thurston, Michael; Ghoshal, Anindya; Lewicki, David

    2013-01-01

    This paper reports the results of an empirical study on the tooth breakage failure mode in spur gears. Of four dominant gear failure modes (breakage, wear, pitting, and scoring), tooth breakage is the most precipitous and often leads to catastrophic failures. The cracks were initiated using a fatigue tester and a custom-designed single-tooth bending fixture to simulate over-load conditions, instead of traditional notching using wire electrical discharge machining (EDM). The cracks were then propagated on a dynamometer. The ground truth of damage level during crack propagation was monitored with crack-propagation sensors. Ten crack propagations have been performed to compare the existing condition indicators (CIs) with respect to their: ability to detect a crack, ability to assess the damage, and sensitivity to sensor placement. Of more than thirty computed CIs, this paper compares five commonly used: raw RMS, FM0, NA4, raw kurtosis, and NP4. The performance of combined CIs was also investigated, using linear, logistic, and boosted regression trees based feature fusion.

  2. Tertiary weathering profiles in central Nigeria as indicators of paleoenvironmental conditions

    Science.gov (United States)

    Zeese, Reinhard

    1996-05-01

    In central Nigeria the Fluviovolcanic Series of the Jos Plateau are composed by deeply weathered volcanic rocks and sediments. The sequence is often capped by ferricretes. Ferricretes, remains of former soils, are also intercalated in the series. Profile sections between two ferricretes are deep (> 10 m). The repeated saprolite/solum sequence sometimes exceeds 100 m. It can be separated into a lower section without bauxitisation, but often with total bleaching of the saprolite, a middle section with bauxite and hematite in the saprolite and an upper kaolinitic section with hematite and goethite. Bleaching by total removal of Fe 2+ and bauxitisation by total desilification are both indicators of wet and hot climates with a high biomass production. In the first case the groundwater was permanently high, in the latter a well drained fluctuating groundwater can be assumed. Changing groundwater conditions may be the result of plateau uplift. Paleoenvironmental changes are, thus, reflected in relatively datable paleosols. Undatable paleosoils with comparable characteristics on the planation surfaces of Central and Northeast Nigeria also result from paleoenvironmental conditions. Consequently, concepts of landscape evolution must take into account changing environments.

  3. SIGNIFICANCE OF PLATELET COUNT AND PLATELET INDICES IN PATIENTS WITH SOME THROMBOCYTOPENIC CONDITIONS

    Directory of Open Access Journals (Sweden)

    Omer Noureldaim Abdalla

    2016-01-01

    Full Text Available Background: Thrombocytopenia is one of the most frequent causes for hematologic consultation in the practice of medicine and can result from a wide variety of conditions. Objective: The study was conducted on behave of platelets count in tie with platelet volume indices to measure their consistency. Methods: The study was “prospective cross-sectional hospital based design” and conducted at Khartoum hospitals (A.Gasim, Jafar I A, and R.ICK. Studied populations texture has stipulated concurred diagnosis of heart disorders (HD, lymphoid neoplasms (LN, hypoplastic bone marrow (HPB, renal transplantation (RT, patients under chemotherapy (CT, and fully checked healthy Sudanese population (HSP. Platelet (PLT count and platelet volume index (PVI were measured using automated method of Sysmex KX-21N and the data was analyzed using SPSS. Results: does established (24 mean and standard for the study population among which (HSP was platelet distribution width (PDW (11.4±1.5 fl, mean platelet volume (MPV (9.3±0.8 fl, platelet large cell ratio (P- LCR (20.6±6.7% and PLT count (245±45 X109/L, and established correlations between PLT count and PVI in thrombocytopenic conditions. Conclusion: we conclude that, PVI has the ability to change from normal to higher or lower than (HSP in thrombocytopenic conditions and Sudanese has PVI mean lower than the mean of reference range, and there are inverse and reverse correlations between PLT count and PVI but not in (HSP and reverse correlation in between PVI except between PDW and P-LCR in (HPB

  4. A method for finding the optimal predictor indices for local wave climate conditions

    Science.gov (United States)

    Camus, Paula; Méndez, Fernando J.; Losada, Inigo J.; Menéndez, Melisa; Espejo, Antonio; Pérez, Jorge; Rueda, Ana; Guanche, Yanira

    2014-07-01

    In this study, a method to obtain local wave predictor indices that take into account the wave generation process is described and applied to several locations. The method is based on a statistical model that relates significant wave height with an atmospheric predictor, defined by sea level pressure fields. The predictor is composed of a local and a regional part, representing the sea and the swell wave components, respectively. The spatial domain of the predictor is determined using the Evaluation of Source and Travel-time of wave Energy reaching a Local Area (ESTELA) method. The regional component of the predictor includes the recent historical atmospheric conditions responsible for the swell wave component at the target point. The regional predictor component has a historical temporal coverage ( n-days) different to the local predictor component (daily coverage). Principal component analysis is applied to the daily predictor in order to detect the dominant variability patterns and their temporal coefficients. Multivariate regression model, fitted at daily scale for different n-days of the regional predictor, determines the optimum historical coverage. The monthly wave predictor indices are selected applying a regression model using the monthly values of the principal components of the daily predictor, with the optimum temporal coverage for the regional predictor. The daily predictor can be used in wave climate projections, while the monthly predictor can help to understand wave climate variability or long-term coastal morphodynamic anomalies.

  5. Online damage detection using recursive principal component analysis and recursive condition indicators

    Science.gov (United States)

    Krishnan, M.; Bhowmik, B.; Tiwari, A. K.; Hazra, B.

    2017-08-01

    In this paper, a novel baseline free approach for continuous online damage detection of multi degree of freedom vibrating structures using recursive principal component analysis (RPCA) in conjunction with online damage indicators is proposed. In this method, the acceleration data is used to obtain recursive proper orthogonal modes in online using the rank-one perturbation method, and subsequently utilized to detect the change in the dynamic behavior of the vibrating system from its pristine state to contiguous linear/nonlinear-states that indicate damage. The RPCA algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants, using the rank-one perturbation method. An online condition indicator (CI) based on the L2 norm of the error between actual response and the response projected using recursive eigenvector matrix updates over successive iterations is proposed. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data. The proposed CI, named recursive residual error, is also adopted for simultaneous spatio-temporal damage detection. Numerical simulations performed on five-degree of freedom nonlinear system under white noise and El Centro excitations, with different levels of nonlinearity simulating the damage scenarios, demonstrate the robustness of the proposed algorithm. Successful results obtained from practical case studies involving experiments performed on a cantilever beam subjected to earthquake excitation, for full sensors and underdetermined cases; and data from recorded responses of the UCLA Factor building (full data and its subset) demonstrate the efficacy of the proposed methodology as an ideal candidate for real-time, reference free structural health monitoring.

  6. Evolving forecasting classifications and applications in health forecasting

    Directory of Open Access Journals (Sweden)

    Soyiri IN

    2012-05-01

    Full Text Available Ireneous N Soyiri1,2, Daniel D Reidpath11Global Public Health, JCSMHS, MONASH University, Selangor, Malaysia; 2School of Public Health, University of Ghana, Legon, Accra, GhanaAbstract: Health forecasting forewarns the health community about future health situations and disease episodes so that health systems can better allocate resources and manage demand. The tools used for developing and measuring the accuracy and validity of health forecasts commonly are not defined although they are usually adapted forms of statistical procedures. This review identifies previous typologies used in classifying the forecasting methods commonly used in forecasting health conditions or situations. It then discusses the strengths and weaknesses of these methods and presents the choices available for measuring the accuracy of health-forecasting models, including a note on the discrepancies in the modes of validation.Keywords: health forecast, health data, electronic health records, accuracy, cross validation, method, strengths and limitations

  7. Innovation Forecasting

    Science.gov (United States)

    1997-11-01

    relating to “ injectors ”) to develop a map of the related technologies [33.] Another approach is to develop a “tree” showing a system branching into its...additional terms such as “trend,” “forecast,” “ delphi ,” “assessment,” and so forth may call up other forecasts and assessments relating to the topic...present and future engine technologies. A preliminary search (Step 1, Table 5) located prior forecasts, in particular, a Delphi study [36]. The Delphi

  8. Indicators of airborne fungal concentrations in urban homes: understanding the conditions that affect indoor fungal exposures.

    Science.gov (United States)

    Crawford, Judith A; Rosenbaum, Paula F; Anagnost, Susan E; Hunt, Andrew; Abraham, Jerrold L

    2015-06-01

    Indoor fungal exposure can compromise respiratory health. Low-income urban areas are of concern because of high asthma and allergy rates and housing disrepair. Understanding the conditions that affect indoor fungal exposures is important for assessing health risks and for developing mitigation strategies. We examined the types and concentrations of airborne fungi inside and outside of homes in low-income areas of Syracuse, NY as well as the effect of snow cover on fungal levels. At 103 homes, air samples for viable fungi were collected, occupants were interviewed and homes were inspected for visible mold, musty odors, water problems and other factors. Multivariable logistic regression was used to relate high fungal levels to home conditions. Predominant indoor fungi included Cladosporium, Penicillium, Aspergillus, Alternaria and hyaline unknowns. Basidiomycetes and an uncommon genus Acrodontium were also found frequently due to analysis methods developed for this project. With snow cover, outdoor total fungal levels were depressed and indoor concentrations were three times higher than outdoor on average with a maximum of 29 times higher. Visible mold was related to elevated levels of Penicillium (OR 4.11 95% CI 1.37-14.0) and bacteria (OR 3.79 95% CI 1.41-11.2). Musty, moldy odors were associated with elevated concentrations of total fungi (OR 3.48 95% CI 1.13-11.6) and basidiomycetes. Cockroaches, an indicator of moisture, were associated with elevated levels of Penicillium (OR 3.66 95% CI 1.16-13.1) and Aspergillus (OR 4.36 95% CI 1.60-13.4). Increasing relative humidity was associated with higher concentrations of Penicillium, yeasts and basidiomycetes. Visible mold, musty odors, indoor humidity and cockroaches are modifiable factors that were important determinants of indoor fungal exposures. Indoor air investigators should interpret indoor:outdoor fungal ratios cautiously when snow cover is present.

