WorldWideScience

Sample records for monthly forecast file

  1. Monthly forecasting of agricultural pests in Switzerland

    Science.gov (United States)

    Hirschi, M.; Dubrovsky, M.; Spirig, C.; Samietz, J.; Calanca, P.; Weigel, A. P.; Fischer, A. M.; Rotach, M. W.

    2012-04-01

    Given the repercussions of pests and diseases on agricultural production, detailed forecasting tools have been developed to simulate the degree of infestation depending on actual weather conditions. The life cycle of pests is most successfully predicted if the micro-climate of the immediate environment (habitat) of the causative organisms can be simulated. Sub-seasonal pest forecasts therefore require weather information for the relevant habitats and the appropriate time scale. The pest forecasting system SOPRA (www.sopra.info) currently in operation in Switzerland relies on such detailed weather information, using hourly weather observations up to the day the forecast is issued, but only a climatology for the forecasting period. Here, we aim at improving the skill of SOPRA forecasts by transforming the weekly information provided by ECMWF monthly forecasts (MOFCs) into hourly weather series as required for the prediction of upcoming life phases of the codling moth, the major insect pest in apple orchards worldwide. Due to the probabilistic nature of operational monthly forecasts and the limited spatial and temporal resolution, their information needs to be post-processed for use in a pest model. In this study, we developed a statistical downscaling approach for MOFCs that includes the following steps: (i) application of a stochastic weather generator to generate a large pool of daily weather series consistent with the climate at a specific location, (ii) a subsequent re-sampling of weather series from this pool to optimally represent the evolution of the weekly MOFC anomalies, and (iii) a final extension to hourly weather series suitable for the pest forecasting model. Results show a clear improvement in the forecast skill of occurrences of upcoming codling moth life phases when incorporating MOFCs as compared to the operational pest forecasting system. This is true both in terms of root mean squared errors and of the continuous rank probability scores of the

  2. monthly energy consumption forecasting using wavelet analysis

    African Journals Online (AJOL)

    User

    ABSTRACT. Monthly energy forecasts help heavy consumers of electric power to prepare adequate budget to pay their electricity bills and also draw the attention of management and stakeholders to electric- ity consumption levels so that energy efficiency measures are put in place to reduce cost. In this paper, a wavelet ...

  3. Interactive Vegetation Phenology, Soil Moisture, and Monthly Temperature Forecasts

    Science.gov (United States)

    Koster, R. D.; Walker, G. K.

    2015-01-01

    The time scales that characterize the variations of vegetation phenology are generally much longer than those that characterize atmospheric processes. The explicit modeling of phenological processes in an atmospheric forecast system thus has the potential to provide skill to subseasonal or seasonal forecasts. We examine this possibility here using a forecast system fitted with a dynamic vegetation phenology model. We perform three experiments, each consisting of 128 independent warm-season monthly forecasts: 1) an experiment in which both soil moisture states and carbon states (e.g., those determining leaf area index) are initialized realistically, 2) an experiment in which the carbon states are prescribed to climatology throughout the forecasts, and 3) an experiment in which both the carbon and soil moisture states are prescribed to climatology throughout the forecasts. Evaluating the monthly forecasts of air temperature in each ensemble against observations, as well as quantifying the inherent predictability of temperature within each ensemble, shows that dynamic phenology can indeed contribute positively to subseasonal forecasts, though only to a small extent, with an impact dwarfed by that of soil moisture.

  4. Forecast of Frost Days Based on Monthly Temperatures

    Science.gov (United States)

    Castellanos, M. T.; Tarquis, A. M.; Morató, M. C.; Saa-Requejo, A.

    2009-04-01

    Although frost can cause considerable crop damage and mitigation practices against forecasted frost exist, frost forecasting technologies have not changed for many years. The paper reports a new method to forecast the monthly number of frost days (FD) for several meteorological stations at Community of Madrid (Spain) based on successive application of two models. The first one is a stochastic model, autoregressive integrated moving average (ARIMA), that forecasts monthly minimum absolute temperature (tmin) and monthly average of minimum temperature (tminav) following Box-Jenkins methodology. The second model relates these monthly temperatures to minimum daily temperature distribution during one month. Three ARIMA models were identified for the time series analyzed with a stational period correspondent to one year. They present the same stational behavior (moving average differenced model) and different non-stational part: autoregressive model (Model 1), moving average differenced model (Model 2) and autoregressive and moving average model (Model 3). At the same time, the results point out that minimum daily temperature (tdmin), for the meteorological stations studied, followed a normal distribution each month with a very similar standard deviation through years. This standard deviation obtained for each station and each month could be used as a risk index for cold months. The application of Model 1 to predict minimum monthly temperatures showed the best FD forecast. This procedure provides a tool for crop managers and crop insurance companies to asses the risk of frost frequency and intensity, so that they can take steps to mitigate against frost damage and estimated the damage that frost would cost. This research was supported by Comunidad de Madrid Research Project 076/92. The cooperation of the Spanish National Meteorological Institute and the Spanish Ministerio de Agricultura, Pesca y Alimentation (MAPA) is gratefully acknowledged.

  5. Modelling and forecasting monthly swordfish catches in the Eastern Mediterranean

    Directory of Open Access Journals (Sweden)

    Konstantinos I. Stergiou

    2003-04-01

    Full Text Available In this study, we used the X-11 census technique for modelling and forecasting the monthly swordfish (Xiphias gladius catches in the Greek Seas during 1982-1996 and 1997 respectively, using catches reported by the National Statistical Service of Greece (NSSG. Forecasts built with X-11 were also compared with those derived from ARIMA andWinter’s exponential smoothing (WES models. The X-11 method captured the features of the study series and outperformed the other two methods, in terms of both fitting and forecasting performance, for all the accuracy measures used. Thus, with the exception of October, November and December 1997, when the corresponding absolute percentage error(APE values were very high (as high as 178.6% because of the low level of the catches, monthly catches during the remaining months of 1997 were predicted accurately, with a mean APE of 12.5%. In contrast, the mean APE values of the other two methods for the same months were higher (ARIMA: 14.6%; WES: 16.6%. The overall good performance of X-11 andthe fact that it provides an insight into the various components (i.e. the seasonal, trend-cycle and irregular components of the time series of interest justify its use in fisheries research. The basic features of the swordfish catches revealed by the application of the X-11 method, the effect of the length of the forecasting horizon on forecasting accuracy and the accuracy of the catches reported by NSSG are also discussed.

  6. A complex autoregressive model and application to monthly temperature forecasts

    Directory of Open Access Journals (Sweden)

    X. Gu

    2005-11-01

    Full Text Available A complex autoregressive model was established based on the mathematic derivation of the least squares for the complex number domain which is referred to as the complex least squares. The model is different from the conventional way that the real number and the imaginary number are separately calculated. An application of this new model shows a better forecast than forecasts from other conventional statistical models, in predicting monthly temperature anomalies in July at 160 meteorological stations in mainland China. The conventional statistical models include an autoregressive model, where the real number and the imaginary number are separately disposed, an autoregressive model in the real number domain, and a persistence-forecast model.

  7. Monthly ENSO Forecast Skill and Lagged Ensemble Size

    Science.gov (United States)

    Trenary, L.; DelSole, T.; Tippett, M. K.; Pegion, K.

    2018-04-01

    The mean square error (MSE) of a lagged ensemble of monthly forecasts of the Niño 3.4 index from the Climate Forecast System (CFSv2) is examined with respect to ensemble size and configuration. Although the real-time forecast is initialized 4 times per day, it is possible to infer the MSE for arbitrary initialization frequency and for burst ensembles by fitting error covariances to a parametric model and then extrapolating to arbitrary ensemble size and initialization frequency. Applying this method to real-time forecasts, we find that the MSE consistently reaches a minimum for a lagged ensemble size between one and eight days, when four initializations per day are included. This ensemble size is consistent with the 8-10 day lagged ensemble configuration used operationally. Interestingly, the skill of both ensemble configurations is close to the estimated skill of the infinite ensemble. The skill of the weighted, lagged, and burst ensembles are found to be comparable. Certain unphysical features of the estimated error growth were tracked down to problems with the climatology and data discontinuities.

  8. Monthly streamflow forecasting with auto-regressive integrated moving average

    Science.gov (United States)

    Nasir, Najah; Samsudin, Ruhaidah; Shabri, Ani

    2017-09-01

    Forecasting of streamflow is one of the many ways that can contribute to better decision making for water resource management. The auto-regressive integrated moving average (ARIMA) model was selected in this research for monthly streamflow forecasting with enhancement made by pre-processing the data using singular spectrum analysis (SSA). This study also proposed an extension of the SSA technique to include a step where clustering was performed on the eigenvector pairs before reconstruction of the time series. The monthly streamflow data of Sungai Muda at Jeniang, Sungai Muda at Jambatan Syed Omar and Sungai Ketil at Kuala Pegang was gathered from the Department of Irrigation and Drainage Malaysia. A ratio of 9:1 was used to divide the data into training and testing sets. The ARIMA, SSA-ARIMA and Clustered SSA-ARIMA models were all developed in R software. Results from the proposed model are then compared to a conventional auto-regressive integrated moving average model using the root-mean-square error and mean absolute error values. It was found that the proposed model can outperform the conventional model.

  9. Forecasting of Average Monthly River Flows in Colombia

    Science.gov (United States)

    Mesa, O. J.; Poveda, G.

    2006-05-01

    The last two decades have witnessed a marked increase in our knowledge of the causes of interannual hydroclimatic variability and our ability to make predictions. Colombia, located near the seat of the ENSO phenomenon, has been shown to experience negative (positive) anomalies in precipitation in concert with El Niño (La Niña). In general besides the Pacific Ocean, Colombia has climatic influences from the Atlantic Ocean and the Caribbean Sea through the tropical forest of the Amazon basin and the savannas of the Orinoco River, in top of the orographic and hydro-climatic effects introduced by the Andes. As in various other countries of the region, hydro-electric power contributes a large proportion (75 %) of the total electricity generation in Colombia. Also, most agriculture is rain-fed dependant, and domestic water supply relies mainly on surface waters from creeks and rivers. Besides, various vector borne tropical diseases intensify in response to rain and temperature changes. Therefore, there is a direct connection between climatic fluctuations and national and regional economies. This talk specifically presents different forecasts of average monthly stream flows for the inflow into the largest reservoir used for hydropower generation in Colombia, and illustrates the potential economic savings of such forecasts. Because of planning of the reservoir operation, the most appropriated time scale for this application is the annual to interannual. Fortunately, this corresponds to the scale at which hydroclimate variability understanding has improved significantly. Among the different possibilities we have explored: traditional statistical ARIMA models, multiple linear regression, natural and constructed analogue models, the linear inverse model, neural network models, the non-parametric regression splines (MARS) model, regime dependant Markovian models and one we termed PREBEO, which is based on spectral bands decomposition using wavelets. Most of the methods make

  10. Monthly Total Precipitation Observation for Climate Prediction Center (CPC)Forecast Divisions

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This ASCII dataset contains monthly total precipitation for 102 Forecast Divisions within the conterminous U.S. It is derived from the monthly NCDC climate division...

  11. Monthly Mean Temperature Observation for Climate Prediction Center (CPC) Forecast Divisions

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This ASCII dataset contains monthly mean temperatures for 102 Forecast Divisions within the conterminous U.S. and is derived from the monthly NCDC climate division...

  12. Forecasting monthly peak demand of electricity in India—A critique

    International Nuclear Information System (INIS)

    Rallapalli, Srinivasa Rao; Ghosh, Sajal

    2012-01-01

    The nature of electricity differs from that of other commodities since electricity is a non-storable good and there have been significant seasonal and diurnal variations of demand. Under such condition, precise forecasting of demand for electricity should be an integral part of the planning process as this enables the policy makers to provide directions on cost-effective investment and on scheduling the operation of the existing and new power plants so that the supply of electricity can be made adequate enough to meet the future demand and its variations. Official load forecasting in India done by Central Electricity Authority (CEA) is often criticized for being overestimated due to inferior techniques used for forecasting. This paper tries to evaluate monthly peak demand forecasting performance predicted by CEA using trend method and compare it with those predicted by Multiplicative Seasonal Autoregressive Integrated Moving Average (MSARIMA) model. It has been found that MSARIMA model outperforms CEA forecasts both in-sample static and out-of-sample dynamic forecast horizons in all five regional grids in India. For better load management and grid discipline, this study suggests employing sophisticated techniques like MSARIMA for peak load forecasting in India. - Highlights: ► This paper evaluates monthly peak demand forecasting performance by CEA. ► Compares CEA forecasts it with those predicted by MSARIMA model. ► MSARIMA model outperforms CEA forecasts in all five regional grids in India. ► Opportunity exists to improve the performance of CEA forecasts.

  13. Monthly reservoir inflow forecasting using a new hybrid SARIMA ...

    Indian Academy of Sciences (India)

    Seasonal autoregressive integrated moving average (SARIMA) models have been frequently ... studied the perfor- mance of stochastic models against ANN models in ..... the model parameters and Se is the standard error. 2.1.3 Nonlinear term ...... ison of regression, ARIMA and ANN models for reservoir inflow forecasting ...

  14. Can we use Earth Observations to improve monthly water level forecasts?

    Science.gov (United States)

    Slater, L. J.; Villarini, G.

    2017-12-01

    Dynamical-statistical hydrologic forecasting approaches benefit from different strengths in comparison with traditional hydrologic forecasting systems: they are computationally efficient, can integrate and `learn' from a broad selection of input data (e.g., General Circulation Model (GCM) forecasts, Earth Observation time series, teleconnection patterns), and can take advantage of recent progress in machine learning (e.g. multi-model blending, post-processing and ensembling techniques). Recent efforts to develop a dynamical-statistical ensemble approach for forecasting seasonal streamflow using both GCM forecasts and changing land cover have shown promising results over the U.S. Midwest. Here, we use climate forecasts from several GCMs of the North American Multi Model Ensemble (NMME) alongside 15-minute stage time series from the National River Flow Archive (NRFA) and land cover classes extracted from the European Space Agency's Climate Change Initiative 300 m annual Global Land Cover time series. With these data, we conduct systematic long-range probabilistic forecasting of monthly water levels in UK catchments over timescales ranging from one to twelve months ahead. We evaluate the improvement in model fit and model forecasting skill that comes from using land cover classes as predictors in the models. This work opens up new possibilities for combining Earth Observation time series with GCM forecasts to predict a variety of hazards from space using data science techniques.

  15. Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa

    Directory of Open Access Journals (Sweden)

    Tadesse Kassahun Birhanu

    2017-12-01

    Full Text Available Knowledge of future river flow information is fundamental for development and management of a river system. In this study, Waterval River flow was forecasted by SARIMA model using GRETL statistical software. Mean monthly flows from 1960 to 2016 were used for modelling and forecasting. Different unit root and Mann–Kendall trend analysis proved the stationarity of the observed flow time series. Based on seasonally differenced correlogram characteristics, different SARIMA models were evaluated; their parameters were optimized, and diagnostic check up of forecasts was made using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AI and Hannan–Quinn (HQ criteria, SARIMA (3, 0, 2 x (3, 1, 312 model was selected for Waterval River flow forecasting. Comparison of forecast performance of SARIMA models with that of computational intelligent forecasting techniques was recommended for future study.

  16. The Research of Regression Method for Forecasting Monthly Electricity Sales Considering Coupled Multi-factor

    Science.gov (United States)

    Wang, Jiangbo; Liu, Junhui; Li, Tiantian; Yin, Shuo; He, Xinhui

    2018-01-01

    The monthly electricity sales forecasting is a basic work to ensure the safety of the power system. This paper presented a monthly electricity sales forecasting method which comprehensively considers the coupled multi-factors of temperature, economic growth, electric power replacement and business expansion. The mathematical model is constructed by using regression method. The simulation results show that the proposed method is accurate and effective.

  17. Knowing what to expect, forecasting monthly emergency department visits: A time-series analysis.

    Science.gov (United States)

    Bergs, Jochen; Heerinckx, Philipe; Verelst, Sandra

    2014-04-01

    To evaluate an automatic forecasting algorithm in order to predict the number of monthly emergency department (ED) visits one year ahead. We collected retrospective data of the number of monthly visiting patients for a 6-year period (2005-2011) from 4 Belgian Hospitals. We used an automated exponential smoothing approach to predict monthly visits during the year 2011 based on the first 5 years of the dataset. Several in- and post-sample forecasting accuracy measures were calculated. The automatic forecasting algorithm was able to predict monthly visits with a mean absolute percentage error ranging from 2.64% to 4.8%, indicating an accurate prediction. The mean absolute scaled error ranged from 0.53 to 0.68 indicating that, on average, the forecast was better compared with in-sample one-step forecast from the naïve method. The applied automated exponential smoothing approach provided useful predictions of the number of monthly visits a year in advance. Copyright © 2013 Elsevier Ltd. All rights reserved.

  18. Predictability of monthly temperature and precipitation using automatic time series forecasting methods

    Science.gov (United States)

    Papacharalampous, Georgia; Tyralis, Hristos; Koutsoyiannis, Demetris

    2018-02-01

    We investigate the predictability of monthly temperature and precipitation by applying automatic univariate time series forecasting methods to a sample of 985 40-year-long monthly temperature and 1552 40-year-long monthly precipitation time series. The methods include a naïve one based on the monthly values of the last year, as well as the random walk (with drift), AutoRegressive Fractionally Integrated Moving Average (ARFIMA), exponential smoothing state-space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (BATS), simple exponential smoothing, Theta and Prophet methods. Prophet is a recently introduced model inspired by the nature of time series forecasted at Facebook and has not been applied to hydrometeorological time series before, while the use of random walk, BATS, simple exponential smoothing and Theta is rare in hydrology. The methods are tested in performing multi-step ahead forecasts for the last 48 months of the data. We further investigate how different choices of handling the seasonality and non-normality affect the performance of the models. The results indicate that: (a) all the examined methods apart from the naïve and random walk ones are accurate enough to be used in long-term applications; (b) monthly temperature and precipitation can be forecasted to a level of accuracy which can barely be improved using other methods; (c) the externally applied classical seasonal decomposition results mostly in better forecasts compared to the automatic seasonal decomposition used by the BATS and Prophet methods; and (d) Prophet is competitive, especially when it is combined with externally applied classical seasonal decomposition.

  19. Sub-seasonal prediction over East Asia during boreal summer using the ECCC monthly forecasting system

    Science.gov (United States)

    Liang, Ping; Lin, Hai

    2018-02-01

    A useful sub-seasonal forecast is of great societal and economical value in the highly populated East Asian region, especially during boreal summer when frequent extreme events such as heat waves and persistent heavy rainfalls occur. Despite recent interest and development in sub-seasonal prediction, it is still unclear how skillful dynamical forecasting systems are in East Asia beyond 2 weeks. In this study we evaluate the sub-seasonal prediction over East Asia during boreal summer in the operational monthly forecasting system of Environment and Climate Change Canada (ECCC).Results show that the climatological intra-seasonal oscillation (CISO) of East Asian summer monsoonis reasonably well captured. Statistically significant forecast skill of 2-meter air temperature (T2m) is achieved for all lead times up to week 4 (days 26-32) over East China and Northeast Asia, which is consistent with the skill in 500 hPa geopotential height (Z500). Significant forecast skill of precipitation, however, is limited to the week of days 5-11. Possible sources of predictability on the sub-seasonal time scale are analyzed. The weekly mean T2m anomaly over East China is found to be linked to an eastward propagating extratropical Rossby wave from the North Atlantic across Europe to East Asia. The Madden-Julian Oscillation (MJO) and El Nino-Southern Oscillation (ENSO) are also likely to influence the forecast skill of T2m at the sub-seasonal timescale over East Asia.

  20. Sensitivity of monthly streamflow forecasts to the quality of rainfall forcing: When do dynamical climate forecasts outperform the Ensemble Streamflow Prediction (ESP) method?

    Science.gov (United States)

    Tanguy, M.; Prudhomme, C.; Harrigan, S.; Smith, K. A.; Parry, S.

    2017-12-01

    Forecasting hydrological extremes is challenging, especially at lead times over 1 month for catchments with limited hydrological memory and variable climates. One simple way to derive monthly or seasonal hydrological forecasts is to use historical climate data to drive hydrological models using the Ensemble Streamflow Prediction (ESP) method. This gives a range of possible future streamflow given known initial hydrologic conditions alone. The degree of skill of ESP depends highly on the forecast initialisation month and catchment type. Using dynamic rainfall forecasts as driving data instead of historical data could potentially improve streamflow predictions. A lot of effort is being invested within the meteorological community to improve these forecasts. However, while recent progress shows promise (e.g. NAO in winter), the skill of these forecasts at monthly to seasonal timescales is generally still limited, and the extent to which they might lead to improved hydrological forecasts is an area of active research. Additionally, these meteorological forecasts are currently being produced at 1 month or seasonal time-steps in the UK, whereas hydrological models require forcings at daily or sub-daily time-steps. Keeping in mind these limitations of available rainfall forecasts, the objectives of this study are to find out (i) how accurate monthly dynamical rainfall forecasts need to be to outperform ESP, and (ii) how the method used to disaggregate monthly rainfall forecasts into daily rainfall time series affects results. For the first objective, synthetic rainfall time series were created by increasingly degrading observed data (proxy for a `perfect forecast') from 0 % to +/-50 % error. For the second objective, three different methods were used to disaggregate monthly rainfall data into daily time series. These were used to force a simple lumped hydrological model (GR4J) to generate streamflow predictions at a one-month lead time for over 300 catchments

  1. Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe

    Science.gov (United States)

    Meißner, Dennis; Klein, Bastian; Ionita, Monica

    2017-12-01

    Traditionally, navigation-related forecasts in central Europe cover short- to medium-range lead times linked to the travel times of vessels to pass the main waterway bottlenecks leaving the loading ports. Without doubt, this aspect is still essential for navigational users, but in light of the growing political intention to use the free capacity of the inland waterway transport in Europe, additional lead time supporting strategic decisions is more and more in demand. However, no such predictions offering extended lead times of several weeks up to several months currently exist for considerable parts of the European waterway network. This paper describes the set-up of a monthly to seasonal forecasting system for the German stretches of the international waterways of the Rhine, Danube and Elbe rivers. Two competitive forecast approaches have been implemented: the dynamical set-up forces a hydrological model with post-processed outputs from ECMWF general circulation model System 4, whereas the statistical approach is based on the empirical relationship (teleconnection) of global oceanic, climate and regional hydro-meteorological data with river flows. The performance of both forecast methods is evaluated in relation to the climatological forecast (ensemble of historical streamflow) and the well-known ensemble streamflow prediction approach (ESP, ensemble based on historical meteorology) using common performance indicators (correlation coefficient; mean absolute error, skill score; mean squared error, skill score; and continuous ranked probability, skill score) and an impact-based evaluation quantifying the potential economic gain. The following four key findings result from this study: (1) as former studies for other regions of central Europe indicate, the accuracy and/or skill of the meteorological forcing used has a larger effect than the quality of initial hydrological conditions for relevant stations along the German waterways. (2) Despite the predictive

  2. Forecasting monthly inflow discharge of the Iffezheim reservoir using data-driven models

    Science.gov (United States)

    Zhang, Qing; Aljoumani, Basem; Hillebrand, Gudrun; Hoffmann, Thomas; Hinkelmann, Reinhard

    2017-04-01

    River stream flow is an essential element in hydrology study fields, especially for reservoir management, since it defines input into reservoirs. Forecasting this stream flow plays an important role in short or long-term planning and management in the reservoir, e.g. optimized reservoir and hydroelectric operation or agricultural irrigation. Highly accurate flow forecasting can significantly reduce economic losses and is always pursued by reservoir operators. Therefore, hydrologic time series forecasting has received tremendous attention of researchers. Many models have been proposed to improve the hydrological forecasting. Due to the fact that most natural phenomena occurring in environmental systems appear to behave in random or probabilistic ways, different cases may need a different methods to forecast the inflow and even a unique treatment to improve the forecast accuracy. The purpose of this study is to determine an appropriate model for forecasting monthly inflow to the Iffezheim reservoir in Germany, which is the last of the barrages in the Upper Rhine. Monthly time series of discharges, measured from 1946 to 2001 at the Plittersdorf station, which is located 6 km downstream of the Iffezheim reservoir, were applied. The accuracies of the used stochastic models - Fiering model and Auto-Regressive Integrated Moving Average models (ARIMA) are compared with Artificial Intelligence (AI) models - single Artificial Neural Network (ANN) and Wavelet ANN models (WANN). The Fiering model is a linear stochastic model and used for generating synthetic monthly data. The basic idea in modeling time series using ARIMA is to identify a simple model with as few model parameters as possible in order to provide a good statistical fit to the data. To identify and fit the ARIMA models, four phase approaches were used: identification, parameter estimation, diagnostic checking, and forecasting. An automatic selection criterion, such as the Akaike information criterion, is utilized

  3. Monthly streamflow forecasting using continuous wavelet and multi-gene genetic programming combination

    Science.gov (United States)

    Hadi, Sinan Jasim; Tombul, Mustafa

    2018-06-01

    Streamflow is an essential component of the hydrologic cycle in the regional and global scale and the main source of fresh water supply. It is highly associated with natural disasters, such as droughts and floods. Therefore, accurate streamflow forecasting is essential. Forecasting streamflow in general and monthly streamflow in particular is a complex process that cannot be handled by data-driven models (DDMs) only and requires pre-processing. Wavelet transformation is a pre-processing technique; however, application of continuous wavelet transformation (CWT) produces many scales that cause deterioration in the performance of any DDM because of the high number of redundant variables. This study proposes multigene genetic programming (MGGP) as a selection tool. After the CWT analysis, it selects important scales to be imposed into the artificial neural network (ANN). A basin located in the southeast of Turkey is selected as case study to prove the forecasting ability of the proposed model. One month ahead downstream flow is used as output, and downstream flow, upstream, rainfall, temperature, and potential evapotranspiration with associated lags are used as inputs. Before modeling, wavelet coherence transformation (WCT) analysis was conducted to analyze the relationship between variables in the time-frequency domain. Several combinations were developed to investigate the effect of the variables on streamflow forecasting. The results indicated a high localized correlation between the streamflow and other variables, especially the upstream. In the models of the standalone layout where the data were entered to ANN and MGGP without CWT, the performance is found poor. In the best-scale layout, where the best scale of the CWT identified as the highest correlated scale is chosen and enters to ANN and MGGP, the performance increased slightly. Using the proposed model, the performance improved dramatically particularly in forecasting the peak values because of the inclusion

  4. Forecasting Monthly Electricity Demands by Wavelet Neuro-Fuzzy System Optimized by Heuristic Algorithms

    Directory of Open Access Journals (Sweden)

    Jeng-Fung Chen

    2018-02-01

    Full Text Available Electricity load forecasting plays a paramount role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate planning and prediction of electricity load are therefore vital. In this study, a novel approach for forecasting monthly electricity demands by wavelet transform and a neuro-fuzzy system is proposed. Firstly, the most appropriate inputs are selected and a dataset is constructed. Then, Haar wavelet transform is utilized to decompose the load data and eliminate noise. In the model, a hierarchical adaptive neuro-fuzzy inference system (HANFIS is suggested to solve the curse-of-dimensionality problem. Several heuristic algorithms including Gravitational Search Algorithm (GSA, Cuckoo Optimization Algorithm (COA, and Cuckoo Search (CS are utilized to optimize the clustering parameters which help form the rule base, and adaptive neuro-fuzzy inference system (ANFIS optimize the parameters in the antecedent and consequent parts of each sub-model. The proposed approach was applied to forecast the electricity load of Hanoi, Vietnam. The constructed models have shown high forecasting performances based on the performance indices calculated. The results demonstrate the validity of the approach. The obtained results were also compared with those of several other well-known methods including autoregressive integrated moving average (ARIMA and multiple linear regression (MLR. In our study, the wavelet CS-HANFIS model outperformed the others and provided more accurate forecasting.

  5. Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information

    Science.gov (United States)

    Yang, Tiantian; Asanjan, Ata Akbari; Welles, Edwin; Gao, Xiaogang; Sorooshian, Soroosh; Liu, Xiaomang

    2017-04-01

    Reservoirs are fundamental human-built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast-informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month-ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Results show (1) three methods are capable of providing monthly reservoir inflows with satisfactory statistics; (2) the results obtained by Random Forest have the best statistical performances compared with the other two methods; (3) another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; (4) climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and (5) different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies.

  6. Data Pre-Analysis and Ensemble of Various Artificial Neural Networks for Monthly Streamflow Forecasting

    Directory of Open Access Journals (Sweden)

    Jianzhong Zhou

    2018-05-01

    Full Text Available This paper introduces three artificial neural network (ANN architectures for monthly streamflow forecasting: a radial basis function network, an extreme learning machine, and the Elman network. Three ensemble techniques, a simple average ensemble, a weighted average ensemble, and an ANN-based ensemble, were used to combine the outputs of the individual ANN models. The objective was to highlight the performance of the general regression neural network-based ensemble technique (GNE through an improvement of monthly streamflow forecasting accuracy. Before the construction of an ANN model, data preanalysis techniques, such as empirical wavelet transform (EWT, were exploited to eliminate the oscillations of the streamflow series. Additionally, a theory of chaos phase space reconstruction was used to select the most relevant and important input variables for forecasting. The proposed GNE ensemble model has been applied for the mean monthly streamflow observation data from the Wudongde hydrological station in the Jinsha River Basin, China. Comparisons and analysis of this study have demonstrated that the denoised streamflow time series was less disordered and unsystematic than was suggested by the original time series according to chaos theory. Thus, EWT can be adopted as an effective data preanalysis technique for the prediction of monthly streamflow. Concurrently, the GNE performed better when compared with other ensemble techniques.

  7. Monthly electric energy demand forecasting with neural networks and Fourier series

    International Nuclear Information System (INIS)

    Gonzalez-Romera, E.; Jaramillo-Moran, M.A.; Carmona-Fernandez, D.

    2008-01-01

    Medium-term electric energy demand forecasting is a useful tool for grid maintenance planning and market research of electric energy companies. Several methods, such as ARIMA, regression or artificial intelligence, have been usually used to carry out those predictions. Some approaches include weather or economic variables, which strongly influence electric energy demand. Economic variables usually influence the general series trend, while weather provides a periodic behavior because of its seasonal nature. This work investigates the periodic behavior of the Spanish monthly electric demand series, obtained by rejecting the trend from the consumption series. A novel hybrid approach is proposed: the periodic behavior is forecasted with a Fourier series while the trend is predicted with a neural network. Satisfactory results have been obtained, with a lower than 2% MAPE, which improve those reached when only neural networks or ARIMA were used for the same purpose. (author)

  8. Monthly streamflow forecasting based on hidden Markov model and Gaussian Mixture Regression

    Science.gov (United States)

    Liu, Yongqi; Ye, Lei; Qin, Hui; Hong, Xiaofeng; Ye, Jiajun; Yin, Xingli

    2018-06-01

    Reliable streamflow forecasts can be highly valuable for water resources planning and management. In this study, we combined a hidden Markov model (HMM) and Gaussian Mixture Regression (GMR) for probabilistic monthly streamflow forecasting. The HMM is initialized using a kernelized K-medoids clustering method, and the Baum-Welch algorithm is then executed to learn the model parameters. GMR derives a conditional probability distribution for the predictand given covariate information, including the antecedent flow at a local station and two surrounding stations. The performance of HMM-GMR was verified based on the mean square error and continuous ranked probability score skill scores. The reliability of the forecasts was assessed by examining the uniformity of the probability integral transform values. The results show that HMM-GMR obtained reasonably high skill scores and the uncertainty spread was appropriate. Different HMM states were assumed to be different climate conditions, which would lead to different types of observed values. We demonstrated that the HMM-GMR approach can handle multimodal and heteroscedastic data.

  9. State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

    Science.gov (United States)

    Gibbs, Matthew S.; McInerney, David; Humphrey, Greer; Thyer, Mark A.; Maier, Holger R.; Dandy, Graeme C.; Kavetski, Dmitri

    2018-02-01

    Monthly to seasonal streamflow forecasts provide useful information for a range of water resource management and planning applications. This work focuses on improving such forecasts by considering the following two aspects: (1) state updating to force the models to match observations from the start of the forecast period, and (2) selection of a shorter calibration period that is more representative of the forecast period, compared to a longer calibration period traditionally used. The analysis is undertaken in the context of using streamflow forecasts for environmental flow water management of an open channel drainage network in southern Australia. Forecasts of monthly streamflow are obtained using a conceptual rainfall-runoff model combined with a post-processor error model for uncertainty analysis. This model set-up is applied to two catchments, one with stronger evidence of non-stationarity than the other. A range of metrics are used to assess different aspects of predictive performance, including reliability, sharpness, bias and accuracy. The results indicate that, for most scenarios and metrics, state updating improves predictive performance for both observed rainfall and forecast rainfall sources. Using the shorter calibration period also improves predictive performance, particularly for the catchment with stronger evidence of non-stationarity. The results highlight that a traditional approach of using a long calibration period can degrade predictive performance when there is evidence of non-stationarity. The techniques presented can form the basis for operational monthly streamflow forecasting systems and provide support for environmental decision-making.

  10. Combination of synoptical-analogous and dynamical methods to increase skill score of monthly air temperature forecasts over Northern Eurasia

    Science.gov (United States)

    Khan, Valentina; Tscepelev, Valery; Vilfand, Roman; Kulikova, Irina; Kruglova, Ekaterina; Tischenko, Vladimir

    2016-04-01

    Long-range forecasts at monthly-seasonal time scale are in great demand of socio-economic sectors for exploiting climate-related risks and opportunities. At the same time, the quality of long-range forecasts is not fully responding to user application necessities. Different approaches, including combination of different prognostic models, are used in forecast centers to increase the prediction skill for specific regions and globally. In the present study, two forecasting methods are considered which are exploited in operational practice of Hydrometeorological Center of Russia. One of them is synoptical-analogous method of forecasting of surface air temperature at monthly scale. Another one is dynamical system based on the global semi-Lagrangian model SL-AV, developed in collaboration of Institute of Numerical Mathematics and Hydrometeorological Centre of Russia. The seasonal version of this model has been used to issue global and regional forecasts at monthly-seasonal time scales. This study presents results of the evaluation of surface air temperature forecasts generated with using above mentioned synoptical-statistical and dynamical models, and their combination to potentially increase skill score over Northern Eurasia. The test sample of operational forecasts is encompassing period from 2010 through 2015. The seasonal and interannual variability of skill scores of these methods has been discussed. It was noticed that the quality of all forecasts is highly dependent on the inertia of macro-circulation processes. The skill scores of forecasts are decreasing during significant alterations of synoptical fields for both dynamical and empirical schemes. Procedure of combination of forecasts from different methods, in some cases, has demonstrated its effectiveness. For this study the support has been provided by Grant of Russian Science Foundation (№14-37-00053).

  11. Long-term generation scheduling of Xiluodu and Xiangjiaba cascade hydro plants considering monthly streamflow forecasting error

    International Nuclear Information System (INIS)

    Xie, Mengfei; Zhou, Jianzhong; Li, Chunlong; Zhu, Shuang

    2015-01-01

    Highlights: • Monthly streamflow forecasting error is considered. • An improved parallel progressive optimality algorithm is proposed. • Forecasting dispatching chart is manufactured accompanying with a set of rules. • Applications in Xiluodu and Xiangjiaba cascade hydro plants. - Abstract: Reliable streamflow forecasts are very significant for reservoir operation and hydropower generation. But for monthly streamflow forecasting, the forecasting result is unreliable and it is hard to be utilized, although it has a certain reference value for long-term hydro generation scheduling. Current researches mainly focus on deterministic scheduling, and few of them consider the uncertainties. So this paper considers the forecasting error which exists in monthly streamflow forecasting and proposes a new long-term hydro generation scheduling method called forecasting dispatching chart for Xiluodu and Xiangjiaba cascade hydro plants. First, in order to consider the uncertainties of inflow, Monte Carlo simulation is employed to generate streamflow data according to the forecasting value and error distribution curves. Then the large amount of data obtained by Monte Carlo simulation is used as inputs for long-term hydro generation scheduling model. Because of the large amount of streamflow data, the computation speed of conventional algorithm cannot meet the demand. So an improved parallel progressive optimality algorithm is proposed to solve the long-term hydro generation scheduling problem and a series of solutions are obtained. These solutions constitute an interval set, unlike the unique solution in the traditional deterministic long-term hydro generation scheduling. At last, the confidence intervals of the solutions are calculated and forecasting dispatching chart is proposed as a new method for long-term hydro generation scheduling. A set of rules are proposed corresponding to forecasting dispatching chart. The chart is tested for practical operations and achieves

  12. Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid Wavelet packet–Support vector regression

    Directory of Open Access Journals (Sweden)

    N. Sujay Raghavendra

    2015-12-01

    Full Text Available This research demonstrates the state-of-the-art capability of Wavelet packet analysis in improving the forecasting efficiency of Support vector regression (SVR through the development of a novel hybrid Wavelet packet–Support vector regression (WP–SVR model for forecasting monthly groundwater level fluctuations observed in three shallow unconfined coastal aquifers. The Sequential Minimal Optimization Algorithm-based SVR model is also employed for comparative study with WP–SVR model. The input variables used for modeling were monthly time series of total rainfall, average temperature, mean tide level, and past groundwater level observations recorded during the period 1996–2006 at three observation wells located near Mangalore, India. The Radial Basis function is employed as a kernel function during SVR modeling. Model parameters are calibrated using the first seven years of data, and the remaining three years data are used for model validation using various input combinations. The performance of both the SVR and WP–SVR models is assessed using different statistical indices. From the comparative result analysis of the developed models, it can be seen that WP–SVR model outperforms the classic SVR model in predicting groundwater levels at all the three well locations (e.g. NRMSE(WP–SVR = 7.14, NRMSE(SVR = 12.27; NSE(WP–SVR = 0.91, NSE(SVR = 0.8 during the test phase with respect to well location at Surathkal. Therefore, using the WP–SVR model is highly acceptable for modeling and forecasting of groundwater level fluctuations.

  13. Predictability of tropical cyclone events on intraseasonal timescales with the ECMWF monthly forecast model

    Science.gov (United States)

    Elsberry, Russell L.; Jordan, Mary S.; Vitart, Frederic

    2010-05-01

    The objective of this study is to provide evidence of predictability on intraseasonal time scales (10-30 days) for western North Pacific tropical cyclone formation and subsequent tracks using the 51-member ECMWF 32-day forecasts made once a week from 5 June through 25 December 2008. Ensemble storms are defined by grouping ensemble member vortices whose positions are within a specified separation distance that is equal to 180 n mi at the initial forecast time t and increases linearly to 420 n mi at Day 14 and then is constant. The 12-h track segments are calculated with a Weighted-Mean Vector Motion technique in which the weighting factor is inversely proportional to the distance from the endpoint of the previous 12-h motion vector. Seventy-six percent of the ensemble storms had five or fewer member vortices. On average, the ensemble storms begin 2.5 days before the first entry of the Joint Typhoon Warning Center (JTWC) best-track file, tend to translate too slowly in the deep tropics, and persist for longer periods over land. A strict objective matching technique with the JTWC storms is combined with a second subjective procedure that is then applied to identify nearby ensemble storms that would indicate a greater likelihood of a tropical cyclone developing in that region with that track orientation. The ensemble storms identified in the ECMWF 32-day forecasts provided guidance on intraseasonal timescales of the formations and tracks of the three strongest typhoons and two other typhoons, but not for two early season typhoons and the late season Dolphin. Four strong tropical storms were predicted consistently over Week-1 through Week-4, as was one weak tropical storm. Two other weak tropical storms, three tropical cyclones that developed from precursor baroclinic systems, and three other tropical depressions were not predicted on intraseasonal timescales. At least for the strongest tropical cyclones during the peak season, the ECMWF 32-day ensemble provides

  14. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

    Science.gov (United States)

    Wang, Wen-Chuan; Chau, Kwok-Wing; Cheng, Chun-Tian; Qiu, Lin

    2009-08-01

    SummaryDeveloping a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation ( R), Nash-Sutcliffe efficiency coefficient ( E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.

  15. Monthly streamflow forecasting at varying spatial scales in the Rhine basin

    Science.gov (United States)

    Schick, Simon; Rössler, Ole; Weingartner, Rolf

    2018-02-01

    Model output statistics (MOS) methods can be used to empirically relate an environmental variable of interest to predictions from earth system models (ESMs). This variable often belongs to a spatial scale not resolved by the ESM. Here, using the linear model fitted by least squares, we regress monthly mean streamflow of the Rhine River at Lobith and Basel against seasonal predictions of precipitation, surface air temperature, and runoff from the European Centre for Medium-Range Weather Forecasts. To address potential effects of a scale mismatch between the ESM's horizontal grid resolution and the hydrological application, the MOS method is further tested with an experiment conducted at the subcatchment scale. This experiment applies the MOS method to 133 additional gauging stations located within the Rhine basin and combines the forecasts from the subcatchments to predict streamflow at Lobith and Basel. In doing so, the MOS method is tested for catchments areas covering 4 orders of magnitude. Using data from the period 1981-2011, the results show that skill, with respect to climatology, is restricted on average to the first month ahead. This result holds for both the predictor combination that mimics the initial conditions and the predictor combinations that additionally include the dynamical seasonal predictions. The latter, however, reduce the mean absolute error of the former in the range of 5 to 12 %, which is consistently reproduced at the subcatchment scale. An additional experiment conducted for 5-day mean streamflow indicates that the dynamical predictions help to reduce uncertainties up to about 20 days ahead, but it also reveals some shortcomings of the present MOS method.

  16. Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach

    Science.gov (United States)

    Moeeni, Hamid; Bonakdari, Hossein; Ebtehaj, Isa

    2017-03-01

    Forecasting reservoir inflow is one of the most important components of water resources and hydroelectric systems operation management. Seasonal autoregressive integrated moving average (SARIMA) models have been frequently used for predicting river flow. SARIMA models are linear and do not consider the random component of statistical data. To overcome this shortcoming, monthly inflow is predicted in this study based on a combination of seasonal autoregressive integrated moving average (SARIMA) and gene expression programming (GEP) models, which is a new hybrid method (SARIMA-GEP). To this end, a four-step process is employed. First, the monthly inflow datasets are pre-processed. Second, the datasets are modelled linearly with SARIMA and in the third stage, the non-linearity of residual series caused by linear modelling is evaluated. After confirming the non-linearity, the residuals are modelled in the fourth step using a gene expression programming (GEP) method. The proposed hybrid model is employed to predict the monthly inflow to the Jamishan Dam in west Iran. Thirty years' worth of site measurements of monthly reservoir dam inflow with extreme seasonal variations are used. The results of this hybrid model (SARIMA-GEP) are compared with SARIMA, GEP, artificial neural network (ANN) and SARIMA-ANN models. The results indicate that the SARIMA-GEP model ( R 2=78.8, VAF =78.8, RMSE =0.89, MAPE =43.4, CRM =0.053) outperforms SARIMA and GEP and SARIMA-ANN ( R 2=68.3, VAF =66.4, RMSE =1.12, MAPE =56.6, CRM =0.032) displays better performance than the SARIMA and ANN models. A comparison of the two hybrid models indicates the superiority of SARIMA-GEP over the SARIMA-ANN model.

  17. Comparing Machine Learning and Decision Making Approaches to Forecast Long Lead Monthly Rainfall: The City of Vancouver, Canada

    Directory of Open Access Journals (Sweden)

    Zahra Zahmatkesh

    2018-01-01

    Full Text Available Estimating maximum possible rainfall is of great value for flood prediction and protection, particularly for regions, such as Canada, where urban and fluvial floods from extreme rainfalls have been known to be a major concern. In this study, a methodology is proposed to forecast real-time rainfall (with one month lead time using different number of spatial inputs with different orders of lags. For this purpose, two types of models are used. The first one is a machine learning data driven-based model, which uses a set of hydrologic variables as inputs, and the second one is an empirical-statistical model that employs the multi-criteria decision analysis method for rainfall forecasting. The data driven model is built based on Artificial Neural Networks (ANNs, while the developed multi-criteria decision analysis model uses Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS approach. A comprehensive set of spatially varying climate variables, including geopotential height, sea surface temperature, sea level pressure, humidity, temperature and pressure with different orders of lags is collected to form input vectors for the forecast models. Then, a feature selection method is employed to identify the most appropriate predictors. Two sets of results from the developed models, i.e., maximum daily rainfall in each month (RMAX and cumulative value of rainfall for each month (RCU, are considered as the target variables for forecast purpose. The results from both modeling approaches are compared using a number of evaluation criteria such as Nash-Sutcliffe Efficiency (NSE. The proposed models are applied for rainfall forecasting for a coastal area in Western Canada: Vancouver, British Columbia. Results indicate although data driven models such as ANNs work well for the simulation purpose, developed TOPSIS model considerably outperforms ANNs for the rainfall forecasting. ANNs show acceptable simulation performance during the

  18. Forecasting and Analysis of Monthly Rainfalls in Ardabil Province by Arima, Autoregrressive, and Winters Models

    Directory of Open Access Journals (Sweden)

    B. Salahi

    2017-01-01

    Full Text Available Introduction: Rainfall has the highest variability at time and place scale. Rainfall fluctuation in different geographical areas reveals the necessity of investigating this climate element and suitable models to forecast the rate of precipitation for regional planning. Ardabil province has always faced rainfall fluctuations and shortage of water supply. Precipitation is one of the most important features of the environment. The amount of precipitation over time and in different places is subject to large fluctuations which may be periodical. Studies show that, due to the certain complexities of rainfall, the models which used to predict future values will also need greater accuracy and less error. Among the forecasting models, Arima has more applications and it has replaced with other models. Materials and Methods: In this research, through order 2 Autoregrressive, Winters, and Arima models, monthly rainfalls of Ardabil synoptic station (representing Ardabil province for a 31-year period (1977-2007 were investigated. To assess the presence or absence of significant changes in mean precipitation of Ardabil synoptic station, rainfall of this station was divided into two periods: 1977-1993 and 1994-2010. T-test was used to statistically examine the difference between the two periods. After adjusting the data, descriptive statistics were applied. In order to model the total monthly precipitation of Ardabil synoptic station, Winters, Autoregressive, and Arima models were used. Among different models, the best options were chosen to predict the time series including the mean absolute deviation (MAD, the mean squared errors (MSE, root mean square errors (RMSE and mean absolute percentage errors (MAPE. In order to select the best model among the available options under investigation, the predicted value of the deviation of the actual value was utilized for the months of 2006-2010. Results and Discussion: Statistical characteristics of the total monthly

  19. Nowcasting and Forecasting the Monthly Food Stamps Data in the US Using Online Search Data

    Science.gov (United States)

    Fantazzini, Dean

    2014-01-01

    We propose the use of Google online search data for nowcasting and forecasting the number of food stamps recipients. We perform a large out-of-sample forecasting exercise with almost 3000 competing models with forecast horizons up to 2 years ahead, and we show that models including Google search data statistically outperform the competing models at all considered horizons. These results hold also with several robustness checks, considering alternative keywords, a falsification test, different out-of-samples, directional accuracy and forecasts at the state-level. PMID:25369315

  20. Modeling and forecasting monthly movement of annual average solar insolation based on the least-squares Fourier-model

    International Nuclear Information System (INIS)

    Yang, Zong-Chang

    2014-01-01

    Highlights: • Introduce a finite Fourier-series model for evaluating monthly movement of annual average solar insolation. • Present a forecast method for predicting its movement based on the extended Fourier-series model in the least-squares. • Shown its movement is well described by a low numbers of harmonics with approximately 6-term Fourier series. • Predict its movement most fitting with less than 6-term Fourier series. - Abstract: Solar insolation is one of the most important measurement parameters in many fields. Modeling and forecasting monthly movement of annual average solar insolation is of increasingly importance in areas of engineering, science and economics. In this study, Fourier-analysis employing finite Fourier-series is proposed for evaluating monthly movement of annual average solar insolation and extended in the least-squares for forecasting. The conventional Fourier analysis, which is the most common analysis method in the frequency domain, cannot be directly applied for prediction. Incorporated with the least-square method, the introduced Fourier-series model is extended to predict its movement. The extended Fourier-series forecasting model obtains its optimums Fourier coefficients in the least-square sense based on its previous monthly movements. The proposed method is applied to experiments and yields satisfying results in the different cities (states). It is indicated that monthly movement of annual average solar insolation is well described by a low numbers of harmonics with approximately 6-term Fourier series. The extended Fourier forecasting model predicts the monthly movement of annual average solar insolation most fitting with less than 6-term Fourier series

  1. The ScaLIng Macroweather Model (SLIMM): using scaling to forecast global-scale macroweather from months to decades

    Science.gov (United States)

    Lovejoy, S.; del Rio Amador, L.; Hébert, R.

    2015-09-01

    On scales of ≈ 10 days (the lifetime of planetary-scale structures), there is a drastic transition from high-frequency weather to low-frequency macroweather. This scale is close to the predictability limits of deterministic atmospheric models; thus, in GCM (general circulation model) macroweather forecasts, the weather is a high-frequency noise. However, neither the GCM noise nor the GCM climate is fully realistic. In this paper we show how simple stochastic models can be developed that use empirical data to force the statistics and climate to be realistic so that even a two-parameter model can perform as well as GCMs for annual global temperature forecasts. The key is to exploit the scaling of the dynamics and the large stochastic memories that we quantify. Since macroweather temporal (but not spatial) intermittency is low, we propose using the simplest model based on fractional Gaussian noise (fGn): the ScaLIng Macroweather Model (SLIMM). SLIMM is based on a stochastic ordinary differential equation, differing from usual linear stochastic models (such as the linear inverse modelling - LIM) in that it is of fractional rather than integer order. Whereas LIM implicitly assumes that there is no low-frequency memory, SLIMM has a huge memory that can be exploited. Although the basic mathematical forecast problem for fGn has been solved, we approach the problem in an original manner, notably using the method of innovations to obtain simpler results on forecast skill and on the size of the effective system memory. A key to successful stochastic forecasts of natural macroweather variability is to first remove the low-frequency anthropogenic component. A previous attempt to use fGn for forecasts had disappointing results because this was not done. We validate our theory using hindcasts of global and Northern Hemisphere temperatures at monthly and annual resolutions. Several nondimensional measures of forecast skill - with no adjustable parameters - show excellent

  2. The Scaling LInear Macroweather model (SLIM): using scaling to forecast global scale macroweather from months to decades

    Science.gov (United States)

    Lovejoy, S.; del Rio Amador, L.; Hébert, R.

    2015-03-01

    At scales of ≈ 10 days (the lifetime of planetary scale structures), there is a drastic transition from high frequency weather to low frequency macroweather. This scale is close to the predictability limits of deterministic atmospheric models; so that in GCM macroweather forecasts, the weather is a high frequency noise. But neither the GCM noise nor the GCM climate is fully realistic. In this paper we show how simple stochastic models can be developped that use empirical data to force the statistics and climate to be realistic so that even a two parameter model can outperform GCM's for annual global temperature forecasts. The key is to exploit the scaling of the dynamics and the enormous stochastic memories that it implies. Since macroweather intermittency is low, we propose using the simplest model based on fractional Gaussian noise (fGn): the Scaling LInear Macroweather model (SLIM). SLIM is based on a stochastic ordinary differential equations, differing from usual linear stochastic models (such as the Linear Inverse Modelling, LIM) in that it is of fractional rather than integer order. Whereas LIM implicitly assumes there is no low frequency memory, SLIM has a huge memory that can be exploited. Although the basic mathematical forecast problem for fGn has been solved, we approach the problem in an original manner notably using the method of innovations to obtain simpler results on forecast skill and on the size of the effective system memory. A key to successful forecasts of natural macroweather variability is to first remove the low frequency anthropogenic component. A previous attempt to use fGn for forecasts had poor results because this was not done. We validate our theory using hindcasts of global and Northern Hemisphere temperatures at monthly and annual resolutions. Several nondimensional measures of forecast skill - with no adjustable parameters - show excellent agreement with hindcasts and these show some skill even at decadal scales. We also compare

  3. Conditional Monthly Weather Resampling Procedure for Operational Seasonal Water Resources Forecasting

    Science.gov (United States)

    Beckers, J.; Weerts, A.; Tijdeman, E.; Welles, E.; McManamon, A.

    2013-12-01

    To provide reliable and accurate seasonal streamflow forecasts for water resources management several operational hydrologic agencies and hydropower companies around the world use the Extended Streamflow Prediction (ESP) procedure. The ESP in its original implementation does not accommodate for any additional information that the forecaster may have about expected deviations from climatology in the near future. Several attempts have been conducted to improve the skill of the ESP forecast, especially for areas which are affected by teleconnetions (e,g. ENSO, PDO) via selection (Hamlet and Lettenmaier, 1999) or weighting schemes (Werner et al., 2004; Wood and Lettenmaier, 2006; Najafi et al., 2012). A disadvantage of such schemes is that they lead to a reduction of the signal to noise ratio of the probabilistic forecast. To overcome this, we propose a resampling method conditional on climate indices to generate meteorological time series to be used in the ESP. The method can be used to generate a large number of meteorological ensemble members in order to improve the statistical properties of the ensemble. The effectiveness of the method was demonstrated in a real-time operational hydrologic seasonal forecasts system for the Columbia River basin operated by the Bonneville Power Administration. The forecast skill of the k-nn resampler was tested against the original ESP for three basins at the long-range seasonal time scale. The BSS and CRPSS were used to compare the results to those of the original ESP method. Positive forecast skill scores were found for the resampler method conditioned on different indices for the prediction of spring peak flows in the Dworshak and Hungry Horse basin. For the Libby Dam basin however, no improvement of skill was found. The proposed resampling method is a promising practical approach that can add skill to ESP forecasts at the seasonal time scale. Further improvement is possible by fine tuning the method and selecting the most

  4. A system-theory-based model for monthly river runoff forecasting: model calibration and optimization

    Directory of Open Access Journals (Sweden)

    Wu Jianhua

    2014-03-01

    Full Text Available River runoff is not only a crucial part of the global water cycle, but it is also an important source for hydropower and an essential element of water balance. This study presents a system-theory-based model for river runoff forecasting taking the Hailiutu River as a case study. The forecasting model, designed for the Hailiutu watershed, was calibrated and verified by long-term precipitation observation data and groundwater exploitation data from the study area. Additionally, frequency analysis, taken as an optimization technique, was applied to improve prediction accuracy. Following model optimization, the overall relative prediction errors are below 10%. The system-theory-based prediction model is applicable to river runoff forecasting, and following optimization by frequency analysis, the prediction error is acceptable.

  5. Assessment of the potential forecasting skill of a global hydrological model in reproducing the occurrence of monthly flow extremes

    Directory of Open Access Journals (Sweden)

    N. Candogan Yossef

    2012-11-01

    Full Text Available As an initial step in assessing the prospect of using global hydrological models (GHMs for hydrological forecasting, this study investigates the skill of the GHM PCR-GLOBWB in reproducing the occurrence of past extremes in monthly discharge on a global scale. Global terrestrial hydrology from 1958 until 2001 is simulated by forcing PCR-GLOBWB with daily meteorological data obtained by downscaling the CRU dataset to daily fields using the ERA-40 reanalysis. Simulated discharge values are compared with observed monthly streamflow records for a selection of 20 large river basins that represent all continents and a wide range of climatic zones.

    We assess model skill in three ways all of which contribute different information on the potential forecasting skill of a GHM. First, the general skill of the model in reproducing hydrographs is evaluated. Second, model skill in reproducing significantly higher and lower flows than the monthly normals is assessed in terms of skill scores used for forecasts of categorical events. Third, model skill in reproducing flood and drought events is assessed by constructing binary contingency tables for floods and droughts for each basin. The skill is then compared to that of a simple estimation of discharge from the water balance (PE.

    The results show that the model has skill in all three types of assessments. After bias correction the model skill in simulating hydrographs is improved considerably. For most basins it is higher than that of the climatology. The skill is highest in reproducing monthly anomalies. The model also has skill in reproducing floods and droughts, with a markedly higher skill in floods. The model skill far exceeds that of the water balance estimate. We conclude that the prospect for using PCR-GLOBWB for monthly and seasonal forecasting of the occurrence of hydrological extremes is positive. We argue that this conclusion applies equally to other similar GHMs and

  6. Short-term forecasting of Czech quarterly GDP using monthly indicators

    Czech Academy of Sciences Publication Activity Database

    Arnoštová, K.; Havrlant, D.; Růžička, L.; Tóth, Peter

    2011-01-01

    Roč. 61, č. 6 (2011), s. 566-583 ISSN 0015-1920 Institutional research plan: CEZ:MSM0021620846 Keywords : GDP forecasting * bridge models * principal components Subject RIV: AH - Economics Impact factor: 0.346, year: 2011 http://journal.fsv.cuni.cz/storage/1235_toth.pdf

  7. A Method for the Monthly Electricity Demand Forecasting in Colombia based on Wavelet Analysis and a Nonlinear Autoregressive Model

    Directory of Open Access Journals (Sweden)

    Cristhian Moreno-Chaparro

    2011-12-01

    Full Text Available This paper proposes a monthly electricity forecast method for the National Interconnected System (SIN of Colombia. The method preprocesses the time series using a Multiresolution Analysis (MRA with Discrete Wavelet Transform (DWT; a study for the selection of the mother wavelet and her order, as well as the level decomposition was carried out. Given that original series follows a non-linear behaviour, a neural nonlinear autoregressive (NAR model was used. The prediction was obtained by adding the forecast trend with the estimated obtained by the residual series combined with further components extracted from preprocessing. A bibliographic review of studies conducted internationally and in Colombia is included, in addition to references to investigations made with wavelet transform applied to electric energy prediction and studies reporting the use of NAR in prediction.

  8. Eight months of clinical experience with the Self-Adjusting File system.

    Science.gov (United States)

    Solomonov, Michael

    2011-06-01

    The Self-Adjusting File (SAF) system (ReDent-Nova, Ra'anana, Israel) has been recently introduced for the simultaneous instrumentation and irrigation of root canals. The SAF is claimed to adapt itself three dimensionally to the root canal, including its cross-section. It is operated with a continuous flow of sodium hypochlorite that is delivered into the root canal through the hollow file and claimed to be activated by sonic agitation of the irrigant. Our aim was to present for the first time clinical cases prepared with the SAF system and to describe a clinical classification of canals, according to their difficulty, with recommendations for endodontic treatment sequences for each category. This report is based on the experience of a single endodontist, who used the system to treat more than 50 consecutive primary endodontic cases over the prior 8 months. A clinical classification was developed which enabled the operator to select a treatment protocol for easy and optimal glide path preparation to be effectively used with the SAF file in the various root canals encountered in the clinical environment. Clinical classification of canal difficulty makes root canal treatment sequences with the SAF simple and predictable. Many types of cases can be treated with the SAF system although a novice user is advised to advance slowly along the learning curve from simpler to more complicated canals. Copyright © 2011 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

  9. Application study of monthly precipitation forecast in Northeast China based on the cold vortex persistence activity index

    Science.gov (United States)

    Gang, Liu; Meihui, Qu; Guolin, Feng; Qucheng, Chu; Jing, Cao; Jie, Yang; Ling, Cao; Yao, Feng

    2018-03-01

    This paper introduces three quantitative indicators to conduct research for characterizing Northeast China cold vortex persistence activity: cold vortex persistence, generalized "cold vortex," and cold vortex precipitation. As discussed in the first part of paper, a hindcast is performed by multiple regressions using Northeast China precipitation from 2012 to 2014 combination with the previous winter 144 air-sea system factors. The results show that the mentioned three cold vortex index series can reflect the spatial and temporal distributions of observational precipitation in 2012-2014 and obtain results. The cold vortex factors are then added to the Forecast System on Dynamical and Analogy Skills (FODAS) to carry out dynamic statistical hindcast of precipitation in Northeast China from 2003 to 2012. Based on the characteristics and significance of each index, precipitation hindcast is carried out for Northeast China in May, June, July, August, May-June, and July-August. It turns out that the Northeast Cold Vortex Index Series, as defined in this paper, can make positive corrections to the FODAS forecast system, and most of the index correction results are higher than the system's own correction value. This study provides quantitative index products and supplies a solid technical foundation and support for monthly precipitation forecast in Northeast China.

  10. Exploiting the atmosphere's memory for monthly, seasonal and interannual temperature forecasting using Scaling LInear Macroweather Model (SLIMM)

    Science.gov (United States)

    Del Rio Amador, Lenin; Lovejoy, Shaun

    2016-04-01

    . The corresponding space-time model (the ScaLIng Macroweather Model (SLIMM) is thus only multifractal in space where the spatial intermittency is associated with different climate zones. SLIMM exploits the power law (scaling) behavior in time of the temperature field and uses the long historical memory of the temperature series to improve the skill. The only model parameter is the fluctuation scaling exponent, H (usually in the range -0.5 - 0), which is directly related to the skill and can be obtained from the data. The results predicted analytically by the model have been tested by performing actual hindcasts in different 5° x 5° regions covering the planet using ERA-Interim, 20CRv2 and NCEP/NCAR reanalysis as reference datasets. We report maps of theoretical skill predicted by the model and we compare it with actual skill based on hindcasts for monthly, seasonal and annual resolutions. We also present maps of calibrated probability hindcasts with their respective validations. Comparisons between our results using SLIMM, some other stochastic autoregressive model, and hindcasts from the Canadian Seasonal to Interannual Prediction System (CanSIPS) and the National Centers for Environmental Prediction (NCEP)'s model CFSv2, are also shown. For seasonal temperature forecasts, SLIMM outperforms the GCM based forecasts in over 90% of the earth's surface. SLIMM forecasts can be accessed online through the site: http://www.to_be_announced.mcgill.ca.

  11. Development of a methodology for monthly forecasting of surface fires of Colombia's vegetation cover, an application to north Andean region

    International Nuclear Information System (INIS)

    Gonzalez Hernandez, Yolanda; Rangel CH, Jesus Orlando

    2004-01-01

    In the present article a methodology is presented for the forecasting of the monthly risk of surface fires of the vegetation cover in Colombia, based on the analysis of meteorological components and variables of climatic and anthropic variability involved in fire risks of the north Andean region. The methodology enables one to regionalize the country, with fire prediction purposes in mind, into ten sub-regions, in each one of which seven height levels are defined to make up separate regions of study. For each of these, a database is built to feed both the logistic regression models and the Poisson models, which identify the variables independent from, and/or associated with the presence or absence of fires

  12. Assessing Intelligent Models in Forecasting Monthly Rainfall by Means of Teleconnection Patterns (Case Study: Khorasan Razavi Province

    Directory of Open Access Journals (Sweden)

    Farzaneh Nazarieh

    2016-02-01

    Full Text Available Introduction: Rainfall is affected by changes in the global sea level change, especially changes in sea surface temperature SST Sea Surface Temperature and sea level pressure SLP Sea level Pressure. Climate anomalies being related to each other at large distance is called teleconnection. As physical relationships between rainfall and teleconnection patterns are not defined clearly, we used intelligent models for forecasting rainfall. The intelligent models used in this study included Fuzzy Inference Systems, neural network and Neuro-fuzzy. In this study, first the teleconnection indices that could affect rainfall in the study area were identified. Then intelligent models were trained for rainfall forecasting. Finally, the most capable model for forecasting rainfall was presented. The study area for this research is the Khorasan Razavi Province. In order to present a model for rainfall forecasting, rainfall data of seven synoptic stations including Mashhad, Golmakan, Nishapur, Sabzevar, Kashmar, Torbate and Sharks since 1991 to 2010 were used. Materials and Methods: Based on previous studies about Teleconnection Patterns in the study area, effective Teleconnection indexes were identified. After calculating the correlation between the identified teleconnection indices and rainfall in one, two and three months ahead for all stations, fourteen teleconnection indices were chosen as inputs for intelligent models. These indices include, SLP Adriatic , SLP northern Red Sea, SLP Mediterranean Sea, SLP Aral sea, SST Sea surface temperature Labrador sea, SST Oman Sea, SST Caspian Sea, SST Persian Gulf, North Pacific pattern, SST Tropical Pacific in NINO12 and NINO3 regions, North Pacific Oscillation, Trans-Nino Index, Multivariable Enso Index. Inputs of the intelligent models include fourteen teleconnection indices, latitude and altitude of each station and their outputs are the prediction of rainfall for one, two and three months ahead. For calibration of

  13. 20 CFR 404.621 - What happens if I file after the first month I meet the requirements for benefits?

    Science.gov (United States)

    2010-04-01

    ... the time limit due to— (i) Circumstances beyond your control, such as extended illness, mental or... you were informed of the need to file an application within the 2-year period and you neglected to do... unable to apply within the 12-month time period because of a physical or mental condition, you may apply...

  14. Monthly water quality forecasting and uncertainty assessment via bootstrapped wavelet neural networks under missing data for Harbin, China.

    Science.gov (United States)

    Wang, Yi; Zheng, Tong; Zhao, Ying; Jiang, Jiping; Wang, Yuanyuan; Guo, Liang; Wang, Peng

    2013-12-01

    In this paper, bootstrapped wavelet neural network (BWNN) was developed for predicting monthly ammonia nitrogen (NH(4+)-N) and dissolved oxygen (DO) in Harbin region, northeast of China. The Morlet wavelet basis function (WBF) was employed as a nonlinear activation function of traditional three-layer artificial neural network (ANN) structure. Prediction intervals (PI) were constructed according to the calculated uncertainties from the model structure and data noise. Performance of BWNN model was also compared with four different models: traditional ANN, WNN, bootstrapped ANN, and autoregressive integrated moving average model. The results showed that BWNN could handle the severely fluctuating and non-seasonal time series data of water quality, and it produced better performance than the other four models. The uncertainty from data noise was smaller than that from the model structure for NH(4+)-N; conversely, the uncertainty from data noise was larger for DO series. Besides, total uncertainties in the low-flow period were the biggest due to complicated processes during the freeze-up period of the Songhua River. Further, a data missing-refilling scheme was designed, and better performances of BWNNs for structural data missing (SD) were observed than incidental data missing (ID). For both ID and SD, temporal method was satisfactory for filling NH(4+)-N series, whereas spatial imputation was fit for DO series. This filling BWNN forecasting method was applied to other areas suffering "real" data missing, and the results demonstrated its efficiency. Thus, the methods introduced here will help managers to obtain informed decisions.

  15. A Comparison Study of Return Ratio-Based Academic Enrollment Forecasting Models. Professional File. Article 129, Spring 2013

    Science.gov (United States)

    Zan, Xinxing Anna; Yoon, Sang Won; Khasawneh, Mohammad; Srihari, Krishnaswami

    2013-01-01

    In an effort to develop a low-cost and user-friendly forecasting model to minimize forecasting error, we have applied average and exponentially weighted return ratios to project undergraduate student enrollment. We tested the proposed forecasting models with different sets of historical enrollment data, such as university-, school-, and…

  16. Configuring calendar variation based on time series regression method for forecasting of monthly currency inflow and outflow in Central Java

    Science.gov (United States)

    Setiawan, Suhartono, Ahmad, Imam Safawi; Rahmawati, Noorgam Ika

    2015-12-01

    Bank Indonesia (BI) as the central bank of Republic Indonesiahas a single overarching objective to establish and maintain rupiah stability. This objective could be achieved by monitoring traffic of inflow and outflow money currency. Inflow and outflow are related to stock and distribution of money currency around Indonesia territory. It will effect of economic activities. Economic activities of Indonesia,as one of Moslem country, absolutely related to Islamic Calendar (lunar calendar), that different with Gregorian calendar. This research aims to forecast the inflow and outflow money currency of Representative Office (RO) of BI Semarang Central Java region. The results of the analysis shows that the characteristics of inflow and outflow money currency influenced by the effects of the calendar variations, that is the day of Eid al-Fitr (moslem holyday) as well as seasonal patterns. In addition, the period of a certain week during Eid al-Fitr also affect the increase of inflow and outflow money currency. The best model based on the value of the smallestRoot Mean Square Error (RMSE) for inflow data is ARIMA model. While the best model for predicting the outflow data in RO of BI Semarang is ARIMAX model or Time Series Regression, because both of them have the same model. The results forecast in a period of 2015 shows an increase of inflow money currency happened in August, while the increase in outflow money currency happened in July.

  17. A new approach for monthly updates of anthropogenic sulfur dioxide emissions from space: Application to China and implications for air quality forecasts

    Science.gov (United States)

    Wang, Yi; Wang, Jun; Xu, Xiaoguang; Henze, Daven K.; Wang, Yuxuan; Qu, Zhen

    2016-09-01

    SO2 emissions, the largest source of anthropogenic aerosols, can respond rapidly to economic and policy driven changes. However, bottom-up SO2 inventories have inherent limitations owing to 24-48 months latency and lack of month-to-month variation in emissions (especially in developing countries). This study develops a new approach that integrates Ozone Monitoring Instrument (OMI) SO2 satellite measurements and GEOS-Chem adjoint model simulations to constrain monthly anthropogenic SO2 emissions. The approach's effectiveness is demonstrated for 14 months in East Asia; resultant posterior emissions not only capture a 20% SO2 emission reduction in Beijing during the 2008 Olympic Games but also improve agreement between modeled and in situ surface measurements. Further analysis reveals that posterior emissions estimates, compared to the prior, lead to significant improvements in forecasting monthly surface and columnar SO2. With the pending availability of geostationary measurements of tropospheric composition, we show that it may soon be possible to rapidly constrain SO2 emissions and associated air quality predictions at fine spatiotemporal scales.

  18. Forecaster Behaviour and Bias in Macroeconomic Forecasts

    OpenAIRE

    Roy Batchelor

    2007-01-01

    This paper documents the presence of systematic bias in the real GDP and inflation forecasts of private sector forecasters in the G7 economies in the years 1990–2005. The data come from the monthly Consensus Economics forecasting service, and bias is measured and tested for significance using parametric fixed effect panel regressions and nonparametric tests on accuracy ranks. We examine patterns across countries and forecasters to establish whether the bias reflects the inefficient use of i...

  19. Long-range forecast of monthly rainfall over India during summer monsoon season using SST in the north Indian Ocean

    Digital Repository Service at National Institute of Oceanography (India)

    Sadhuram, Y.

    only SS T. Utilizing the rainfall data of Pa r tha - sarathy et al. 6 and SSTA data (5 ? 5 d e- gree grids) of Kaplan et al. 8 , it is found that the SSTA over the AS du r ing winter (DJF ? 1/0 year) in the region 15 ? 20 ?N; 60 ? 70 ?E... and b ). The corr e lations are almost zero du r ing April and May, which is not shown. SSTA during fall in the CEIO is strongly and positively corr e lated with the seasonal and monthly rai n fall. It is w eak during other months (Figure 1 c...

  20. Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model

    Science.gov (United States)

    Rounaghi, Mohammad Mahdi; Nassir Zadeh, Farzaneh

    2016-08-01

    We investigated the presence and changes in, long memory features in the returns and volatility dynamics of S&P 500 and London Stock Exchange using ARMA model. Recently, multifractal analysis has been evolved as an important way to explain the complexity of financial markets which can hardly be described by linear methods of efficient market theory. In financial markets, the weak form of the efficient market hypothesis implies that price returns are serially uncorrelated sequences. In other words, prices should follow a random walk behavior. The random walk hypothesis is evaluated against alternatives accommodating either unifractality or multifractality. Several studies find that the return volatility of stocks tends to exhibit long-range dependence, heavy tails, and clustering. Because stochastic processes with self-similarity possess long-range dependence and heavy tails, it has been suggested that self-similar processes be employed to capture these characteristics in return volatility modeling. The present study applies monthly and yearly forecasting of Time Series Stock Returns in S&P 500 and London Stock Exchange using ARMA model. The statistical analysis of S&P 500 shows that the ARMA model for S&P 500 outperforms the London stock exchange and it is capable for predicting medium or long horizons using real known values. The statistical analysis in London Stock Exchange shows that the ARMA model for monthly stock returns outperforms the yearly. ​A comparison between S&P 500 and London Stock Exchange shows that both markets are efficient and have Financial Stability during periods of boom and bust.