  9. [Indicator condition guided human immunodeficiency virus requesting in primary health care: results of a collaboration].

    Science.gov (United States)

    Cayuelas-Redondo, Laia; Menacho-Pascual, Ignacio; Noguera-Sánchez, Pablo; Goicoa-Gago, Carmen; Pollio-Peña, Gernónimo; Blanco-Delgado, Rebeca; Barba-Ávila, Olga; Sequeira-Aymar, Ethel; Muns, Mercè; Clusa, Thais; García, Felipe; León, Agathe

    2015-12-01

    The search of HIV infected patients guided by indicator conditions (IC) is a strategy used to increase the early detection of HIV. The objective is to analyze whether a collaboration to raise awareness of the importance of early detection of HIV in 3 primary care centers influenced the proportion of HIV serology requested. Multicenter retrospective study was conducted comparing the baseline and a post-collaboration period. The collaboration consisted of training sessions and participation in the HIDES study (years 2009-2010). Patients between 18 and 64 years old with newly diagnosed herpes zoster, seborrheic eczema, mononucleosis syndrome, and leucopenia/thrombocytopenia in 3 primary care centers in 2008 (baseline period) and 2012 (post-collaboration period). The sociodemographic variables, HIV risk conditions, requests for HIV serology, and outcomes were evaluated. A total of 1,219 ICs were included (558 in 2008 and 661 in 2012). In 2008 the number of HIV tests in patients with an IC was 3.9%, and rose to 11.8% in 2012 (P<.0001). The HIV infection rate was 2.2% (95% CI: 0.4-7.3) (n=2). It was estimated that 25 new cases (12 in 2008 and 13 in 2012) would have been diagnosed if they had performed the test on all patients with IC. Predictors of HIV request were, having an IC in 2012, a younger age, having an mononucleosis syndrome, and not being Spanish. The HIV request demand tripled, after the collaboration with primary care centers, however in 88% the test was not requested, resulting in diagnostic losses. New strategies are needed to raise awareness of the importance of early detection of HIV. Copyright © 2015 Elsevier España, S.L.U. y Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.

  10. Functional traits of selected mangrove species in Brazil as biological indicators of different environmental conditions

    Energy Technology Data Exchange (ETDEWEB)

    Arrivabene, Hiulana Pereira [Universidade Federal do Espírito Santo, Centro de Ciências Humanas e Naturais, Departamento de Ciências Biológicas, 29075-910 Vitória, Espírito Santo (Brazil); Souza, Iara [Universidade Federal de São Carlos, Centro de Ciências Biológicas e da Saúde, Departamento de Ciências Fisiológicas, 13565-905 São Carlos (Brazil); Có, Walter Luiz Oliveira [Associação Educational de Vitória, Departamento de Biologia, 29053-360 Vitória (Brazil); Rodella, Roberto Antônio [Universidade Estadual Paulista Júlio de Mesquita Filho, Campus de Botucatu, Instituto de Biociências, Departamento de Botânica, C. Postal 510, 18618-000 Botucatu, São Paulo (Brazil); Wunderlin, Daniel Alberto, E-mail: dwunder@fcq.unc.edu.ar [Instituto de Ciencia y Tecnología de Alimentos Córdoba (ICYTAC), CONICET, Dpto. Qca. Orgánica, Fac. Cs. Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, 5000, Córdoba (Argentina); and others

    2014-04-01

    Ecological studies on phenotypic plasticity illustrate the relevance of this phenomenon in nature. Conditions of biota reflect environmental changes, highlighting the adaptability of resident species that can be used as bioindicators of such changes. We report the morpho-anatomical plasticity of leaves of Avicennia schaueriana Stapf and Leechm. ex Moldenke, Laguncularia racemosa (L.) C.F.Gaertn. and Rhizophora mangle L., evaluated in three estuaries (Vitória bay, Santa Cruz and Itaúnas River; state of Espírito Santo, Brazil), considering five areas of mangrove ecosystems with diverse environmental issues. Two sampling sites are part of the Ecological Station Lameirão Island in Vitória bay, close to a harbor. A third sampling site in Cariacica (Vitória bay) is inside the Vitória harbor and also is influenced by domestic sewage. The fourth studied area (Santa Cruz) is part of Piraquê Mangrove Ecological Reservation, while the fifth (Itaúnas River) is a small mangrove, with sandy sediment and greater photosynthetically active radiation, also not strongly influenced by anthropic activity. Results pointed out the morpho-anatomical plasticity in studied species, showing that A. schaueriana and L. racemosa might be considered the most appropriate bioindicators to indicate different settings and environmental conditions. Particularly, the dry mass per leaf area (LMA) of A. schaueriana was the main biomarker measured. In our study, LMA of A. schaueriana was positively correlated with salinity (Spearman 0.71), Mn content (0.81) and pH (0.82) but negatively correlated with phosphorus content (− 0.63). Thus, the evaluation of modification in LMA of A. schaueriana pointed out changes among five studied sites, suggesting its use to reflect changes in the environment, which could be also useful in the future to evaluate the climate change. - Highlights: • We investigated adaptive modifications in plants in response to differences among three estuaries. • We used

  11. Strategic Forecasting

    DEFF Research Database (Denmark)

    Duus, Henrik Johannsen

    2016-01-01

    Purpose: The purpose of this article is to present an overview of the area of strategic forecasting and its research directions and to put forward some ideas for improving management decisions. Design/methodology/approach: This article is conceptual but also informed by the author’s long contact...... and collaboration with various business firms. It starts by presenting an overview of the area and argues that the area is as much a way of thinking as a toolbox of theories and methodologies. It then spells out a number of research directions and ideas for management. Findings: Strategic forecasting is seen...... as a rebirth of long range planning, albeit with new methods and theories. Firms should make the building of strategic forecasting capability a priority. Research limitations/implications: The article subdivides strategic forecasting into three research avenues and suggests avenues for further research efforts...

  12. Exposure Forecaster

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Exposure Forecaster Database (ExpoCastDB) is EPA's database for aggregating chemical exposure information and can be used to help with chemical exposure...

  13. Weighting of NMME temperature and precipitation forecasts across Europe

    Science.gov (United States)

    Slater, Louise J.; Villarini, Gabriele; Bradley, A. Allen

    2017-09-01

    Multi-model ensemble forecasts are obtained by weighting multiple General Circulation Model (GCM) outputs to heighten forecast skill and reduce uncertainties. The North American Multi-Model Ensemble (NMME) project facilitates the development of such multi-model forecasting schemes by providing publicly-available hindcasts and forecasts online. Here, temperature and precipitation forecasts are enhanced by leveraging the strengths of eight NMME GCMs (CCSM3, CCSM4, CanCM3, CanCM4, CFSv2, GEOS5, GFDL2.1, and FLORb01) across all forecast months and lead times, for four broad climatic European regions: Temperate, Mediterranean, Humid-Continental and Subarctic-Polar. We compare five different approaches to multi-model weighting based on the equally weighted eight single-model ensembles (EW-8), Bayesian updating (BU) of the eight single-model ensembles (BU-8), BU of the 94 model members (BU-94), BU of the principal components of the eight single-model ensembles (BU-PCA-8) and BU of the principal components of the 94 model members (BU-PCA-94). We assess the forecasting skill of these five multi-models and evaluate their ability to predict some of the costliest historical droughts and floods in recent decades. Results indicate that the simplest approach based on EW-8 preserves model skill, but has considerable biases. The BU and BU-PCA approaches reduce the unconditional biases and negative skill in the forecasts considerably, but they can also sometimes diminish the positive skill in the original forecasts. The BU-PCA models tend to produce lower conditional biases than the BU models and have more homogeneous skill than the other multi-models, but with some loss of skill. The use of 94 NMME model members does not present significant benefits over the use of the 8 single model ensembles. These findings may provide valuable insights for the development of skillful, operational multi-model forecasting systems.

  14. On the reliability of Seasonal Climate Forecasts

    CERN Document Server

    Weisheimer, Antje

    2013-01-01

    Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: How good are seasonal climate forecasts on a scale of 1-5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal climate forecasts are made from ensembles of integrations of numerical models of climate. We argue that goodness should be assessed primarily in terms of the probabilistic reliability of these ensemble-based forecasts and that a 5 should be reserved for systems which are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as a world leading operational institute producing seasonal climate forecasts. A wide range of goodness rankings, depending on regio...

  15. Accounting for three sources of uncertainty in ensemble hydrological forecasting

    Directory of Open Access Journals (Sweden)

    A. Thiboult

    2015-07-01

    Full Text Available Seeking for more accuracy and reliability, the hydrometeorological community has developed several tools to decipher the different sources of uncertainty in relevant modeling processes. Among them, the Ensemble Kalman Filter, multimodel approaches and meteorological ensemble forecasting proved to have the capability to improve upon deterministic hydrological forecast. This study aims at untangling the sources of uncertainty by studying the combination of these tools and assessing their contribution to the overall forecast quality. Each of these components is able to capture a certain aspect of the total uncertainty and improve the forecast at different stage in the forecasting process by using different means. Their combination outperforms any of the tool used solely. The EnKF is shown to contribute largely to the ensemble accuracy and dispersion, indicating that the initial condition uncertainty is dominant. However, it fails to maintain the required dispersion throughout the entire forecast horizon and needs to be supported by a multimodel approach to take into account structural uncertainty. Moreover, the multimodel approach contributes to improve the general forecasting performance and prevents from falling into the model selection pitfall since models differ strongly in their ability. Finally, the use of probabilistic meteorological forcing was found to contribute mostly to long lead time reliability. Particular attention needs to be paid to the combination of the tools, especially in the Ensemble Kalman Filter tuning to avoid overlapping in error deciphering.