  1. Pronosticando la inflación mensual en Colombia un paso hacia delante: una aproximación "de abajo hacia arriba" || Forecasting the Colombian Monthly Inflation One Step Ahead: A "Bottom to Top" Approach

    Directory of Open Access Journals (Sweden)

    Alonso, Julio César

    2017-06-01

    Full Text Available La estructura jerárquica del Índice de Precios al Consumidor (IPC de Colombia permite calcular la inflación como una combinación lineal de sus subcomponentes. Nuestra aproximación implica emplear modelos SARIMA para pronosticar cada componente del IPC y crear un pronóstico de la inflación como una combinación lineal de los pronósticos individuales; es decir, una aproximación "de abajo hacia arriba". Se evalúa el desempeño fuera de muestra de los pronósticos para el siguiente mes de 12 métodos que emplean una aproximación "de abajo hacia arriba". Estos métodos son comparados con un pronóstico agregado de la inflación empleando un modelo SARIMA para el IPC total. Nuestros resultados muestran que emplear un método "de abajo hacia arriba" para pronosticar la inflación del siguiente mes tiene un mejor comportamiento que emplear un modelo SARIMA agregado. || The hierarchical structure of the Colombian Consumer Price Index (CPI makes possible to calculate inflation as a linear combination of its subcomponents. We use SARIMA models to forecast each component of CPI and construct an forecast of inflation using a lineal combination of the forecasts of these components, i.e. a "bottom to top" approach. In this paper, we asses the out-of-sample performance of the one-step ahead forecast of 12 "bottom to top" methodologies. These methods are compared with an aggregate forecast using a SARIMA model. Our results show that a "bottom to top" method to forecast inflation outperforms an aggregate approach for the case of monthly inflation in Colombia.

  2. Forecast Combinations

    OpenAIRE

    Timmermann, Allan G

    2005-01-01

    Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this paper we analyse theoretically the factors that determine the advantages from combining forecasts (for example, the d...

  3. Forecast combinations

    OpenAIRE

    Aiolfi, Marco; Capistrán, Carlos; Timmermann, Allan

    2010-01-01

    We consider combinations of subjective survey forecasts and model-based forecasts from linear and non-linear univariate specifications as well as multivariate factor-augmented models. Empirical results suggest that a simple equal-weighted average of survey forecasts outperform the best model-based forecasts for a majority of macroeconomic variables and forecast horizons. Additional improvements can in some cases be gained by using a simple equal-weighted average of survey and model-based fore...

  4. Forecasting Skill

    Science.gov (United States)

    1981-01-01

    for the third and fourth day precipitation forecasts. A marked improvement was shown for the consensus 24 hour precipitation forecast, and small... Zuckerberg (1980) found a small long term skill increase in forecasts of heavy snow events for nine eastern cities. Other National Weather Service...and maximum temperature) are each awarded marks 2, 1, or 0 according to whether the forecast is correct, 8 - *- -**■*- ———"—- - -■ t0m 1 MM—IB I

  5. Combining SKU-level sales forecasts from models and experts

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); R. Legerstee (Rianne)

    2009-01-01

    textabstractWe study the performance of SKU-level sales forecasts which linearly combine statistical model forecasts and expert forecasts. Using a large and unique database containing model forecasts for monthly sales of various pharmaceutical products and forecasts given by about fifty experts, we

  6. Climate Prediction Center - monthly Outlook

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Site Map News Outlooks monthly Climate Outlooks Banner OFFICIAL Forecasts June 2018 [UPDATED MONTHLY FORECASTS SERVICE ) Canonical Correlation Analysis ECCA - Ensemble Canonical Correlation Analysis Optimal Climate Normals

  7. Load forecasting

    International Nuclear Information System (INIS)

    Mak, H.

    1995-01-01

    Slides used in a presentation at The Power of Change Conference in Vancouver, BC in April 1995 about the changing needs for load forecasting were presented. Technological innovations and population increase were said to be the prime driving forces behind the changing needs in load forecasting. Structural changes, market place changes, electricity supply planning changes, and changes in planning objectives were other factors discussed. It was concluded that load forecasting was a form of information gathering, that provided important market intelligence

  8. Forecast of auroral activity

    International Nuclear Information System (INIS)

    Lui, A.T.Y.

    2004-01-01

    A new technique is developed to predict auroral activity based on a sample of over 9000 auroral sites identified in global auroral images obtained by an ultraviolet imager on the NASA Polar satellite during a 6-month period. Four attributes of auroral activity sites are utilized in forecasting, namely, the area, the power, and the rates of change in area and power. This new technique is quite accurate, as indicated by the high true skill scores for forecasting three different levels of auroral dissipation during the activity lifetime. The corresponding advanced warning time ranges from 22 to 79 min from low to high dissipation levels

  9. 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...

  10. 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...

  11. 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...

  12. Gridded Monthly Time-Mean Observation minus Forecast (omf) Values 0.5 x 0.667 degree V001 (MA_AMSUA_NOAA16_OMF) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — The differences between the observations and the forecast background used for the analysis (the innovations or O-F for short) and those between the observations and...

  13. Forecasting global atmospheric CO2

    International Nuclear Information System (INIS)

    Agusti-Panareda, A.; Massart, S.; Boussetta, S.; Balsamo, G.; Beljaars, A.; Engelen, R.; Jones, L.; Peuch, V.H.; Chevallier, F.; Ciais, P.; Paris, J.D.; Sherlock, V.

    2014-01-01

    A new global atmospheric carbon dioxide (CO 2 ) real-time forecast is now available as part of the preoperational Monitoring of Atmospheric Composition and Climate - Interim Implementation (MACC-II) service using the infrastructure of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). One of the strengths of the CO 2 forecasting system is that the land surface, including vegetation CO 2 fluxes, is modelled online within the IFS. Other CO 2 fluxes are prescribed from inventories and from off-line statistical and physical models. The CO 2 forecast also benefits from the transport modelling from a state-of-the-art numerical weather prediction (NWP) system initialized daily with a wealth of meteorological observations. This paper describes the capability of the forecast in modelling the variability of CO 2 on different temporal and spatial scales compared to observations. The modulation of the amplitude of the CO 2 diurnal cycle by near-surface winds and boundary layer height is generally well represented in the forecast. The CO 2 forecast also has high skill in simulating day-to-day synoptic variability. In the atmospheric boundary layer, this skill is significantly enhanced by modelling the day-to-day variability of the CO 2 fluxes from vegetation compared to using equivalent monthly mean fluxes with a diurnal cycle. However, biases in the modelled CO 2 fluxes also lead to accumulating errors in the CO 2 forecast. These biases vary with season with an underestimation of the amplitude of the seasonal cycle both for the CO 2 fluxes compared to total optimized fluxes and the atmospheric CO 2 compared to observations. The largest biases in the atmospheric CO 2 forecast are found in spring, corresponding to the onset of the growing season in the Northern Hemisphere. In the future, the forecast will be re-initialized regularly with atmospheric CO 2 analyses based on the assimilation of CO 2 products retrieved from satellite

  14. Bayesian analyses of seasonal runoff forecasts

    Science.gov (United States)

    Krzysztofowicz, R.; Reese, S.

    1991-12-01

    Forecasts of seasonal snowmelt runoff volume provide indispensable information for rational decision making by water project operators, irrigation district managers, and farmers in the western United States. Bayesian statistical models and communication frames have been researched in order to enhance the forecast information disseminated to the users, and to characterize forecast skill from the decision maker's point of view. Four products are presented: (i) a Bayesian Processor of Forecasts, which provides a statistical filter for calibrating the forecasts, and a procedure for estimating the posterior probability distribution of the seasonal runoff; (ii) the Bayesian Correlation Score, a new measure of forecast skill, which is related monotonically to the ex ante economic value of forecasts for decision making; (iii) a statistical predictor of monthly cumulative runoffs within the snowmelt season, conditional on the total seasonal runoff forecast; and (iv) a framing of the forecast message that conveys the uncertainty associated with the forecast estimates to the users. All analyses are illustrated with numerical examples of forecasts for six gauging stations from the period 1971 1988.

  15. Four methodologies to improve healthcare demand forecasting.

    Science.gov (United States)

    Côté, M J; Tucker, S L

    2001-05-01

    Forecasting demand for health services is an important step in managerial decision making for all healthcare organizations. This task, which often is assumed by financial managers, first requires the compilation and examination of historical information. Although many quantitative forecasting methods exist, four common methods of forecasting are percent adjustment, 12-month moving average, trendline, and seasonalized forecast. These four methods are all based upon the organization's recent historical demand. Healthcare financial managers who want to project demand for healthcare services in their facility should understand the advantages and disadvantages of each method and then select the method that will best meet the organization's needs.

  16. kosh 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. kpdt 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. kewr 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. kiso 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. kpga 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...

  1. kbkw 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...

  2. ktcl 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...

  3. pgwt 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...

  4. kpsp 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...

  5. kbih 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...

  6. kdnl 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...

  7. kart 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...

  8. kilm 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...

  9. kpne 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...

  10. kabi 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...

  11. ptpn 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...

  12. kblf 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. panc 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. kpbi 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. kgdv 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. kcmx 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. kdls 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. koaj 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. krhi 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. kbpk 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...

  1. khuf 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...

  2. kbpi Terminal Aerodrome Forecast

    Data.gov (United States)

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  13. kecg 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. khbg 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. kpbf 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. konp 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. pkwa 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. ktvf 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. paga 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. khks 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...

  1. kdsm 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...

  2. kpsm 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...

  3. kgrb 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...

  4. kgmu 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...

  5. papg 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...

  6. kbgm 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...

  7. pamc 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...

  8. klrd 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...

  9. ksan 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...

  10. patk 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...

  11. kowb 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...

  12. klru 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. kfxe 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. kjct 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. 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...

  16. paaq 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. kaex 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. klbx 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. kmia 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. kpit 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...

  1. kcrw 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...

  2. paen 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...

  3. kast 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...

  4. kuin 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...

  5. kmht 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...

  6. kcys 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...

  7. kflo 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...

  8. pakn 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...

  9. pabt 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...

  10. krdg 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...

  11. khdn 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...

  12. 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...

  13. kphx 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. Robust forecast comparison

    OpenAIRE

    Jin, Sainan; Corradi, Valentina; Swanson, Norman

    2015-01-01

    Forecast accuracy is typically measured in terms of a given loss function. However, as a consequence of the use of misspecified models in multiple model comparisons, relative forecast rankings are loss function dependent. This paper addresses this issue by using a novel criterion for forecast evaluation which is based on the entire distribution of forecast errors. We introduce the concepts of general-loss (GL) forecast superiority and convex-loss (CL) forecast superiority, and we establish a ...

  15. Weather forecast

    CERN Document Server

    Courtier, P

    1994-02-07

    Weather prediction is performed using the numerical model of the atmosphere evolution.The evolution equations are derived from the Navier Stokes equation for the adiabatic part but the are very much complicated by the change of phase of water, the radiation porocess and the boundary layer.The technique used operationally is described. Weather prediction is an initial value problem and accurate initial conditions need to be specified. Due to the small number of observations available (105 ) as compared to the dimension of the model state variable (107),the problem is largely underdetermined. Techniques of optimal control and inverse problems are used and have been adapted to the large dimension of our problem. our problem.The at mosphere is a chaotic system; the implication for weather prediction is discussed. Ensemble prediction is used operationally and the technique for generating initial conditions which lead to a numerical divergence of the subsequent forecasts is described.

  16. National Forecast Charts

    Science.gov (United States)

    code. Press enter or select the go button to submit request Local forecast by "City, St" or Prediction Center on Twitter NCEP Quarterly Newsletter WPC Home Analyses and Forecasts National Forecast to all federal, state, and local government web resources and services. National Forecast Charts

  17. Are Forecast Updates Progressive?

    NARCIS (Netherlands)

    C-L. Chang (Chia-Lin); Ph.H.B.F. Franses (Philip Hans); M.J. McAleer (Michael)

    2010-01-01

    textabstractMacro-economic forecasts typically involve both a model component, which is replicable, as well as intuition, which is non-replicable. Intuition is expert knowledge possessed by a forecaster. If forecast updates are progressive, forecast updates should become more accurate, on average,

  18. Forecasting freight flows

    DEFF Research Database (Denmark)

    Lyk-Jensen, Stéphanie

    2011-01-01

    Trade patterns and transport markets are changing as a result of the growth and globalization of international trade, and forecasting future freight flow has to rely on trade forecasts. Forecasting freight flows is critical for matching infrastructure supply to demand and for assessing investment...... constitute a valuable input to freight models for forecasting future capacity problems.......Trade patterns and transport markets are changing as a result of the growth and globalization of international trade, and forecasting future freight flow has to rely on trade forecasts. Forecasting freight flows is critical for matching infrastructure supply to demand and for assessing investment...

  19. Forecasting risks of natural gas consumption in Slovenia

    Energy Technology Data Exchange (ETDEWEB)

    Potocnik, Primoz; Govekar, Edvard; Grabec, Igor [Laboratory of Synergetics, Ljubljana (Slovenia). Faculty of Mechanical Engineering; Thaler, Marko; Poredos, Alojz [Laboratory for Refrigeration, Ljubljana (Slovenia). Faculty of Mechanical Engineering

    2007-08-15

    Efficient operation of modern energy distribution systems often requires forecasting future energy demand. This paper proposes a strategy to estimate forecasting risk. The objective of the proposed method is to improve knowledge about expected forecasting risk and to estimate the expected cash flow in advance, based on the risk model. The strategy combines an energy demand forecasting model, an economic incentive model and a risk model. Basic guidelines are given for the construction of a forecasting model that combines past energy consumption data, weather data and weather forecast. The forecasting model is required to estimate expected forecasting errors that are the basis for forecasting risk estimation. The risk estimation strategy also requires an economic incentive model that describes the influence of forecasting accuracy on the energy distribution systems' cash flow. The economic model defines the critical forecasting error levels that most strongly influence cash flow. Based on the forecasting model and the economic model, the development of a risk model is proposed. The risk model is associated with critical forecasting error levels in the context of various influential parameters such as seasonal data, month, day of the week and temperature. The risk model is applicable to estimating the daily forecasting risk based on the influential parameters. The proposed approach is illustrated by a case study of a Slovenian natural gas distribution company. (author)

  20. Forecasting risks of natural gas consumption in Slovenia

    International Nuclear Information System (INIS)

    Potocnik, Primoz; Thaler, Marko; Govekar, Edvard; Grabec, Igor; Poredos, Alojz

    2007-01-01

    Efficient operation of modern energy distribution systems often requires forecasting future energy demand. This paper proposes a strategy to estimate forecasting risk. The objective of the proposed method is to improve knowledge about expected forecasting risk and to estimate the expected cash flow in advance, based on the risk model. The strategy combines an energy demand forecasting model, an economic incentive model and a risk model. Basic guidelines are given for the construction of a forecasting model that combines past energy consumption data, weather data and weather forecast. The forecasting model is required to estimate expected forecasting errors that are the basis for forecasting risk estimation. The risk estimation strategy also requires an economic incentive model that describes the influence of forecasting accuracy on the energy distribution systems' cash flow. The economic model defines the critical forecasting error levels that most strongly influence cash flow. Based on the forecasting model and the economic model, the development of a risk model is proposed. The risk model is associated with critical forecasting error levels in the context of various influential parameters such as seasonal data, month, day of the week and temperature. The risk model is applicable to estimating the daily forecasting risk based on the influential parameters. The proposed approach is illustrated by a case study of a Slovenian natural gas distribution company

  1. Solid low-level waste forecasting guide

    International Nuclear Information System (INIS)

    Templeton, K.J.; Dirks, L.L.

    1995-03-01

    Guidance for forecasting solid low-level waste (LLW) on a site-wide basis is described in this document. Forecasting is defined as an approach for collecting information about future waste receipts. The forecasting approach discussed in this document is based solely on hanford's experience within the last six years. Hanford's forecasting technique is not a statistical forecast based upon past receipts. Due to waste generator mission changes, startup of new facilities, and waste generator uncertainties, statistical methods have proven to be inadequate for the site. It is recommended that an approach similar to Hanford's annual forecasting strategy be implemented at each US Department of Energy (DOE) installation to ensure that forecast data are collected in a consistent manner across the DOE complex. Hanford's forecasting strategy consists of a forecast cycle that can take 12 to 30 months to complete. The duration of the cycle depends on the number of LLW generators and staff experience; however, the duration has been reduced with each new cycle. Several uncertainties are associated with collecting data about future waste receipts. Volume, shipping schedule, and characterization data are often reported as estimates with some level of uncertainty. At Hanford, several methods have been implemented to capture the level of uncertainty. Collection of a maximum and minimum volume range has been implemented as well as questionnaires to assess the relative certainty in the requested data

  2. Skilful seasonal forecasts of streamflow over Europe?

    Science.gov (United States)

    Arnal, Louise; Cloke, Hannah L.; Stephens, Elisabeth; Wetterhall, Fredrik; Prudhomme, Christel; Neumann, Jessica; Krzeminski, Blazej; Pappenberger, Florian

    2018-04-01

    This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate

  3. Flood forecasting and uncertainty of precipitation forecasts

    International Nuclear Information System (INIS)

    Kobold, Mira; Suselj, Kay

    2004-01-01

    The timely and accurate flood forecasting is essential for the reliable flood warning. The effectiveness of flood warning is dependent on the forecast accuracy of certain physical parameters, such as the peak magnitude of the flood, its timing, location and duration. The conceptual rainfall - runoff models enable the estimation of these parameters and lead to useful operational forecasts. The accurate rainfall is the most important input into hydrological models. The input for the rainfall can be real time rain-gauges data, or weather radar data, or meteorological forecasted precipitation. The torrential nature of streams and fast runoff are characteristic for the most of the Slovenian rivers. Extensive damage is caused almost every year- by rainstorms affecting different regions of Slovenia' The lag time between rainfall and runoff is very short for Slovenian territory and on-line data are used only for now casting. Forecasted precipitations are necessary for hydrological forecast for some days ahead. ECMWF (European Centre for Medium-Range Weather Forecasts) gives general forecast for several days ahead while more detailed precipitation data with limited area ALADIN/Sl model are available for two days ahead. There is a certain degree of uncertainty using such precipitation forecasts based on meteorological models. The variability of precipitation is very high in Slovenia and the uncertainty of ECMWF predicted precipitation is very large for Slovenian territory. ECMWF model can predict precipitation events correctly, but underestimates amount of precipitation in general The average underestimation is about 60% for Slovenian region. The predictions of limited area ALADIN/Si model up to; 48 hours ahead show greater applicability in hydrological forecasting. The hydrological models are sensitive to precipitation input. The deviation of runoff is much bigger than the rainfall deviation. Runoff to rainfall error fraction is about 1.6. If spatial and time distribution

  4. Downscaling of GCM forecasts to streamflow over Scandinavia

    DEFF Research Database (Denmark)

    Nilsson, P.; Uvo, C.B.; Landman, W.A.

    2008-01-01

    flows. The technique includes model output statistics (MOS) based on a non-linear Neural Network (NN) approach. Results show that streamflow forecasts from Global Circulation Model (GCM) predictions, for the Scandinavia region are viable and highest skill values were found for basins located in south......A seasonal forecasting technique to produce probabilistic and deterministic streamflow forecasts for 23 basins in Norway and northern Sweden is developed in this work. Large scale circulation and moisture fields, forecasted by the ECHAM4.5 model 4 months in advance, are used to forecast spring...

  5. Long-range forecasting of intermittent streamflow

    OpenAIRE

    F. F. van Ogtrop; R. W. Vervoort; G. Z. Heller; D. M. Stasinopoulos; R. A. Rigby

    2011-01-01

    Long-range forecasting of intermittent streamflow in semi-arid Australia poses a number of major challenges. One of the challenges relates to modelling zero, skewed, non-stationary, and non-linear data. To address this, a statistical model to forecast streamflow up to 12 months ahead is applied to five semi-arid catchments in South Western Queensland. The model uses logistic regression through Generalised Additive Models for Location, Scale and Shape (GAMLSS) to determine th...

  6. Long-range forecasting of intermittent streamflow

    OpenAIRE

    F. F. van Ogtrop; R. W. Vervoort; G. Z. Heller; D. M. Stasinopoulos; R. A. Rigby

    2011-01-01

    Long-range forecasting of intermittent streamflow in semi-arid Australia poses a number of major challenges. One of the challenges relates to modelling zero, skewed, non-stationary, and non-linear data. To address this, a probabilistic statistical model to forecast streamflow 12 months ahead is applied to five semi-arid catchments in South Western Queensland. The model uses logistic regression through Generalised Additive Models for Location, Scale and Shape (GAMLSS) to determine the probabil...

  7. Download this PDF file

    African Journals Online (AJOL)

    Dr Kazungu

    Governments need financial resources to provide public goods and services to the citizens. ... governments budgetary procedures frameworks, fiscal forecasting and ... In Tanzania, the Public Finance Act (2001) stipulates the government budget .... monthly and annual total tax revenue collection data published by the Bank ...

  8. Medium Range Forecasts Representation (and Long Range Forecasts?)

    Science.gov (United States)

    Vincendon, J.-C.

    2009-09-01

    interesting on Fridays because it gives then a first outlook of the weather for the second weekend. There also, an example will illustrate that. Finally, we lead an experiment for some months to go beyond and supply a tendency of weather forecasts over the period D+10 / D+14, whom we also call " tendency for week 2 ". It is a question at the moment of producing a small text describing the global evolution of the temperatures and the precipitation, there is no graphic production. All this is completed by a sentence summarizing the tendencies expected from the temperature for weeks 3 and 4. We thus begin to think seriously about the production of a monthly forecast for the public within the framework of our operational activities. We have to establish under which graphic shape this one can be made.

  9. Fuel cycle forecasting - there are forecasts and there are forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Puechl, K H

    1975-12-01

    The FORECAST-NUCLEAR computer program described recognizes that forecasts are made to answer a variety of questions and, therefore, that no single forecast is universally appropriate. Also, it recognizes that no two individuals will completely agree as to the input data that are appropriate for obtaining an answer to even a single simple question. Accordingly, the program was written from a utilitarian standpoint: it allows working with multiple projections; data inputting is simple to allow game-playing; computation time is short to minimize the cost of 'what if' assessements; and detail is internally carried to allow meaningful analysis.

  10. Fuel cycle forecasting - there are forecasts and there are forecasts

    International Nuclear Information System (INIS)

    Puechl, K.H.

    1975-01-01

    The FORECAST-NUCLEAR computer program described recognizes that forecasts are made to answer a variety of questions and, therefore, that no single forecast is universally appropriate. Also, it recognizes that no two individuals will completely agree as to the input data that are appropriate for obtaining an answer to even a single simple question. Accordingly, the program was written from a utilitarian standpoint: it allows working with multiple projections; data inputting is simple to allow game-playing; computation time is short to minimize the cost of 'what if' assessements; and detail is internally carried to allow meaningful analysis. (author)

  11. Using Seasonal Forecasting Data for Vessel Routing

    Science.gov (United States)

    Bell, Ray; Kirtman, Ben

    2017-04-01

    We present an assessment of seasonal forecasting of surface wind speed, significant wave height and ocean surface current speed in the North Pacific for potential use of vessel routing from Singapore to San Diego. WaveWatchIII is forced with surface winds and ocean surface currents from the Community Climate System Model 4 (CCSM4) retrospective forecasts for the period of 1982-2015. Several lead time forecasts are used from zero months to six months resulting in 2,720 model years, ensuring the findings from this study are robust. July surface wind speed and significant wave height can be skillfully forecast with a one month lead time, with the western North Pacific being the most predictable region. Beyond May initial conditions (lead time of two months) the El Niño Southern Oscillation (ENSO) Spring predictability barrier limits skill of significant wave height but there is skill for surface wind speed with January initial conditions (lead time of six months). In a separate study of vessel routing between Norfolk, Virginia and Gibraltar we demonstrate the benefit of a multimodel approach using the North American Multimodel Ensemble (NMME). In collaboration with Charles River Analytics an all-encompassing forecast is presented by using machine learning on the various ensembles which can be using used for industry applications.

  12. Robust Approaches to Forecasting

    OpenAIRE

    Jennifer Castle; David Hendry; Michael P. Clements

    2014-01-01

    We investigate alternative robust approaches to forecasting, using a new class of robust devices, contrasted with equilibrium correction models. Their forecasting properties are derived facing a range of likely empirical problems at the forecast origin, including measurement errors, implulses, omitted variables, unanticipated location shifts and incorrectly included variables that experience a shift. We derive the resulting forecast biases and error variances, and indicate when the methods ar...

  13. Inflation Forecast Contracts

    OpenAIRE

    Gersbach, Hans; Hahn, Volker

    2012-01-01

    We introduce a new type of incentive contract for central bankers: inflation forecast contracts, which make central bankers’ remunerations contingent on the precision of their inflation forecasts. We show that such contracts enable central bankers to influence inflation expectations more effectively, thus facilitating more successful stabilization of current inflation. Inflation forecast contracts improve the accuracy of inflation forecasts, but have adverse consequences for output. On balanc...

  14. Statistical forecasting of met-ocean parameters in the Cochin estuarine system, southwest coast of India

    Digital Repository Service at National Institute of Oceanography (India)

    Srinivas, K.; Revichandran, C.; DineshKumar, P.K.

    Three different statistical forecasting techniques - autoregressive, sinusoidal and exponentially weighted moving average (EWMA) were used to forecast monthly values of meteorological and oceanographic (met-ocean) parameters viz. sea surface...

  15. An Optimization of Inventory Demand Forecasting in University Healthcare Centre

    Science.gov (United States)

    Bon, A. T.; Ng, T. K.

    2017-01-01

    Healthcare industry becomes an important field for human beings nowadays as it concerns about one’s health. With that, forecasting demand for health services is an important step in managerial decision making for all healthcare organizations. Hence, a case study was conducted in University Health Centre to collect historical demand data of Panadol 650mg for 68 months from January 2009 until August 2014. The aim of the research is to optimize the overall inventory demand through forecasting techniques. Quantitative forecasting or time series forecasting model was used in the case study to forecast future data as a function of past data. Furthermore, the data pattern needs to be identified first before applying the forecasting techniques. Trend is the data pattern and then ten forecasting techniques are applied using Risk Simulator Software. Lastly, the best forecasting techniques will be find out with the least forecasting error. Among the ten forecasting techniques include single moving average, single exponential smoothing, double moving average, double exponential smoothing, regression, Holt-Winter’s additive, Seasonal additive, Holt-Winter’s multiplicative, seasonal multiplicative and Autoregressive Integrated Moving Average (ARIMA). According to the forecasting accuracy measurement, the best forecasting technique is regression analysis.

  16. Spatiotemporal drought forecasting using nonlinear models

    Science.gov (United States)

    Vasiliades, Lampros; Loukas, Athanasios

    2010-05-01

    Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. In order to achieve spatiotemporal forecasting, some mature analysis tools, e.g., time series and spatial statistics are extended to the spatial dimension and the temporal dimension, respectively. Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Despite the widespread application of nonlinear mathematical models, comparative studies on spatiotemporal drought forecasting using different models are still a huge task for modellers. This study uses a promising approach, the Gamma Test (GT), to select the input variables and the training data length, so that the trial and error workload could be greatly reduced. The GT enables to quickly evaluate and estimate the best mean squared error that can be achieved by a smooth model on any unseen data for a given selection of inputs, prior to model construction. The GT is applied to forecast droughts using monthly Standardized Precipitation Index (SPI) timeseries at multiple timescales in several precipitation stations at Pinios river basin in Thessaly region, Greece. Several nonlinear models have been developed efficiently, with the aid of the GT, for 1-month up to 12-month ahead forecasting. Several temporal and spatial statistical indices were considered for the performance evaluation of the models. The predicted results show reasonably good agreement with the actual data for short lead times, whereas the forecasting accuracy decreases with

  17. Electricity demand forecasting techniques

    International Nuclear Information System (INIS)

    Gnanalingam, K.

    1994-01-01

    Electricity demand forecasting plays an important role in power generation. The two areas of data that have to be forecasted in a power system are peak demand which determines the capacity (MW) of the plant required and annual energy demand (GWH). Methods used in electricity demand forecasting include time trend analysis and econometric methods. In forecasting, identification of manpower demand, identification of key planning factors, decision on planning horizon, differentiation between prediction and projection (i.e. development of different scenarios) and choosing from different forecasting techniques are important

  18. Spatial electric load forecasting

    CERN Document Server

    Willis, H Lee

    2002-01-01

    Containing 12 new chapters, this second edition contains offers increased-coverage of weather correction and normalization of forecasts, anticipation of redevelopment, determining the validity of announced developments, and minimizing risk from over- or under-planning. It provides specific examples and detailed explanations of key points to consider for both standard and unusual utility forecasting situations, information on new algorithms and concepts in forecasting, a review of forecasting pitfalls and mistakes, case studies depicting challenging forecast environments, and load models illustrating various types of demand.

  19. Long term file migration. Part I: file reference patterns

    International Nuclear Information System (INIS)

    Smith, A.J.

    1978-08-01

    In most large computer installations, files are moved between on-line disk and mass storage (tape, integrated mass storage device) either automatically by the system or specifically at the direction of the user. This is the first of two papers which study the selection of algorithms for the automatic migration of files between mass storage and disk. The use of the text editor data sets at the Stanford Linear Accelerator Center (SLAC) computer installation is examined through the analysis of thirteen months of file reference data. Most files are used very few times. Of those that are used sufficiently frequently that their reference patterns may be examined, about a third show declining rates of reference during their lifetime; of the remainder, very few (about 5%) show correlated interreference intervals, and interreference intervals (in days) appear to be more skewed than would occur with the Bernoulli process. Thus, about two-thirds of all sufficiently active files appear to be referenced as a renewal process with a skewed interreference distribution. A large number of other file reference statistics (file lifetimes, interference distributions, moments, means, number of uses/file, file sizes, file rates of reference, etc.) are computed and presented. The results are applied in the following paper to the development and comparative evaluation of file migration algorithms. 17 figures, 13 tables

  20. Forecasting of Currency Crises in East Asia

    Directory of Open Access Journals (Sweden)

    Chi-Young Song

    2005-06-01

    Full Text Available In this paper, we have developed a forecasting system for currency crisis in East Asia based on a signaling approach. Our system uses 15 monthly indicators of five East Asian countries including Indonesia, Korea, Malaysia, the Philippines and Thailand that were severely hit by the currency crisis in 1997. We investigate the performance of the system through deploying out-of-sample forecasting for the periods both before and after the 1997 East Asian currency crisis. Unlike the existing research based on the signaling approach, our out-of-sample forecasting does not fix the in-sample period. The out-of-sample forecasting between July 1995 and June 1997 shows that prior to breakout of the crisis, several indicators including real exchange rates and exports sent frequent warnings to all crisis-hit East Asian countries except the Philippines. This may indicate that a signaling-based early warning system for currency crisis could have been an useful method of forecasting the East Asian crisis. On the other hand, we also find that our forecasting system often generates warning signals during the out-of-sample period between July 1999 and June 2001. Since we have not observed any currency crisis in this region after 1998, these are all false alarms, indicating that our system may be seriously exposed to the type II error. We can, however, mitigate this problem if we adjust the optimal critical values of indicators depending on the preferences of forecasting system manager.

  1. File sharing

    NARCIS (Netherlands)

    van Eijk, N.

    2011-01-01

    File sharing’ has become generally accepted on the Internet. Users share files for downloading music, films, games, software etc. In this note, we have a closer look at the definition of file sharing, the legal and policy-based context as well as enforcement issues. The economic and cultural

  2. Forecasting Analysis of Shanghai Stock Index Based on ARIMA Model

    Directory of Open Access Journals (Sweden)

    Li Chenggang

    2017-01-01

    Full Text Available Prediction and analysis of the Shanghai Composite Index is conducive for investors to investing in the stock market, and providing investors with reference. This paper selects Shanghai Composite Index monthly closing price from Jan, 2005 to Oct, 2016 to construct ARIMA model. This paper carries on the forecast of the last three monthly closing price of Shanghai Stock Index that have occurred, and compared it with the actual value, which tests the accuracy and feasibility of the model in the short term Shanghai Stock Index forecast. At last, this paper uses the ARIMA model to forecast the Shanghai Composite Index closing price of the last two months in 2016.

  3. Inaccuracy in traffic forecasts

    DEFF Research Database (Denmark)

    Flyvbjerg, Bent; Holm, Mette K. Skamris; Buhl, Søren Ladegaard

    2006-01-01

    This paper presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth US$58 billion. The study shows with very high statistical significance...... that forecasters generally do a poor job of estimating the demand for transportation infrastructure projects. The result is substantial downside financial and economic risk. Forecasts have not become more accurate over the 30-year period studied. If techniques and skills for arriving at accurate demand forecasts...... forecasting. Highly inaccurate traffic forecasts combined with large standard deviations translate into large financial and economic risks. But such risks are typically ignored or downplayed by planners and decision-makers, to the detriment of social and economic welfare. The paper presents the data...

  4. Seasonal time series forecasting: a comparative study of arima and ...

    African Journals Online (AJOL)

    This paper addresses the concerns of Faraway and Chatfield (1998) who questioned the forecasting ability of Artificial Neural Networks (ANN). In particular the paper compares the performance of Artificial Neural Networks (ANN) and ARIMA models in forecasting of seasonal (monthly) Time series. Using the Airline data ...

  5. FORECASTING MODELS IN MANAGEMENT

    OpenAIRE

    Sindelar, Jiri

    2008-01-01

    This article deals with the problems of forecasting models. First part of the article is dedicated to definition of the relevant areas (vertical and horizontal pillar of definition) and then the forecasting model itself is defined; as article presents theoretical background for further primary research, this definition is crucial. Finally the position of forecasting models within the management system is identified. The paper is a part of the outputs of FEM CULS grant no. 1312/11/3121.

  6. Forecasting in Planning

    OpenAIRE

    Ike, P.; Voogd, Henk; Voogd, Henk; Linden, Gerard

    2004-01-01

    This chapter begins with a discussion of qualitative forecasting by describing a number of methods that depend on judgements made by stakeholders, experts or other interested parties to arrive at forecasts. Two qualitative approaches are illuminated, the Delphi and scenario methods respectively. Quantitative forecasting is illustrated with a brief overview of time series methods. Both qualitative and quantitative methods are illustrated by an example. The role and relative importance of forec...

  7. The strategy of professional forecasting

    DEFF Research Database (Denmark)

    Ottaviani, Marco; Sørensen, Peter Norman

    2006-01-01

    We develop and compare two theories of professional forecasters’ strategic behavior. The first theory, reputational cheap talk, posits that forecasters endeavor to convince the market that they are well informed. The market evaluates their forecasting talent on the basis of the forecasts...... and the realized state. If the market expects forecasters to report their posterior expectations honestly, then forecasts are shaded toward the prior mean. With correct market expectations, equilibrium forecasts are imprecise but not shaded. The second theory posits that forecasters compete in a forecasting...... contest with pre-specified rules. In a winner-take-all contest, equilibrium forecasts are excessively differentiated...

  8. Daddy Months

    OpenAIRE

    Volker Meier; Helmut Rainer

    2014-01-01

    We consider a bargaining model in which husband and wife decide on the allocation of time and disposable income. Since her bargaining power would go down otherwise more strongly, the wife agrees to having a child only if the husband also leaves the labor market for a while. The daddy months subsidy enables the couple to overcome a hold-up problem and thereby improves efficiency. However, the same ruling harms cooperative couples and may also reduce welfare in an endogenous taxation framework.