  16. Forecasting metal prices: Do forecasters herd?

    DEFF Research Database (Denmark)

    Pierdzioch, C.; Rulke, J. C.; Stadtmann, G.

    2013-01-01

    We analyze more than 20,000 forecasts of nine metal prices at four different forecast horizons. We document that forecasts are heterogeneous and report that anti-herding appears to be a source of this heterogeneity. Forecaster anti-herding reflects strategic interactions among forecasters...

  17. The Impact of Sunlight Conditions on the Consistency of Vegetation Indices in Croplands—Effective Usage of Vegetation Indices from Continuous Ground-Based Spectral Measurements

    Directory of Open Access Journals (Sweden)

    Mitsunori Ishihara

    2015-10-01

    Full Text Available A ground-based network of spectral observations is useful for ecosystem monitoring and validation of satellite data. However, these observations contain inherent uncertainties due to the change of sunlight conditions. This study investigated the impact of changing solar zenith angles and diffuse/direct light conditions on the consistency of vegetation indices (normalized difference vegetation index (NDVI and green-red vegetation index (GRVI derived from ground-based spectral measurements in three different types of cropland (paddy field, upland field, cultivated grassland in Japan. In general, the vegetation indices decreased with decreasing solar zenith angle. This response was affected significantly by the growth stage and diffuse/direct light conditions. The decreasing response of the NDVI to the decreasing solar zenith angle was high during the middle growth stage (0.4 < NDVI < 0.8. On the other hand, a similar response of the GRVI was evident except in the early growth stage (GRVI < 0. The response of vegetation indices to the solar zenith angle was evident under clear sky conditions but almost negligible under cloudy sky conditions. At large solar zenith angles, neither the NDVI nor the GRVI were affected by diffuse/direct light conditions in any growth stage. These experimental results were supported well by the results of simulations based on a physically-based canopy reflectance model (PROSAIL. Systematic selection of the data from continuous diurnal spectral measurements in consideration of the solar light conditions would be effective for accurate and consistent assessment of the canopy structure and functioning.

  18. Mapping soil resistance under different soil water content conditions using indicator kriging

    Science.gov (United States)

    Miras-Avalos, J. M.; Bonnin-Acosta, J.; Sande-Fouz, P.; Pereira-Lanças, K.; Paz-Gonzalez, A.

    2009-04-01

    In many agricultural problems, it is of interest to map the zones where the variable under study shows the probability of being greater than a threshold value. Soil resistances higher than 2 MPa might difficult the establishment of cultures; therefore, further management or tillage techniques should be undertaken. The aim of this work was to map soil resistance using geostatistical techniques, therefore, an analysis of the spatial distribution of soil compaction and the influence of soil water content on the resistance to penetration was carried out. The studied clay-textured soil was managed under no-tillage practices. Soil resistance was described by the cone index which was obtained using a penetrometer. This attribute was assessed at 5 different depths, i.e. 0-10 cm, 10-20 cm, 20-30 cm, 30-40 cm and deeper than 40 cm, whereas soil water content was described at 0-20 cm and 20-40 cm. In the end, 73 data points were surveyed. Soil water conditions varied during the five different samplings. Statistical analysis showed that datasets followed a normal distribution, therefore, no transformation was required. Studied attributes showed low and non-significant correlation coefficients which impeded the application of cross-variogram and cokriging techniques. Because of the limited number of measured data, only the omnidirectional semivariogram was computed, and hence the spatial variability is assumed to be identical in all directions. Spatial dependence was observed in 33 out of 35 data series, both for cone index and soil water content. Fitted theoretical structures corresponded to exponential models in 20 cases, 10 Gaussian models and 3 spherical models. Nugget effect varied from 0 to 44.4 depending on the dataset and spatial dependence maximum range was 90 m. A strong spatial dependence was observed in 18 of the data sets whereas only 2 showed a weak autocorrelation. Taking into account the 2 MPa threshold, indicator kriging was used to map the soil resistance

  19. Weather Forecasting Systems and Methods

    Science.gov (United States)

    Mecikalski, John (Inventor); MacKenzie, Wayne M., Jr. (Inventor); Walker, John Robert (Inventor)

    2014-01-01

    A weather forecasting system has weather forecasting logic that receives raw image data from a satellite. The raw image data has values indicative of light and radiance data from the Earth as measured by the satellite, and the weather forecasting logic processes such data to identify cumulus clouds within the satellite images. For each identified cumulus cloud, the weather forecasting logic applies interest field tests to determine a score indicating the likelihood of the cumulus cloud forming precipitation and/or lightning in the future within a certain time period. Based on such scores, the weather forecasting logic predicts in which geographic regions the identified cumulus clouds will produce precipitation and/or lighting within during the time period. Such predictions may then be used to provide a weather map thereby providing users with a graphical illustration of the areas predicted to be affected by precipitation within the time period.

  20. Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island

    Directory of Open Access Journals (Sweden)

    Julien Morel

    2014-07-01

    Full Text Available Estimating sugarcane biomass is difficult to achieve when working with highly variable spatial distributions of growing conditions, like on Reunion Island. We used a dataset of in-farm fields with contrasted climatic conditions and farming practices to compare three methods of yield estimation based on remote sensing: (1 an empirical relationship method with a growing season-integrated Normalized Difference Vegetation Index NDVI, (2 the Kumar-Monteith efficiency model, and (3 a forced-coupling method with a sugarcane crop model (MOSICAS and satellite-derived fraction of absorbed photosynthetically active radiation. These models were compared with the crop model alone and discussed to provide recommendations for a satellite-based system for the estimation of yield at the field scale. Results showed that the linear empirical model produced the best results (RMSE = 10.4 t∙ha−1. Because this method is also the simplest to set up and requires less input data, it appears that it is the most suitable for performing operational estimations and forecasts of sugarcane yield at the field scale. The main limitation is the acquisition of a minimum of five satellite images. The upcoming open-access Sentinel-2 Earth observation system should overcome this limitation because it will provide 10-m resolution satellite images with a 5-day frequency.

  1. Validation of Body Condition Indices and Quantitative Magnetic Resonance in Estimating Body Composition in a Small Lizard

    Science.gov (United States)

    WARNER, DANIEL A.; JOHNSON, MARIA S.; NAGY, TIM R.

    2017-01-01

    Measurements of body condition are typically used to assess an individual’s quality, health, or energetic state. Most indices of body condition are based on linear relationships between body length and mass. Although these indices are simple to obtain, nonlethal, and useful indications of energetic state, their accuracy at predicting constituents of body condition (e.g., fat and lean mass) are often unknown. The objectives of this research were to (1) validate the accuracy of another simple and noninvasive method, quantitative magnetic resonance (QMR), at estimating body composition in a small-bodied lizard, Anolis sagrei, and (2) evaluate the accuracy of two indices of body condition (based on length–mass relationships) at predicting body fat, lean, and water mass. Comparisons of results from QMR scans to those from chemical carcass analysis reveal that QMR measures body fat, lean, and water mass with excellent accuracy in male and female lizards. With minor calibration from regression equations, QMR will be a reliable method of estimating body composition of A. sagrei. Body condition indices were positively related to absolute estimates of each constituent of body composition, but these relationships showed considerable variation around regression lines. In addition, condition indices did not predict fat, lean, or water mass when adjusted for body mass. Thus, our results emphasize the need for caution when interpreting body condition based upon linear measurements of animals. Overall, QMR provides an alternative noninvasive method for accurately measuring fat, lean, and water mass in these small-bodied animals. PMID:28035770

  2. A Recursive Kalman Filter Forecasting Approach

    OpenAIRE

    Douglas R. Kahl; Johannes Ledolter

    1983-01-01

    This paper examines the forecasting accuracy and the cost effectiveness of time series models with time-varying coefficients. A simulation study investigates the potential forecasting benefits of a proposed Kalman filter type adaptive estimation and forecasting approach. It is found that: (1) When appropriate, the time-varying coefficient approach leads to better forecasts than the constant coefficient procedures. (2) A simple decision rule, which indicates whether time-varying coefficient mo...

  3. Manganese-oxide-coated redox bars as an indicator of reducing conditions in soils.

    Science.gov (United States)

    Dorau, Kristof; Mansfeldt, Tim

    2015-03-01

    Identification of reducing conditions in soils is of concern not only for pedogenesis but also for nutrient and pollutant dynamics. We manufactured manganese (Mn)-oxide-coated polyvinyl chloride bars and proved their suitability for the identification of reducing soil conditions. Birnessite was synthesized and coated onto white polyvinyl chloride bars. The dark brown coatings were homogenous and durable. As revealed by microcosm devices with adjusted redox potentials (E), under oxidizing conditions (E ∼450 mV at pH 7) there was no Mn-oxide removal. Reductive dissolution of Mn-oxides, which is expressed by the removal of the coatings, started under weakly reducing conditions (E ∼175 mV) and was more intensive under moderately reducing conditions (∼80 mV). According to thermodynamics, the removal of Mn-oxide coatings (225 mm d) exceeded the removal of iron (Fe)-oxide coatings (118 mm d) in soil column experiments. This was confirmed in a soil with a shallow and strongly fluctuating water table where both types of redox bars were inserted. Consequently, it was possible to identify reducing conditions in soils using Mn-oxide-coated bars. We recommend this methodology for short-term monitoring because tri- and tetravalent Mn is the preferred electron acceptor compared with trivalent Fe, and this additionally offers the possibility of distinguishing between weakly and moderately reducing conditions. If dissolved Fe is abundant in soils, the possibility of nonenzymatic reduction of Mn has to be taken into account.