  9. Seasonal Drought Forecasting for Latin America Using the ECMWF S4 Forecast System

    Directory of Open Access Journals (Sweden)

    Hugo Carrão

    2018-06-01

    Full Text Available Meaningful seasonal prediction of drought conditions is key information for end-users and water managers, particularly in Latin America where crop and livestock production are key for many regional economies. However, there are still not many studies of the feasibility of such a forecasts at continental level in the region. In this study, precipitation predictions from the European Centre for Medium Range Weather (ECMWF seasonal forecast system S4 are combined with observed precipitation data to generate forecasts of the standardized precipitation index (SPI for Latin America, and their skill is evaluated over the hindcast period 1981–2010. The value-added utility in using the ensemble S4 forecast to predict the SPI is identified by comparing the skill of its forecasts with a baseline skill based solely on their climatological characteristics. As expected, skill of the S4-generated SPI forecasts depends on the season, location, and the specific aggregation period considered (the 3- and 6-month SPI were evaluated. Added skill from the S4 for lead times equaling the SPI accumulation periods is primarily present in regions with high intra-annual precipitation variability, and is found mostly for the months at the end of the dry seasons for 3-month SPI, and half-yearly periods for 6-month SPI. The ECMWF forecast system behaves better than the climatology for clustered grid points in the North of South America, the Northeast of Argentina, Uruguay, southern Brazil and Mexico. The skillful regions are similar for the SPI3 and -6, but become reduced in extent for the severest SPI categories. Forecasting different magnitudes of meteorological drought intensity on a seasonal time scale still remains a challenge. However, the ECMWF S4 forecasting system does capture the occurrence of drought events for the aforementioned regions and seasons reasonably well. In the near term, the largest advances in the prediction of meteorological drought for Latin

  10. House Price Forecasts, Forecaster Herding, and the Recent Crisis

    Directory of Open Access Journals (Sweden)

    Christian Pierdzioch

    2012-11-01

    Full Text Available We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-herding of forecasters. Forecasts are consistent with herding (anti-herding of forecasters if forecasts are biased towards (away from the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time.

  11. House Price Forecasts, Forecaster Herding, and the Recent Crisis

    DEFF Research Database (Denmark)

    Stadtmann, Georg; Pierdzioch; Ruelke

    2013-01-01

    We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-)herding of forecasters. Forecasts are consistent with herding (anti-herding) of forecasters if forecasts are biased towards (away from) t......) the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time....

  12. World Area Forecast System (WAFS)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The World Area Forecast System (WAFS) is a worldwide system by which world area forecast centers provide aeronautical meteorological en-route forecasts in uniform...

  13. Forecasting in Planning

    NARCIS (Netherlands)

    Ike, P.; Voogd, Henk; Voogd, Henk; Linden, Gerard

    2004-01-01

    This chapter begins with a discussion of qualitative forecasting by describing a number of methods that depend on judgements made by stakeholders, experts or other interested parties to arrive at forecasts. Two qualitative approaches are illuminated, the Delphi and scenario methods respectively.

  14. Improving Garch Volatility Forecasts

    NARCIS (Netherlands)

    Klaassen, F.J.G.M.

    1998-01-01

    Many researchers use GARCH models to generate volatility forecasts. We show, however, that such forecasts are too variable. To correct for this, we extend the GARCH model by distinguishing two regimes with different volatility levels. GARCH effects are allowed within each regime, so that our model

  15. 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

  16. Forecast Accuracy Uncertainty and Momentum

    OpenAIRE

    Bing Han; Dong Hong; Mitch Warachka

    2009-01-01

    We demonstrate that stock price momentum and earnings momentum can result from uncertainty surrounding the accuracy of cash flow forecasts. Our model has multiple information sources issuing cash flow forecasts for a stock. The investor combines these forecasts into an aggregate cash flow estimate that has minimal mean-squared forecast error. This aggregate estimate weights each cash flow forecast by the estimated accuracy of its issuer, which is obtained from their past forecast errors. Mome...

  17. The GOCF/AWAP system - forecasting temperature extremes

    International Nuclear Information System (INIS)

    Fawcett, Robert; Hume, Timothy

    2010-01-01

    Gridded hourly temperature forecasts from the Bureau of Meteorology's Gridded Operational Consensus Forecasting (GOCF) system are combined in real time with the Australian Water Availability Project (AWAP) gridded daily temperature analyses to produce gridded daily maximum and minimum temperature forecasts with lead times from one to five days. These forecasts are compared against the historical record of AWAP daily temperature analyses (1911 to present), to identify regions where record or near-record temperatures are predicted to occur. This paper describes the GOCF/AWAP system, showing how the daily maximum and minimum temperature forecasts are prepared from the hourly forecasts, and how they are bias-corrected in real time using the AWAP analyses, against which they are subsequently verified. Using monthly climatologies of long-term daily mean, standard deviation and all-time highest and lowest on record, derived forecast products (for both maximum and minimum temperature) include ordinary and standardised anomalies, 'forecast - highest on record' and 'forecast - lowest on record'. Compensation for the climatological variation across the country is achieved in these last two products, which provide the necessary guidance as to whether or not record-breaking temperatures are expected, by expressing the forecast departure from the previous record in both 0 C and standard deviations.

  18. A short-term ensemble wind speed forecasting system for wind power applications

    Science.gov (United States)

    Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

    2011-12-01

    This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

  19. A hybrid spatiotemporal drought forecasting model for operational use

    Science.gov (United States)

    Vasiliades, L.; Loukas, A.

    2010-09-01

    Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. This study develops a hybrid spatiotemporal scheme for integrated spatial and temporal forecasting. Temporal forecasting is achieved using feed-forward neural networks and the temporal forecasts are extended to the spatial dimension using a spatial recurrent neural network model. The methodology is demonstrated for an operational meteorological drought index the Standardized Precipitation Index (SPI) calculated at multiple timescales. 48 precipitation stations and 18 independent precipitation stations, located at Pinios river basin in Thessaly region, Greece, were used for the development and spatiotemporal validation of the hybrid spatiotemporal scheme. Several quantitative temporal and spatial statistical indices were considered for the performance evaluation of the models. Furthermore, qualitative statistical criteria based on contingency tables between observed and forecasted drought episodes were calculated. The results show that the lead time of forecasting for operational use depends on the SPI timescale. The hybrid spatiotemporal drought forecasting model could be operationally used for forecasting up to three months ahead for SPI short timescales (e.g. 3-6 months) up to six months ahead for large SPI timescales (e.g. 24 months). The above findings could be useful in developing a drought preparedness plan in the region.

  20. High-Resolution Hydrological Sub-Seasonal Forecasting for Water Resources Management Over Europe

    Science.gov (United States)

    Wood, E. F.; Wanders, N.; Pan, M.; Sheffield, J.; Samaniego, L. E.; Thober, S.; Kumar, R.; Prudhomme, C.; Houghton-Carr, H.

    2017-12-01

    For decision-making at the sub-seasonal and seasonal time scale, hydrological forecasts with a high temporal and spatial resolution are required by water managers. So far such forecasts have been unavailable due to 1) lack of availability of meteorological seasonal forecasts, 2) coarse temporal resolution of meteorological seasonal forecasts, requiring temporal downscaling, 3) lack of consistency between observations and seasonal forecasts, requiring bias-correction. The EDgE (End-to-end Demonstrator for improved decision making in the water sector in Europe) project commissioned by the ECMWF (C3S) created a unique dataset of hydrological seasonal forecasts derived from four global climate models (CanCM4, FLOR-B01, ECMF, LFPW) in combination with four global hydrological models (PCR-GLOBWB, VIC, mHM, Noah-MP), resulting in 208 forecasts for any given day. The forecasts provide a daily temporal and 5-km spatial resolution, and are bias corrected against E-OBS meteorological observations. The forecasts are communicated to stakeholders via Sectoral Climate Impact Indicators (SCIIs), created in collaboration with the end-user community of the EDgE project (e.g. the percentage of ensemble realizations above the 10th percentile of monthly river flow, or below the 90th). Results show skillful forecasts for discharge from 3 months to 6 months (latter for N Europe due to snow); for soil moisture up to three months due precipitation forecast skill and short initial condition memory; and for groundwater greater than 6 months (lowest skill in western Europe.) The SCIIs are effective in communicating both forecast skill and uncertainty. Overall the new system provides an unprecedented ensemble for seasonal forecasts with significant skill over Europe to support water management. The consistency in both the GCM forecasts and the LSM parameterization ensures a stable and reliable forecast framework and methodology, even if additional GCMs or LSMs are added in the future.

  1. Spatial electric load forecasting

    CERN Document Server

    Willis, H Lee

    2002-01-01

    Spatial Electric Load Forecasting Consumer Demand for Power and ReliabilityCoincidence and Load BehaviorLoad Curve and End-Use ModelingWeather and Electric LoadWeather Design Criteria and Forecast NormalizationSpatial Load Growth BehaviorSpatial Forecast Accuracy and Error MeasuresTrending MethodsSimulation Method: Basic ConceptsA Detailed Look at the Simulation MethodBasics of Computerized SimulationAnalytical Building Blocks for Spatial SimulationAdvanced Elements of Computerized SimulationHybrid Trending-Simulation MethodsAdvanced

  2. Model for forecasting of monthly average insulation at ground level taking into account the radiation absorption losses crossing atmosphere in the thermal solar applications; Modelo de previsao da insolacao media mensal ao nivel do solo levando em conta a perda por absorcao na atmosfera em aplicacoes solares termicas

    Energy Technology Data Exchange (ETDEWEB)

    Camargo, J.C.; Apolinario, F.R.; Silva, E.P. da [Universidade Estadual de Campinas, SP (Brazil). Lab. de Hidrogenio]. E-mails: joaoc@fem.unicamp.br; rezende@ifi.unicamp.br; lh2ennio@ifi.unicamp.br

    2000-07-01

    The use of the solar energy, for thermal or photovoltaic ends, depends basically on the amount of radiation that reaches the ground in the place where desires to carry through this use, defining the necessary area of the collectors, or panels, that in turn are the main components of the final cost of the system and, therefore, of the viability or not on its use. The incident radiation in the terrestrial surface is minor that one reaches the top of the atmosphere due to the absorption and dispersion factors. The objective of this work is to present a model of forecast the monthly average radiation for ends of use in systems of flat solar collectors for heating water, in the city of Campinas - Sao Paulo, Brazil. This work has been developed by the Hydrogen Laboratory of the Institute of Physics of the UNICAMP, being also used for other applications with solar energy. Based in the radiation data, taken from a local station, a theoretical study was developed to calculate a parameter of loss of radiation when this cross the atmosphere. This Kt loss factor, has basic importance for the knowledge of the effective available energy for use. With this data it is possible to determine, on the basis of the incident radiation in the top of the atmosphere, the value of the radiation on a surface. (author)

  3. Monthly reservoir inflow forecasting using a new hybrid SARIMA ...

    Indian Academy of Sciences (India)

    SARIMA models are linear and do not consider the random component of statistical data. To overcome this ... lake levels using artificial intelligence models and found that, compared with other models such as. ANN, GEP has .... that should be evaluated before modelling, and if they exist, they should be removed. The com-.

  4. Application of the North American Multi-Model Ensemble to seasonal water supply forecasting in the Great Lakes basin through the use of the Great Lakes Seasonal Climate Forecast Tool

    Science.gov (United States)

    Gronewold, A.; Apps, D.; Fry, L. M.; Bolinger, R.

    2017-12-01

    The U.S. Army Corps of Engineers (USACE) contribution to the internationally coordinated 6-month forecast of Great Lakes water levels relies on several water supply models, including a regression model relating a coming month's water supply to past water supplies, previous months' precipitation and temperature, and forecasted precipitation and temperature. Probabilistic forecasts of precipitation and temperature depicted in the Climate Prediction Center's seasonal outlook maps are considered to be standard for use in operational forecasting for seasonal time horizons, and have provided the basis for computing a coming month's precipitation and temperature for use in the USACE water supply regression models. The CPC outlook maps are a useful forecast product offering insight into interpretation of climate models through the prognostic discussion and graphical forecasts. However, recent evolution of USACE forecast procedures to accommodate automated data transfer and manipulation offers a new opportunity for direct incorporation of ensemble climate forecast data into probabilistic outlooks of water supply using existing models that have previously been implemented in a deterministic fashion. We will present results from a study investigating the potential for applying data from the North American Multi-Model Ensemble to operational water supply forecasts. The use of NMME forecasts is facilitated by a new, publicly available, Great Lakes Seasonal Climate Forecast Tool that provides operational forecasts of monthly average temperatures and monthly total precipitation summarized for each lake basin.

  5. About the National Forecast Chart

    Science.gov (United States)

    code. Press enter or select the go button to submit request Local forecast by "City, St" or Prediction Center on Twitter NCEP Quarterly Newsletter WPC Home Analyses and Forecasts National Forecast to all federal, state, and local government web resources and services. The National Forecast Charts

  6. Marine Point Forecasts

    Science.gov (United States)

    will link to the zone forecast and then allow further zooming to the point of interest whereas on the Honolulu, HI Chicago, IL Northern Indiana, IN Lake Charles, LA New Orleans, LA Boston, MA Caribou, ME

  7. Socioeconomic Forecasting : [Technical Summary

    Science.gov (United States)

    2012-01-01

    Because the traffic forecasts produced by the Indiana : Statewide Travel Demand Model (ISTDM) are driven by : the demographic and socioeconomic inputs to the model, : particular attention must be given to obtaining the most : accurate demographic and...

  8. 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.

  9. Inflow forecasting at BPA

    Energy Technology Data Exchange (ETDEWEB)

    McManamon, A. [Bonneville Power Administration, Portland, OR (United States)

    2007-07-01

    The Columbia River Power System operates with consideration for flood control, endangered species, navigation, irrigation, water supply, recreation, other fish and wildlife concerns and power production. The Bonneville Power Association (BPA) located in Portland, Oregon is responsible for 35-40 per cent of the power consumed within the region. This presentation discussed inflow power concerns at BPA. The presentation illustrated elevational relief of projects; annual and daily variability; the hydrologic cycle; national river service weather forecasting service (NRSWFS); components of NRSWFS; and hydrologic forecast locations. Project operations and inventory were included along with a comparison of the 71-year average unregulated flow with regulated flow at the Dalles. Consistency between short-term and long-term forecasts and long-term streamflow forecasts were also illustrated in graphical format. The presentation also discussed the issue of reducing model and parameter uncertainty; reducing initial conditions uncertainty; snow updating; and reducing meteorological uncertainty. tabs., figs.

  10. CCAA seasonal forecasting

    International Development Research Centre (IDRC) Digital Library (Canada)

    Integrating meteorological and indigenous knowledge-based seasonal climate forecasts in ..... Explanation is based on spiritual and social values. Taught by .... that provided medicine and food became the subject of strict rules and practices ...

  11. Forecast Icing Product

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Forecast Icing Product (FIP) is an automatically-generated index suitable for depicting areas of potentially hazardous airframe icing. The FIP algorithm uses...

  12. 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

  13. Verification of space weather forecasts at the UK Met Office

    Science.gov (United States)

    Bingham, S.; Sharpe, M.; Jackson, D.; Murray, S.

    2017-12-01

    The UK Met Office Space Weather Operations Centre (MOSWOC) has produced space weather guidance twice a day since its official opening in 2014. Guidance includes 4-day probabilistic forecasts of X-ray flares, geomagnetic storms, high-energy electron events and high-energy proton events. Evaluation of such forecasts is important to forecasters, stakeholders, model developers and users to understand the performance of these forecasts and also strengths and weaknesses to enable further development. Met Office terrestrial near real-time verification systems have been adapted to provide verification of X-ray flare and geomagnetic storm forecasts. Verification is updated daily to produce Relative Operating Characteristic (ROC) curves and Reliability diagrams, and rolling Ranked Probability Skill Scores (RPSSs) thus providing understanding of forecast performance and skill. Results suggest that the MOSWOC issued X-ray flare forecasts are usually not statistically significantly better than a benchmark climatological forecast (where the climatology is based on observations from the previous few months). By contrast, the issued geomagnetic storm activity forecast typically performs better against this climatological benchmark.

  14. Objective Lightning Probability Forecasts for East-Central Florida Airports

    Science.gov (United States)

    Crawford, Winfred C.

    2013-01-01

    The forecasters at the National Weather Service in Melbourne, FL, (NWS MLB) identified a need to make more accurate lightning forecasts to help alleviate delays due to thunderstorms in the vicinity of several commercial airports in central Florida at which they are responsible for issuing terminal aerodrome forecasts. Such forecasts would also provide safer ground operations around terminals, and would be of value to Center Weather Service Units serving air traffic controllers in Florida. To improve the forecast, the AMU was tasked to develop an objective lightning probability forecast tool for the airports using data from the National Lightning Detection Network (NLDN). The resulting forecast tool is similar to that developed by the AMU to support space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) for use by the 45th Weather Squadron (45 WS) in previous tasks (Lambert and Wheeler 2005, Lambert 2007). The lightning probability forecasts are valid for the time periods and areas needed by the NWS MLB forecasters in the warm season months, defined in this task as May-September.

  15. Conditional Probabilistic Population Forecasting

    OpenAIRE

    Sanderson, W.C.; Scherbov, S.; O'Neill, B.C.; Lutz, W.

    2003-01-01

    Since policy makers often prefer to think in terms of scenarios, the question has arisen as to whether it is possible to make conditional population forecasts in a probabilistic context. This paper shows that it is both possible and useful to make these forecasts. We do this with two different kinds of examples. The first is the probabilistic analog of deterministic scenario analysis. Conditional probabilistic scenario analysis is essential for policy makers it allows them to answer "what if"...

  16. Conditional probabilistic population forecasting

    OpenAIRE

    Sanderson, Warren; Scherbov, Sergei; O'Neill, Brian; Lutz, Wolfgang

    2003-01-01

    Since policy-makers often prefer to think in terms of alternative scenarios, the question has arisen as to whether it is possible to make conditional population forecasts in a probabilistic context. This paper shows that it is both possible and useful to make these forecasts. We do this with two different kinds of examples. The first is the probabilistic analog of deterministic scenario analysis. Conditional probabilistic scenario analysis is essential for policy-makers because it allows them...

  17. Conditional Probabilistic Population Forecasting

    OpenAIRE

    Sanderson, Warren C.; Scherbov, Sergei; O'Neill, Brian C.; Lutz, Wolfgang

    2004-01-01

    Since policy-makers often prefer to think in terms of alternative scenarios, the question has arisen as to whether it is possible to make conditional population forecasts in a probabilistic context. This paper shows that it is both possible and useful to make these forecasts. We do this with two different kinds of examples. The first is the probabilistic analog of deterministic scenario analysis. Conditional probabilistic scenario analysis is essential for policy-makers because...

  18. EU pharmaceutical expenditure forecast

    OpenAIRE

    Urbinati, Duccio; Rémuzat, Cécile; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

    2014-01-01

    Background and Objectives: With constant incentives for healthcare payers to contain their pharmaceutical budgets, forecasting has become critically important. Some countries have, for instance, developed pharmaceutical horizon scanning units. The objective of this project was to build a model to assess the net effect of the entrance of new patented medicinal products versus medicinal products going off-patent, with a defined forecast horizon, on selected European Union (EU) Member States’ ph...

  19. Problems of Forecast

    OpenAIRE

    Kucharavy , Dmitry; De Guio , Roland

    2005-01-01

    International audience; The ability to foresee future technology is a key task of Innovative Design. The paper focuses on the obstacles to reliable prediction of technological evolution for the purpose of Innovative Design. First, a brief analysis of problems for existing forecasting methods is presented. The causes for the complexity of technology prediction are discussed in the context of reduction of the forecast errors. Second, using a contradiction analysis, a set of problems related to ...

  20. Climate Prediction Center (CPC) Monthly Drought Outlook (MDO)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A CPC forecaster (from a rotating schedule of 5 as of August 2013) creates the Monthly Drought Outlook map and narratives. The map, produced using GIS, shows where...

  1. Peak Wind Tool for General Forecasting

    Science.gov (United States)

    Barrett, Joe H., III

    2010-01-01

    The expected peak wind speed of the day is an important forecast element in the 45th Weather Squadron's (45 WS) daily 24-Hour and Weekly Planning Forecasts. The forecasts are used for ground and space launch operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45 WS also issues wind advisories for KSC/CCAFS when they expect wind gusts to meet or exceed 25 kt, 35 kt and 50 kt thresholds at any level from the surface to 300 ft. The 45 WS forecasters have indicated peak wind speeds are challenging to forecast, particularly in the cool season months of October - April. In Phase I of this task, the Applied Meteorology Unit (AMU) developed a tool to help the 45 WS forecast non-convective winds at KSC/CCAFS for the 24-hour period of 0800 to 0800 local time. The tool was delivered as a Microsoft Excel graphical user interface (GUI). The GUI displayed the forecast of peak wind speed, 5-minute average wind speed at the time of the peak wind, timing of the peak wind and probability the peak speed would meet or exceed 25 kt, 35 kt and 50 kt. For the current task (Phase II ), the 45 WS requested additional observations be used for the creation of the forecast equations by expanding the period of record (POR). Additional parameters were evaluated as predictors, including wind speeds between 500 ft and 3000 ft, static stability classification, Bulk Richardson Number, mixing depth, vertical wind shear, temperature inversion strength and depth and wind direction. Using a verification data set, the AMU compared the performance of the Phase I and II prediction methods. Just as in Phase I, the tool was delivered as a Microsoft Excel GUI. The 45 WS requested the tool also be available in the Meteorological Interactive Data Display System (MIDDS). The AMU first expanded the POR by two years by adding tower observations, surface observations and CCAFS (XMR) soundings for the cool season months of March 2007 to April 2009. The POR was expanded

  2. 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.

  3. Long-range forecasting of intermittent streamflow

    Science.gov (United States)

    van Ogtrop, F. F.; Vervoort, R. W.; Heller, G. Z.; Stasinopoulos, D. M.; Rigby, R. A.

    2011-11-01

    Long-range forecasting of intermittent streamflow in semi-arid Australia poses a number of major challenges. One of the challenges relates to modelling zero, skewed, non-stationary, and non-linear data. To address this, a statistical model to forecast streamflow up to 12 months ahead is applied to five semi-arid catchments in South Western Queensland. The model uses logistic regression through Generalised Additive Models for Location, Scale and Shape (GAMLSS) to determine the probability of flow occurring in any of the systems. We then use the same regression framework in combination with a right-skewed distribution, the Box-Cox t distribution, to model the intensity (depth) of the non-zero streamflows. Time, seasonality and climate indices, describing the Pacific and Indian Ocean sea surface temperatures, are tested as covariates in the GAMLSS model to make probabilistic 6 and 12-month forecasts of the occurrence and intensity of streamflow. The output reveals that in the study region the occurrence and variability of flow is driven by sea surface temperatures and therefore forecasts can be made with some skill.

  4. Long-range forecasting of intermittent streamflow

    Directory of Open Access Journals (Sweden)

    F. F. van Ogtrop

    2011-11-01

    Full Text Available Long-range forecasting of intermittent streamflow in semi-arid Australia poses a number of major challenges. One of the challenges relates to modelling zero, skewed, non-stationary, and non-linear data. To address this, a statistical model to forecast streamflow up to 12 months ahead is applied to five semi-arid catchments in South Western Queensland. The model uses logistic regression through Generalised Additive Models for Location, Scale and Shape (GAMLSS to determine the probability of flow occurring in any of the systems. We then use the same regression framework in combination with a right-skewed distribution, the Box-Cox t distribution, to model the intensity (depth of the non-zero streamflows. Time, seasonality and climate indices, describing the Pacific and Indian Ocean sea surface temperatures, are tested as covariates in the GAMLSS model to make probabilistic 6 and 12-month forecasts of the occurrence and intensity of streamflow. The output reveals that in the study region the occurrence and variability of flow is driven by sea surface temperatures and therefore forecasts can be made with some skill.

  5. ACONC Files

    Data.gov (United States)

    U.S. Environmental Protection Agency — ACONC files containing simulated ozone and PM2.5 fields that were used to create the model difference plots shown in the journal article. This dataset is associated...

  6. XML Files

    Science.gov (United States)

    ... this page: https://medlineplus.gov/xml.html MedlinePlus XML Files To use the sharing features on this page, please enable JavaScript. MedlinePlus produces XML data sets that you are welcome to download ...

  7. 831 Files

    Data.gov (United States)

    Social Security Administration — SSA-831 file is a collection of initial and reconsideration adjudicative level DDS disability determinations. (A few hearing level cases are also present, but the...

  8. A novel hybrid ensemble learning paradigm for tourism forecasting

    Science.gov (United States)

    Shabri, Ani

    2015-02-01

    In this paper, a hybrid forecasting model based on Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) is proposed to forecast tourism demand. This methodology first decomposes the original visitor arrival series into several Intrinsic Model Function (IMFs) components and one residual component by EMD technique. Then, IMFs components and the residual components is forecasted respectively using GMDH model whose input variables are selected by using Partial Autocorrelation Function (PACF). The final forecasted result for tourism series is produced by aggregating all the forecasted results. For evaluating the performance of the proposed EMD-GMDH methodologies, the monthly data of tourist arrivals from Singapore to Malaysia are used as an illustrative example. Empirical results show that the proposed EMD-GMDH model outperforms the EMD-ARIMA as well as the GMDH and ARIMA (Autoregressive Integrated Moving Average) models without time series decomposition.

  9. Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

    Science.gov (United States)

    Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

    2009-04-01

    In part I of this study, the operational flood forecasting system in Bavaria and an approach to identify and quantify forecast uncertainty was introduced. The approach is split into the calculation of an empirical 'overall error' from archived forecasts and the calculation of an empirical 'model error' based on hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The 'model error' can especially in upstream catchments where forecast uncertainty is strongly dependent on the current predictability of the atrmosphere be superimposed on the spread of a hydrometeorological ensemble forecast. In Bavaria, two meteorological ensemble prediction systems are currently tested for operational use: the 16-member COSMO-LEPS forecast and a poor man's ensemble composed of DWD GME, DWD Cosmo-EU, NCEP GFS, Aladin-Austria, MeteoSwiss Cosmo-7. The determination of the overall forecast uncertainty is dependent on the catchment characteristics: 1. Upstream catchment with high influence of weather forecast a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. b) Corresponding to the characteristics of the meteorological ensemble forecast, each resulting forecast hydrograph can be regarded as equally likely. c) The 'model error' distribution, with parameters dependent on hydrological case and lead time, is added to each forecast timestep of each ensemble member d) For each forecast timestep, the overall (i.e. over all 'model error' distribution of each ensemble member) error distribution is calculated e) From this distribution, the uncertainty range on a desired level (here: the 10% and 90% percentile) is extracted and drawn as forecast envelope. f) As the mean or median of an ensemble forecast does not necessarily exhibit meteorologically sound temporal evolution, a single hydrological forecast termed 'lead forecast' is chosen and shown in addition to the uncertainty bounds. This can be

  10. Space Weather Forecasting at IZMIRAN

    Science.gov (United States)

    Gaidash, S. P.; Belov, A. V.; Abunina, M. A.; Abunin, A. A.

    2017-12-01

    Since 1998, the Institute of Terrestrial Magnetism, Ionosphere, and Radio Wave Propagation (IZMIRAN) has had an operating heliogeophysical service—the Center for Space Weather Forecasts. This center transfers the results of basic research in solar-terrestrial physics into daily forecasting of various space weather parameters for various lead times. The forecasts are promptly available to interested consumers. This article describes the center and the main types of forecasts it provides: solar and geomagnetic activity, magnetospheric electron fluxes, and probabilities of proton increases. The challenges associated with the forecasting of effects of coronal mass ejections and coronal holes are discussed. Verification data are provided for the center's forecasts.

  11. Verification of different forecasts of Hungarian Meteorological Service

    Science.gov (United States)

    Feher, B.

    2009-09-01

    In this paper I show the results of the forecasts made by the Hungarian Meteorological Service. I focus on the general short- and medium-range forecasts, which contains cloudiness, precipitation, wind speed and temperature for six regions of Hungary. I would like to show the results of some special forecasts as well, such as precipitation predictions which are made for the catchment area of Danube and Tisza rivers, and daily mean temperature predictions used by Hungarian energy companies. The product received by the user is made by the general forecaster, but these predictions are based on the ALADIN and ECMWF outputs. Because of these, the product of the forecaster and the models were also verified. Method like this is able to show us, which weather elements are more difficult to forecast or which regions have higher errors. During the verification procedure the basic errors (mean error, mean absolute error) are calculated. Precipitation amount is classified into five categories, and scores like POD, TS, PC,…etc. were defined by contingency table determined by these categories. The procedure runs fully automatically, all the things forecasters have to do is to print the daily result each morning. Beside the daily result, verification is also made for longer periods like week, month or year. Analyzing the results of longer periods we can say that the best predictions are made for the first few days, and precipitation forecasts are less good for mountainous areas, even, the scores of the forecasters sometimes are higher than the errors of the models. Since forecaster receive results next day, it can helps him/her to reduce mistakes and learn the weakness of the models. This paper contains the verification scores, their trends, the method by which these scores are calculated, and some case studies on worse forecasts.

  12. Monthly hydrometeorological ensemble prediction of streamflow droughts and corresponding drought indices

    Directory of Open Access Journals (Sweden)

    F. Fundel

    2013-01-01

    Full Text Available Streamflow droughts, characterized by low runoff as consequence of a drought event, affect numerous aspects of life. Economic sectors that are impacted by low streamflow are, e.g., power production, agriculture, tourism, water quality management and shipping. Those sectors could potentially benefit from forecasts of streamflow drought events, even of short events on the monthly time scales or below. Numerical hydrometeorological models have increasingly been used to forecast low streamflow and have become the focus of recent research. Here, we consider daily ensemble runoff forecasts for the river Thur, which has its source in the Swiss Alps. We focus on the evaluation of low streamflow and of the derived indices as duration, severity and magnitude, characterizing streamflow droughts up to a lead time of one month.

    The ECMWF VarEPS 5-member ensemble reforecast, which covers 18 yr, is used as forcing for the hydrological model PREVAH. A thorough verification reveals that, compared to probabilistic peak-flow forecasts, which show skill up to a lead time of two weeks, forecasts of streamflow droughts are skilful over the entire forecast range of one month. For forecasts at the lower end of the runoff regime, the quality of the initial state seems to be crucial to achieve a good forecast quality in the longer range. It is shown that the states used in this study to initialize forecasts satisfy this requirement. The produced forecasts of streamflow drought indices, derived from the ensemble forecasts, could be beneficially included in a decision-making process. This is valid for probabilistic forecasts of streamflow drought events falling below a daily varying threshold, based on a quantile derived from a runoff climatology. Although the forecasts have a tendency to overpredict streamflow droughts, it is shown that the relative economic value of the ensemble forecasts reaches up to 60%, in case a forecast user is able to take preventive

  13. Monthly hydrometeorological ensemble prediction of streamflow droughts and corresponding drought indices

    Science.gov (United States)

    Fundel, F.; Jörg-Hess, S.; Zappa, M.

    2013-01-01

    Streamflow droughts, characterized by low runoff as consequence of a drought event, affect numerous aspects of life. Economic sectors that are impacted by low streamflow are, e.g., power production, agriculture, tourism, water quality management and shipping. Those sectors could potentially benefit from forecasts of streamflow drought events, even of short events on the monthly time scales or below. Numerical hydrometeorological models have increasingly been used to forecast low streamflow and have become the focus of recent research. Here, we consider daily ensemble runoff forecasts for the river Thur, which has its source in the Swiss Alps. We focus on the evaluation of low streamflow and of the derived indices as duration, severity and magnitude, characterizing streamflow droughts up to a lead time of one month. The ECMWF VarEPS 5-member ensemble reforecast, which covers 18 yr, is used as forcing for the hydrological model PREVAH. A thorough verification reveals that, compared to probabilistic peak-flow forecasts, which show skill up to a lead time of two weeks, forecasts of streamflow droughts are skilful over the entire forecast range of one month. For forecasts at the lower end of the runoff regime, the quality of the initial state seems to be crucial to achieve a good forecast quality in the longer range. It is shown that the states used in this study to initialize forecasts satisfy this requirement. The produced forecasts of streamflow drought indices, derived from the ensemble forecasts, could be beneficially included in a decision-making process. This is valid for probabilistic forecasts of streamflow drought events falling below a daily varying threshold, based on a quantile derived from a runoff climatology. Although the forecasts have a tendency to overpredict streamflow droughts, it is shown that the relative economic value of the ensemble forecasts reaches up to 60%, in case a forecast user is able to take preventive action based on the forecast.

  14. Assessment of storm forecast

    DEFF Research Database (Denmark)

    Cutululis, Nicolaos Antonio; Hahmann, Andrea N.; Huus Bjerge, Martin

    When wind speed exceeds a certain value, wind turbines shut-down in order to protect their structure. This leads to sudden wind plants shut down and to new challenges concerning the secure operation of the pan-European electric system with future large scale offshore wind power. This task aims...... stopped, completely or partially, producing due to extreme wind speeds. Wind speed and power measurements from those events are presented and compared to the forecast available at Energinet.dk. The analysis looked at wind speed and wind power forecast. The main conclusion of the analysis is that the wind...... to consider it an EWP) and that the available wind speed forecasts are given as a mean wind speed over a rather large area. At wind power level, the analysis shows that prediction of accurate production levels from a wind farm experiencing EWP is rather poor. This is partially because the power curve...

  15. Financial Analysts’ Forecasts

    DEFF Research Database (Denmark)

    Stæhr, Simone

    . The primary focus is on financial analysts in the task of conducting earnings forecasts while a secondary focus is on investors’ abilities to interpret and make use of these forecasts. Simply put, financial analysts can be seen as information intermediators receiving inputs to their analyses from firm...... in the decision making and the magnitude of these constraints does sometimes vary with personal traits. Therefore, to the extent that financial analysts are subjects to behavioral biases their outputs to the investors are likely to be biased by their interpretation of information. Because investors need accuracy...... management and providing outputs to the investors. Amongst various outputs from the analysts are forecasts of earnings. According to decision theories mostly from the literature in psychology all humans are affected by cognitive constraints to some degree. These constraints may lead to unintentional biases...

  16. Wind power forecast

    Energy Technology Data Exchange (ETDEWEB)

    Pestana, Rui [Rede Electrica Nacional (REN), S.A., Lisboa (Portugal). Dept. Systems and Development System Operator; Trancoso, Ana Rosa; Delgado Domingos, Jose [Univ. Tecnica de Lisboa (Portugal). Seccao de Ambiente e Energia

    2012-07-01

    Accurate wind power forecast are needed to reduce integration costs in the electric grid caused by wind inherent variability. Currently, Portugal has a significant wind power penetration level and consequently the need to have reliable wind power forecasts at different temporal scales, including localized events such as ramps. This paper provides an overview of the methodologies used by REN to forecast wind power at national level, based on statistical and probabilistic combinations of NWP and measured data with the aim of improving accuracy of pure NWP. Results show that significant improvement can be achieved with statistical combination with persistence in the short-term and with probabilistic combination in the medium-term. NWP are also able to detect ramp events with 3 day notice to the operational planning. (orig.)