  4. 49 CFR 236.512 - Cab signal indication when locomotive enters block where restrictive conditions obtain.

    Science.gov (United States)

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false Cab signal indication when locomotive enters block... TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.512 Cab signal indication when locomotive enters block where...

  5. Winterkill indicator model, Crop Condition Assessment Division (CCAD) data base interface driver, user's manual

    Science.gov (United States)

    Hansen, R. F. (Principal Investigator)

    1981-01-01

    Instructions are given for using the Winterkill indicator model CCAD data base interface driver. The purpose of the system is to interface the Winterkill Indicator Model with the CCAD operational data base. The interface driver routine decides what meteorological stations should be processed and calls the proper subroutines to process the stations.

  6. Wheat stress indicator model, Crop Condition Assessment Division (CCAD) data base interface driver, user's manual

    Science.gov (United States)

    Hansen, R. F. (Principal Investigator)

    1981-01-01

    The use of the wheat stress indicator model CCAD data base interface driver is described. The purpose of this system is to interface the wheat stress indicator model with the CCAD operational data base. The interface driver routine decides what meteorological stations should be processed and calls the proper subroutines to process the stations.

  7. a note on condition indices for adult male impala, aepycero-s ...

    African Journals Online (AJOL)

    haematocrit value (packed cell volume) using gaduated small calibre ... mass and heart girth are changing parameters which could be divided ..... Evaluating condition in free ranging red deer with special reference to New Zealand. N.Z. J- Sci.

  8. Assessing the condition of riverine systems using multimetric indices: An example from Oregon's Calapooia basin

    Science.gov (United States)

    Streams and rivers of the Western United States are susceptible to the combined influences of climate change and an expanding human population. Empirical tools for assessing instream conditions play a critical role in monitoring change, preventing degradation, and mitigating imp...

  9. Diagnostic measures for assessing numerical forecasts of African Easterly Waves

    Directory of Open Access Journals (Sweden)

    Nicole Sander

    2008-04-01

    Full Text Available The utility of a number of diagnostic measures for assessing forecasts of the synoptic-scale flow over West Africa and the eastern Atlantic is investigated. The forecasts were carried out using the COSMO Model provided by the Deutscher Wetterdienst (DWD for a three week period in 2004. During this period a number of African Easterly Waves (AEWs were observed, three of which subsequently developed into the Hurricanes Danielle, Frances and Ivan. A sequence of 72 h forecasts were initialised twice daily from the DWD global analysis, using analyses and 12 h forecasts for the boundary conditions. A variety of diagnostics were used to assess the forecasts including objective analyses of jet and trough axes and Hovmoeller plots. The zonal wind was averaged along the objectively analysed jet axes over West Africa and the Atlantic for the forecasts and analyses. This provides a robust measure of the jet strength that takes into account the spatial variability of the jet location and is not tied to either the maximum wind speed or a particular geographic location. Application of this measure to assess the forecasts showed that overall the jet strength was well represented. The largest errors were associated with local jet variations due to misrepresentation of the African Easterly Waves in the forecasts. The objectively analysed trough axes are used to give a visual indication of the forecast quality. Hovmoeller plots proved useful for assessing the evolution of the AEWs, although the interpretation was difficult when convection in the model produced small-scale but strong vorticity anomalies. The results of this study will be applied to future case studies based on the African Monsoon: Multidisciplinary Analysis (AMMA special observing periods.

  10. Earthquake forecasting: Statistics and Information

    CERN Document Server

    Gertsik, V; Krichevets, A

    2013-01-01

    We present an axiomatic approach to earthquake forecasting in terms of multi-component random fields on a lattice. This approach provides a method for constructing point estimates and confidence intervals for conditional probabilities of strong earthquakes under conditions on the levels of precursors. Also, it provides an approach for setting multilevel alarm system and hypothesis testing for binary alarms. We use a method of comparison for different earthquake forecasts in terms of the increase of Shannon information. 'Forecasting' and 'prediction' of earthquakes are equivalent in this approach.

  11. Forecasting of the processing time as the base of simulation of the production system behavior in real conditions

    Directory of Open Access Journals (Sweden)

    Vukićević Milan

    2005-01-01

    Full Text Available The absence of precise information on the magnitudes that determine the behavior of the production system generates the disturbances of the system. The consequence is the low efficacy of the system and the high costs. Therefore, it is necessary to create the base for the prediction of individual magnitudes and thus enable the simulation of the production system behavior in real conditions. The information on time norms has a special significance. It is the base of planning the terms and of defining a part of direct costs. Modern approach in the identification of standard times should be established on new foundations. It should appreciate the specificities of the present moment, as well as the future tendencies in wood processing. They are the production system dynamistic, conditioned predominantly by discontinuous production, as well as by the necessity of cooperation of the production systems. In this study, the approach to the identification of standard times is original, supporting the modern tendencies in wood processing and it has an applicative character.

  12. Using Acid Number as a Leading Indicator of Refrigeration and Air Conditioning System Performance

    Energy Technology Data Exchange (ETDEWEB)

    Dennis Cartlidge; Hans Schellhase

    2003-07-31

    number (TAN), which includes both mineral acids and organic acids, is therefore a useful indicator which can be used to monitor the condition of the system in order to perform remedial maintenance, when required, to prevent system failure. The critical TAN value is the acid level at which remedial action should be taken to prevent the onset of rapid acid formation which can result in system failure. The level of 0.05 mg KOH/g of oil was established for CFC/mineral oil systems based on analysis of 700 used lubricants from operating systems and failed units. There is no consensus within the refrigeration industry as to the critical TAN value for HFC/POE systems, however, the value will be higher than the CFC/mineral oil systems critical TAN value because of the much weaker organic acids produced from POE. A similar study of used POE lubricants should be performed to establish a critical TAN limit for POE systems. Titrimetric analysis per ASTM procedures is the most commonly used method to determine TAN values in lubricants in the refrigeration industry and other industries dealing with lubricating oils. For field measurements, acid test kits are often used since they provide rapid, semi-quantitative TAN results.

  13. EXTREME WINTERS IN XX–XXI CENTURIES AS INDICATORS OF SNOWINESS AND AVALANCHE HAZARD IN THE PAST AND EXPECTED CLIMATE CHANGE CONDITIONS

    Directory of Open Access Journals (Sweden)

    A. D. Oleynikov

    2012-01-01

    Full Text Available Currently, due to the global climate change and increasing frequency of weather events focus is on prediction of climate extremes. Large-scale meteorological anomalies can cause long-term paralysis of social and economic infrastructure of the major mountain regions and even individual states. In winter periods, these anomalies are associated with prolonged heavy snowfalls and associated with them catastrophic avalanches which cause significant social and economic damage. The climate system maintains a certain momentum during periods of adjustment and transition to other conditions in the ratio of heat and moisture and contains a climate «signal» of the climates of the past and the future. In our view seasonal and yearly extremes perform the role of these indicators, study of which enables for a deeper understanding and appreciation of the real situation of the climate periods related to the modern ones. The paper provides an overview of the criteria for selection of extreme winters. Identification of extremely cold winters during the period of instrumental observation and assessment of their snowiness and avalanche activity done for the Elbrus region, which is a model site for study of the avalanche regime in the Central Caucasus. The studies aim to identify the extreme winters in the Greater Caucasus, assess their frequency of occurrence, characterize the scale and intensity of the avalanche formation. The data obtained can be used to identify winter-analogues in the reconstruction and long-term forecast of avalanches. 

  14. The potential value of seasonal forecasts in a changing climate

    Science.gov (United States)

    Winsemius, H. C.; Dutra, E.; Engelbrecht, F. A.; Archer Van Garderen, E.; Wetterhall, F.; Pappenberger, F.; Werner, M. G. F.

    2013-12-01

    Subsistence farming in Southern Africa is vulnerable to extreme weather conditions. The yield of rain-fed agriculture depends largely on rainfall-related factors such as total seasonal rainfall, anomalous onsets and lengths of the rainy season and the frequency of occurrence of dry spells. Livestock, in turn, may be seriously impacted by climatic stress with, for example, exceptionally hot days, affecting condition, reproduction, vulnerability to pests and pathogens and, ultimately, morbidity and mortality. Climate change may affect the frequency and severity of extreme weather conditions, impacting on the success of subsistence farming. A potentially interesting adaptation measure comprises the timely forecasting and warning of such extreme events, combined with mitigation measures that allow farmers to prepare for the event occurring. This paper investigates how the frequency of extreme events may change in the future due to climate change over southern Africa and, in more detail, the Limpopo basin using a set of climate change projections from several regional climate model downscalings. Furthermore the paper assesses the predictability of these indicators by seasonal meteorological forecasts of the European Centre for Medium-range Weather Forecasts (ECMWF) seasonal forecasting system. The focus is on the frequency of dry spells as well as the frequency of heat stress conditions expressed in the Temperature Heat Index. In areas where their frequency of occurrence increases in the future and predictability is found, seasonal forecasts will gain importance in the future as they can more often lead to informed decision making to implement mitigation measures. The multi-model climate projections suggest that the frequency of dry spells is not likely to increase substantially, whereas there is a clear and coherent signal among the models, of an increase in the frequency of heat stress conditions by the end of the century. The skill analysis of the seasonal forecast

  15. General forecasting correcting formula

    OpenAIRE

    Harin, Alexander

    2009-01-01

    A general forecasting correcting formula, as a framework for long-use and standardized forecasts, is created. The formula provides new forecasting resources and new possibilities for expansion of forecasting including economic forecasting into the areas of municipal needs, middle-size and small-size business and, even, to individual forecasting.

  16. General forecasting correcting formula

    OpenAIRE

    2009-01-01

    A general forecasting correcting formula, as a framework for long-use and standardized forecasts, is created. The formula provides new forecasting resources and new possibilities for expansion of forecasting including economic forecasting into the areas of municipal needs, middle-size and small-size business and, even, to individual forecasting.

  17. Information Forecasting.

    Science.gov (United States)

    Hanneman, Gerhard J.

    Information forecasting provides a means of anticipating future message needs of a society or predicting the necessary types of information that will allow smooth social functioning. Periods of unrest and uncertainty in societies contribute to "societal information overload," whereby an abundance of information channels can create communication…

  18. Accuracy of forecasts in strategic intelligence.

    Science.gov (United States)

    Mandel, David R; Barnes, Alan

    2014-07-29

    The accuracy of 1,514 strategic intelligence forecasts abstracted from intelligence reports was assessed. The results show that both discrimination and calibration of forecasts was very good. Discrimination was better for senior (versus junior) analysts and for easier (versus harder) forecasts. Miscalibration was mainly due to underconfidence such that analysts assigned more uncertainty than needed given their high level of discrimination. Underconfidence was more pronounced for harder (versus easier) forecasts and for forecasts deemed more (versus less) important for policy decision making. Despite the observed underconfidence, there was a paucity of forecasts in the least informative 0.4-0.6 probability range. Recalibrating the forecasts substantially reduced underconfidence. The findings offer cause for tempered optimism about the accuracy of strategic intelligence forecasts and indicate that intelligence producers aim to promote informativeness while avoiding overstatement.

  19. Deficiency indices of a differential operator satisfying certain matching interface conditions

    Directory of Open Access Journals (Sweden)

    Pallav Kumar Baruah

    2005-03-01

    Full Text Available A pair of differential operators with matching interface conditions appears in many physical applications such as: oceanography, the study of step index fiber in optical fiber communication, and one dimensional scattering in quantum theory. Here we initiate the study the deficiency index theory of such operators which precedes the study of the spectral theory.

  20. The use of morphological and histological features as nutritional condition indices of Pagrus pagrus larvae

    Directory of Open Access Journals (Sweden)

    Marina Vera Diaz

    Full Text Available Morphometrical and histological techniques were employed to characterize Pagrus pagrus larvae nutritional condition. Larvae were reared in laboratory under controlled conditions with the main objective of testing whether these methodologies allowed finding differences between larvae from different feeding treatments. Once yolk was consumed (three days after hatching larvae were assigned to a feeding treatment: starved during the whole experiment; delayed feeding, starved during three days; fed during the entire experiment. Algae (Nannochloropsis oculata and rotifers (Brachionus plicatilis were provided to larvae for feed treatments. Larvae were fixed daily; for morphometrical purposes in 5% formaldehyde solution, and in Bouin for histological sections. Results herein obtained showed that both methodologies are sensitive enough to distinguish larvae characterized by different nutritional condition states obtained from the feeding treatments. Consequently, these methodologies could be employed in wild red porgy larvae in order to asses their nutritional condition. These techniques could also be employed to check larval quality obtained with aquaculture purposes to estimate the effects of changes in rearing protocols or kind of food supply and thus, to guaranty a higher survival of early developmental stages of reared larvae.

  1. Circulation indices over the Mediterranean and Europe and their relationship with rainfall conditions across the Mediterranean

    Science.gov (United States)

    Kutiel, H.; Maheras, P.; Guika, S.

    1996-09-01

    Circulation types were identified by means of zonal and meridional indices calculated separately over ten different regions of 20° × 20° over the Mediterranean and Europe. Seasonal and annual rainfall totals in four stations Lisbon, Luqa (Malta), Athens and Jerusalem, were compared with circulation types for the period 1873 1991. Correlation coefficients of circulation indices with precipitation, for each station in each season were calculated and mapped. An oscillation in the meridional index during the winter and the spring, between the western and eastern Mediterranean, was detected. Time series analysis of the circulation indices demonstrates a significant reduction in zonality and an increase in meridionality mainly in spring and in summer, over most of the study area.

  2. Dynamic preload indicators fail to predict fluid responsiveness in open-chest conditions

    NARCIS (Netherlands)

    de Waal, Eric E. C.; Rex, Steffen; Kruitwagen, Cas L. J. J.; Kalkman, Cor J.; Buhre, Wolfgang F.

    2009-01-01

    Objective: Dynamic preload indicators like pulse pressure variation (PPV) and stroke volume variation (SVV) are increasingly being used for optimizing cardiac preload since they have been demonstrated to predict fluid responsiveness in a variety of perioperative settings. However, in open-chest cond

  3. Population of soil litter invertebrates as an indicator of critical condition indegrading forest ecosystems

    Directory of Open Access Journals (Sweden)

    A. K. Ibragimov

    2005-09-01

    Full Text Available Conception of three critical levels of the anthropogenic degradation of native ecosystems is proposed. The total crisis of the ecosystem correlates with the entire destruction of the soil environment. The soil invertebrates population may serve as an indicator of this process.

  4. [THE MICRO-ECOLOGY OF DIGESTIVE TRACT AS AN INDICATOR OF HUMAN HEALTH CONDITIONS].

    Science.gov (United States)

    Samoukina, A M; Mikhailova, E S; Chervinets, V M; Mironov A Yu; Alekseeva, Yu A

    2015-06-01

    The study was carried out to analyze qualitative and quantitative parameters of oral fluid and feces in 74 healthy individuals of different age groups. In most of the cases, alterations of micro-ecology are established characterizing by decreasing of amount of indigenous micro-flora and increasing of number of opportunistic pathogenic microorganisms of genera of Staphylococcus, Bacillus, Candida. The degree of evidence of these alterations reliably increases with age. It is established that microbiota, initial and terminal biotopes of digestive tract are closely interrelated and have number of common characteristics depending on age, hormonal and immune status and reflect conditions of micro-biocenosis of digestive tract in general. The character and degree of evidence of alterations of micro-biocenosis can be an effective diagnostic criterion for complex evaluation of human health conditions with following formation of risk groups in need of particular volume of correction activities.

  5. Energy efficiency index to artificially conditioned buildings; Indice de eficiencia energetica para edificios climatizados artificialmente

    Energy Technology Data Exchange (ETDEWEB)

    Jota, Patricia Romeiro da Silva; Santos, Carla da Silva; Costa, Kelly Luciene C. [Centro Federal de Educacao Tecnologica de Minas Gerais (CEMIG/CEFET), Belo Horizonte, MG (Brazil). Centro de Pesquisa em Energia Inteligente

    2010-07-01

    Conditioning buildings has been growing in number and are responsible for a significant portion of the energy used worldwide. The building energy use can be measured by the index of energy performance and specific fuel consumption (EC). The specific consumption is an index where the energy is normalized by the factors that affect energy use in order to obtain an index to explain variations in consumption. In this paper, we present a methodology to obtain a specific consumption that takes into account one of the factors that most affect energy use in these buildings, that is, the external temperature. The study is based on analysis of consumption of air conditioning system according to temperature. Through this analysis we obtain a function to facilitate the standardization of energy use, depending on the temperature outside. This methodology was tested in previous work on real buildings without stratification of energy, and this work will be presented a case study of a building whose energy measurement is stratified. The proposed index is the ratio between the energy consumption of air conditioning system corrected by the temperature through the function K(T). It was possible to demonstrate the efficiency of the index to eliminate the effect of temperature and thus to evaluate the evolution of specific consumption over the months analyzed. (author)

  6. A comparison of several indices for assessing body condition of Mongolian gazelle

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Riney kidney fat index (RKFI), whole kidney fat index (WKFI), femur marrow fat index (FMFI), and tibia marrow fat index (TMFI) of 51 Mongolian gazelles (Procapra gutturosa), collected in Hulunbeier grassland, Inner Mongolian, China, were measured during spring, autumn and winter in 1997-98. These different indexes were compared for using them in assessing the body condition. There was a linear relationship (y=0.9444x-20.139; r=0.9454; p<0.01) between RKFI and KMFI. A linear relationship (y=0.9348x+1.1843; r=0.9875; P<0.01) between TMFI and FMFI also occurred for gazelles. There was a curvilinear relationship (y=31.44Ln(x) -44.403; r=0.8643; P<0.01) between FMFI and RKFI. FMFI remained high, while RKFI decreased to a certain extent. After most of the kidney fat was used, the femur marrow fat abruptly decreased. The results showed that the kidney fat index is more adequate for evaluating the population nutrition in good condition, but marrow fat index was more useful for assessing in poorer nutritional condition.

  7. Exploring What Determines the Use of Forecasts of Varying Time Periods in Guanacaste, Costa Rica

    Science.gov (United States)

    Babcock, M.; Wong-Parodi, G.; Grossmann, I.; Small, M. J.

    2016-12-01

    Weather and climate forecasts are promoted as ways to improve water management, especially in the face of changing environmental conditions. However, studies indicate many stakeholders who may benefit from such information do not use it. This study sought to better understand which personal factors (e.g., trust in forecast sources, perceptions of accuracy) were important determinants of the use of 4-day, 3-month, and 12-month rainfall forecasts by stakeholders in water management-related sectors in the seasonally dry province of Guanacaste, Costa Rica. From August to October 2015, we surveyed 87 stakeholders from a mix of government agencies, local water committees, large farms, tourist businesses, environmental NGO's, and the public. The result of an exploratory factor analysis suggests that trust in "informal" forecast sources (traditional methods, family advice) and in "formal" sources (government, university and private company science) are independent of each other. The result of logistic regression analyses suggest that 1) greater understanding of forecasts is associated with a greater probability of 4-day and 3-month forecast use, but not 12-month forecast use, 2) a greater probability of 3-month forecast use is associated with a lower level of trust in "informal" sources, and 3), feeling less secure about water resources, and regularly using many sources of information (and specifically formal meetings and reports) are each associated with a greater probability of using 12-month forecasts. While limited by the sample size, and affected by the factoring method and regression model assumptions, these results do appear to suggest that while forecasts of all times scales are used to some extent, local decision makers' decisions to use 4-day and 3-month forecasts appear to be more intrinsically motivated (based on their level of understanding and trust) and the use of 12-month forecasts seems to be more motivated by a sense of requirement or mandate.