  17. Seasonal forecasting of discharge for the Raccoon River, Iowa

    Science.gov (United States)

    Slater, Louise; Villarini, Gabriele; Bradley, Allen; Vecchi, Gabriel

    2016-04-01

    The state of Iowa (central United States) is regularly afflicted by severe natural hazards such as the 2008/2013 floods and the 2012 drought. To improve preparedness for these catastrophic events and allow Iowans to make more informed decisions about the most suitable water management strategies, we have developed a framework for medium to long range probabilistic seasonal streamflow forecasting for the Raccoon River at Van Meter, a 8900-km2 catchment located in central-western Iowa. Our flow forecasts use statistical models to predict seasonal discharge for low to high flows, with lead forecasting times ranging from one to ten months. Historical measurements of daily discharge are obtained from the U.S. Geological Survey (USGS) at the Van Meter stream gage, and used to compute quantile time series from minimum to maximum seasonal flow. The model is forced with basin-averaged total seasonal precipitation records from the PRISM Climate Group and annual row crop production acreage from the U.S. Department of Agriculture's National Agricultural Statistics Services database. For the forecasts, we use corn and soybean production from the previous year (persistence forecast) as a proxy for the impacts of agricultural practices on streamflow. The monthly precipitation forecasts are provided by eight Global Climate Models (GCMs) from the North American Multi-Model Ensemble (NMME), with lead times ranging from 0.5 to 11.5 months, and a resolution of 1 decimal degree. Additionally, precipitation from the month preceding each season is used to characterize antecedent soil moisture conditions. The accuracy of our modelled (1927-2015) and forecasted (2001-2015) discharge values is assessed by comparison with the observed USGS data. We explore the sensitivity of forecast skill over the full range of lead times, flow quantiles, forecast seasons, and with each GCM. Forecast skill is also examined using different formulations of the statistical models, as well as NMME forecast

  18. Forecast Inaccuracies in Power Plant Projects From Project Managers' Perspectives

    Science.gov (United States)

    Sanabria, Orlando

    Guided by organizational theory, this phenomenological study explored the factors affecting forecast preparation and inaccuracies during the construction of fossil fuel-fired power plants in the United States. Forecast inaccuracies can create financial stress and uncertain profits during the project construction phase. A combination of purposeful and snowball sampling supported the selection of participants. Twenty project managers with over 15 years of experience in power generation and project experience across the United States were interviewed within a 2-month period. From the inductive codification and descriptive analysis, 5 themes emerged: (a) project monitoring, (b) cost control, (c) management review frequency, (d) factors to achieve a precise forecast, and (e) factors causing forecast inaccuracies. The findings of the study showed the factors necessary to achieve a precise forecast includes a detailed project schedule, accurate labor cost estimates, monthly project reviews and risk assessment, and proper utilization of accounting systems to monitor costs. The primary factors reported as causing forecast inaccuracies were cost overruns by subcontractors, scope gaps, labor cost and availability of labor, and equipment and material cost. Results of this study could improve planning accuracy and the effective use of resources during construction of power plants. The study results could contribute to social change by providing a framework to project managers to lessen forecast inaccuracies, and promote construction of power plants that will generate employment opportunities and economic development.

  19. Forecasting Turbine Icing Events

    DEFF Research Database (Denmark)

    Davis, Neil; Hahmann, Andrea N.; Clausen, Niels-Erik

    2012-01-01

    In this study, we present a method for forecasting icing events. The method is validated at two European wind farms in with known icing events. The icing model used was developed using current ice accretion methods, and newly developed ablation algorithms. The model is driven by inputs from the WRF...... mesoscale model, allowing for both climatological estimates of icing and short term icing forecasts. The current model was able to detect periods of icing reasonably well at the warmer site. However at the cold climate site, the model was not able to remove ice quickly enough leading to large ice...

  20. Spatial load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Willis, H.L.; Engel, M.V.; Buri, M.J.

    1995-04-01

    The reliability, efficiency, and economy of a power delivery system depend mainly on how well its substations, transmission lines, and distribution feeders are located within the utility service area, and how well their capacities match power needs in their respective localities. Often, utility planners are forced to commit to sites, rights of way, and equipment capacities year in advance. A necessary element of effective expansion planning is a forecast of where and how much demand must be served by the future T and D system. This article reports that a three-stage method forecasts with accuracy and detail, allowing meaningful determination of sties and sizes for future substation, transmission, and distribution facilities.

  1. Using soil moisture forecasts for sub-seasonal summer temperature predictions in Europe

    Science.gov (United States)

    Orth, René; Seneviratne, Sonia I.

    2014-12-01

    Soil moisture exhibits outstanding memory characteristics and plays a key role within the climate system. Especially through its impacts on the evapotranspiration of soils and plants, it may influence the land energy balance and therefore surface temperature. These attributes make soil moisture an important variable in the context of weather and climate forecasting. In this study we investigate the value of (initial) soil moisture information for sub-seasonal temperature forecasts. For this purpose we employ a simple water balance model to infer soil moisture from streamflow observations in 400 catchments across Europe. Running this model with forecasted atmospheric forcing, we derive soil moisture forecasts, which we then translate into temperature forecasts using simple linear relationships. The resulting temperature forecasts show skill beyond climatology up to 2 weeks in most of the considered catchments. Even if forecasting skills are rather small at longer lead times with significant skill only in some catchments at lead times of 3 and 4 weeks, this soil moisture-based approach shows local improvements compared to the monthly European Centre for Medium Range Weather Forecasting (ECMWF) temperature forecasts at these lead times. For both products (soil moisture-only forecast and ECMWF forecast), we find comparable or better forecast performance in the case of extreme events, especially at long lead times. Even though a product based on soil moisture information alone is not of practical relevance, our results indicate that soil moisture (memory) is a potentially valuable contributor to temperature forecast skill. Investigating the underlying soil moisture of the ECMWF forecasts we find good agreement with the simple model forecasts, especially at longer lead times. Analyzing the drivers of the temperature forecast skills we find that they are mainly controlled by the strengths of (1) the soil moisture-temperature coupling and (2) the soil moisture memory. We

  2. Drought forecasting in Luanhe River basin involving climatic indices

    Science.gov (United States)

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

    2017-11-01

    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

  3. Forecasting Housing Approvals in Australia: Do Forecasters Herd?

    DEFF Research Database (Denmark)

    Stadtmann, Georg; Pierdzioch; Rülke

    2012-01-01

    Price trends in housing markets may reflect herding of market participants. A natural question is whether such herding, to the extent that it occurred, reflects herding in forecasts of professional forecasters. Using more than 6,000 forecasts of housing approvals for Australia, we did not find...

  4. Seasonal forecasting of fire over Kalimantan, Indonesia

    Science.gov (United States)

    Spessa, A. C.; Field, R. D.; Pappenberger, F.; Langner, A.; Englhart, S.; Weber, U.; Stockdale, T.; Siegert, F.; Kaiser, J. W.; Moore, J.

    2015-03-01

    Large-scale fires occur frequently across Indonesia, particularly in the southern region of Kalimantan and eastern Sumatra. They have considerable impacts on carbon emissions, haze production, biodiversity, health, and economic activities. In this study, we demonstrate that severe fire and haze events in Indonesia can generally be predicted months in advance using predictions of seasonal rainfall from the ECMWF System 4 coupled ocean-atmosphere model. Based on analyses of long, up-to-date series observations on burnt area, rainfall, and tree cover, we demonstrate that fire activity is negatively correlated with rainfall and is positively associated with deforestation in Indonesia. There is a contrast between the southern region of Kalimantan (high fire activity, high tree cover loss, and strong non-linear correlation between observed rainfall and fire) and the central region of Kalimantan (low fire activity, low tree cover loss, and weak, non-linear correlation between observed rainfall and fire). The ECMWF seasonal forecast provides skilled forecasts of burnt and fire-affected area with several months lead time explaining at least 70% of the variance between rainfall and burnt and fire-affected area. Results are strongly influenced by El Niño years which show a consistent positive bias. Overall, our findings point to a high potential for using a more physical-based method for predicting fires with several months lead time in the tropics rather than one based on indexes only. We argue that seasonal precipitation forecasts should be central to Indonesia's evolving fire management policy.

  5. CDM Convective Forecast Planning guidance

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The CDM Convective Forecast Planning (CCFP) guidance product provides a foreast of en-route aviation convective hazards. The forecasts are updated every 2 hours and...

  6. Are demand forecasting techniques applicable to libraries?

    OpenAIRE

    Sridhar, M. S.

    1984-01-01

    Examines the nature and limitations of demand forecasting, discuses plausible methods of forecasting demand for information, suggests some useful hints for demand forecasting and concludes by emphasizing unified approach to demand forecasting.

  7. RRB / SSI Interface Checkwriting Integrated Computer Operation Extract File (CHICO)

    Data.gov (United States)

    Social Security Administration — This monthly file provides SSA with information about benefit payments made to railroad retirement beneficiaries. SSA uses this data to verify Supplemental Security...

  8. A new forecast presentation tool for offshore contractors

    Science.gov (United States)

    Jørgensen, M.

    2009-09-01

    Contractors working off shore are often very sensitive to both sea and weather conditions, and it's essential that they have easy access to reliable information on coming conditions to enable planning of when to start or shut down offshore operations to avoid loss of life and materials. Danish Meteorological Institute, DMI, recently, in cooperation with business partners in the field, developed a new application to accommodate that need. The "Marine Forecast Service” is a browser based forecast presentation tool. It provides an interface for the user to enable easy and quick access to all relevant meteorological and oceanographic forecasts and observations for a given area of interest. Each customer gains access to the application via a standard login/password procedure. Once logged in, the user can inspect animated forecast maps of parameters like wind, gust, wave height, swell and current among others. Supplementing the general maps, the user can choose to look at forecast graphs for each of the locations where the user is running operations. These forecast graphs can also be overlaid with the user's own in situ observations, if such exist. Furthermore, the data from the graphs can be exported as data files that the customer can use in his own applications as he desires. As part of the application, a forecaster's view on the current and near future weather situation is presented to the user as well, adding further value to the information presented through maps and graphs. Among other features of the product, animated radar and satellite images could be mentioned. And finally the application provides the possibility of a "second opinion” through traditional weather charts from another recognized provider of weather forecasts. The presentation will provide more detailed insights into the contents of the applications as well as some of the experiences with the product.

  9. Load Forecasting in Electric Utility Integrated Resource Planning

    Energy Technology Data Exchange (ETDEWEB)

    Carvallo, Juan Pablo [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Larsen, Peter H. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sanstad, Alan H [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Goldman, Charles A. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2017-07-19

    Integrated resource planning (IRP) is a process used by many vertically-integrated U.S. electric utilities to determine least-cost/risk supply and demand-side resources that meet government policy objectives and future obligations to customers and, in many cases, shareholders. Forecasts of energy and peak demand are a critical component of the IRP process. There have been few, if any, quantitative studies of IRP long-run (planning horizons of two decades) load forecast performance and its relationship to resource planning and actual procurement decisions. In this paper, we evaluate load forecasting methods, assumptions, and outcomes for 12 Western U.S. utilities by examining and comparing plans filed in the early 2000s against recent plans, up to year 2014. We find a convergence in the methods and data sources used. We also find that forecasts in more recent IRPs generally took account of new information, but that there continued to be a systematic over-estimation of load growth rates during the period studied. We compare planned and procured resource expansion against customer load and year-to-year load growth rates, but do not find a direct relationship. Load sensitivities performed in resource plans do not appear to be related to later procurement strategies even in the presence of large forecast errors. These findings suggest that resource procurement decisions may be driven by other factors than customer load growth. Our results have important implications for the integrated resource planning process, namely that load forecast accuracy may not be as important for resource procurement as is generally believed, that load forecast sensitivities could be used to improve the procurement process, and that management of load uncertainty should be prioritized over more complex forecasting techniques.

  10. Virtual file system for PSDS

    Science.gov (United States)

    Runnels, Tyson D.

    1993-01-01

    This is a case study. It deals with the use of a 'virtual file system' (VFS) for Boeing's UNIX-based Product Standards Data System (PSDS). One of the objectives of PSDS is to store digital standards documents. The file-storage requirements are that the files must be rapidly accessible, stored for long periods of time - as though they were paper, protected from disaster, and accumulative to about 80 billion characters (80 gigabytes). This volume of data will be approached in the first two years of the project's operation. The approach chosen is to install a hierarchical file migration system using optical disk cartridges. Files are migrated from high-performance media to lower performance optical media based on a least-frequency-used algorithm. The optical media are less expensive per character stored and are removable. Vital statistics about the removable optical disk cartridges are maintained in a database. The assembly of hardware and software acts as a single virtual file system transparent to the PSDS user. The files are copied to 'backup-and-recover' media whose vital statistics are also stored in the database. Seventeen months into operation, PSDS is storing 49 gigabytes. A number of operational and performance problems were overcome. Costs are under control. New and/or alternative uses for the VFS are being considered.

  11. Improving Seasonal Crop Monitoring and Forecasting for Soybean and Corn in Iowa

    Science.gov (United States)

    Togliatti, K.; Archontoulis, S.; Dietzel, R.; VanLoocke, A.

    2016-12-01

    Accurately forecasting crop yield in advance of harvest could greatly benefit farmers, however few evaluations have been conducted to determine the effectiveness of forecasting methods. We tested one such method that used a combination of short-term weather forecasting from the Weather Research and Forecasting Model (WRF) to predict in season weather variables, such as, maximum and minimum temperature, precipitation and radiation at 4 different forecast lengths (2 weeks, 1 week, 3 days, and 0 days). This forecasted weather data along with the current and historic (previous 35 years) data from the Iowa Environmental Mesonet was combined to drive Agricultural Production Systems sIMulator (APSIM) simulations to forecast soybean and corn yields in 2015 and 2016. The goal of this study is to find the forecast length that reduces the variability of simulated yield predictions while also increasing the accuracy of those predictions. APSIM simulations of crop variables were evaluated against bi-weekly field measurements of phenology, biomass, and leaf area index from early and late planted soybean plots located at the Agricultural Engineering and Agronomy Research Farm in central Iowa as well as the Northwest Research Farm in northwestern Iowa. WRF model predictions were evaluated against observed weather data collected at the experimental fields. Maximum temperature was the most accurately predicted variable, followed by minimum temperature and radiation, and precipitation was least accurate according to RMSE values and the number of days that were forecasted within a 20% error of the observed weather. Our analysis indicated that for the majority of months in the growing season the 3 day forecast performed the best. The 1 week forecast came in second and the 2 week forecast was the least accurate for the majority of months. Preliminary results for yield indicate that the 2 week forecast is the least variable of the forecast lengths, however it also is the least accurate

  12. Forecasting of superconducting compounds

    International Nuclear Information System (INIS)

    Savitskii, E.M.; Gribulya, V.G.; Kiseleva, N.N.

    1981-01-01

    In forecasting new superconducting intermetallic compounds of the A15 and Mo 3 Se types most promising from the viewpoint of high critical temperature Tsub(c), high critical magnetic fields Hsub(c), and high critical currents and in estimating their transition temperature it is proposed to apply cybernetic methods of computer learning

  13. Forecast of nuclear energetics

    Energy Technology Data Exchange (ETDEWEB)

    Sikora, W

    1976-01-01

    The forecast concerning the development of nuclear energetics is presented. Some information on economics of nuclear power plants is given. The nuclear fuel reserves are estimated on the background of power resources of the world. The safety and environment protection problems are mentioned.

  14. Climate Forecast System

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Forecast System Home News Organization Web portal to all Federal, state and local government Web resources and services. The NCEP Climate when using the CFS Reanalysis (CFSR) data. Saha, Suranjana, and Coauthors, 2010: The NCEP Climate

  15. Foresight and Forecasts

    DEFF Research Database (Denmark)

    Kilbourn, Kyle; Bay, Marie Brøndum

    In predicting areas of growth, public innovation projects may rely on optimistic visions of technology still in development as a way of ensuring novelty for funding. This paper explores what happens when forecasts of robotic technology meets the practice of sterile supply in a preliminary stage...

  16. Hydrology and flow forecasting

    NARCIS (Netherlands)

    Vrijling, J.K.; Kwadijk, J.; Van Duivendijk, J.; Van Gelder, P.; Pang, H.; Rao, S.Q.; Wang, G.Q.; Huang, X.Q.

    2002-01-01

    We have studied and applied the statistic model (i.e. MMC) and hydrological models to Upper Yellow River. This report introduces the results and some conclusions from the model. The three models, MMC, MWBM and NAM, have be applied in the research area. The forecasted discharge by the three models

  17. NWS Marine Forecast Areas

    Science.gov (United States)

    of Commerce Ocean Prediction Center National Oceanic and Atmospheric Administration Analysis & Unified Surface Analysis Ocean Ocean Products Ice & Icebergs NIC Ice Products NAIS Iceberg Analysis Social Media Facebook Twitter YouTube Search Search For Go NWS All NOAA NWS Marine Forecast Areas

  18. The Latest Forecast.

    Science.gov (United States)

    Laurence, David

    2002-01-01

    Discusses the "latest forecast" for the future of English departments. Addresses departmental and institutional staffing practices, employment opportunities for PhDs, the acceleration of change in the institution, and the general state of the study and teaching of English. (RS)

  19. Ecological forecasts: An emerging imperative

    Science.gov (United States)

    James S. Clark; Steven R. Carpenter; Mary Barber; Scott Collins; Andy Dobson; Jonathan A. Foley; David M. Lodge; Mercedes Pascual; Roger Pielke; William Pizer; Cathy Pringle; Walter V. Reid; Kenneth A. Rose; Osvaldo Sala; William H. Schlesinger; Diana H. Wall; David Wear

    2001-01-01

    Planning and decision-making can be improved by access to reliable forecasts of ecosystem state, ecosystem services, and natural capital. Availability of new data sets, together with progress in computation and statistics, will increase our ability to forecast ecosystem change. An agenda that would lead toward a capacity to produce, evaluate, and communicate forecasts...

  20. Air Pollution Forecasts: An Overview

    Science.gov (United States)

    Bai, Lu; Wang, Jianzhou; Lu, Haiyan

    2018-01-01

    Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies. PMID:29673227

  1. Air Pollution Forecasts: An Overview.

    Science.gov (United States)

    Bai, Lu; Wang, Jianzhou; Ma, Xuejiao; Lu, Haiyan

    2018-04-17

    Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.

  2. Air Pollution Forecasts: An Overview

    Directory of Open Access Journals (Sweden)

    Lu Bai

    2018-04-01

    Full Text Available Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.

  3. Storm Prediction Center Forecast Products

    Science.gov (United States)

    select the go button to submit request Local forecast by "City, St" or "ZIP" City, St Archive NOAA Weather Radio Research Non-op. Products Forecast Tools Svr. Tstm. Events SPC Publications SPC services. Forecast Products Current Weather Watches This is the current graphic showing any severe

  4. An Assessment of the Subseasonal Forecast Performance in the Extended Global Ensemble Forecast System (GEFS)

    Science.gov (United States)

    Sinsky, E.; Zhu, Y.; Li, W.; Guan, H.; Melhauser, C.

    2017-12-01

    Optimal forecast quality is crucial for the preservation of life and property. Improving monthly forecast performance over both the tropics and extra-tropics requires attention to various physical aspects such as the representation of the underlying SST, model physics and the representation of the model physics uncertainty for an ensemble forecast system. This work focuses on the impact of stochastic physics, SST and the convection scheme on forecast performance for the sub-seasonal scale over the tropics and extra-tropics with emphasis on the Madden-Julian Oscillation (MJO). A 2-year period is evaluated using the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS). Three experiments with different configurations than the operational GEFS were performed to illustrate the impact of the stochastic physics, SST and convection scheme. These experiments are compared against a control experiment (CTL) which consists of the operational GEFS but its integration is extended from 16 to 35 days. The three configurations are: 1) SPs, which uses a Stochastically Perturbed Physics Tendencies (SPPT), Stochastic Perturbed Humidity (SHUM) and Stochastic Kinetic Energy Backscatter (SKEB); 2) SPs+SST_bc, which uses a combination of SPs and a bias-corrected forecast SST from the NCEP Climate Forecast System Version 2 (CFSv2); and 3) SPs+SST_bc+SA_CV, which combines SPs, a bias-corrected forecast SST and a scale aware convection scheme. When comparing to the CTL experiment, SPs shows substantial improvement. The MJO skill has improved by about 4 lead days during the 2-year period. Improvement is also seen over the extra-tropics due to the updated stochastic physics, where there is a 3.1% and a 4.2% improvement during weeks 3 and 4 over the northern hemisphere and southern hemisphere, respectively. Improvement is also seen when the bias-corrected CFSv2 SST is combined with SPs. Additionally, forecast performance enhances when the scale aware

  5. A quality assessment of the MARS crop yield forecasting system for the European Union

    Science.gov (United States)

    van der Velde, Marijn; Bareuth, Bettina

    2015-04-01

    Timely information on crop production forecasts can become of increasing importance as commodity markets are more and more interconnected. Impacts across large crop production areas due to (e.g.) extreme weather and pest outbreaks can create ripple effects that may affect food prices and availability elsewhere. The MARS Unit (Monitoring Agricultural ResourceS), DG Joint Research Centre, European Commission, has been providing forecasts of European crop production levels since 1993. The operational crop production forecasting is carried out with the MARS Crop Yield Forecasting System (M-CYFS). The M-CYFS is used to monitor crop growth development, evaluate short-term effects of anomalous meteorological events, and provide monthly forecasts of crop yield at national and European Union level. The crop production forecasts are published in the so-called MARS bulletins. Forecasting crop yield over large areas in the operational context requires quality benchmarks. Here we present an analysis of the accuracy and skill of past crop yield forecasts of the main crops (e.g. soft wheat, grain maize), throughout the growing season, and specifically for the final forecast before harvest. Two simple benchmarks to assess the skill of the forecasts were defined as comparing the forecasts to 1) a forecast equal to the average yield and 2) a forecast using a linear trend established through the crop yield time-series. These reveal a variability in performance as a function of crop and Member State. In terms of production, the yield forecasts of 67% of the EU-28 soft wheat production and 80% of the EU-28 maize production have been forecast superior to both benchmarks during the 1993-2013 period. In a changing and increasingly variable climate crop yield forecasts can become increasingly valuable - provided they are used wisely. We end our presentation by discussing research activities that could contribute to this goal.

  6. Load forecasting for supermarket refrigeration

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Aalborg Nielsen, Henrik

    This report presents a study of models for forecasting the load for supermarket refrigeration. The data used for building the forecasting models consists of load measurements, local climate measurements and weather forecasts. The load measurements are from a supermarket located in a village...... in Denmark. The load for refrigeration is the sum of all cabinets in the supermarket, both low and medium temperature cabinets, and spans a period of one year. As input to the forecasting models the ambient temperature observed near the supermarket together with weather forecasts are used. Every hour...

  7. Solar Storm GIC Forecasting: Solar Shield Extension Development of the End-User Forecasting System Requirements

    Science.gov (United States)

    Pulkkinen, A.; Mahmood, S.; Ngwira, C.; Balch, C.; Lordan, R.; Fugate, D.; Jacobs, W.; Honkonen, I.

    2015-01-01

    A NASA Goddard Space Flight Center Heliophysics Science Division-led team that includes NOAA Space Weather Prediction Center, the Catholic University of America, Electric Power Research Institute (EPRI), and Electric Research and Management, Inc., recently partnered with the Department of Homeland Security (DHS) Science and Technology Directorate (S&T) to better understand the impact of Geomagnetically Induced Currents (GIC) on the electric power industry. This effort builds on a previous NASA-sponsored Applied Sciences Program for predicting GIC, known as Solar Shield. The focus of the new DHS S&T funded effort is to revise and extend the existing Solar Shield system to enhance its forecasting capability and provide tailored, timely, actionable information for electric utility decision makers. To enhance the forecasting capabilities of the new Solar Shield, a key undertaking is to extend the prediction system coverage across Contiguous United States (CONUS), as the previous version was only applicable to high latitudes. The team also leverages the latest enhancements in space weather modeling capacity residing at Community Coordinated Modeling Center to increase the Technological Readiness Level, or Applications Readiness Level of the system http://www.nasa.gov/sites/default/files/files/ExpandedARLDefinitions4813.pdf.

  8. Wind field forecast for accidental release of radiative materials

    International Nuclear Information System (INIS)

    Kang Ling; Chen Jiayi; Cai Xuhui

    2003-01-01

    A meso-scale wind field forecast model was designed for emergency environmental assessment in case of accidental release of radiative materials from a nuclear power station. Actual practice of the model showed that it runs fast, has wind field prediction function, and the result given is accurate. With meteorological data collected from weather stations, and pre-treated by a wind field diagnostic model, the initial wind fields at different times were inputted as initial values and assimilation fields for the forecasting model. The model, in turn, worked out to forecast meso-scale wind field of 24 hours in a horizontal domain of 205 km x 205 km. And then, the diagnostic model was employed again with the forecasting data to obtain more detail information of disturbed wind field by local terrain in a smaller domain of 20.5 km x 20.5 km, of which the nuclear power station is at the center. Using observation data in January, April, July and October of 1996 over the area of Hangzhou Bay, wind fields in these 4 months were simulated by different assimilation time and number of the weather stations for a sensitive test. Results indicated that the method used here has increased accuracy of the forecasted wind fields. And incorporating diagnostic method with the wind field forecast model has greatly increased efficiency of the wind field forecast for the smaller domain. This model and scheme have been used in Environmental Consequence Assessment System of Nuclear Accident in Qinshan Area

  9. Forecasting military expenditure

    Directory of Open Access Journals (Sweden)

    Tobias Böhmelt

    2014-05-01

    Full Text Available To what extent do frequently cited determinants of military spending allow us to predict and forecast future levels of expenditure? The authors draw on the data and specifications of a recent model on military expenditure and assess the predictive power of its variables using in-sample predictions, out-of-sample forecasts and Bayesian model averaging. To this end, this paper provides guidelines for prediction exercises in general using these three techniques. More substantially, however, the findings emphasize that previous levels of military spending as well as a country’s institutional and economic characteristics particularly improve our ability to predict future levels of investment in the military. Variables pertaining to the international security environment also matter, but seem less important. In addition, the results highlight that the updated model, which drops weak predictors, is not only more parsimonious, but also slightly more accurate than the original specification.

  10. The forecaster's added value

    Science.gov (United States)

    Turco, M.; Milelli, M.

    2009-09-01

    To the authors' knowledge there are relatively few studies that try to answer this topic: "Are humans able to add value to computer-generated forecasts and warnings ?". Moreover, the answers are not always positive. In particular some postprocessing method is competitive or superior to human forecast (see for instance Baars et al., 2005, Charba et al., 2002, Doswell C., 2003, Roebber et al., 1996, Sanders F., 1986). Within the alert system of ARPA Piemonte it is possible to study in an objective manner if the human forecaster is able to add value with respect to computer-generated forecasts. Every day the meteorology group of the Centro Funzionale of Regione Piemonte produces the HQPF (Human QPF) in terms of an areal average for each of the 13 regional warning areas, which have been created according to meteo-hydrological criteria. This allows the decision makers to produce an evaluation of the expected effects by comparing these HQPFs with predefined rainfall thresholds. Another important ingredient in this study is the very dense non-GTS network of rain gauges available that makes possible a high resolution verification. In this context the most useful verification approach is the measure of the QPF and HQPF skills by first converting precipitation expressed as continuous amounts into ‘‘exceedance'' categories (yes-no statements indicating whether precipitation equals or exceeds selected thresholds) and then computing the performances for each threshold. In particular in this work we compare the performances of the latest three years of QPF derived from two meteorological models COSMO-I7 (the Italian version of the COSMO Model, a mesoscale model developed in the framework of the COSMO Consortium) and IFS (the ECMWF global model) with the HQPF. In this analysis it is possible to introduce the hypothesis test developed by Hamill (1999), in which a confidence interval is calculated with the bootstrap method in order to establish the real difference between the

  11. Forecasting carbon dioxide emissions.

    Science.gov (United States)

    Zhao, Xiaobing; Du, Ding

    2015-09-01

    This study extends the literature on forecasting carbon dioxide (CO2) emissions by applying the reduced-form econometrics approach of Schmalensee et al. (1998) to a more recent sample period, the post-1997 period. Using the post-1997 period is motivated by the observation that the strengthening pace of global climate policy may have been accelerated since 1997. Based on our parameter estimates, we project 25% reduction in CO2 emissions by 2050 according to an economic and population growth scenario that is more consistent with recent global trends. Our forecasts are conservative due to that we do not have sufficient data to fully take into account recent developments in the global economy. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Forecasting potential crises

    International Nuclear Information System (INIS)

    Neufeld, W.P.

    1984-01-01

    Recently, the Trend Analysis Program (TAP) of the American Council of Life Insurance commissioned the Futures Group of Glastonbury, Connecticut, to examine the potential for large-scale catastrophic events in the near future. TAP was specifically concerned with five potential crises: the warming of the earth's atmosphere, the water shortage, the collapse of the physical infrastructure, the global financial crisis, and the threat of nuclear war. We are often unprepared to take action; in these cases, we lose an advantage we might have otherwise had. This is the whole idea behind forecasting: to foresee possibilities and to project how we can respond. If we are able to create forecasts against which we can test policy options and choices, we may have the luxury of adopting policies ahead of events. Rather than simply fighting fires, we have the option of creating a future more to our choosing. Short descriptions of these five potential crises and, in some cases, possible solutions are presented

  13. Seasonal UK Drought Forecasting using Statistical Methods

    Science.gov (United States)

    Richardson, Doug; Fowler, Hayley; Kilsby, Chris; Serinaldi, Francesco

    2016-04-01

    In the UK drought is a recurrent feature of climate with potentially large impacts on public water supply. Water companies' ability to mitigate the impacts of drought by managing diminishing availability depends on forward planning and it would be extremely valuable to improve forecasts of drought on monthly to seasonal time scales. By focusing on statistical forecasting methods, this research aims to provide techniques that are simpler, faster and computationally cheaper than physically based models. In general, statistical forecasting is done by relating the variable of interest (some hydro-meteorological variable such as rainfall or streamflow, or a drought index) to one or more predictors via some formal dependence. These predictors are generally antecedent values of the response variable or external factors such as teleconnections. A candidate model is Generalised Additive Models for Location, Scale and Shape parameters (GAMLSS). GAMLSS is a very flexible class allowing for more general distribution functions (e.g. highly skewed and/or kurtotic distributions) and the modelling of not just the location parameter but also the scale and shape parameters. Additionally GAMLSS permits the forecasting of an entire distribution, allowing the output to be assessed in probabilistic terms rather than simply the mean and confidence intervals. Exploratory analysis of the relationship between long-memory processes (e.g. large-scale atmospheric circulation patterns, sea surface temperatures and soil moisture content) and drought should result in the identification of suitable predictors to be included in the forecasting model, and further our understanding of the drivers of UK drought.

  14. Forecasting Dry Bulk Freight Index with Improved SVM

    Directory of Open Access Journals (Sweden)

    Qianqian Han

    2014-01-01

    Full Text Available An improved SVM model is presented to forecast dry bulk freight index (BDI in this paper, which is a powerful tool for operators and investors to manage the market trend and avoid price risking shipping industry. The BDI is influenced by many factors, especially the random incidents in dry bulk market, inducing the difficulty in forecasting of BDI. Therefore, to eliminate the impact of random incidents in dry bulk market, wavelet transform is adopted to denoise the BDI data series. Hence, the combined model of wavelet transform and support vector machine is developed to forecast BDI in this paper. Lastly, the BDI data in 2005 to 2012 are presented to test the proposed model. The 84 prior consecutive monthly BDI data are the inputs of the model, and the last 12 monthly BDI data are the outputs of model. The parameters of the model are optimized by genetic algorithm and the final model is conformed through SVM training. This paper compares the forecasting result of proposed method and three other forecasting methods. The result shows that the proposed method has higher accuracy and could be used to forecast the short-term trend of the BDI.

  15. Benchmark analysis of forecasted seasonal temperature over different climatic areas

    Science.gov (United States)

    Giunta, G.; Salerno, R.; Ceppi, A.; Ercolani, G.; Mancini, M.

    2015-12-01

    From a long-term perspective, an improvement of seasonal forecasting, which is often exclusively based on climatology, could provide a new capability for the management of energy resources in a time scale of just a few months. This paper regards a benchmark analysis in relation to long-term temperature forecasts over Italy in the year 2010, comparing the eni-kassandra meteo forecast (e-kmf®) model, the Climate Forecast System-National Centers for Environmental Prediction (CFS-NCEP) model, and the climatological reference (based on 25-year data) with observations. Statistical indexes are used to understand the reliability of the prediction of 2-m monthly air temperatures with a perspective of 12 weeks ahead. The results show how the best performance is achieved by the e-kmf® system which improves the reliability for long-term forecasts compared to climatology and the CFS-NCEP model. By using the reliable high-performance forecast system, it is possible to optimize the natural gas portfolio and management operations, thereby obtaining a competitive advantage in the European energy market.

  16. Validation Test Report for the BioCast Optical Forecast Model Version 1.0

    Science.gov (United States)

    2015-04-09

    hour forecast sequence have been established for ten months of continuous daily forecasting sequence trails in the northern Gulf of Mexico and the...beam-c field) is combined with today’s NCOM hourly forecast currents to predict the turbidity distribution (c)1 in 24 hours. The following day’s...proxy for turbidity ) coupled with the currents derived from the NCOM, BioCast enables the currents to advect the turbidity pixel information

  17. Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast

    OpenAIRE

    Chen, Junfei; Li, Ming; Wang, Weiguang

    2012-01-01

    Drought is part of natural climate variability and ranks the first natural disaster in the world. Drought forecasting plays an important role in mitigating impacts on agriculture and water resources. In this study, a drought forecast model based on the random forest method is proposed to predict the time series of monthly standardized precipitation index (SPI). We demonstrate model application by four stations in the Haihe river basin, China. The random-forest- (RF-) based forecast model has ...

  18. Forecasting oilfield economic performance

    International Nuclear Information System (INIS)

    Bradley, M.E.; Wood, A.R.O.

    1994-01-01

    This paper presents a general method for forecasting oilfield economic performance that integrates cost data with operational, reservoir, and financial information. Practices are developed for determining economic limits for an oil field and its components. The economic limits of marginal wells and the role of underground competition receive special attention. Also examined is the influence of oil prices on operating costs. Examples illustrate application of these concepts. Categorization of costs for historical tracking and projections is recommended

  19. Frost Forecasting for Fruitgrowers

    Science.gov (United States)

    Martsolf, J. D.; Chen, E.

    1983-01-01

    Progress in forecasting from satellite data reviewed. University study found data from satellites displayed in color and used to predict frost are valuable aid to agriculture. Study evaluated scheme to use Earth-temperature data from Geostationary Operational Environmental Satellite in computer model that determines when and where freezing temperatures endanger developing fruit crops, such as apples, peaches and cherries in spring and citrus crops in winter.

  20. Uranium price forecasting methods

    International Nuclear Information System (INIS)

    Fuller, D.M.

    1994-01-01

    This article reviews a number of forecasting methods that have been applied to uranium prices and compares their relative strengths and weaknesses. The methods reviewed are: (1) judgemental methods, (2) technical analysis, (3) time-series methods, (4) fundamental analysis, and (5) econometric methods. Historically, none of these methods has performed very well, but a well-thought-out model is still useful as a basis from which to adjust to new circumstances and try again

  1. Application of seasonal rainfall forecasts and satellite rainfall observations to crop yield forecasting for Africa

    Science.gov (United States)

    Greatrex, H. L.; Grimes, D. I. F.; Wheeler, T. R.

    2009-04-01

    Rain-fed agriculture is of utmost importance in sub-Saharan Africa; the FAO estimates that over 90% of food consumed in the region is grown in rain-fed farming systems. As the climate in sub-Saharan Africa has a high interannual variability, this dependence on rainfall can leave communities extremely vulnerable to food shortages, especially when coupled with a lack of crop management options. The ability to make a regional forecast of crop yield on a timescale of months would be of enormous benefit; it would enable both governmental and non-governmental organisations to be alerted in advance to crop failure and could facilitate national and regional economic planning. Such a system would also enable individual communities to make more informed crop management decisions, increasing their resilience to climate variability and change. It should be noted that the majority of crops in the region are rainfall limited, therefore the ability to create a seasonal crop forecast depends on the ability to forecast rainfall at a monthly or seasonal timescale and to temporally downscale this to a daily time-series of rainfall. The aim of this project is to develop a regional-scale seasonal forecast for sub-Saharan crops, utilising the General Large Area Model for annual crops (GLAM). GLAM would initially be driven using both dynamical and statistical seasonal rainfall forecasts to provide an initial estimate of crop yield. The system would then be continuously updated throughout the season by replacing the seasonal rainfall forecast with daily weather observations. TAMSAT satellite rainfall estimates are used rather than rain-gauge data due to the scarcity of ground based observations. An important feature of the system is the use of the geo-statistical method of sequential simulation to create an ensemble of daily weather inputs from both the statistical seasonal rainfall forecasts and the satellite rainfall estimates. This allows a range of possible yield outputs to be

  2. MOPITT Gridded Monthly CO Retrievals (Thermal Infrared Radiances) V006

    Data.gov (United States)

    National Aeronautics and Space Administration — The MOPITT L3 files contain daily and monthly mean gridded versions of the daily L2 CO profile and total column retrievals. The averaging kernels associated with...