  8. High-resolution hydrological seasonal forecasting for water resources management over Europe

    Science.gov (United States)

    Pan, Ming; Wanders, Niko; Wood, Eric; Sheffield, Justin; Samaniego, Luis; Thober, Stephan; Kumar, Rohini; Prudhomme, Christel; Houghton-Carr, Helen

    2017-04-01

    To support the decision-making process at the seasonal time scale, hydrological forecasts with a high temporal and spatial resolution are required to provide the level of information needed by water managers. So far high-resolution seasonal forecasts have been unavailable due to 1) lack of availability in meteorological seasonal forecasts, 2) the coarse temporal resolution of meteorological seasonal forecasts, requiring temporal downscaling, and 3) lack of consistency between observations and seasonal forecasts, requiring bias-correction. As part of the EDgE (End-to-end Demonstrator for improved decision making in the water sector in Europe) project, we have created a unique dataset of hydrological seasonal forecasts derived from four atmospheric circulation models (CanCM4, FLOR-B01, ECMF, LFPW) in combination with four global hydrological models (PCR-GLOBWB, VIC, mHM, Noah-MP). The forecasts provide daily values at 5-km spatial resolution and are bias corrected against E-OBS meteorological observations. Consistency in the LSM parameterization ensures synergy in the hydrological forecasts, resulting in 208 forecasts at any given day over Europe. The forecast results are communicated to stakeholders via Sectoral Climate Impact Indicators (SCIIs) that have been co-designed in collaboration with end-users and stakeholders inside the EDgE project. An example of an SCII is the percentage of ensemble realizations above the 10th percentile of monthly river flow or below the 90th percentile, including the persistency in the forecast with increasing lead times. Results show that skillful discharge forecasts can be made throughout Europe 3 months in advance, with predictability up to 6 months for Northern Europe due to the impact of snow. The predictability of soil moisture is limited to the first three months, due to the significant impact of precipitation and the short memory in the initial conditions (only for the first month). The groundwater recharge predictability

  9. Integrating Condition Indicators and Usage Parameters for Improved Spiral Bevel Gear Health Monitoring

    Science.gov (United States)

    Dempsey, Paula J.; Handschuh, Robert F.; Delgado, Irebert, R.

    2013-01-01

    The objective of this study was to illustrate the importance of combining Health Usage Monitoring Systems (HUMS) data with usage monitoring system data when detecting rotorcraft transmission health. Three gear sets were tested in the NASA Glenn Spiral Bevel Gear Fatigue Rig. Damage was initiated and progressed on the gear and pinion teeth. Damage progression was measured by debris generation and documented with inspection photos at varying torque values. A contact fatigue analysis was applied to the gear design indicating the effect temperature, load and reliability had on gear life. Results of this study illustrated the benefits of combining HUMS data and actual usage data to indicate progression of damage for spiral bevel gears.

  10. Can Body Condition and Somatic Indices be Used to Evaluate Metal-Induced Stress in Wild Small Mammals?

    Directory of Open Access Journals (Sweden)

    Nicolas Tête

    Full Text Available Wildlife is exposed to natural (e.g., food availability and quality, parasitism and anthropogenic stressors (e.g., habitat fragmentation, toxicants. Individual variables (e.g., age, gender affect behaviour and physiology of animals. Together, these parameters can create both great inter-individual variations in health indicators and interpretation difficulties. We investigated the relevance of body condition and somatic indices (liver, kidneys as indicators of health status in wood mice (Apodemus sylvaticus, n = 560 captured along a metal pollution gradient in four landscape types (30 sampling squares 500-m sided. The indices were calculated using a recently proposed standard major axis regression instead of an ordinary least square regression. After considering age and gender for the body condition index, no landscape type influence was detected in the indices. However, important index variability was observed between sampling squares; this effect was included as a random effect in linear models. After integrating all individual and environmental variables that may affect the indices, cadmium (Cd concentrations in both the liver and kidneys were negatively related to body condition and liver indices only for individuals from highly contaminated sites. Lead in the liver was negatively related to the liver index, and Cd in kidneys was positively linked to the kidney index, potentially suggesting metal-induced stress. However, interpretation of these indices as a wildlife ecotoxicology tool should be performed with caution due to the sensitivity of potentially confounding variables (e.g., individual factors and environmental parameters.

  11. Post Processing Numerical Weather Prediction Model Rainfall Forecasts for Use in Ensemble Streamflow Forecasting in Australia

    Science.gov (United States)

    Shrestha, D. L.; Robertson, D.; Bennett, J.; Ward, P.; Wang, Q. J.

    2012-12-01

    Through the water information research and development alliance (WIRADA) project, CSIRO is conducting research to improve flood and short-term streamflow forecasting services delivered by the Australian Bureau of Meteorology. WIRADA aims to build and test systems to generate ensemble flood and short-term streamflow forecasts with lead times of up to 10 days by integrating rainfall forecasts from Numerical Weather Prediction (NWP) models and hydrological modelling. Here we present an overview of the latest progress towards developing this system. Rainfall during the forecast period is a major source of uncertainty in streamflow forecasting. Ensemble rainfall forecasts are used in streamflow forecasting to characterise the rainfall uncertainty. In Australia, NWP models provide forecasts of rainfall and other weather conditions for lead times of up to 10 days. However, rainfall forecasts from Australian NWP models are deterministic and often contain systematic errors. We use a simplified Bayesian joint probability (BJP) method to post-process rainfall forecasts from the latest generation of Australian NWP models. The BJP method generates reliable and skilful ensemble rainfall forecasts. The post-processed rainfall ensembles are then used to force a semi-distributed conceptual rainfall runoff model to produce ensemble streamflow forecasts. The performance of the ensemble streamflow forecasts is evaluated on a number of Australian catchments and the benefits of using post processed rainfall forecasts are demonstrated.

  12. Consequences of keeping Mytilus in the laboratory as assessed by different cellular condition indices

    Science.gov (United States)

    Cajaraville, M. P.; Díez, G.; Marigómez, I. A.; Angulo, E.

    1991-12-01

    Mytilus galloprovincialis Lmk. were maintained in the laboratory for three months in a semicontinuous water flow system. Animals were fed a commercial filter-feeder food and sampled after 0, 21, 35, 49, 77, and 91 days. In order to establish whether laboratory conditions and the food used were deleterious to mussels, their health status was assessed by quantifying different histological parameters of the digestive gland tissue. It was concluded that mussels kept for more than 35 days under the described laboratory conditions showed signs of stress presumably caused by the reproductive state of the mussels investigated. The food used and the nutrition-related health status of the animals were adequate, as shown by transmission electron microscopical studies after the 91-day maintenance period. A stress response was also evoked by a 10-day starvation period, which was reflected by an increased proportion of type I and type IV digestive tubules, and a reduced “Mean Epithelial Thickness” (MET). Finally, the results demonstrate the sensitivity of quantitative histological diagnosis in comparison to subjective tubule grading procedures in the assessment of the degree of stress experienced by mussels.

  13. US of the elbow: indications, technique, normal anatomy, and pathologic conditions.

    Science.gov (United States)

    Konin, Gabrielle P; Nazarian, Levon N; Walz, Daniel M

    2013-01-01

    The elbow, a synovial hinge joint, is a common site of disease. Ultrasonography (US) has become an important imaging modality for evaluating pathologic conditions of the elbow. This powerful imaging tool has the advantages of outstanding spatial resolution, clinical correlation with direct patient interaction, dynamic assessment of disease, and the ability to guide interventions. Unlike most other imaging modalities, US allows the contralateral elbow to be imaged simultaneously, providing an internal control and comparison with normal anatomy. A useful approach to US evaluation of the elbow is to divide it into four compartments: anterior, lateral, medial, and posterior. US of the elbow has varied clinical applications, including evaluation and treatment of lateral and medial epicondylitis, imaging of biceps and triceps musculotendinous injuries, evaluation of ulnar collateral ligament laxity, diagnosis of joint effusions and intraarticular bodies, and evaluation of peripheral nerves for neuropathy and subluxation. US can also be used to evaluate soft-tissue masses about the elbow. Knowledge of the normal US anatomy of the elbow, familiarity with the technique of elbow US, and awareness of the US appearances of common pathologic conditions of the elbow along with their potential treatment options will optimize radiologists' diagnostic assessment and improve patient care. Supplemental material available at http://radiographics.rsna.org/lookup/suppl/doi:10.1148/rg.334125059/-/DC1.

  14. Soil pollution indices conditioned by medieval metallurgical activity - A case study from Krakow (Poland).

    Science.gov (United States)

    Kowalska, Joanna; Mazurek, Ryszard; Gąsiorek, Michał; Setlak, Marcin; Zaleski, Tomasz; Waroszewski, Jaroslaw

    2016-11-01

    The studied soil profile under the Main Market Square (MMS) in Krakow was characterised by the influence of medieval metallurgical activity. In the presented soil section lithological discontinuity (LD) was found, which manifests itself in the form of cultural layers (CLs). Moreover, in this paper LD detection methods based on soil texture are presented. For the first time, three different ways to identify the presence of LD in the urban soils are suggested. The presence of LD had an influence on the content and distribution of heavy metals within the soil profile. The content of heavy metals in the CLs under the MMS in Krakow was significantly higher than the content in natural horizons. In addition, there were distinct differences in the content of heavy metals within CLs. Profile variability and differences in the content of heavy metals and phosphorus within the CLs under the MMS were activity indicators of Krakow inhabitants in the past. This paper presents alternative methods for the assessment of the degree of heavy metal contamination in urban soils using selected pollution indices. On the basis of the studied total concentration of heavy metals (Zn, Pb, Cu, Mn, Cr, Cd, Ni, Sn, Ag) and total phosphorus content, the Geoaccumulation Index (Igeo), Enrichment Factor (EF), Sum of Pollution Index (PIsum), Single Pollution Index (PI), Nemerow Pollution Index (PINemerow) and Potential Ecological Risk (RI) were calculated using different local and reference geochemical backgrounds. The use of various geochemical backgrounds is helpful to evaluate the assessment of soil pollution. The individual CLs differed from each other according to the degree of pollution. The different values of pollution indices within the studied soil profile showed that LDS should not be evaluated in terms of contamination as one, homogeneous soil profile but each separate CL should be treated individually.