  3. MOPITT Gridded Monthly CO Retrievals (Near Infrared Radiances) V006

    Data.gov (United States)

    National Aeronautics and Space Administration — The MOPITT L3 files contain daily and monthly mean gridded versions of the daily L2 CO profile and total column retrievals. The averaging kernels associated with...

  4. Ensemble Streamflow Forecast Improvements in NYC's Operations Support Tool

    Science.gov (United States)

    Wang, L.; Weiss, W. J.; Porter, J.; Schaake, J. C.; Day, G. N.; Sheer, D. P.

    2013-12-01

    Like most other water supply utilities, New York City's Department of Environmental Protection (DEP) has operational challenges associated with drought and wet weather events. During drought conditions, DEP must maintain water supply reliability to 9 million customers as well as meet environmental release requirements downstream of its reservoirs. During and after wet weather events, DEP must maintain turbidity compliance in its unfiltered Catskill and Delaware reservoir systems and minimize spills to mitigate downstream flooding. Proactive reservoir management - such as release restrictions to prepare for a drought or preventative drawdown in advance of a large storm - can alleviate negative impacts associated with extreme events. It is important for water managers to understand the risks associated with proactive operations so unintended consequences such as endangering water supply reliability with excessive drawdown prior to a storm event are minimized. Probabilistic hydrologic forecasts are a critical tool in quantifying these risks and allow water managers to make more informed operational decisions. DEP has recently completed development of an Operations Support Tool (OST) that integrates ensemble streamflow forecasts, real-time observations, and a reservoir system operations model into a user-friendly graphical interface that allows its water managers to take robust and defensible proactive measures in the face of challenging system conditions. Since initial development of OST was first presented at the 2011 AGU Fall Meeting, significant improvements have been made to the forecast system. First, the monthly AR1 forecasts ('Hirsch method') were upgraded with a generalized linear model (GLM) utilizing historical daily correlations ('Extended Hirsch method' or 'eHirsch'). The development of eHirsch forecasts improved predictive skill over the Hirsch method in the first week to a month from the forecast date and produced more realistic hydrographs on the tail

  5. PyForecastTools

    Energy Technology Data Exchange (ETDEWEB)

    2017-09-22

    The PyForecastTools package provides Python routines for calculating metrics for model validation, forecast verification and model comparison. For continuous predictands the package provides functions for calculating bias (mean error, mean percentage error, median log accuracy, symmetric signed bias), and for calculating accuracy (mean squared error, mean absolute error, mean absolute scaled error, normalized RMSE, median symmetric accuracy). Convenience routines to calculate the component parts (e.g. forecast error, scaled error) of each metric are also provided. To compare models the package provides: generic skill score; percent better. Robust measures of scale including median absolute deviation, robust standard deviation, robust coefficient of variation and the Sn estimator are all provided by the package. Finally, the package implements Python classes for NxN contingency tables. In the case of a multi-class prediction, accuracy and skill metrics such as proportion correct and the Heidke and Peirce skill scores are provided as object methods. The special case of a 2x2 contingency table inherits from the NxN class and provides many additional metrics for binary classification: probability of detection, probability of false detection, false alarm ration, threat score, equitable threat score, bias. Confidence intervals for many of these quantities can be calculated using either the Wald method or Agresti-Coull intervals.

  6. Seismic forecast using geostatistics

    International Nuclear Information System (INIS)

    Grecu, Valeriu; Mateiciuc, Doru

    2007-01-01

    The main idea of this research direction consists in the special way of constructing a new type of mathematical function as being a correlation between a computed statistical quantity and another physical quantity. This type of function called 'position function' was taken over by the authors of this study in the field of seismology with the hope of solving - at least partially - the difficult problem of seismic forecast. The geostatistic method of analysis focuses on the process of energy accumulation in a given seismic area, completing this analysis by a so-called loading function. This function - in fact a temporal function - describes the process of energy accumulation during a seismic cycle from a given seismic area. It was possible to discover a law of evolution of the seismic cycles that was materialized in a so-called characteristic function. This special function will help us to forecast the magnitude and the occurrence moment of the largest earthquake in the analysed area. Since 2000, the authors have been evolving to a new stage of testing: real - time analysis, in order to verify the quality of the method. There were five large earthquakes forecasts. (authors)

  7. Statistical methods for forecasting

    CERN Document Server

    Abraham, Bovas

    2009-01-01

    The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists."This book, it must be said, lives up to the words on its advertising cover: ''Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical, intermediate level discussion of a variety of forecasting tools, and explains how they relate to one another, both in theory and practice.'' It does just that!"-Journal of the Royal Statistical Society"A well-written work that deals with statistical methods and models that can be used to produce short-term forecasts, this book has wide-ranging applications. It could be used in the context of a study of regression, forecasting, and time series ...

  8. Forecasting droughts in West Africa: Operational practice and refined seasonal precipitation forecasts

    Science.gov (United States)

    Bliefernicht, Jan; Siegmund, Jonatan; Seidel, Jochen; Arnold, Hanna; Waongo, Moussa; Laux, Patrick; Kunstmann, Harald

    2016-04-01

    Precipitation forecasts for the upcoming rainy seasons are one of the most important sources of information for an early warning of droughts and water scarcity in West Africa. The meteorological services in West Africa perform seasonal precipitation forecasts within the framework of PRESAO (the West African climate outlook forum) since the end of the 1990s. Various sources of information and statistical techniques are used by the individual services to provide a harmonized seasonal precipitation forecasts for decision makers in West Africa. In this study, we present a detailed overview of the operational practice in West Africa including a first statistical assessment of the performance of the precipitation forecasts for drought situations for the past 18 years (1998 to 2015). In addition, a long-term hindcasts (1982 to 2009) and a semi-operational experiment for the rainy season 2013 using statistical and/or dynamical downscaling are performed to refine the precipitation forecasts from the Climate Forecast System Version 2 (CFSv2), a global ensemble prediction system. This information is post-processed to provide user-oriented precipitation indices such as the onset of the rainy season for supporting water and land use management for rain-fed agriculture. The evaluation of the individual techniques is performed focusing on water-scarce regions of the Volta basin in Burkina Faso and Ghana. The forecasts of the individual techniques are compared to state-of-the-art global observed precipitation products and a novel precipitation database based on long-term daily rain-gage measurements provided by the national meteorological services. The statistical assessment of the PRESAO forecasts indicates skillful seasonal precipitation forecasts for many locations in the Volta basin, particularly for years with water deficits. The operational experiment for the rainy season 2013 illustrates the high potential of a physically-based downscaling for this region but still shows

  9. JENDL special purpose file

    International Nuclear Information System (INIS)

    Nakagawa, Tsuneo

    1995-01-01

    In JENDL-3,2, the data on all the reactions having significant cross section over the neutron energy from 0.01 meV to 20 MeV are given for 340 nuclides. The object range of application extends widely, such as the neutron engineering, shield and others of fast reactors, thermal neutron reactors and nuclear fusion reactors. This is a general purpose data file. On the contrary to this, the file in which only the data required for a specific application field are collected is called special purpose file. The file for dosimetry is a typical special purpose file. The Nuclear Data Center, Japan Atomic Energy Research Institute, is making ten kinds of JENDL special purpose files. The files, of which the working groups of Sigma Committee are in charge, are listed. As to the format of the files, ENDF format is used similarly to JENDL-3,2. Dosimetry file, activation cross section file, (α, n) reaction data file, fusion file, actinoid file, high energy data file, photonuclear data file, PKA/KERMA file, gas production cross section file and decay data file are described on their contents, the course of development and their verification. Dosimetry file and gas production cross section file have been completed already. As for the others, the expected time of completion is shown. When these files are completed, they are opened to the public. (K.I.)

  10. Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast

    Science.gov (United States)

    Zhu, Jiang; Stevens, E.; Zavodsky, B. T.; Zhang, X.; Heinrichs, T.; Broderson, D.

    2014-01-01

    Data assimilation has been demonstrated very useful in improving both global and regional numerical weather prediction. Alaska has very coarser surface observation sites. On the other hand, it gets much more satellite overpass than lower 48 states. How to utilize satellite data to improve numerical prediction is one of hot topics among weather forecast community in Alaska. The Geographic Information Network of Alaska (GINA) at University of Alaska is conducting study on satellite data assimilation for WRF model. AIRS/CRIS sounder profile data are used to assimilate the initial condition for the customized regional WRF model (GINA-WRF model). Normalized standard deviation, RMSE, and correlation statistic analysis methods are applied to analyze one case of 48 hours forecasts and one month of 24-hour forecasts in order to evaluate the improvement of regional numerical model from Data assimilation. The final goal of the research is to provide improved real-time short-time forecast for Alaska regions.

  11. Affective Forecasting and Self-Rated Symptoms of Depression, Anxiety, and Hypomania: Evidence for a Dysphoric Forecasting Bias

    Science.gov (United States)

    Hoerger, Michael; Quirk, Stuart W.; Chapman, Benjamin P.; Duberstein, Paul R.

    2011-01-01

    Emerging research has examined individual differences in affective forecasting; however, we are aware of no published study to date linking psychopathology symptoms to affective forecasting problems. Pitting cognitive theory against depressive realism theory, we examined whether dysphoria was associated with negatively biased affective forecasts or greater accuracy. Participants (n = 325) supplied predicted and actual emotional reactions for three days surrounding an emotionally-evocative relational event, Valentine’s Day. Predictions were made a month prior to the holiday. Consistent with cognitive theory, we found evidence for a dysphoric forecasting bias – the tendency of individuals in dysphoric states to overpredict negative emotional reactions to future events. The dysphoric forecasting bias was robust across ratings of positive and negative affect, forecasts for pleasant and unpleasant scenarios, continuous and categorical operationalizations of dysphoria, and three time points of observation. Similar biases were not observed in analyses examining the independent effects of anxiety and hypomania. Findings provide empirical evidence for the long assumed influence of depressive symptoms on future expectations. The present investigation has implications for affective forecasting studies examining information processing constructs, decision making, and broader domains of psychopathology. PMID:22397734

  12. Affective forecasting and self-rated symptoms of depression, anxiety, and hypomania: evidence for a dysphoric forecasting bias.

    Science.gov (United States)

    Hoerger, Michael; Quirk, Stuart W; Chapman, Benjamin P; Duberstein, Paul R

    2012-01-01

    Emerging research has examined individual differences in affective forecasting; however, we are aware of no published study to date linking psychopathology symptoms to affective forecasting problems. Pitting cognitive theory against depressive realism theory, we examined whether dysphoria was associated with negatively biased affective forecasts or greater accuracy. Participants (n=325) supplied predicted and actual emotional reactions for three days surrounding an emotionally evocative relational event, Valentine's Day. Predictions were made a month prior to the holiday. Consistent with cognitive theory, we found evidence for a dysphoric forecasting bias-the tendency of individuals in dysphoric states to overpredict negative emotional reactions to future events. The dysphoric forecasting bias was robust across ratings of positive and negative affect, forecasts for pleasant and unpleasant scenarios, continuous and categorical operationalisations of dysphoria, and three time points of observation. Similar biases were not observed in analyses examining the independent effects of anxiety and hypomania. Findings provide empirical evidence for the long-assumed influence of depressive symptoms on future expectations. The present investigation has implications for affective forecasting studies examining information-processing constructs, decision making, and broader domains of psychopathology.

  13. Analysing UK real estate market forecast disagreement

    OpenAIRE

    McAllister, Patrick; Newell, G.; Matysiak, George

    2005-01-01

    Given the significance of forecasting in real estate investment decisions, this paper investigates forecast uncertainty and disagreement in real estate market forecasts. Using the Investment Property Forum (IPF) quarterly survey amongst UK independent real estate forecasters, these real estate forecasts are compared with actual real estate performance to assess a number of real estate forecasting issues in the UK over 1999-2004, including real estate forecast error, bias and consensus. The re...

  14. Effective Feature Preprocessing for Time Series Forecasting

    DEFF Research Database (Denmark)

    Zhao, Junhua; Dong, Zhaoyang; Xu, Zhao

    2006-01-01

    Time series forecasting is an important area in data mining research. Feature preprocessing techniques have significant influence on forecasting accuracy, therefore are essential in a forecasting model. Although several feature preprocessing techniques have been applied in time series forecasting...... performance in time series forecasting. It is demonstrated in our experiment that, effective feature preprocessing can significantly enhance forecasting accuracy. This research can be a useful guidance for researchers on effectively selecting feature preprocessing techniques and integrating them with time...... series forecasting models....

  15. EU pharmaceutical expenditure forecast.

    Science.gov (United States)

    Urbinati, Duccio; Rémuzat, Cécile; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

    2014-01-01

    With constant incentives for healthcare payers to contain their pharmaceutical budgets, forecasting has become critically important. Some countries have, for instance, developed pharmaceutical horizon scanning units. The objective of this project was to build a model to assess the net effect of the entrance of new patented medicinal products versus medicinal products going off-patent, with a defined forecast horizon, on selected European Union (EU) Member States' pharmaceutical budgets. This model took into account population ageing, as well as current and future country-specific pricing, reimbursement, and market access policies (the project was performed for the European Commission; see http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). In order to have a representative heterogeneity of EU Member States, the following countries were selected for the analysis: France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. A forecasting period of 5 years (2012-2016) was chosen to assess the net pharmaceutical budget impact. A model for generics and biosimilars was developed for each country. The model estimated a separate and combined effect of the direct and indirect impacts of the patent cliff. A second model, estimating the sales development and the risk of development failure, was developed for new drugs. New drugs were reviewed individually to assess their clinical potential and translate it into commercial potential. The forecast was carried out according to three perspectives (healthcare public payer, society, and manufacturer), and several types of distribution chains (retail, hospital, and combined retail and hospital). Probabilistic and deterministic sensitivity analyses were carried out. According to the model, all countries experienced drug budget reductions except Poland (+€41 million). Savings were expected to be the highest in the United Kingdom (-€9,367 million), France (-€5,589 million), and, far behind them

  16. Short term and medium term power distribution load forecasting by neural networks

    International Nuclear Information System (INIS)

    Yalcinoz, T.; Eminoglu, U.

    2005-01-01

    Load forecasting is an important subject for power distribution systems and has been studied from different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short term, medium term and long term forecasts. Several research groups have proposed various techniques for either short term load forecasting or medium term load forecasting or long term load forecasting. This paper presents a neural network (NN) model for short term peak load forecasting, short term total load forecasting and medium term monthly load forecasting in power distribution systems. The NN is used to learn the relationships among past, current and future temperatures and loads. The neural network was trained to recognize the peak load of the day, total load of the day and monthly electricity consumption. The suitability of the proposed approach is illustrated through an application to real load shapes from the Turkish Electricity Distribution Corporation (TEDAS) in Nigde. The data represents the daily and monthly electricity consumption in Nigde, Turkey

  17. GloFAS-Seasonal: Operational Seasonal Ensemble River Flow Forecasts at the Global Scale

    Science.gov (United States)

    Emerton, Rebecca; Zsoter, Ervin; Smith, Paul; Salamon, Peter

    2017-04-01

    Seasonal hydrological forecasting has potential benefits for many sectors, including agriculture, water resources management and humanitarian aid. At present, no global scale seasonal hydrological forecasting system exists operationally; although smaller scale systems have begun to emerge around the globe over the past decade, a system providing consistent global scale seasonal forecasts would be of great benefit in regions where no other forecasting system exists, and to organisations operating at the global scale, such as disaster relief. We present here a new operational global ensemble seasonal hydrological forecast, currently under development at ECMWF as part of the Global Flood Awareness System (GloFAS). The proposed system, which builds upon the current version of GloFAS, takes the long-range forecasts from the ECMWF System4 ensemble seasonal forecast system (which incorporates the HTESSEL land surface scheme) and uses this runoff as input to the Lisflood routing model, producing a seasonal river flow forecast out to 4 months lead time, for the global river network. The seasonal forecasts will be evaluated using the global river discharge reanalysis, and observations where available, to determine the potential value of the forecasts across the globe. The seasonal forecasts will be presented as a new layer in the GloFAS interface, which will provide a global map of river catchments, indicating whether the catchment-averaged discharge forecast is showing abnormally high or low flows during the 4-month lead time. Each catchment will display the corresponding forecast as an ensemble hydrograph of the weekly-averaged discharge forecast out to 4 months, with percentile thresholds shown for comparison with the discharge climatology. The forecast visualisation is based on a combination of the current medium-range GloFAS forecasts and the operational EFAS (European Flood Awareness System) seasonal outlook, and aims to effectively communicate the nature of a seasonal

  18. Online short-term forecast of greenhouse heat load using a weather forecast service

    DEFF Research Database (Denmark)

    Vogler-Finck, P. J.C.; Bacher, P.; Madsen, Henrik

    2017-01-01

    the performance of recursive least squares for predicting the heat load of individual greenhouses in an online manner. Predictor inputs (weekly curves terms and weather forecast inputs) are selected in an automated manner using a forward selection approach. Historical load measurements from 5 Danish greenhouses...... mean square error of the prediction was within 8–20% of the peak load for the set of consumers over the 8 months period considered....

  19. Forecasting the Seasonal Timing of Maine's Lobster Fishery

    Directory of Open Access Journals (Sweden)

    Katherine E. Mills

    2017-11-01

    Full Text Available The fishery for American lobster is currently the highest-valued commercial fishery in the United States, worth over US$620 million in dockside value in 2015. During a marine heat wave in 2012, the fishery was disrupted by the early warming of spring ocean temperatures and subsequent influx of lobster landings. This situation resulted in a price collapse, as the supply chain was not prepared for the early and abundant landings of lobsters. Motivated by this series of events, we have developed a forecast of when the Maine (USA lobster fishery will shift into its high volume summer landings period. The forecast uses a regression approach to relate spring ocean temperatures derived from four NERACOOS buoys along the coast of Maine to the start day of the high landings period of the fishery. Tested against conditions in past years, the forecast is able to predict the start day to within 1 week of the actual start, and the forecast can be issued 3–4 months prior to the onset of the high-landings period, providing valuable lead-time for the fishery and its associated supply chain to prepare for the upcoming season. Forecast results are conveyed in a probabilistic manner and are updated weekly over a 6-week forecasting period so that users can assess the certainty and consistency of the forecast and factor the uncertainty into their use of the information in a given year. By focusing on the timing of events, this type of seasonal forecast provides climate-relevant information to users at time scales that are meaningful for operational decisions. As climate change alters seasonal phenology and reduces the reliability of past experience as a guide for future expectations, this type of forecast can enable fishing industry participants to better adjust to and prepare for operating in the context of climate change.

  20. Evaluation of Probabilistic Disease Forecasts.

    Science.gov (United States)

    Hughes, Gareth; Burnett, Fiona J

    2017-10-01

    The statistical evaluation of probabilistic disease forecasts often involves calculation of metrics defined conditionally on disease status, such as sensitivity and specificity. However, for the purpose of disease management decision making, metrics defined conditionally on the result of the forecast-predictive values-are also important, although less frequently reported. In this context, the application of scoring rules in the evaluation of probabilistic disease forecasts is discussed. An index of separation with application in the evaluation of probabilistic disease forecasts, described in the clinical literature, is also considered and its relation to scoring rules illustrated. Scoring rules provide a principled basis for the evaluation of probabilistic forecasts used in plant disease management. In particular, the decomposition of scoring rules into interpretable components is an advantageous feature of their application in the evaluation of disease forecasts.

  1. A Practical Model for Forecasting New Freshman Enrollment during the Application Period.

    Science.gov (United States)

    Paulsen, Michael B.

    1989-01-01

    A simple and effective model for forecasting freshman enrollment during the application period is presented step by step. The model requires minimal and readily available information, uses a simple linear regression analysis on a personal computer, and provides updated monthly forecasts. (MSE)

  2. Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice

    DEFF Research Database (Denmark)

    Callot, Laurent; Kock, Anders Bredahl; Medeiros, Marcelo C.

    stocks and find that these dynamics are not stable as the data is aggregated from the daily to the weekly and monthly frequency. The theoretical performance guarantees on our forecasts are illustrated on the Dow Jones index. In particular, we can beat our benchmark by a wide margin at the longer forecast...

  3. Operational hydrological forecasting in Bavaria. Part I: Forecast uncertainty

    Science.gov (United States)

    Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

    2009-04-01

    In Bavaria, operational flood forecasting has been established since the disastrous flood of 1999. Nowadays, forecasts based on rainfall information from about 700 raingauges and 600 rivergauges are calculated and issued for nearly 100 rivergauges. With the added experience of the 2002 and 2005 floods, awareness grew that the standard deterministic forecast, neglecting the uncertainty associated with each forecast is misleading, creating a false feeling of unambiguousness. As a consequence, a system to identify, quantify and communicate the sources and magnitude of forecast uncertainty has been developed, which will be presented in part I of this study. In this system, the use of ensemble meteorological forecasts plays a key role which will be presented in part II. Developing the system, several constraints stemming from the range of hydrological regimes and operational requirements had to be met: Firstly, operational time constraints obviate the variation of all components of the modeling chain as would be done in a full Monte Carlo simulation. Therefore, an approach was chosen where only the most relevant sources of uncertainty were dynamically considered while the others were jointly accounted for by static error distributions from offline analysis. Secondly, the dominant sources of uncertainty vary over the wide range of forecasted catchments: In alpine headwater catchments, typically of a few hundred square kilometers in size, rainfall forecast uncertainty is the key factor for forecast uncertainty, with a magnitude dynamically changing with the prevailing predictability of the atmosphere. In lowland catchments encompassing several thousands of square kilometers, forecast uncertainty in the desired range (usually up to two days) is mainly dependent on upstream gauge observation quality, routing and unpredictable human impact such as reservoir operation. The determination of forecast uncertainty comprised the following steps: a) From comparison of gauge

  4. Added value of dynamical downscaling of winter seasonal forecasts over North America

    Science.gov (United States)

    Tefera Diro, Gulilat; Sushama, Laxmi

    2017-04-01

    Skillful seasonal forecasts have enormous potential benefits for socio-economic sectors that are sensitive to weather and climate conditions, as the early warning routines could reduce the vulnerability of such sectors. In this study, individual ensemble members of the ECMWF global ensemble seasonal forecasts are dynamically downscaled to produce ensemble of regional seasonal forecasts over North America using the fifth generation Canadian Regional Climate Model (CRCM5). CRCM5 forecasts are initialized on November 1st of each year and are integrated for four months for the 1991-2001 period at 0.22 degree resolution to produce a one-month lead-time forecast. The initial conditions for atmospheric variables are obtained from ERA-Interim reanalysis, whereas the initial conditions for land surface are obtained from a separate ERA-interim driven CRCM5 simulation with spectral nudging applied to the interior domain. The global and regional ensemble forecasts were then verified to investigate the skill and economic benefits of dynamical downscaling. Results indicate that both the global and regional climate models produce skillful precipitation forecast over the southern Great Plains and eastern coasts of the U.S and skillful temperature forecasts over the northern U.S. and most of Canada. In comparison to ECMWF forecasts, CRCM5 forecasts improved the temperature forecast skill over most part of the domain, but the improvements for precipitation is limited to regions with complex topography, where it improves the frequency of intense daily precipitation. CRCM5 forecast also yields a better economic value compared to ECMWF precipitation forecasts, for users whose cost to loss ratio is smaller than 0.5.

  5. Global Energy Forecasting Competition 2012

    DEFF Research Database (Denmark)

    Hong, Tao; Pinson, Pierre; Fan, Shu

    2014-01-01

    The Global Energy Forecasting Competition (GEFCom2012) attracted hundreds of participants worldwide, who contributed many novel ideas to the energy forecasting field. This paper introduces both tracks of GEFCom2012, hierarchical load forecasting and wind power forecasting, with details...... on the aspects of the problem, the data, and a summary of the methods used by selected top entries. We also discuss the lessons learned from this competition from the organizers’ perspective. The complete data set, including the solution data, is published along with this paper, in an effort to establish...

  6. Forecasting in Complex Systems

    Science.gov (United States)

    Rundle, J. B.; Holliday, J. R.; Graves, W. R.; Turcotte, D. L.; Donnellan, A.

    2014-12-01

    Complex nonlinear systems are typically characterized by many degrees of freedom, as well as interactions between the elements. Interesting examples can be found in the areas of earthquakes and finance. In these two systems, fat tails play an important role in the statistical dynamics. For earthquake systems, the Gutenberg-Richter magnitude-frequency is applicable, whereas for daily returns for the securities in the financial markets are known to be characterized by leptokurtotic statistics in which the tails are power law. Very large fluctuations are present in both systems. In earthquake systems, one has the example of great earthquakes such as the M9.1, March 11, 2011 Tohoku event. In financial systems, one has the example of the market crash of October 19, 1987. Both were largely unexpected events that severely impacted the earth and financial systems systemically. Other examples include the M9.3 Andaman earthquake of December 26, 2004, and the Great Recession which began with the fall of Lehman Brothers investment bank on September 12, 2013. Forecasting the occurrence of these damaging events has great societal importance. In recent years, national funding agencies in a variety of countries have emphasized the importance of societal relevance in research, and in particular, the goal of improved forecasting technology. Previous work has shown that both earthquakes and financial crashes can be described by a common Landau-Ginzburg-type free energy model. These metastable systems are characterized by fat tail statistics near the classical spinodal. Correlations in these systems can grow and recede, but do not imply causation, a common source of misunderstanding. In both systems, a common set of techniques can be used to compute the probabilities of future earthquakes or crashes. In this talk, we describe the basic phenomenology of these systems and emphasize their similarities and differences. We also consider the problem of forecast validation and verification

  7. HUD GIS Boundary Files

    Data.gov (United States)

    Department of Housing and Urban Development — The HUD GIS Boundary Files are intended to supplement boundary files available from the U.S. Census Bureau. The files are for community planners interested in...

  8. Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast

    Directory of Open Access Journals (Sweden)

    Jinyin Ye

    2016-01-01

    Full Text Available TIGGE (THORPEX International Grand Global Ensemble was a major part of the THORPEX (Observing System Research and Predictability Experiment. It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability forecast, extend the lead time of the flood forecast, and gain more time for decision-makers to make the right decision. In this study, precipitation ensemble forecast products from ECMWF, NCEP, and CMA are used to drive distributed hydrologic model TOPX. We focus on Yi River catchment and aim to build a flood forecast and early warning system. The results show that the meteorologic-hydrologic coupled model can satisfactorily predict the flow-process of four flood events. The predicted occurrence time of peak discharges is close to the observations. However, the magnitude of the peak discharges is significantly different due to various performances of the ensemble prediction systems. The coupled forecasting model can accurately predict occurrence of the peak time and the corresponding risk probability of peak discharge based on the probability distribution of peak time and flood warning, which can provide users a strong theoretical foundation and valuable information as a promising new approach.

  9. Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs

    Science.gov (United States)

    Pelorosso, Leandro; Diehl, Alexandra; Matković, Krešimir; Delrieux, Claudio; Ruiz, Juan; Gröeller, M. Eduard; Bruckner, Stefan

    2016-04-01

    accurate measure of forecast uncertainty that could result in better decision-making. It offers different level of abstractions to help with the recalibration of the RAR method. It also has an inspection tool that displays the selected analogs, their observations and statistical data. It gives the users access to inner parts of the method, unveiling hidden information. References [GR05] GNEITING T., RAFTERY A. E.: Weather forecasting with ensemble methods. Science 310, 5746, 248-249, 2005. [KAL03] KALNAY E.: Atmospheric modeling, data assimilation and predictability. Cambridge University Press, 2003. [PH06] PALMER T., HAGEDORN R.: Predictability of weather and climate. Cambridge University Press, 2006. [HW06] HAMILL T. M., WHITAKER J. S.: Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and application. Monthly Weather Review 134, 11, 3209-3229, 2006. [DE06] DEITRICK S., EDSALL R.: The influence of uncertainty visualization on decision making: An empirical evaluation. Springer, 2006. [KMS08] KEIM D. A., MANSMANN F., SCHNEIDEWIND J., THOMAS J., ZIEGLER H.: Visual analytics: Scope and challenges. Springer, 2008.

  10. The new Met Office strategy for seasonal forecasts

    Science.gov (United States)

    Hewson, T. D.

    2012-04-01

    In October 2011 the Met Office began issuing a new-format UK seasonal forecast, called "The 3-month Outlook". Government interest in a UK-relevant product had been heightened by infrastructure issues arising during the severe cold of previous winters. At the same time there was evidence that the Met Office's "GLOSEA4" long range forecasting system exhibited some hindcast skill for the UK, that was comparable to its hindcast skill for the larger (and therefore less useful) 'northern Europe' region. Also, the NAO- and AO- signals prevailing in the previous two winters had been highlighted by the GLOSEA4 model well in advance. This presentation will initially give a brief overview of GLOSEA4, describing key features such as evolving sea-ice, a well-resolved stratosphere, and the perturbation strategy. Skill measures will be shown, along with forecasts for the last 3 winters. The new structure 3-month outlook will then be described and presented. Previously, our seasonal forecasts had been based on a tercile approach. The new format outlook aims to substantially improve upon this by illustrating graphically, and with text, the full range of possible outcomes, and by placing those outcomes in the context of climatology. In one key component the forecast pdfs (probability density functions) are displayed alongside climatological pdfs. To generate the forecast pdf we take the bias-corrected GLOSEA4 output (42 members), and then incorporate, via expert team, all other relevant information. Firstly model forecasts from other centres are examined. Then external 'forcing factors', such as solar, and the state of the land-ocean-ice system, are referenced, assessing how well the models represent their influence, and bringing in statistical relationships where appropriate. The expert team thereby decides upon any changes to the GLOSEA4 data, employing an interactive tool to shift, expand or contract the forecast pdfs accordingly. The full modification process will be illustrated

  11. Canadian natural gas price forecast

    International Nuclear Information System (INIS)

    Jones, D.

    1998-01-01

    The basic factors that influenced NYMEX gas prices during the winter of 1997/1998 - warm temperatures, low fuel prices, new production in the Gulf of Mexico, and the fact that forecasters had predicted a mild spring due to El Nino - were reviewed. However, it was noted that for the last 18 months the basic factors had less of an impact on market direction because of an increase in Fund and technical trader participation. Overall, gas prices were strong through most of the year. For the winter of 1998-1999 the prediction was that NYMEX gas prices will remain below $2.00 through to the end of October 1998 because of high U.S. storage levels and moderate temperatures. NYMEX gas prices are expected to peak in January 1999 at $3.25. AECO natural gas prices were predicted to decrease in the short term because of increasing levels of Canadian storage, and because of delays in Northern Border pipeline expansions. It was also predicted that AECO prices will peak in January 1999 and will remain relatively strong through the summer of 1999. tabs., figs

  12. AIRPACT Air Quality Forecasting for August 2001

    Science.gov (United States)

    Vaughan, J. K.; Lamb, B. K.; Westberg, H. H.; Fritz, B. G.; Bamesberger, L.; Bowman, C.; Figueroa-Kaminsky, C.; Otterson, S.; Wilson, R.; Arnold, J. R.; Mass, C.; Albright, M.; Jaffe, D. A.; Barrie, L. A.; Barchet, W. R.; Fast, J. D.; Jobson, B. T.

    2002-12-01

    The AIRPACT air-quality forecasting system was operational during the month of August, 2001, and provided daily forecasts of ozone and associated species throughout the PNW2001 period. The AIRPACT (air indicator report for public awareness and community tracking) project was supported by the U.S. EPA through the EMPACT program. The modeling effort within this project resulted in the assembly of a highly automated air quality forecasting system using MM5 meteorology coupled with a regional emissions sub-system, which both drove the CALMET-CALGRID Eulerian air-quality model. Results were posted to the project web-site and distributed via ftp each morning before operations decisions were finalized. Modeling outputs included 24-hour animations of estimated gridded area emissions and predicted gridded hourly average mixing ratios for ozone, among other species. A verification system for comparing AIRPACT results against the Washington Department of Ecology telemetered surface monitor data was in development during PNW2001. The various measurement components of PNW2001, in combination with the Ecology monitoring network, provided an excellent opportunity to compare AIRPACT ozone predictions with ozone observations from multiple measurement schemes, including surface monitors, aircraft sampling, and ozonesondes. The AIRPACT prediction verification against surface monitors at six downwind sites near Seattle, WA for August 2001 resulted in a normalized bias of 15% and a normalized gross error of 51%. Comparisons of AIRPACT predictions against ozonesondes and aircraft measurements are presented graphically in this poster.

  13. FORECASTING NEW PRODUCT SALES

    Directory of Open Access Journals (Sweden)

    R. Siriram

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: This paper tests the accuracy of using Linear regression, Logistics regression, and Bass curves in selected new product rollouts, based on sales data. The selected new products come from the electronics and electrical engineering and information and communications technology industries. The eight selected products are: electronic switchgear, electric motors, supervisory control and data acquisition systems, programmable logic controllers, cell phones, wireless modules, routers, and antennas. We compare the Linear regression, Logistics regression and Bass curves with respect to forecasting using analysis of variance. The accuracy of these three curves is studied and conclusions are drawn. We use an expert panel to compare the different curves and provide lessons for managers to improve forecasting new product sales. In addition, comparison between the two industries is drawn, and areas for further research are indicated.

    AFRIKAANSE OPSOMMING: Hierdie artikel toets die akkuraatheid van die gebruik van linêere regressie, logistiese regressie en Bass-krommes by die bekendstelling van nuwe produkte gebaseer op verkoopsdata. Die geselekteerde nuwe produkte is uit die elektriese en elektroniese asook informasietegnologie- en kommunikasie bedrywe. Linêere regressie, logistiese regressie en Bass-krommes word vergelyk ten opsigte van vooruitskatting deur variansie te ontleed. Die akkuraatheid word ontleed en gevolgtrekkings gemaak. Die doel is om vooruitskatting van nuwe produkverkope te verbeter.

  14. Issues in Forecasting CMEs

    Science.gov (United States)

    Pizzo, V. J.

    2017-12-01

    I will present my view of the current status of space weather forecasting abilities related to CMEs. This talk will address the large-scale aspects, but specifically not energetic particle phenomena. A key point is that all models, whether sophisticated numerical contraptions or quasi-empirical ones, are only as good as the data you feed them. Hence the emphasis will be on observations and analysis methods. First I will review where we stand with regard to the near-Sun quantitative data needed to drive any model, no matter how complex or simple-minded, and I will discuss technological roadblocks that suggest it may be some time before we see any meaningful improvements beyond what we have today. Then I cover issues related to characterizing CME propagation out through the corona and into interplanetary space, as well as to observational limitations in the vicinity of 1 AU. Since none of these observational constraints are likely to be resolved anytime soon, the real challenge is to make more informed use of what is available. Thus, this talk will focus on how we may identify and pursue the most profitable approaches, for both forecast and research applications. The discussion will highlight a number of promising leads, including those related to inclusion of solar backside information, joint magnetograph observations from L5 and Earth, how to use (not just run) ensembles, more rational use of HI observations, and suggestions for using cube-sats for deep space observations of CMEs and MCs.

  15. Inflation, Forecast Intervals and Long Memory Regression Models

    NARCIS (Netherlands)

    C.S. Bos (Charles); Ph.H.B.F. Franses (Philip Hans); M. Ooms (Marius)

    2001-01-01

    textabstractWe examine recursive out-of-sample forecasting of monthly postwar U.S. core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading

  16. Inflation, Forecast Intervals and Long Memory Regression Models

    NARCIS (Netherlands)

    Ooms, M.; Bos, C.S.; Franses, P.H.

    2003-01-01

    We examine recursive out-of-sample forecasting of monthly postwar US core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators

  17. Insights from a history of seasonal inflow forecasting with a conceptual hydrologic model

    Science.gov (United States)

    Druce, Donald J.

    2001-08-01

    BC Hydro has used a conceptual hydrologic model for forecasting the seasonal inflows to its Mica project, on the Columbia River, for more than 20 years. The model estimates the snowpack on the forecast date using recently observed weather data and then calculates the runoff response to approximately 30 historical weather sequences over the remainder of the year. The ensemble of equally likely seasonal inflows makes up the forecast. Forecasts are issued as of the first of each month from January to August. The same model and modeller have made all of such forecasts for the Mica project. While some may see this as a failure to progress, others may see it as an unique opportunity to learn how well an older hydrologic model has performed, in practice, over the long term. Comments on both perspectives are offered, after the analyses of the forecasts have been presented. The forecasts have been analysed, as of each of the monthly forecast dates, to determine the accuracy of the mean and to establish any differences between ex post and ex ante measures of uncertainty. Results are then compared with those from a regression model that has also been used for forecasting the seasonal inflows to the Mica project over the same period of record.

  18. AN EVALUATION OF POINT AND DENSITY FORECASTS FOR SELECTED EU FARM GATE MILK PRICES

    Directory of Open Access Journals (Sweden)

    Dennis Bergmann

    2018-01-01

    Full Text Available Fundamental changes to the common agricultural policy (CAP have led to greater market orientation which in turn has resulted in sharply increased variability of EU farm gate milk prices and thus farmers’ income. In this market environment reliable forecasts of farm gate milk prices are extremely important as farmers can make improved decisions with regards to cash flow management and budget preparation. In addition these forecasts may be used in setting fixed priced contracts between dairy farmers and processors thus providing certainty and reducing risk. In this study both point and density forecasts from various time series models for farm gate milk prices in Germany, Ireland and for an average EU price series are evaluated using a rolling window framework. Additionally forecasts of the individual models are combined using different combination schemes. The results of the out of sample evaluation show that ARIMA type models perform well on short forecast horizons (1 to 3 month while the structural time series approach performs well on longer forecast horizons (12 month. Finally combining individual forecasts of different models significantly improves the forecast performance for all forecast horizons.

  19. Towards a GME ensemble forecasting system: Ensemble initialization using the breeding technique

    Directory of Open Access Journals (Sweden)

    Jan D. Keller

    2008-12-01

    Full Text Available The quantitative forecast of precipitation requires a probabilistic background particularly with regard to forecast lead times of more than 3 days. As only ensemble simulations can provide useful information of the underlying probability density function, we built a new ensemble forecasting system (GME-EFS based on the GME model of the German Meteorological Service (DWD. For the generation of appropriate initial ensemble perturbations we chose the breeding technique developed by Toth and Kalnay (1993, 1997, which develops perturbations by estimating the regions of largest model error induced uncertainty. This method is applied and tested in the framework of quasi-operational forecasts for a three month period in 2007. The performance of the resulting ensemble forecasts are compared to the operational ensemble prediction systems ECMWF EPS and NCEP GFS by means of ensemble spread of free atmosphere parameters (geopotential and temperature and ensemble skill of precipitation forecasting. This comparison indicates that the GME ensemble forecasting system (GME-EFS provides reasonable forecasts with spread skill score comparable to that of the NCEP GFS. An analysis with the continuous ranked probability score exhibits a lack of resolution for the GME forecasts compared to the operational ensembles. However, with significant enhancements during the 3 month test period, the first results of our work with the GME-EFS indicate possibilities for further development as well as the potential for later operational usage.

  20. Global Ensemble Forecast System (GEFS) [1 Deg.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...

  1. The Application of TAPM for Site Specific Wind Energy Forecasting

    Directory of Open Access Journals (Sweden)

    Merlinde Kay

    2016-02-01

    Full Text Available The energy industry uses weather forecasts for determining future electricity demand variations due to the impact of weather, e.g., temperature and precipitation. However, as a greater component of electricity generation comes from intermittent renewable sources such as wind and solar, weather forecasting techniques need to now also focus on predicting renewable energy supply, which means adapting our prediction models to these site specific resources. This work assesses the performance of The Air Pollution Model (TAPM, and demonstrates that significant improvements can be made to only wind speed forecasts from a mesoscale Numerical Weather Prediction (NWP model. For this study, a wind farm site situated in North-west Tasmania, Australia was investigated. I present an analysis of the accuracy of hourly NWP and bias corrected wind speed forecasts over 12 months spanning 2005. This extensive time frame allows an in-depth analysis of various wind speed regimes of importance for wind-farm operation, as well as extreme weather risk scenarios. A further correction is made to the basic bias correction to improve the forecast accuracy further, that makes use of real-time wind-turbine data and a smoothing function to correct for timing-related issues. With full correction applied, a reduction in the error in the magnitude of the wind speed by as much as 50% for “hour ahead” forecasts specific to the wind-farm site has been obtained.

  2. Should we use seasonnal meteorological ensemble forecasts for hydrological forecasting? A case study for nordic watersheds in Canada.

    Science.gov (United States)

    Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert; Guay, Catherine

    2017-04-01

    Hydro-electricity is a major source of energy for many countries throughout the world, including Canada. Long lead-time streamflow forecasts are all the more valuable as they help decision making and dam management. Different techniques exist for long-term hydrological forecasting. Perhaps the most well-known is 'Extended Streamflow Prediction' (ESP), which considers past meteorological scenarios as possible, often equiprobable, future scenarios. In the ESP framework, those past-observed meteorological scenarios (climatology) are used in turn as the inputs of a chosen hydrological model to produce ensemble forecasts (one member corresponding to each year in the available database). Many hydropower companies, including Hydro-Québec (province of Quebec, Canada) use variants of the above described ESP system operationally for long-term operation planning. The ESP system accounts for the hydrological initial conditions and for the natural variability of the meteorological variables. However, it cannot consider the current initial state of the atmosphere. Climate models can help remedy this drawback. In the context of a changing climate, dynamical forecasts issued from climate models seem to be an interesting avenue to improve upon the ESP method and could help hydropower companies to adapt their management practices to an evolving climate. Long-range forecasts from climate models can also be helpful for water management at locations where records of past meteorological conditions are short or nonexistent. In this study, we compare 7-month hydrological forecasts obtained from climate model outputs to an ESP system. The ESP system mimics the one used operationally at Hydro-Québec. The dynamical climate forecasts are produced by the European Center for Medium range Weather Forecasts (ECMWF) System4. Forecasts quality is assessed using numerical scores such as the Continuous Ranked Probability Score (CRPS) and the Ignorance score and also graphical tools such as the

  3. Guidelines for forecasting energy demand

    International Nuclear Information System (INIS)

    Sonino, T.

    1976-11-01

    Four methodologies for forecasting energy demand are reviewed here after considering the role of energy in the economy and the analysis of energy use in different economic sectors. The special case of Israel is considered throughout, and some forecasts for energy demands in the year 2000 are presented. An energy supply mix that may be considered feasible is proposed. (author)

  4. Regional-seasonal weather forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Abarbanel, H.; Foley, H.; MacDonald, G.; Rothaus, O.; Rudermann, M.; Vesecky, J.

    1980-08-01

    In the interest of allocating heating fuels optimally, the state-of-the-art for seasonal weather forecasting is reviewed. A model using an enormous data base of past weather data is contemplated to improve seasonal forecasts, but present skills do not make that practicable. 90 references. (PSB)

  5. Forecasting the future of biodiversity

    DEFF Research Database (Denmark)

    Fitzpatrick, M. C.; Sanders, Nate; Ferrier, Simon

    2011-01-01

    , but their application to forecasting climate change impacts on biodiversity has been limited. Here we compare forecasts of changes in patterns of ant biodiversity in North America derived from ensembles of single-species models to those from a multi-species modeling approach, Generalized Dissimilarity Modeling (GDM...... climate change impacts on biodiversity....

  6. Forecasts: uncertain, inaccurate and biased?

    DEFF Research Database (Denmark)

    Nicolaisen, Morten Skou; Ambrasaite, Inga; Salling, Kim Bang

    2012-01-01

    Cost Benefit Analysis (CBA) is the dominating methodology for appraisal of transport infrastructure projects across the globe. In order to adequately assess the costs and benefits of such projects two types of forecasts are crucial to the validity of the appraisal. First are the forecasts of cons....... It is recommended that more attention is given to monitoring completed projects so future forecasts can benefit from better data availability through systematic ex-post evaluations, and an example of how to utilize such data in practice is presented.......Cost Benefit Analysis (CBA) is the dominating methodology for appraisal of transport infrastructure projects across the globe. In order to adequately assess the costs and benefits of such projects two types of forecasts are crucial to the validity of the appraisal. First are the forecasts...... of construction costs, which account for the majority of total project costs. Second are the forecasts of travel time savings, which account for the majority of total project benefits. The latter of these is, inter alia, determined by forecasts of travel demand, which we shall use as a proxy for the forecasting...

  7. Ensemble hydromoeteorological forecasting in Denmark

    DEFF Research Database (Denmark)

    Lucatero Villasenor, Diana

    forecasts where a dampening of the differences of precipitation quality occurs. Seasonal meteorological forecasts are possible due to changes of large scale patterns of the ocean and land, such as el Niño, that evolve at a much slower pace than the atmosphere, which can have an impact on its evolution later...

  8. Now, Here's the Weather Forecast...

    Science.gov (United States)

    Richardson, Mathew

    2013-01-01

    The Met Office has a long history of weather forecasting, creating tailored weather forecasts for customers across the world. Based in Exeter, the Met Office is also home to the Met Office Hadley Centre, a world-leading centre for the study of climate change and its potential impacts. Climate information from the Met Office Hadley Centre is used…

  9. Method of forecasting power distribution

    International Nuclear Information System (INIS)

    Kaneto, Kunikazu.

    1981-01-01

    Purpose: To obtain forecasting results at high accuracy by reflecting the signals from neutron detectors disposed in the reactor core on the forecasting results. Method: An on-line computer transfers, to a simulator, those process data such as temperature and flow rate for coolants in each of the sections and various measuring signals such as control rod positions from the nuclear reactor. The simulator calculates the present power distribution before the control operation. The signals from the neutron detectors at each of the positions in the reactor core are estimated from the power distribution and errors are determined based on the estimated values and the measured values to determine the smooth error distribution in the axial direction. Then, input conditions at the time to be forecast are set by a data setter. The simulator calculates the forecast power distribution after the control operation based on the set conditions. The forecast power distribution is corrected using the error distribution. (Yoshino, Y.)

  10. Energy forecasts, perspectives and methods

    Energy Technology Data Exchange (ETDEWEB)

    Svensson, J E; Mogren, A

    1984-01-01

    The authors have analyzed different methods for long term energy prognoses, in particular energy consumption forecasts. Energy supply and price prognoses are also treated, but in a less detailed manner. After defining and discussing the various methods/models used in forecasts, a generalized discussion of the influence on the prognoses from the perspectives (background factors, world view, norms, ideology) of the prognosis makers is given. Some basic formal demands that should be asked from any rational forecast are formulated and discussed. The authors conclude that different forecasting methodologies are supplementing each other. There is no best method, forecasts should be accepted as views of the future from differing perspectives. The primary prognostic problem is to show the possible futures, selecting the wanted future is a question of political process.

  11. International Aftershock Forecasting: Lessons from the Gorkha Earthquake

    Science.gov (United States)

    Michael, A. J.; Blanpied, M. L.; Brady, S. R.; van der Elst, N.; Hardebeck, J.; Mayberry, G. C.; Page, M. T.; Smoczyk, G. M.; Wein, A. M.

    2015-12-01

    Following the M7.8 Gorhka, Nepal, earthquake of April 25, 2015 the USGS issued a series of aftershock forecasts. The initial impetus for these forecasts was a request from the USAID Office of US Foreign Disaster Assistance to support their Disaster Assistance Response Team (DART) which coordinated US Government disaster response, including search and rescue, with the Government of Nepal. Because of the possible utility of the forecasts to people in the region and other response teams, the USGS released these forecasts publicly through the USGS Earthquake Program web site. The initial forecast used the Reasenberg and Jones (Science, 1989) model with generic parameters developed for active deep continental regions based on the Garcia et al. (BSSA, 2012) tectonic regionalization. These were then updated to reflect a lower productivity and higher decay rate based on the observed aftershocks, although relying on teleseismic observations, with a high magnitude-of-completeness, limited the amount of data. After the 12 May M7.3 aftershock, the forecasts used an Epidemic Type Aftershock Sequence model to better characterize the multiple sources of earthquake clustering. This model provided better estimates of aftershock uncertainty. These forecast messages were crafted based on lessons learned from the Christchurch earthquake along with input from the U.S. Embassy staff in Kathmandu. Challenges included how to balance simple messaging with forecasts over a variety of time periods (week, month, and year), whether to characterize probabilities with words such as those suggested by the IPCC (IPCC, 2010), how to word the messages in a way that would translate accurately into Nepali and not alarm the public, and how to present the probabilities of unlikely but possible large and potentially damaging aftershocks, such as the M7.3 event, which had an estimated probability of only 1-in-200 for the week in which it occurred.

  12. Download this PDF file

    African Journals Online (AJOL)

    User

    EFFECTIVENESS OF FIREFLY ALGORITHM BASED NEURAL NETWORK IN. TIME SERIES FORECASTING .... Most forecasting problems employs the traditional NN often called ..... SAGA 2009, Lecture Notes in Computer. Sciences 5792: ...

  13. Model Forecast Skill and Sensitivity to Initial Conditions in the Seasonal Sea Ice Outlook

    Science.gov (United States)

    Blanchard-Wrigglesworth, E.; Cullather, R. I.; Wang, W.; Zhang, J.; Bitz, C. M.

    2015-01-01

    We explore the skill of predictions of September Arctic sea ice extent from dynamical models participating in the Sea Ice Outlook (SIO). Forecasts submitted in August, at roughly 2 month lead times, are skillful. However, skill is lower in forecasts submitted to SIO, which began in 2008, than in hindcasts (retrospective forecasts) of the last few decades. The multimodel mean SIO predictions offer slightly higher skill than the single-model SIO predictions, but neither beats a damped persistence forecast at longer than 2 month lead times. The models are largely unsuccessful at predicting each other, indicating a large difference in model physics and/or initial conditions. Motivated by this, we perform an initial condition sensitivity experiment with four SIO models, applying a fixed -1 m perturbation to the initial sea ice thickness. The significant range of the response among the models suggests that different model physics make a significant contribution to forecast uncertainty.

  14. DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA

    Directory of Open Access Journals (Sweden)

    J. Rhee

    2016-06-01

    Full Text Available The reduction of drought impacts may be achieved through sustainable drought management and proactive measures against drought disaster. Accurate and timely provision of drought information is essential. In this study, drought forecasting models to provide high-resolution drought information based on drought indicators for ungauged areas were developed. The developed models predict drought indices of the 6-month Standardized Precipitation Index (SPI6 and the 6-month Standardized Precipitation Evapotranspiration Index (SPEI6. An interpolation method based on multiquadric spline interpolation method as well as three machine learning models were tested. Three machine learning models of Decision Tree, Random Forest, and Extremely Randomized Trees were tested to enhance the provision of drought initial conditions based on remote sensing data, since initial conditions is one of the most important factors for drought forecasting. Machine learning-based methods performed better than interpolation methods for both classification and regression, and the methods using climatology data outperformed the methods using long-range forecast. The model based on climatological data and the machine learning method outperformed overall.

  15. AIRS/Aqua Level 3 Monthly standard physical retrieval (AIRS-only) V005

    Data.gov (United States)

    National Aeronautics and Space Administration — The AIRS Only Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers a calendar month....

  16. AIRS/Aqua Level 3 Monthly standard physical retrieval (AIRS+AMSU) V005

    Data.gov (United States)

    National Aeronautics and Space Administration — The AIRS Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers a calendar month. The...

  17. Aqua AIRS Level 3 Monthly Standard Physical Retrieval (AIRS-only) V006

    Data.gov (United States)

    National Aeronautics and Space Administration — The AIRS Only Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers a calendar month....

  18. Aqua AIRS Level 3 Monthly Standard Physical Retrieval (AIRS+AMSU) V006

    Data.gov (United States)

    National Aeronautics and Space Administration — The AIRS Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers a calendar month. The...

  19. Verification of ECMWF System 4 for seasonal hydrological forecasting in a northern climate

    Science.gov (United States)

    Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert

    2017-11-01

    Hydropower production requires optimal dam and reservoir management to prevent flooding damage and avoid operation losses. In a northern climate, where spring freshet constitutes the main inflow volume, seasonal forecasts can help to establish a yearly strategy. Long-term hydrological forecasts often rely on past observations of streamflow or meteorological data. Another alternative is to use ensemble meteorological forecasts produced by climate models. In this paper, those produced by the ECMWF (European Centre for Medium-Range Forecast) System 4 are examined and bias is characterized. Bias correction, through the linear scaling method, improves the performance of the raw ensemble meteorological forecasts in terms of continuous ranked probability score (CRPS). Then, three seasonal ensemble hydrological forecasting systems are compared: (1) the climatology of simulated streamflow, (2) the ensemble hydrological forecasts based on climatology (ESP) and (3) the hydrological forecasts based on bias-corrected ensemble meteorological forecasts from System 4 (corr-DSP). Simulated streamflow computed using observed meteorological data is used as benchmark. Accounting for initial conditions is valuable even for long-term forecasts. ESP and corr-DSP both outperform the climatology of simulated streamflow for lead times from 1 to 5 months depending on the season and watershed. Integrating information about future meteorological conditions also improves monthly volume forecasts. For the 1-month lead time, a gain exists for almost all watersheds during winter, summer and fall. However, volume forecasts performance for spring varies from one watershed to another. For most of them, the performance is close to the performance of ESP. For longer lead times, the CRPS skill score is mostly in favour of ESP, even if for many watersheds, ESP and corr-DSP have comparable skill. Corr-DSP appears quite reliable but, in some cases, under-dispersion or bias is observed. A more complex bias

  20. Models for Train Passenger Forecasting of Java and Sumatra

    Science.gov (United States)

    Sartono

    2017-04-01

    People tend to take public transportation to avoid high traffic, especially in Java. In Jakarta, the number of railway passengers is over than the capacity of the train at peak time. This is an opportunity as well as a challenge. If it is managed well then the company can get high profit. Otherwise, it may lead to disaster. This article discusses models for the train passengers, hence, finding the reasonable models to make a prediction overtimes. The Box-Jenkins method is occupied to develop a basic model. Then, this model is compared to models obtained using exponential smoothing method and regression method. The result shows that Holt-Winters model is better to predict for one-month, three-month, and six-month ahead for the passenger in Java. In addition, SARIMA(1,1,0)(2,0,0) is more accurate for nine-month and twelve-month oversee. On the other hand, for Sumatra passenger forecasting, SARIMA(1,1,1)(0,0,2) gives a better approximation for one-month ahead, and ARIMA model is best for three-month ahead prediction. The rest, Trend Seasonal and Liner Model has the least of RMSE to forecast for six-month, nine-month, and twelve-month ahead.

  1. Sirocco - Fukushima Forecast Description

    International Nuclear Information System (INIS)

    2011-01-01

    SYMPHONIE-NH is the non-hydrostatic ocean model following the Boussinesq hydrostatic SYMPHONIE-2010 model developed by the Sirocco system team (CNRS and Toulouse University). Both are using an Arakawa type finite difference method for the C grid. The R and D team generally gives priority to a physically based approach of modelling (global conservation of the mechanical energy, consistency of pressure and density, accuracy of the bottom pressure torque,...) that tends to favour low order and robust numerical schemes. Most of the physical and numerical options (Non-Hydrostatic, free surface, generalised coordinates combined to an ALE method,...) are particularly suitable for the coastal area. At the request of the International Atomic Energy Agency (IAEA, March 14, 2011), SIROCCO is delivering every day a real time 6-day forecast bulletin of the dispersion in seawater of radionuclides emitted by the Fukushima nuclear plant. The simulations are based on the S2010.18 release of the 3D SIROCCO ocean circulation model. The system is operational since March 24 and the bulletin is available on an 'open-access' basis since March 28. The model uses a stretched horizontal grid with a variable horizontal resolution: from 600 m x 600 m at the nearest grid point from Fukushima, to 5 km x 5 km offshore. The initial fields (T, S, U, V, SSH) and the lateral open boundary conditions are provided by the Mercator PSY4V1R3 system (one field per day, horizontal resolution 1/12 deg. x 1/12 deg.). At the sea surface, the ocean model is forced by the meteorological fluxes delivered every 3 hours by ECMWF.i The tidal forcing at the lateral open boundaries is provided by the T-UGO model, implemented for this purpose by the SIROCCO team on the Japanese Pacific coast. Some details are given on the methodology: Bathymetry, Initialization and large scale forcing, Tides, Atmospheric forcing, Forecast protocol, and Scenario for radioactive tracers

  2. Sirocco - Fukushima Forecast Description

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2011-04-10

    SYMPHONIE-NH is the non-hydrostatic ocean model following the Boussinesq hydrostatic SYMPHONIE-2010 model developed by the Sirocco system team (CNRS and Toulouse University). Both are using an Arakawa type finite difference method for the C grid. The R and D team generally gives priority to a physically based approach of modelling (global conservation of the mechanical energy, consistency of pressure and density, accuracy of the bottom pressure torque,...) that tends to favour low order and robust numerical schemes. Most of the physical and numerical options (Non-Hydrostatic, free surface, generalised coordinates combined to an ALE method,...) are particularly suitable for the coastal area. At the request of the International Atomic Energy Agency (IAEA, March 14, 2011), SIROCCO is delivering every day a real time 6-day forecast bulletin of the dispersion in seawater of radionuclides emitted by the Fukushima nuclear plant. The simulations are based on the S2010.18 release of the 3D SIROCCO ocean circulation model. The system is operational since March 24 and the bulletin is available on an 'open-access' basis since March 28. The model uses a stretched horizontal grid with a variable horizontal resolution: from 600 m x 600 m at the nearest grid point from Fukushima, to 5 km x 5 km offshore. The initial fields (T, S, U, V, SSH) and the lateral open boundary conditions are provided by the Mercator PSY4V1R3 system (one field per day, horizontal resolution 1/12 deg. x 1/12 deg.). At the sea surface, the ocean model is forced by the meteorological fluxes delivered every 3 hours by ECMWF.i The tidal forcing at the lateral open boundaries is provided by the T-UGO model, implemented for this purpose by the SIROCCO team on the Japanese Pacific coast. Some details are given on the methodology: Bathymetry, Initialization and large scale forcing, Tides, Atmospheric forcing, Forecast protocol, and Scenario for radioactive tracers

  3. Application and verification of ECMWF seasonal forecast for wind energy

    Science.gov (United States)

    Žagar, Mark; Marić, Tomislav; Qvist, Martin; Gulstad, Line

    2015-04-01

    to the wind speed anomalies. On the other hand, in some cases and areas where turbines operate close to, or above the rated power, the sensitivity of power forecast is reduced. Thus, the seasonal power forecasting system requires good knowledge of the changes in frequency of events with sufficient wind speeds to have acceptable skill. The scientific background for the Vestas seasonal power forecasting system is described and the relationship between predicted monthly wind speed anomalies and observed wind energy production are investigated for a number of operating wind farms in different climate zones. Current challenges will be discussed and some future research and development areas identified.

  4. Provider of Services File

    Data.gov (United States)

    U.S. Department of Health & Human Services — The POS file consists of two data files, one for CLIA labs and one for 18 other provider types. The file names are CLIA and OTHER. If downloading the file, note it...

  5. Types of Forecast and Weather-Related Information Used among Tourism Businesses in Coastal North Carolina

    Science.gov (United States)

    Ayscue, Emily P.

    This study profiles the coastal tourism sector, a large and diverse consumer of climate and weather information. It is crucial to provide reliable, accurate and relevant resources for the climate and weather-sensitive portions of this stakeholder group in order to guide them in capitalizing on current climate and weather conditions and to prepare them for potential changes. An online survey of tourism business owners, managers and support specialists was conducted within the eight North Carolina oceanfront counties asking respondents about forecasts they use and for what purposes as well as why certain forecasts are not used. Respondents were also asked about their perceived dependency of their business on climate and weather as well as how valuable different forecasts are to their decision-making. Business types represented include: Agriculture, Outdoor Recreation, Accommodations, Food Services, Parks and Heritage, and Other. Weekly forecasts were the most popular forecasts with Monthly and Seasonal being the least used. MANOVA and ANOVA analyses revealed outdoor-oriented businesses (Agriculture and Outdoor Recreation) as perceiving themselves significantly more dependent on climate and weather than indoor-oriented ones (Food Services and Accommodations). Outdoor businesses also valued short-range forecasts significantly more than indoor businesses. This suggests a positive relationship between perceived climate and weather dependency and forecast value. The low perceived dependency and value of short-range forecasts of indoor businesses presents an opportunity to create climate and weather information resources directed at how they can capitalize on positive climate and weather forecasts and how to counter negative effects with forecasted adverse conditions. The low use of long-range forecasts among all business types can be related to the low value placed on these forecasts. However, these forecasts are still important in that they are used to make more

  6. PC Graphic file programing

    International Nuclear Information System (INIS)

    Yang, Jin Seok

    1993-04-01

    This book gives description of basic of graphic knowledge and understanding and realization of graphic file form. The first part deals with graphic with graphic data, store of graphic data and compress of data, programing language such as assembling, stack, compile and link of program and practice and debugging. The next part mentions graphic file form such as Mac paint file, GEM/IMG file, PCX file, GIF file, and TIFF file, consideration of hardware like mono screen driver and color screen driver in high speed, basic conception of dithering and conversion of formality.

  7. Some aspects of the file organization and retrieval strategy in large data-bases

    International Nuclear Information System (INIS)

    Arnaudov, D.D.; Govorun, N.N.

    1977-01-01

    Methods of organizing a big information retrieval system are discribed. A special attention is paid to the file organization. An adapting file structure is described in more detail. The discussed method gives one the opportunity to organize large files in such a way that the response time of the system can be minimized, when the file is increasing. In connection with the retrieval strategy a method is proposed, which uses the frequencies of the descr/iptors and the couples of the descriptors to forecast the expected number of the relevant documents. Programmes are made, on the base of these methods, which are used in the information retrieval systems of JINR

  8. Forecasting Natural Rubber Price In Malaysia Using Arima

    Science.gov (United States)

    Zahari, Fatin Z.; Khalid, Kamil; Roslan, Rozaini; Sufahani, Suliadi; Mohamad, Mahathir; Saifullah Rusiman, Mohd; Ali, Maselan

    2018-04-01

    This paper contains introduction, materials and methods, results and discussions, conclusions and references. Based on the title mentioned, high volatility of the price of natural rubber nowadays will give the significant risk to the producers, traders, consumers, and others parties involved in the production of natural rubber. To help them in making decisions, forecasting is needed to predict the price of natural rubber. The main objective of the research is to forecast the upcoming price of natural rubber by using the reliable statistical method. The data are gathered from Malaysia Rubber Board which the data are from January 2000 until December 2015. In this research, average monthly price of Standard Malaysia Rubber 20 (SMR20) will be forecast by using Box-Jenkins approach. Time series plot is used to determine the pattern of the data. The data have trend pattern which indicates the data is non-stationary data and the data need to be transformed. By using the Box-Jenkins method, the best fit model for the time series data is ARIMA (1, 1, 0) which this model satisfy all the criteria needed. Hence, ARIMA (1, 1, 0) is the best fitted model and the model will be used to forecast the average monthly price of Standard Malaysia Rubber 20 (SMR20) for twelve months ahead.

  9. Climate Forecast System Version 2 (CFSv2) Operational Forecasts

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Climate Forecast System Version 2 (CFSv2) produced by the NOAA National Centers for Environmental Prediction (NCEP) is a fully coupled model representing the...

  10. Black Sea coastal forecasting system

    Directory of Open Access Journals (Sweden)

    A. I. Kubryakov

    2012-03-01

    Full Text Available The Black Sea coastal nowcasting and forecasting system was built within the framework of EU FP6 ECOOP (European COastalshelf sea OPerational observing and forecasting system project for five regions: the south-western basin along the coasts of Bulgaria and Turkey, the north-western shelf along the Romanian and Ukrainian coasts, coastal zone around of the Crimea peninsula, the north-eastern Russian coastal zone and the coastal zone of Georgia. The system operates in the real-time mode during the ECOOP project and afterwards. The forecasts include temperature, salinity and current velocity fields. Ecosystem model operates in the off-line mode near the Crimea coast.

  11. Assessing the Skill of Chlorophyll Forecasts: Latest Development and Challenges Ahead Using the Case of the Equatorial Pacific

    Science.gov (United States)

    Rousseaux, Cecile S.; Gregg, Watson W.

    2018-01-01

    Using a global ocean biogeochemical model combined with a forecast of physical oceanic and atmospheric variables from the NASA Global Modeling and Assimilation Office, we assess the skill of a chlorophyll concentrations forecast in the Equatorial Pacific for the period 2012-2015 with a focus on the forecast of the onset of the 2015 El Nino event. Using a series of retrospective 9-month hindcasts, we assess the uncertainties of the forecasted chlorophyll by comparing the monthly total chlorophyll concentration from the forecast with the corresponding monthly ocean chlorophyll data from the Suomi-National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (S-NPP VIIRS) satellite. The forecast was able to reproduce the phasing of the variability in chlorophyll concentration in the Equatorial Pacific, including the beginning of the 2015-2016 El Nino. The anomaly correlation coefficient (ACC) was significant (p less than 0.05) for forecast at 1-month (R=0.33), 8-month (R=0.42) and 9-month (R=0.41) lead times. The root mean square error (RMSE) increased from 0.0399 microgram chl L(exp -1) for the 1-month lead forecast to a maximum of 0.0472 microgram chl L(exp -1) for the 9-month lead forecast indicating that the forecast of the amplitude of chlorophyll concentration variability was getting worse. Forecasts with a 3-month lead time were on average the closest to the S-NPP VIIRS data (23% or 0.033 microgram chl L(exp -1)) while the forecast with a 9-month lead time were the furthest (31% or 0.042 microgram chl L(exp -1)). These results indicate the potential for forecasting chlorophyll concentration in this region but also highlights various deficiencies and suggestions for improvements to the current biogeochemical forecasting system. This system provides an initial basis for future applications including the effects of El Nino events on fisheries and other ocean resources given improvements identified in the analysis of these results.

  12. Wave ensemble forecast system for tropical cyclones in the Australian region

    Science.gov (United States)

    Zieger, Stefan; Greenslade, Diana; Kepert, Jeffrey D.

    2018-05-01

    Forecasting of waves under extreme conditions such as tropical cyclones is vitally important for many offshore industries, but there remain many challenges. For Northwest Western Australia (NW WA), wave forecasts issued by the Australian Bureau of Meteorology have previously been limited to products from deterministic operational wave models forced by deterministic atmospheric models. The wave models are run over global (resolution 1/4∘) and regional (resolution 1/10∘) domains with forecast ranges of + 7 and + 3 day respectively. Because of this relatively coarse resolution (both in the wave models and in the forcing fields), the accuracy of these products is limited under tropical cyclone conditions. Given this limited accuracy, a new ensemble-based wave forecasting system for the NW WA region has been developed. To achieve this, a new dedicated 8-km resolution grid was nested in the global wave model. Over this grid, the wave model is forced with winds from a bias-corrected European Centre for Medium Range Weather Forecast atmospheric ensemble that comprises 51 ensemble members to take into account the uncertainties in location, intensity and structure of a tropical cyclone system. A unique technique is used to select restart files for each wave ensemble member. The system is designed to operate in real time during the cyclone season providing + 10-day forecasts. This paper will describe the wave forecast components of this system and present the verification metrics and skill for specific events.

  13. Essays on forecasting stationary and nonstationary economic time series

    Science.gov (United States)

    Bachmeier, Lance Joseph

    This dissertation consists of three essays. Chapter II considers the question of whether M2 growth can be used to forecast inflation at horizons of up to ten years. A vector error correction (VEC) model serves as our benchmark model. We find that M2 growth does have marginal predictive content for inflation at horizons of more than two years, but only when allowing for cointegration and when the cointegrating rank and vector are specified a priori. When estimating the cointegration vector or failing to impose cointegration, there is no longer evidence of causality running from M2 growth to inflation at any forecast horizon. Finally, we present evidence that M2 needs to be redefined, as forecasts of the VEC model using data on M2 observed after 1993 are worse than the forecasts of an autoregressive model of inflation. Chapter III reconsiders the evidence for a "rockets and feathers" effect in gasoline markets. We estimate an error correction model of gasoline prices using daily data for the period 1985--1998 and fail to find any evidence of asymmetry. We show that previous work suffered from two problems. First, nonstationarity in some of the regressors was ignored, leading to invalid inference. Second, the weekly data used in previous work leads to a temporal aggregation problem, and thus biased estimates of impulse response functions. Chapter IV tests for a forecasting relationship between the volume of litigation and macroeconomic variables. We analyze annual data for the period 1960--2000 on the number of cases filed, real GDP, real consumption expenditures, inflation, unemployment, and interest rates. Bivariate Granger causality tests show that several of the macroeconomic variables can be used to forecast the volume of litigation, but show no evidence that the volume of litigation can be used to forecast any of the macroeconomic variables. The analysis is then extended to bivariate and multivariate regression models, and we find similar evidence to that of the

  14. Magnetogram Forecast: An All-Clear Space Weather Forecasting System

    Science.gov (United States)

    Barghouty, Nasser; Falconer, David

    2015-01-01

    Solar flares and coronal mass ejections (CMEs) are the drivers of severe space weather. Forecasting the probability of their occurrence is critical in improving space weather forecasts. The National Oceanic and Atmospheric Administration (NOAA) currently uses the McIntosh active region category system, in which each active region on the disk is assigned to one of 60 categories, and uses the historical flare rates of that category to make an initial forecast that can then be adjusted by the NOAA forecaster. Flares and CMEs are caused by the sudden release of energy from the coronal magnetic field by magnetic reconnection. It is believed that the rate of flare and CME occurrence in an active region is correlated with the free energy of an active region. While the free energy cannot be measured directly with present observations, proxies of the free energy can instead be used to characterize the relative free energy of an active region. The Magnetogram Forecast (MAG4) (output is available at the Community Coordinated Modeling Center) was conceived and designed to be a databased, all-clear forecasting system to support the operational goals of NASA's Space Radiation Analysis Group. The MAG4 system automatically downloads nearreal- time line-of-sight Helioseismic and Magnetic Imager (HMI) magnetograms on the Solar Dynamics Observatory (SDO) satellite, identifies active regions on the solar disk, measures a free-energy proxy, and then applies forecasting curves to convert the free-energy proxy into predicted event rates for X-class flares, M- and X-class flares, CMEs, fast CMEs, and solar energetic particle events (SPEs). The forecast curves themselves are derived from a sample of 40,000 magnetograms from 1,300 active region samples, observed by the Solar and Heliospheric Observatory Michelson Doppler Imager. Figure 1 is an example of MAG4 visual output

  15. Hybrid Stochastic Forecasting Model for Management of Large Open Water Reservoir with Storage Function

    Science.gov (United States)

    Kozel, Tomas; Stary, Milos

    2017-12-01

    The main advantage of stochastic forecasting is fan of possible value whose deterministic method of forecasting could not give us. Future development of random process is described better by stochastic then deterministic forecasting. Discharge in measurement profile could be categorized as random process. Content of article is construction and application of forecasting model for managed large open water reservoir with supply function. Model is based on neural networks (NS) and zone models, which forecasting values of average monthly flow from inputs values of average monthly flow, learned neural network and random numbers. Part of data was sorted to one moving zone. The zone is created around last measurement average monthly flow. Matrix of correlation was assembled only from data belonging to zone. The model was compiled for forecast of 1 to 12 month with using backward month flows (NS inputs) from 2 to 11 months for model construction. Data was got ridded of asymmetry with help of Box-Cox rule (Box, Cox, 1964), value r was found by optimization. In next step were data transform to standard normal distribution. The data were with monthly step and forecast is not recurring. 90 years long real flow series was used for compile of the model. First 75 years were used for calibration of model (matrix input-output relationship), last 15 years were used only for validation. Outputs of model were compared with real flow series. For comparison between real flow series (100% successfully of forecast) and forecasts, was used application to management of artificially made reservoir. Course of water reservoir management using Genetic algorithm (GE) + real flow series was compared with Fuzzy model (Fuzzy) + forecast made by Moving zone model. During evaluation process was founding the best size of zone. Results show that the highest number of input did not give the best results and ideal size of zone is in interval from 25 to 35, when course of management was almost same for

  16. Earthquake number forecasts testing

    Science.gov (United States)

    Kagan, Yan Y.