  15. SOFT project: a new forecasting system based on satellite data

    Science.gov (United States)

    Pascual, Ananda; Orfila, A.; Alvarez, Alberto; Hernandez, E.; Gomis, D.; Barth, Alexander; Tintore, Joaquim

    2002-01-01

    The aim of the SOFT project is to develop a new ocean forecasting system by using a combination of satellite dat, evolutionary programming and numerical ocean models. To achieve this objective two steps are proved: (1) to obtain an accurate ocean forecasting system using genetic algorithms based on satellite data; and (2) to integrate the above new system into existing deterministic numerical models. Evolutionary programming will be employed to build 'intelligent' systems that, learning form the past ocean variability and considering the present ocean state, will be able to infer near future ocean conditions. Validation of the forecast skill will be carried out by comparing the forecasts fields with satellite and in situ observations. Validation with satellite observations will provide the expected errors in the forecasting system. Validation with in situ data will indicate the capabilities of the satellite based forecast information to improve the performance of the numerical ocean models. This later validation will be accomplished considering in situ measurements in a specific oceanographic area at two different periods of time. The first set of observations will be employed to feed the hybrid systems while the second set will be used to validate the hybrid and traditional numerical model results.

  16. 风速与风电功率的联合条件概率预测方法%Joint Conditions Probability Forecast Method for Wind Speed and Wind Power

    Institute of Scientific and Technical Information of China (English)

    王松岩; 于继来

    2011-01-01

    采用确定性预测模型对风速和风电功率进行预测,无法传递结果的概率可信程度,不适应风险分析与调控应用的需要.为此,建立了以当前时段实测风速和下一时段预报风速为联合条件的离散预报误差概率统计(forecast errorprobability distribution,FEPD)模型,并以该模型对未来时段的预报误差概率分布进行预测.首先由历史统计结果确定修正因子,利用风速波动分布特征(speed disturbance probability distribution,DPD)对预报误差概率分布进行偏度修正,再将修正后的预报误差概率分布与由确定性预测算法得到的结果相结合,从而得到风速概率性预测结果.实例表明,所提方法可以较好地预测未来时段风速及风电功率变化的概率分布结果.%Certainty forecast method could not meet the need of risk analysis and system operation because probability information was not included. This paper chose measured speed and forecast speed as joint conditions and made a discrete forecast error probability distribution (FEPD) model which could be used for next-time wind speed probability forecast. Firstly speed disturbance probability distribution (DPD) was adopted to modify skewness of FEPD with modification factor, then modified FEPD and certainty forecast value were combined to obtain wind speed probability distribution forecast result. DPD, FEPD and modification factor were all obtained from historical data with different statistical periods. In modification process, samples real-time scrolling technology was used to guarantee the distribution reasonable and promptly. Result shows that this model has a good effect when forecasting wind speed or power output probability distribution in next time range.

  17. The influence of visual impairment on separate indicators of a functional condition of touch systems of pupils of middle classes

    Directory of Open Access Journals (Sweden)

    Lydmyla Shesterova

    2015-08-01

    Full Text Available Purpose: to define and to compare separate indicators of a functional condition of touch systems of pupils of middle classes with visual impairment and their contemporaries with normal vision. Materials and Methods: pupils of middle classes with visual impairment and pupils of middle classes with normal vision took part in the research. During the research such methods were applied: analysis and synthesis of references, perimetry, determination of visual acuity by means of a special table, acumetry, esthesiometry, determination of resistance of a vestibular mechanism to rotary loadings, methods of mathematical statistics. Results: the analysis of separate indicators of a functional condition of visual, acoustical, vestibular and tactile analyzers of pupils with visual impairment and without them is carried out. Conclusions: it is established that the studied indicators of a functional condition of touch systems at pupils of middle classes with visual impairment are better, than at their contemporaries with normal vision

  18. Assessment of students’ health condition by indicators of adaptation potential, biological age and bio-energetic reserves of organism

    Directory of Open Access Journals (Sweden)

    Martyniuk O.V.

    2015-06-01

    Full Text Available Purpose: to assess students’ health condition by indicators of adaptation potential, biological age and express-assessment. Material: in the research 47 first and second year girl students participated, who belonged to main health group. Results: we distributed the girl students into three groups: 14.89% of them were included in group with “safe” health condition; 34.04% - in group of “third state”; 51.06% were related to group with “ dangerous” health condition. We established that dangerous level was characterized by energy potential of below middle and low level. It is accompanied by accelerated processes of organism’s age destructions and tension of regulation mechanisms. Conclusions: the received results permit to further develop and generalize the data of students’ health’s assessment by indicators of adaptation potentials, biological age and physical health’s condition.

  19. Indices for physiological assessment of nutritional condition in pregnant collared peccaries (Tayassu tajacu).

    Science.gov (United States)

    Lochmiller, R L; Hellgren, E C; Varner, L W; Grant, W E

    1988-07-01

    Hematological and serum biochemical responses to two levels of dietary energy (high energy [HE], 3300 kcal digestible energy [DE]/kg; moderate energy [ME], 2300 kcal DE/kg) and protein (high protein [HP], 16.0% crude protein; moderate protein [MP], 8.4% crude protein) during gestation in 15 collared peccaries (Tayassu tajacu) were examined. Dietary energy and protein levels influenced body weight gain during gestation. Red blood cell counts and lymphocyte concentrations were higher and neutrophil concentrations were lower among females fed an HP diet compared to those fed an MP diet. Alkaline phosphatase and alpha-2 globulin concentrations were higher among females fed an MP diet. Aspartate aminotransferase and cholesterol concentrations were higher and calcium and thyroxine concentrations were lower among females fed ME diets compared to those fed HE diets. These results suggest that physiological indices used in combination with morphological measurements can be useful in assessing collared peccary nutritional health during gestation.

  20. Use of the Auroral Boundary Index for potential forecasting of ionospheric scintillation

    Science.gov (United States)

    Griffin, James M.; Connor, Thomas C.; Snell, Hilary E.

    2012-01-01

    The Hardy-Gussenhoven Auroral Dosing Model (HGADM) was developed to compute electron characteristic energy and energy flux values onto the global grid and is often used to generate the inputs for other phenomenological models. Forecasting auroral conditions is limited by rapid changes in the ionosphere due to variable solar conditions. However, through a statistical analysis of Auroral Boundary Index data we have developed a technique which allows us to forecast/predict the appropriate inputs to the HGADM, thereby providing a means of forecasting the characteristic energy and energy flux values. This paper will initially discuss the statistical analysis and the development of the forecast mode for the HGADM. We then discuss the possibility that aurora-based indices along with other environmental indicators can be correlated to ionospheric disturbances.

  1. Hungarian surveillance of germinal mutations. Lack of detectable increase in indicator conditions caused by germinal mutations following the Chernobyl accident

    Energy Technology Data Exchange (ETDEWEB)

    Czeizel, A. (National Inst. of Hygiene, Budapest (Hungary). Dept. of Human Genetics and Teratology)

    1989-07-01

    The Hungarian surveillance of germinal mutations is based on three indicator conditions seen in offspring, i.e., 15 sentinel anomalies, Down syndrome and component anomaly pairs of unidentified multiple congenital anomalies. It is an 'opportunistic program', because the necessary data are available from the Hungarian Congenital Malformation Registry. This system is described and the criteria of a good registry are summarized. The analysis of indicator conditions caused by germinal mutations did not reveal any measurable mutagenic effects in Hungary following the accident at the Chernobyl nuclear power plant. The pros and cons of germinal mutation surveillance are discussed. (orig.).

  2. Bromine partitioning between olivine and melt at OIB source conditions: Indication for volatile recycling

    Science.gov (United States)

    Joachim, Bastian; Ruzié, Lorraine; Burgess, Ray; Pawley, Alison; Clay, Patricia L.; Ballentine, Christopher J.

    2016-04-01

    Halogens play a key role in our understanding of volatile transport processes in the Earth's mantle. Their moderate (fluorine) to highly (iodine) incompatible and volatile behavior implies that their distribution is influenced by partial melting, fractionation and degassing processes as well as fluid mobilities. The heavy halogens, particularly bromine and iodine, are far more depleted in the Earth's mantle than expected from their condensation temperature (Palme and O'Neill 2014), so that their very low abundances in basalts and peridotites (ppb-range) make it analytically challenging to investigate their concentrations in Earth's mantle reservoirs and their behavior during transport processes (Pyle and Mather, 2009). We used a new experimental technique, which combines the irradiation technique (Johnson et al. 2000), laser ablation and conventional mass spectrometry. This enables us to present the first experimentally derived bromine partition coefficient between olivine and melt. Partitioning experiments were performed at 1500° C and 2.3 GPa, a P-T condition that is representative for partial melting processes in the OIB source region (Davis et al. 2011). The bromine partition coefficient between olivine and silicate melt at this condition has been determined to DBrol/melt = 4.37•10-4± 1.96•10-4. Results show that bromine is significantly more incompatible than chlorine (˜1.5 orders of magnitude) and fluorine (˜2 orders of magnitude) due to its larger ionic radius. We have used our bromine partitioning data to estimate minimum bromine abundances in EM1 and EM2 source regions. We used minimum bromine bulk rock concentrations determined in an EM1 (Pitcairn: 1066 ppb) and EM2 (Society: 2063 ppb) basalt (Kendrick et al. 2012), together with an estimated minimum melt fraction of 0.01 in OIB source regions (Dasgupta et al. 2007). The almost perfect bromine incompatibility results in minimum bromine abundances in EM1 and EM2 OIB source regions of 11 ppb and 20

  3. On forecasting Exchange Rate Volatility.

    OpenAIRE

    Hafner, Christian

    2003-01-01

    In an efficient market, foreign exchange rates have to guarantee absence of triangular arbitrage. This note shows that the no-arbitrage condition can be exploited for forecasting the volatility of a single rate by using the information contained in the other rates. Linearly transforming the volatility forecasts of a bivariate model is shown to be more efficient than using a univariate model for the cross-rate.

  4. Rationality and Momentum in Real Estate Investment Forecasts

    OpenAIRE

    2014-01-01

    This study examines the rationality and momentum in forecasts for rental, capital value and total returns for the real estate investment market in the United Kingdom. In order to investigate if forecasters are affected by the general economic conditions present at the time of forecast we incorporate into the analysis Gross Domestic Product (GDP) and the Default Spread (DS). The empirical findings show high levels of momentum in the forecasts, with highly persistent forecast errors. The result...

  5. Rationality and momentum in real estate investment forecasts\\ud

    OpenAIRE

    2014-01-01

    This study examines the rationality and momentum in forecasts for rental, capital value and total returns for the real estate investment market in the United Kingdom. In order to investigate if forecasters are affected by the general economic conditions present at the time of forecast we incorporate into the analysis Gross Domestic Product(GDP) and the Default Spread (DS). The empirical findings show high levels of momentum in the forecasts, with highly persistent forecast errors. The results...

  6. Uses and Applications of Climate Forecasts for Power Utilities.

    Science.gov (United States)

    Changnon, Stanley A.; Changnon, Joyce M.; Changnon, David

    1995-05-01

    The uses and potential applications of climate forecasts for electric and gas utilities were assessed 1) to discern needs for improving climate forecasts and guiding future research, and 2) to assist utilities in making wise use of forecasts. In-depth structured interviews were conducted with 56 decision makers in six utilities to assess existing and potential uses of climate forecasts. Only 3 of the 56 use forecasts. Eighty percent of those sampled envisioned applications of climate forecasts, given certain changes and additional information. Primary applications exist in power trading, load forecasting, fuel acquisition, and systems planning, with slight differences in interests between utilities. Utility staff understand probability-based forecasts but desire climatological information related to forecasted outcomes, including analogs similar to the forecasts, and explanations of the forecasts. Desired lead times vary from a week to three months, along with forecasts of up to four seasons ahead. The new NOAA forecasts initiated in 1995 provide the lead times and longer-term forecasts desired. Major hindrances to use of forecasts are hard-to-understand formats, lack of corporate acceptance, and lack of access to expertise. Recent changes in government regulations altered the utility industry, leading to a more competitive world wherein information about future weather conditions assumes much more value. Outreach efforts by government forecast agencies appear valuable to help achieve the appropriate and enhanced use of climate forecasts by the utility industry. An opportunity for service exists also for the private weather sector.

  7. Mineralogical and isotopic indicators of palaeoclimatological conditions during Precambrian time, Aldan Shield, Siberia

    Science.gov (United States)

    Guliy, Vasyl

    2014-05-01

    , and minerals of the last generations form big crystals (up to 5 cm). The studied minerals are characterized by mainly positive sulphur isotopic values (up to +32.1 permil). Relatively low sulphur isotopic values (5.7 - 10.6 permil) have barites. Isotopic compositions of first anhydrite generations yield sulphur isotopic values from 5.4 to 6.9 permil, and its following generations are distinctly higher - from 20.3 to 32.1 permil. The studied gypsums are characterized by similar high sulphur isotopic values - from 22.5 to 30.1 permil as well as celestine - 24.0 permil. The isotopic investigations showed that first generations of minerals commonly have more heavy sulfur isotopic compositions. It is in accordance with isotopic C and O records in coexisting carbonates, which are enriched in 13C. Apatite-sulfate-carbonate rocks are products of complex alternation of sedimentary processes at different regimes of basin salinity occasionally marked by the decomposition of older sediments under sub-aerial conditions. High sulphur isotopic values in late generations of sulfates are connected with depletion of carbon and oxygen isotopic compositions. They were produced by post sedimentation alterations under the effect of surface waters and/or metasomatic processes.

  8. STUDY ON THE CORRELATIONS BETWEEN CERTAIN FUNCTIONAL INDICES AND THE CONDITIONAL MOTOR SKILLS ON AGE GROUPS IN ATHLETES

    Directory of Open Access Journals (Sweden)

    CARMINA LIANA MUSAT

    2010-12-01

    Full Text Available Effort represents in sports one of the essential environmental factors, vital both in the body’s harmonious development, and in maintaining its health. Motor activity, through the phenomena of adaptation, compensation and overcompensation that it generates, stimulates and in some cases guides in growth and development, has twocomponents: a genetic one, referring to inborn characteristics, the genetic information making up the genotype and manifested as the fenotype (the external manifestation form, and an acquired one, obtained through exercises influenced by environmental conditions. The present study undertaken by means of multivaried analysis of conditional motor skills evinced that the indices of conditional motor skills may remain strongly connected during many years with certain functional indices, and then these correlations may suffer essential alterations

  9. Indicator condition based HIV testing: Missed opportunities for earlier diagnosis in men who have sex with men.

    Science.gov (United States)

    Espinel, Marco; Belza, María José; Cabeza-de-Vaca, Cristina; Arranz, Beatriz; Guerras, Juan Miguel; Garcia-Soltero, Jennifer; Hoyos, Juan

    2017-10-07

    Contact with the healthcare system by a sample of seropositive men who have sex with men (MSM) prior to their HIV diagnosis are analysed, and missed opportunities (MO) for an earlier HIV diagnosis are identified. Between 2012-2013, an online survey was conducted among HIV-positive MSM, mainly recruited from gay websites. Those who were diagnosed with HIV between 2010-2013 were analysed. MO were defined as episodes prior to the HIV diagnosis in which the healthcare system was contacted due to an indicator condition of HIV infection and the test was not suggested. The proportion of missed opportunities were compared according to the type of indicator condition, the department consulted and the healthcare professional's knowledge that the patient was MSM. Overall, 639 participants (66% of 966) reported 1,145 episodes with some indicator condition, the majority of these being identified in primary care (n=527; 46%). The highest percentage of MOs is also observed in primary care (63%). Although the indicator condition with the highest number of MOs was STIs (n=124), the highest percentage of MOs was observed in consultations due to diarrhoea with no known cause (69.8%). The percentage of MOs when the doctor knew that the patient was MSM was 40 vs. 70% when the doctor did not know. The majority of HIV-positive MSM analysed in this study went to healthcare services for HIV-infection indicator conditions prior to their HIV diagnosis. Primary care was the most-frequently-visited department and is also where the most opportunities were missed to perform an HIV test, even when it was known that the patient was a MSM. Copyright © 2017 Elsevier España, S.L.U. and Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.

  10. Life-history dependent relationships between body condition and immunity, between immunity indices in male Eurasian tree sparrows.

    Science.gov (United States)

    Zhao, Yuliang; Li, Mo; Sun, Yanfeng; Wu, Wei; Kou, Guanqun; Guo, Lingling; Xing, Danning; Wu, Yuefeng; Li, Dongming; Zhao, Baohua

    2017-08-01

    In free-living animals, recent evidence indicates that innate, and acquired, immunity varies with annual variation in the demand for, and availability of, food resources. However, little is known about how animals adjust the relationships between immunity and body condition, and between innate and acquired immunity to optimize survival over winter and reproductive success during the breeding stage. Here, we measured indices of body condition (size-corrected mass [SCM], and hematocrit [Hct]), constitutive innate immunity (plasma total complement hemolysis activity [CH50]) and acquired immunity (plasma immunoglobulin A [IgA]), plus heterophil/lymphocyte (H/L) ratios, in male Eurasian tree sparrows (Passer montanus) during the wintering and the breeding stages. We found that birds during the wintering stage had higher IgA levels than those from the breeding stage. Two indices of body condition were both negatively correlated with plasma CH50 activities, and positively with IgA levels in wintering birds, but this was not the case in the breeding birds. However, there was no correlation between CH50 activities and IgA levels in both stages. These results suggest that the relationships between body condition and immunity can vary across life-history stage, and there are no correlations between innate and acquired immunity independent of life-history stage, in male Eurasian tree sparrows. Therefore, body condition indices predict immunological state, especially during the non-breeding stage, which can be useful indicators of individual immunocompetences for understanding the variations in innate and acquired immunity in free-living animals. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Forecasting Macedonian Business Cycle Turning Points Using Qual Var Model

    Directory of Open Access Journals (Sweden)

    Petrovska Magdalena

    2016-09-01

    Full Text Available This paper aims at assessing the usefulness of leading indicators in business cycle research and forecast. Initially we test the predictive power of the economic sentiment indicator (ESI within a static probit model as a leading indicator, commonly perceived to be able to provide a reliable summary of the current economic conditions. We further proceed analyzing how well an extended set of indicators performs in forecasting turning points of the Macedonian business cycle by employing the Qual VAR approach of Dueker (2005. In continuation, we evaluate the quality of the selected indicators in pseudo-out-of-sample context. The results show that the use of survey-based indicators as a complement to macroeconomic data work satisfactory well in capturing the business cycle developments in Macedonia.

  12. kwmc Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  13. kont Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  14. kcrg Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  15. kjac Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  16. krdu Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  17. kiwd Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  18. krbl Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  19. kssf Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  20. ksaw Terminal Aerodrome Forecast

    Data.gov (United States)

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