    2017-10-01

    We study the distributions of earthquake numbers in two global earthquake catalogues: Global Centroid-Moment Tensor and Preliminary Determinations of Epicenters. The properties of these distributions are especially required to develop the number test for our forecasts of future seismic activity rate, tested by the Collaboratory for Study of Earthquake Predictability (CSEP). A common assumption, as used in the CSEP tests, is that the numbers are described by the Poisson distribution. It is clear, however, that the Poisson assumption for the earthquake number distribution is incorrect, especially for the catalogues with a lower magnitude threshold. In contrast to the one-parameter Poisson distribution so widely used to describe earthquake occurrences, the negative-binomial distribution (NBD) has two parameters. The second parameter can be used to characterize the clustering or overdispersion of a process. We also introduce and study a more complex three-parameter beta negative-binomial distribution. We investigate the dependence of parameters for both Poisson and NBD distributions on the catalogue magnitude threshold and on temporal subdivision of catalogue duration. First, we study whether the Poisson law can be statistically rejected for various catalogue subdivisions. We find that for most cases of interest, the Poisson distribution can be shown to be rejected statistically at a high significance level in favour of the NBD. Thereafter, we investigate whether these distributions fit the observed distributions of seismicity. For this purpose, we study upper statistical moments of earthquake numbers (skewness and kurtosis) and compare them to the theoretical values for both distributions. Empirical values for the skewness and the kurtosis increase for the smaller magnitude threshold and increase with even greater intensity for small temporal subdivision of catalogues. The Poisson distribution for large rate values approaches the Gaussian law, therefore its skewness

  17. 25 years of time series forecasting

    NARCIS (Netherlands)

    de Gooijer, J.G.; Hyndman, R.J.

    2006-01-01

    We review the past 25 years of research into time series forecasting. In this silver jubilee issue, we naturally highlight results published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982-1985 and International Journal of Forecasting 1985-2005). During

  18. Estimates of Uncertainty around the RBA's Forecasts

    OpenAIRE

    Peter Tulip; Stephanie Wallace

    2012-01-01

    We use past forecast errors to construct confidence intervals and other estimates of uncertainty around the Reserve Bank of Australia's forecasts of key macroeconomic variables. Our estimates suggest that uncertainty about forecasts is high. We find that the RBA's forecasts have substantial explanatory power for the inflation rate but not for GDP growth.

  19. Evaluation of bias-correction methods for ensemble streamflow volume forecasts

    Directory of Open Access Journals (Sweden)

    T. Hashino

    2007-01-01

    Full Text Available Ensemble prediction systems are used operationally to make probabilistic streamflow forecasts for seasonal time scales. However, hydrological models used for ensemble streamflow prediction often have simulation biases that degrade forecast quality and limit the operational usefulness of the forecasts. This study evaluates three bias-correction methods for ensemble streamflow volume forecasts. All three adjust the ensemble traces using a transformation derived with simulated and observed flows from a historical simulation. The quality of probabilistic forecasts issued when using the three bias-correction methods is evaluated using a distributions-oriented verification approach. Comparisons are made of retrospective forecasts of monthly flow volumes for a north-central United States basin (Des Moines River, Iowa, issued sequentially for each month over a 48-year record. The results show that all three bias-correction methods significantly improve forecast quality by eliminating unconditional biases and enhancing the potential skill. Still, subtle differences in the attributes of the bias-corrected forecasts have important implications for their use in operational decision-making. Diagnostic verification distinguishes these attributes in a context meaningful for decision-making, providing criteria to choose among bias-correction methods with comparable skill.

  20. Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting

    International Nuclear Information System (INIS)

    Zhang, Wen Yu; Hong, Wei-Chiang; Dong, Yucheng; Tsai, Gary; Sung, Jing-Tian; Fan, Guo-feng

    2012-01-01

    The electric load forecasting is complicated, and it sometimes reveals cyclic changes due to cyclic economic activities or climate seasonal nature, such as hourly peak in a working day, weekly peak in a business week, and monthly peak in a demand planned year. Hybridization of support vector regression (SVR) with chaotic sequence and evolutionary algorithms has successfully been applied to improve forecasting accuracy, and to effectively avoid trapping in a local optimum. However, it has not been widely explored to employ SVR-based model to deal with cyclic electric load forecasting. This paper will firstly investigate the potentiality of a novel hybrid algorithm, namely chaotic genetic algorithm-simulated annealing algorithm (CGASA), with an SVR model to improve load forecasting accurate performance. In which, the proposed CGASA employs internal randomness of chaotic iterations to overcome premature local optimum. Secondly, the seasonal mechanism will then be applied to well adjust the cyclic load tendency. Finally, a numerical example from an existed reference is employed to compare the forecasting performance of the proposed SSVRCGASA model. The forecasting results show that the SSVRCGASA model yields more accurate forecasting results than ARIMA and TF-ε-SVR-SA models. -- Highlights: ► Hybridizing the seasonal adjustment mechanism into an SVR model. ► Employing chaotic sequence to improve the premature convergence of genetic algorithm and simulated annealing algorithm. ► Successfully providing significant accurate monthly load demand forecasting.

  1. ECMWF seasonal forecast system 3 and its prediction of sea surface temperature

    Energy Technology Data Exchange (ETDEWEB)

    Stockdale, Timothy N.; Anderson, David L.T.; Balmaseda, Magdalena A.; Ferranti, Laura; Mogensen, Kristian; Palmer, Timothy N.; Molteni, Franco; Vitart, Frederic [ECMWF, Reading (United Kingdom); Doblas-Reyes, Francisco [ECMWF, Reading (United Kingdom); Institut Catala de Ciencies del Clima (IC3), Barcelona (Spain)

    2011-08-15

    The latest operational version of the ECMWF seasonal forecasting system is described. It shows noticeably improved skill for sea surface temperature (SST) prediction compared with previous versions, particularly with respect to El Nino related variability. Substantial skill is shown for lead times up to 1 year, although at this range the spread in the ensemble forecast implies a loss of predictability large enough to account for most of the forecast error variance, suggesting only moderate scope for improving long range El Nino forecasts. At shorter ranges, particularly 3-6 months, skill is still substantially below the model-estimated predictability limit. SST forecast skill is higher for more recent periods than earlier ones. Analysis shows that although various factors can affect scores in particular periods, the improvement from 1994 onwards seems to be robust, and is most plausibly due to improvements in the observing system made at that time. The improvement in forecast skill is most evident for 3-month forecasts starting in February, where predictions of NINO3.4 SST from 1994 to present have been almost without fault. It is argued that in situations where the impact of model error is small, the value of improved observational data can be seen most clearly. Significant skill is also shown in the equatorial Indian Ocean, although predictive skill in parts of the tropical Atlantic are relatively poor. SST forecast errors can be especially high in the Southern Ocean. (orig.)

  2. Two quantitative forecasting methods for macroeconomic indicators in Czech Republic

    Directory of Open Access Journals (Sweden)

    Mihaela BRATU (SIMIONESCU

    2012-03-01

    Full Text Available Econometric modelling and exponential smoothing techniques are two quantitative forecasting methods with good results in practice, but the objective of the research was to find out which of the two techniques are better for short run predictions. Therefore, for inflation, unemployment and interest rate in Czech Republic some accuracy indicators were calculated for the predictions based on these methods. Short run forecasts on a horizon of 3 months were made for December 2011-February 2012, the econometric models being updated. For Czech Republic, the exponential smoothing techniques provided more accurate forecasts than the econometric models (VAR(2 models, ARMA procedure and models with lagged variables. One explication for the better performance of smoothing techniques would be that in the chosen countries the short run predictions more influenced by the recent evolution of the indicators.

  3. Forecasting tourist arrivals to balearic islands using genetic programming

    Directory of Open Access Journals (Sweden)

    Rosselló-Nadal, Jaume

    2007-01-01

    Full Text Available Traditionally, univariate time-series models have largely dominated forecasting for international tourism demand. In this paper, the ability of a Genetic Program (GP to predict monthly tourist arrivals from UK and Germany to Balearic Islands (Spain is explored. GP has already been employed satisfactorily in different scientific areas, including economics. The technique shows different advantages regarding to other forecasting methods. Firstly, it does not assume a priori a rigid functional form of the model. Secondly, it is more robust and easy-to-use than other non-parametric methods. Finally, it provides explicitly a mathematical equation which allows a simple ad hoc interpretation of the results. Comparing the performance of the proposed technique against other method commonly used in tourism forecasting (no-change model, Moving Average and ARIMA, the empirical results reveal that GP can be a valuable tool in this field.

  4. An Assessment of the Skill of GEOS-5 Seasonal Forecasts

    Science.gov (United States)

    Ham, Yoo-Geun; Schubert, Siegfried D.; Rienecker, Michele M.

    2013-01-01

    The seasonal forecast skill of the NASA Global Modeling and Assimilation Office coupled global climate model (CGCM) is evaluated based on an ensemble of 9-month lead forecasts for the period 1993 to 2010. The results from the current version (V2) of the CGCM consisting of the GEOS-5 AGM coupled to the MOM4 ocean model are compared with those from an earlier version (V1) in which the AGCM (the NSIPP model) was coupled to the Poseidon Ocean Model. It was found that the correlation skill of the Sea Surface Temperature (SST) forecasts is generally better in V2, especially over the sub-tropical and tropical central and eastern Pacific, Atlantic, and Indian Ocean. Furthermore, the improvement in skill in V2 mainly comes from better forecasts of the developing phase of ENSO from boreal spring to summer. The skill of ENSO forecasts initiated during the boreal winter season, however, shows no improvement in terms of correlation skill, and is in fact slightly worse in terms of root mean square error (RMSE). The degradation of skill is found to be due to an excessive ENSO amplitude. For V1, the ENSO amplitude is too strong in forecasts starting in boreal spring and summer, which causes large RMSE in the forecast. For V2, the ENSO amplitude is slightly stronger than that in observations and V1 for forecasts starting in boreal winter season. An analysis of the terms in the SST tendency equation, shows that this is mainly due to an excessive zonal advective feedback. In addition, V2 forecasts that are initiated during boreal winter season, exhibit a slower phase transition of El Nino, which is consistent with larger amplitude of ENSO after the ENSO peak season. It is found that this is due to weak discharge of equatorial Warm Water Volume (WWV). In both observations and V1, the discharge of equatorial WWV leads the equatorial geostrophic easterly current so as to damp the El Nino starting in January. This process is delayed by about 2 months in V2 due to the slower phase

  5. Recurrent networks for wave forecasting

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Prabaharan, N.

    , merchant vessel routing, nearshore construction, etc. more efficiently and safely. This paper presents an application of the Artificial Neural Network, namely Backpropagation Recurrent Neural Network (BRNN) with rprop update algorithm for wave forecasting...

  6. Ensemble forecasting of species distributions.

    Science.gov (United States)

    Araújo, Miguel B; New, Mark

    2007-01-01

    Concern over implications of climate change for biodiversity has led to the use of bioclimatic models to forecast the range shifts of species under future climate-change scenarios. Recent studies have demonstrated that projections by alternative models can be so variable as to compromise their usefulness for guiding policy decisions. Here, we advocate the use of multiple models within an ensemble forecasting framework and describe alternative approaches to the analysis of bioclimatic ensembles, including bounding box, consensus and probabilistic techniques. We argue that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if ensemble forecasts are produced and analysed appropriately.

  7. Forecasting and management of technology

    National Research Council Canada - National Science Library

    Roper, A. T

    2011-01-01

    .... The scope of this edition has broadened to include management of technology content that is relevant to now to executives in organizations while updating and strengthening the technology forecasting...

  8. Evaluating the spatio-temporal performance of sky imager based solar irradiance analysis and forecasts

    Science.gov (United States)

    Schmidt, T.; Kalisch, J.; Lorenz, E.; Heinemann, D.

    2015-10-01

    Clouds are the dominant source of variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the world-wide electric power supply increases the need for accurate solar radiation forecasts. In this work, we present results of a shortest-term global horizontal irradiance (GHI) forecast experiment based on hemispheric sky images. A two month dataset with images from one sky imager and high resolutive GHI measurements from 99 pyranometers distributed over 10 km by 12 km is used for validation. We developed a multi-step model and processed GHI forecasts up to 25 min with an update interval of 15 s. A cloud type classification is used to separate the time series in different cloud scenarios. Overall, the sky imager based forecasts do not outperform the reference persistence forecasts. Nevertheless, we find that analysis and forecast performance depend strongly on the predominant cloud conditions. Especially convective type clouds lead to high temporal and spatial GHI variability. For cumulus cloud conditions, the analysis error is found to be lower than that introduced by a single pyranometer if it is used representatively for the whole area in distances from the camera larger than 1-2 km. Moreover, forecast skill is much higher for these conditions compared to overcast or clear sky situations causing low GHI variability which is easier to predict by persistence. In order to generalize the cloud-induced forecast error, we identify a variability threshold indicating conditions with positive forecast skill.

  9. Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm

    International Nuclear Information System (INIS)

    Hong, Wei-Chiang

    2011-01-01

    Support vector regression (SVR), with hybrid chaotic sequence and evolutionary algorithms to determine suitable values of its three parameters, not only can effectively avoid converging prematurely (i.e., trapping into a local optimum), but also reveals its superior forecasting performance. Electric load sometimes demonstrates a seasonal (cyclic) tendency due to economic activities or climate cyclic nature. The applications of SVR models to deal with seasonal (cyclic) electric load forecasting have not been widely explored. In addition, the concept of recurrent neural networks (RNNs), focused on using past information to capture detailed information, is helpful to be combined into an SVR model. This investigation presents an electric load forecasting model which combines the seasonal recurrent support vector regression model with chaotic artificial bee colony algorithm (namely SRSVRCABC) to improve the forecasting performance. The proposed SRSVRCABC employs the chaotic behavior of honey bees which is with better performance in function optimization to overcome premature local optimum. A numerical example from an existed reference is used to elucidate the forecasting performance of the proposed SRSVRCABC model. The forecasting results indicate that the proposed model yields more accurate forecasting results than ARIMA and TF-ε-SVR-SA models. Therefore, the SRSVRCABC model is a promising alternative for electric load forecasting. -- Highlights: → Hybridizing the seasonal adjustment and the recurrent mechanism into an SVR model. → Employing chaotic sequence to improve the premature convergence of artificial bee colony algorithm. → Successfully providing significant accurate monthly load demand forecasting.

  10. Forecasting Croatian inbound tourism demand

    OpenAIRE

    Tica, Josip; Kožić, Ivan

    2015-01-01

    The aim of this paper is to present a forecasting model for the overnight stays of foreign tourists in Croatia. Tourism is one of the most important parts of the Croatian economy. It is particularly important in the context of the services sector. Regular and significant surpluses and the consumption of foreign guests are an important element of budget revenues, especially VAT. The ability to forecast the development of inbound tourism demand in a timely manner is crucial for both business...

  11. Preparing for an Uncertain Forecast

    Science.gov (United States)

    Karolak, Eric

    2011-01-01

    Navigating the world of government relations and public policy can be a little like predicting the weather. One can't always be sure what's in store or how it will affect him/her down the road. But there are common patterns and a few basic steps that can help one best prepare for a change in the forecast. Though the forecast is uncertain, early…

  12. Forecasting with Dynamic Regression Models

    CERN Document Server

    Pankratz, Alan

    2012-01-01

    One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.

  13. Forecasting market developments

    International Nuclear Information System (INIS)

    Weller, T.

    1997-01-01

    Traditional planning in essence consists of linear extrapolation of established facts and experience. This approach was good enough until recently, when progress would be relatively foreseeable within a stable system. The situation has been changing with developments and modifications in the global economic sector proceeding at accelerated pace, so that conventional planning methods become hopelessly inadequate. The past is of low significance to emerging markets; planners today have to keep abreast with and take into account the possible and emerging influencing factors. Experience is a factor to be replaced by intelligent analysis and conclusion within the framework of system networks. Modern scenario modelling methods are based on this approach: They are able to simulate and forecast a whole range of ''possible futures'', derived from perceivable trends. The article illustrates the novel planning methodology by assessing the future of the renewable energy sources, applying a computerized planning method (vision design) which is based on intelligent comparative analysis of all relevant trends. (Orig./RHM) [de

  14. Monthly Weather Review

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Supplements to the Monthly Weather Review publication. The Weather Bureau published the Monthly weather review Supplement irregularly from 1914 to 1949. The...

  15. Operational on-line coupled chemical weather forecasts for Europe with WRF/Chem

    Science.gov (United States)

    Hirtl, Marcus; Mantovani, Simone; Krüger, Bernd C.; Flandorfer, Claudia; Langer, Matthias

    2014-05-01

    Air quality is a key element for the well-being and quality of life of European citizens. Air pollution measurements and modeling tools are essential for the assessment of air quality according to EU legislation. The responsibilities of ZAMG as the national weather service of Austria include the support of the federal states and the public in questions connected to the protection of the environment in the frame of advisory and counseling services as well as expert opinions. ZAMG conducts daily Air-Quality forecasts using the on-line coupled model WRF/Chem. Meteorology is simulated simultaneously with the emissions, turbulent mixing, transport, transformation, and fate of trace gases and aerosols. The emphasis of the application is on predicting pollutants over Austria. Two domains are used for the simulations: the mother domain covers Europe with a resolution of 12 km, the inner domain includes the alpine region with a horizontal resolution of 4 km; 45 model levels are used in the vertical direction. The model runs 2 times per day for a period of 72 hours and is initialized with ECMWF forecasts. On-line coupled models allow considering two-way interactions between different atmospheric processes including chemistry (both gases and aerosols), clouds, radiation, boundary layer, emissions, meteorology and climate. In the operational set-up direct-, indirect and semi-direct effects between meteorology and air chemistry are enabled. The model is running on the HPCF (High Performance Computing Facility) of the ZAMG. In the current set-up 1248 CPUs are used. As the simulations need a big amount of computing resources, a method to safe I/O-time was implemented. Every MPI task writes all its output into the shared memory filesystem of the compute nodes. Once the WRF/Chem integration is finished, all split NetCDF-files are merged and saved on the global file system. The merge-routine is based on parallel-NetCDF. With this method the model runs about 30% faster on the SGI

  16. Ensemble empirical model decomposition and neuro-fuzzy conjunction model for middle and long-term runoff forecast

    Science.gov (United States)

    Tan, Q.

    2017-12-01

    Forecasting the runoff over longer periods, such as months and years, is one of the important tasks for hydrologists and water resource managers to maximize the potential of the limited water. However, due to the nonlinear and nonstationary characteristic of the natural runoff, it is hard to forecast the middle and long-term runoff with a satisfactory accuracy. It has been proven that the forecast performance can be improved by using signal decomposition techniques to product more cleaner signals as model inputs. In this study, a new conjunction model (EEMD-neuro-fuzzy) with adaptive ability is proposed. The ensemble empirical model decomposition (EEMD) is used to decompose the runoff time series into several components, which are with different frequencies and more cleaner than the original time series. Then the neuro-fuzzy model is developed for each component. The final forecast results can be obtained by summing the outputs of all neuro-fuzzy models. Unlike the conventional forecast model, the decomposition and forecast models in this study are adjusted adaptively as long as new runoff information is added. The proposed models are applied to forecast the monthly runoff of Yichang station, located in Yangtze River of China. The results show that the performance of adaptive forecast model we proposed outperforms than the conventional forecast model, the Nash-Sutcliffe efficiency coefficient can reach to 0.9392. Due to its ability to process the nonstationary data, the forecast accuracy, especially in flood season, is improved significantly.

  17. Quantile forecast discrimination ability and value

    DEFF Research Database (Denmark)

    Ben Bouallègue, Zied; Pinson, Pierre; Friederichs, Petra

    2015-01-01

    While probabilistic forecast verification for categorical forecasts is well established, some of the existing concepts and methods have not found their equivalent for the case of continuous variables. New tools dedicated to the assessment of forecast discrimination ability and forecast value are ...... is illustrated based on synthetic datasets, as well as for the case of global radiation forecasts from the high resolution ensemble COSMO-DE-EPS of the German Weather Service....

  18. Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

    Science.gov (United States)

    Roberts, J. Brent; Robertson, Franklin R.; Bosilovich, Michael; Lyon, Bradfield; Funk, Chris

    2013-01-01

    The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period

  19. Forecast of Antarctic Sea Ice and Meteorological Fields

    Science.gov (United States)

    Barreira, S.; Orquera, F.

    2017-12-01

    Since 2001, we have been forecasting the climatic fields of the Antarctic sea ice (SI) and surface air temperature, surface pressure and precipitation anomalies for the Southern Hemisphere at the Meteorological Department of the Argentine Naval Hydrographic Service with different techniques that have evolved with the years. Forecast is based on the results of Principal Components Analysis applied to SI series (S-Mode) that gives patterns of temporal series with validity areas (these series are important to determine which areas in Antarctica will have positive or negative SI anomalies based on what happen in the atmosphere) and, on the other hand, to SI fields (T-Mode) that give us the form of the SI fields anomalies based on a classification of 16 patterns. Each T-Mode pattern has unique atmospheric fields associated to them. Therefore, it is possible to forecast whichever atmosphere variable we decide for the Southern Hemisphere. When the forecast is obtained, each pattern has a probability of occurrence and sometimes it is necessary to compose more than one of them to obtain the final result. S-Mode and T-Mode are monthly updated with new data, for that reason the forecasts improved with the increase of cases since 2001. We used the Monthly Polar Gridded Sea Ice Concentrations database derived from satellite information generated by NASA Team algorithm provided monthly by the National Snow and Ice Data Center of USA that begins in November 1978. Recently, we have been experimenting with multilayer Perceptron (neuronal network) with supervised learning and a back-propagation algorithm to improve the forecast. The Perceptron is the most common Artificial Neural Network topology dedicated to image pattern recognition. It was implemented through the use of temperature and pressure anomalies field images that were associated with a the different sea ice anomaly patterns. The variables analyzed included only composites of surface air temperature and pressure anomalies

  20. Evaluating long term forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Lady, George M. [Department of Economics, College of Liberal Arts, Temple University, Philadelphia, PA 19122 (United States)

    2010-03-15

    The U.S. Department of Energy's Energy Information Administration (EIA), and its predecessor organizations, has published projections of U.S. energy production, consumption, distribution and prices annually for over 30 years. A natural issue to raise in evaluating the projections is an assessment of their accuracy compared to eventual outcomes. A related issue is the determination of the sources of 'error' in the projections that are due to differences between the actual versus realized values of the associated assumptions. One way to do this would be to run the computer-based model from which the projections are derived at the time the projected values are realized, using actual rather than assumed values for model assumptions; and, compare these results to the original projections. For long term forecasts, this approach would require that the model's software and hardware configuration be archived and available for many years, possibly decades, into the future. Such archival creates many practical problems; and, in general, it is not being done. This paper reports on an alternative approach for evaluating the projections. In the alternative approach, the model is run many times for cases in which important assumptions are changed individually and in combinations. A database is assembled from the solutions and a regression analysis is conducted for each important projected variable with the associated assumptions chosen as exogenous variables. When actual data are eventually available, the regression results are then used to estimate the sources of the differences in the projections of the endogenous variables compared to their eventual outcomes. The results presented here are for residential and commercial sector natural gas and electricity consumption. (author)

  1. Decay data file based on the ENSDF file

    Energy Technology Data Exchange (ETDEWEB)

    Katakura, J. [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

    1997-03-01

    A decay data file with the JENDL (Japanese Evaluated Nuclear Data Library) format based on the ENSDF (Evaluated Nuclear Structure Data File) file was produced as a tentative one of special purpose files of JENDL. The problem using the ENSDF file as primary source data of the JENDL decay data file is presented. (author)

  2. Gold sales forecasting: The Box-Jenkins methodology

    Directory of Open Access Journals (Sweden)

    Johannes Tshepiso Tsoku

    2017-02-01

    Full Text Available The study employs the Box-Jenkins Methodology to forecast South African gold sales. For a resource economy like South Africa where metals and minerals account for a high proportion of GDP and export earnings, the decline in gold sales is very disturbing. Box-Jenkins time series technique was used to perform time series analysis of monthly gold sales for the period January 2000 to June 2013 with the following steps: model identification, model estimation, diagnostic checking and forecasting. Furthermore, the prediction accuracy is tested using mean absolute percentage error (MAPE. From the analysis, a seasonal ARIMA(4,1,4×(0,1,112 was found to be the “best fit model” with an MAPE value of 11% indicating that the model is fit to be used to predict or forecast future gold sales for South Africa. In addition, the forecast values show that there will be a decrease in the overall gold sales for the first six months of 2014. It is hoped that the study will help the public and private sectors to understand the gold sales or output scenario and later plan the gold mining activities in South Africa. Furthermore, it is hoped that this research paper has demonstrated the significance of Box-Jenkins technique for this area of research and that they will be applied in the future.

  3. Ridge Regression: A tool to forecast wheat area and production

    Directory of Open Access Journals (Sweden)

    Nasir Jamal

    2007-07-01

    Full Text Available This research study is designed to develop forecasting models for acreage and production of wheat crop for Chakwal district of Rawalpindi region keeping in view the assumptions of OLS estimation. The forecasting models are developed on the basis of 15 years data from 1984-85 to 1998-99 then wheat area and production for next five years from 1999-2000 to 2003-04 is forecasted through the models and compared with the actual figures. After evaluating the accuracy of the models, final models are developed on the basis of 20 years data for the period 1984-85 to 2003-04. These linear models can be used to forecast wheat area and production of next five years. The Urea fertilizer, DAP fertilizer and manures plays a significant role to enhance the production of wheat crop. Number of ploughs in the wheat fields is significant factor to increase the production of wheat crop. Good rains in the month of October and November significantly contributes to increase the production of wheat crop and mean maximum temperature in the month of March is a significant factor to reduce the production of wheat crop.

  4. Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

    Science.gov (United States)

    Abbasi, Maryam; El Hanandeh, Ali

    2016-10-01

    Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. Application of seasonal forecasting for the drought forecasting in Catalonia (Spain)

    Science.gov (United States)

    Llasat, Maria-Carmen; Zaragoza, Albert; Aznar, Blanca; Cabot, Jordi

    2010-05-01

    Low flows and droughts are a hydro-climatic feature in Spain (Alvarez et al, 2008). The construction of dams as water reservoirs has been a usual tool to manage the water resources for agriculture and livestock, industries and human needs (MIMAM, 2000, 2007). The last drought that has affected Spain has last four years in Catalonia, from 2004 to the spring of 2008, and it has been particularly hard as a consequence of the precipitation deficit in the upper part of the rivers that nourish the main dams. This problem increases when the water scarcity affects very populated areas, like big cities. The Barcelona city, with more than 3.000.000 people concentrated in the downtown and surrounding areas is a clear example. One of the objectives of the SOSTAQUA project is to improve the water resources management in real time, in order to improve the water supply in the cities in the framework of sustainable development. The work presented here deals with the application of seasonal forecasting to improve the water management in Catalonia, particularly in drought conditions. A seasonal prediction index has been created as a linear combination of climatic data and the ECM4 prediction that has been validated too. This information has implemented into a hydrological model and it has been applied to the last drought considering the real water demands of population, as well as to the water storage evolution in the last months. It has been found a considerable advance in the forecasting of water volume into reservoirs. The advantage of this methodology is that it only requires seasonal forecasting free through internet. Due to the fact that the principal rivers that supply water to Barcelona, birth on the Pyrenees and Pre-Pyrenees region, the analysis and precipitation forecasting is focused on this region (Zaragoza, 2008).

  6. The impact of different sea-surface temperature prediction scenarios on Southern African seasonal climate forecast skill

    CSIR Research Space (South Africa)

    Landman, WA

    2009-12-01

    Full Text Available Retro-active forecasts produced at a 1-month lead-time by the ECHAM4.5 AGCM are statistically downscaled to South African district rainfall totals for the austral mid-summer season of December to February. The AGCM is forced with SST forecasts...

  7. Evaluation of the Plant-Craig stochastic convection scheme in an ensemble forecasting system

    Science.gov (United States)

    Keane, R. J.; Plant, R. S.; Tennant, W. J.

    2015-12-01

    The Plant-Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic element only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant-Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant-Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.

  8. Forecasting electricity spot market prices with a k-factor GIGARCH process

    International Nuclear Information System (INIS)

    Diongue, Abdou Ka; Guegan, Dominique; Vignal, Bertrand

    2009-01-01

    In this article, we investigate conditional mean and conditional variance forecasts using a dynamic model following a k-factor GIGARCH process. Particularly, we provide the analytical expression of the conditional variance of the prediction error. We apply this method to the German electricity price market for the period August 15, 2000-December 31, 2002 and we test spot prices forecasts until one-month ahead forecast. The forecasting performance of the model is compared with a SARIMA-GARCH benchmark model using the year 2003 as the out-of-sample. The proposed model outperforms clearly the benchmark model. We conclude that the k-factor GIGARCH process is a suitable tool to forecast spot prices, using the classical RMSE criteria. (author)

  9. UPIN Group File

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Group Unique Physician Identifier Number (UPIN) File is the business entity file that contains the group practice UPIN and descriptive information. It does NOT...

  10. Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond

    DEFF Research Database (Denmark)

    Hong, Tao; Pinson, Pierre; Fan, Shu

    2016-01-01

    The energy industry has been going through a significant modernization process over the last decade. Its infrastructure is being upgraded rapidly. The supply, demand and prices are becoming more volatile and less predictable than ever before. Even its business model is being challenged fundamenta......The energy industry has been going through a significant modernization process over the last decade. Its infrastructure is being upgraded rapidly. The supply, demand and prices are becoming more volatile and less predictable than ever before. Even its business model is being challenged...... fundamentally. In this competitive and dynamic environment, many decision-making processes rely on probabilistic forecasts to quantify the uncertain future. Although most of the papers in the energy forecasting literature focus on point or singlevalued forecasts, the research interest in probabilistic energy...

  11. PCF File Format.

    Energy Technology Data Exchange (ETDEWEB)

    Thoreson, Gregory G [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-08-01

    PCF files are binary files designed to contain gamma spectra and neutron count rates from radiation sensors. It is the native format for the GAmma Detector Response and Analysis Software (GADRAS) package [1]. It can contain multiple spectra and information about each spectrum such as energy calibration. This document outlines the format of the file that would allow one to write a computer program to parse and write such files.

  12. Bayesian flood forecasting methods: A review

    Science.gov (United States)

    Han, Shasha; Coulibaly, Paulin

    2017-08-01

    Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been

  13. Dynamic SEP event probability forecasts

    Science.gov (United States)

    Kahler, S. W.; Ling, A.

    2015-10-01

    The forecasting of solar energetic particle (SEP) event probabilities at Earth has been based primarily on the estimates of magnetic free energy in active regions and on the observations of peak fluxes and fluences of large (≥ M2) solar X-ray flares. These forecasts are typically issued for the next 24 h or with no definite expiration time, which can be deficient for time-critical operations when no SEP event appears following a large X-ray flare. It is therefore important to decrease the event probability forecast with time as a SEP event fails to appear. We use the NOAA listing of major (≥10 pfu) SEP events from 1976 to 2014 to plot the delay times from X-ray peaks to SEP threshold onsets as a function of solar source longitude. An algorithm is derived to decrease the SEP event probabilities with time when no event is observed to reach the 10 pfu threshold. In addition, we use known SEP event size distributions to modify probability forecasts when SEP intensity increases occur below the 10 pfu event threshold. An algorithm to provide a dynamic SEP event forecast, Pd, for both situations of SEP intensities following a large flare is derived.

  14. Automation of energy demand forecasting

    Science.gov (United States)

    Siddique, Sanzad

    Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.

  15. Natural gas monthly

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-01-01

    The Natural Gas Monthly highlights activities, events, and analyses of interest to public and private sector organizations associated with the natural gas industry. Volume and price data are presented each month for natural gas production, distribution, consumption, and interstate pipeline activities. Producer-related activities and underground storage data are also reported. From time to time, the Natural Gas Monthly features articles designed to assist readers in using and interpreting natural gas information.

  16. A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments

    Directory of Open Access Journals (Sweden)

    A. Schepen

    2018-03-01

    Full Text Available Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal timescales. In seasonal forecasting, global climate models (GCMs are now the go-to source for rainfall forecasts. For hydrological applications however, GCM forecasts are often biased and unreliable in uncertainty spread, and calibration is therefore required before use. There are sophisticated statistical techniques for calibrating monthly and seasonal aggregations of the forecasts. However, calibration of seasonal forecasts at the daily time step typically uses very simple statistical methods or climate analogue methods. These methods generally lack the sophistication to achieve unbiased, reliable and coherent forecasts of daily amounts and seasonal accumulated totals. In this study, we propose and evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S, which is based on the Bayesian joint probability modelling approach for calibrating daily forecasts and the Schaake Shuffle for connecting the daily ensemble members of different lead times. We apply the method to post-process ACCESS-S forecasts for 12 perennial and ephemeral catchments across Australia and for 12 initialisation dates. RPP-S significantly reduces bias in raw forecasts and improves both skill and reliability. RPP-S forecasts are also more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S post-processed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications.

  17. A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments

    Science.gov (United States)

    Schepen, Andrew; Zhao, Tongtiegang; Wang, Quan J.; Robertson, David E.

    2018-03-01

    Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal timescales. In seasonal forecasting, global climate models (GCMs) are now the go-to source for rainfall forecasts. For hydrological applications however, GCM forecasts are often biased and unreliable in uncertainty spread, and calibration is therefore required before use. There are sophisticated statistical techniques for calibrating monthly and seasonal aggregations of the forecasts. However, calibration of seasonal forecasts at the daily time step typically uses very simple statistical methods or climate analogue methods. These methods generally lack the sophistication to achieve unbiased, reliable and coherent forecasts of daily amounts and seasonal accumulated totals. In this study, we propose and evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S), which is based on the Bayesian joint probability modelling approach for calibrating daily forecasts and the Schaake Shuffle for connecting the daily ensemble members of different lead times. We apply the method to post-process ACCESS-S forecasts for 12 perennial and ephemeral catchments across Australia and for 12 initialisation dates. RPP-S significantly reduces bias in raw forecasts and improves both skill and reliability. RPP-S forecasts are also more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S post-processed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications.

  18. A File Archival System

    Science.gov (United States)

    Fanselow, J. L.; Vavrus, J. L.

    1984-01-01

    ARCH, file archival system for DEC VAX, provides for easy offline storage and retrieval of arbitrary files on DEC VAX system. System designed to eliminate situations that tie up disk space and lead to confusion when different programers develop different versions of same programs and associated files.

  19. Text File Comparator

    Science.gov (United States)

    Kotler, R. S.

    1983-01-01

    File Comparator program IFCOMP, is text file comparator for IBM OS/VScompatable systems. IFCOMP accepts as input two text files and produces listing of differences in pseudo-update form. IFCOMP is very useful in monitoring changes made to software at the source code level.

  20. Economic impact analysis of load forecasting

    International Nuclear Information System (INIS)

    Ranaweera, D.K.; Karady, G.G.; Farmer, R.G.

    1997-01-01

    Short term load forecasting is an essential function in electric power system operations and planning. Forecasts are needed for a variety of utility activities such as generation scheduling, scheduling of fuel purchases, maintenance scheduling and security analysis. Depending on power system characteristics, significant forecasting errors can lead to either excessively conservative scheduling or very marginal scheduling. Either can induce heavy economic penalties. This paper examines the economic impact of inaccurate load forecasts. Monte Carlo simulations were used to study the effect of different load forecasting accuracy. Investigations into the effect of improving the daily peak load forecasts, effect of different seasons of the year and effect of utilization factors are presented