WorldWideScience

Sample records for monthly forecast file

  1. Forecasting Monthly Prices of Japanese Logs

    Directory of Open Access Journals (Sweden)

    Tetsuya Michinaka

    2016-04-01

    Full Text Available Forecasts of prices can help industries in their risk management. This is especially true for Japanese logs, which experience sharp fluctuations in price. In this research, the authors used an exponential smoothing method (ETS and autoregressive integrated moving average (ARIMA models to forecast the monthly prices of domestic logs of three of the most important species in Japan: sugi (Japanese cedar, Cryptomeria japonica D. Don, hinoki (Japanese cypress, Chamaecyparis obtusa (Sieb. et Zucc. Endl., and karamatsu (Japanese larch, Larix kaempferi (Lamb. Carr.. For the 12-month forecasting periods, forecasting intervals of 80% and 95% were given. By measuring the accuracy of forecasts of 12- and 6-month forecasting periods, it was found that ARIMA gave better results than did the ETS in the majority of cases. However, the combined method of averaging ETS and ARIMA forecasts gave the best results for hinoki in several cases.

  2. 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

  3. Monthly streamflow forecasting in the Rhine basin

    Science.gov (United States)

    Schick, Simon; Rössler, Ole; Weingartner, Rolf

    2017-04-01

    Forecasting seasonal streamflow of the Rhine river is of societal relevance as the Rhine is an important water way and water resource in Western Europe. The present study investigates the predictability of monthly mean streamflow at lead times of zero, one, and two months with the focus on potential benefits by the integration of seasonal climate predictions. Specifically, we use seasonal predictions of precipitation and surface air temperature released by the European Centre for Medium-Range Weather Forecasts (ECMWF) for a regression analysis. In order to disentangle forecast uncertainty, the 'Reverse Ensemble Streamflow Prediction' framework is adapted here to the context of regression: By using appropriate subsets of predictors the regression model is constrained to either the initial conditions, the meteorological forcing, or both. An operational application is mimicked by equipping the model with the seasonal climate predictions provided by ECMWF. Finally, to mitigate the spatial aggregation of the meteorological fields the model is also applied at the subcatchment scale, and the resulting predictions are combined afterwards. The hindcast experiment is carried out for the period 1982-2011 in cross validation mode at two gauging stations, namely the Rhine at Lobith and Basel. The results show that monthly forecasts are skillful with respect to climatology only at zero lead time. In addition, at zero lead time the integration of seasonal climate predictions decreases the mean absolute error by 5 to 10 percentage compared to forecasts which are solely based on initial conditions. This reduction most likely is induced by the seasonal prediction of precipitation and not air temperature. The study is completed by bench marking the regression model with runoff simulations from ECMWFs seasonal forecast system. By simply using basin averages followed by a linear bias correction, these runoff simulations translate well to monthly streamflow. Though the regression model

  4. 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.

  5. 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.

  6. NMME Monthly / Seasonal Forecasts for NASA SERVIR Applications Science

    Science.gov (United States)

    Robertson, Franklin R.; Roberts, Jason B.

    2014-01-01

    This work details use of the North American Multi-Model Ensemble (NMME) experimental forecasts as drivers for Decision Support Systems (DSSs) in the NASA / USAID initiative, SERVIR (a Spanish acronym meaning "to serve"). SERVIR integrates satellite observations, ground-based data and forecast models to monitor and forecast environmental changes and to improve response to natural disasters. Through the use of DSSs whose "front ends" are physically based models, the SERVIR activity provides a natural testbed to determine the extent to which NMME monthly to seasonal projections enable scientists, educators, project managers and policy implementers in developing countries to better use probabilistic outlooks of seasonal hydrologic anomalies in assessing agricultural / food security impacts, water availability, and risk to societal infrastructure. The multi-model NMME framework provides a "best practices" approach to probabilistic forecasting. The NMME forecasts are generated at resolution more coarse than that required to support DSS models; downscaling in both space and time is necessary. The methodology adopted here applied model output statistics where we use NMME ensemble monthly projections of sea-surface temperature (SST) and precipitation from 30 years of hindcasts with observations of precipitation and temperature for target regions. Since raw model forecasts are well-known to have structural biases, a cross-validated multivariate regression methodology (CCA) is used to link the model projected states as predictors to the predictands of the target region. The target regions include a number of basins in East and South Africa as well as the Ganges / Baramaputra / Meghna basin complex. The MOS approach used address spatial downscaling. Temporal disaggregation of monthly seasonal forecasts is achieved through use of a tercile bootstrapping approach. We interpret the results of these studies, the levels of skill by several metrics, and key uncertainties.

  7. Seasonality, nonstationarity and the forecasting of monthly time series

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans)

    1991-01-01

    textabstractWe focus on two forecasting models for a monthly time series. The first model requires that the variable is first order and seasonally differenced. The second model considers the series only in its first differences, while seasonality is modeled with a constant and seasonal dummies. A me

  8. Seasonality, nonstationarity and the forecasting of monthly time series

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans)

    1991-01-01

    textabstractWe focus on two forecasting models for a monthly time series. The first model requires that the variable is first order and seasonally differenced. The second model considers the series only in its first differences, while seasonality is modeled with a constant and seasonal dummies. A me

  9. Seasonality, nonstationarity and the forecasting of monthly time series

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans)

    1991-01-01

    textabstractWe focus on two forecasting models for a monthly time series. The first model requires that the variable is first order and seasonally differenced. The second model considers the series only in its first differences, while seasonality is modeled with a constant and seasonal dummies. A

  10. A verification of monthly weather forecasts in the seventies

    NARCIS (Netherlands)

    Oerlemans, J.; Nap, J.L.; Dool, H.M. van den

    1981-01-01

    Monthly forecasts of temperature, rainfall and sunshine have been verified during the period 1970–79. The predictions were based on seven different schemes. Of the seven methods, five refer to De Bilt (The Netherlands), one to southeast England and one to the Federal Republic of Germany. The results

  11. 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.

  12. 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.

  13. Forecasting daily and monthly exchange rates with machine learning techniques

    OpenAIRE

    Papadimitriou, Theophilos; Gogas, Periklis; Plakandaras, Vasilios

    2013-01-01

    We combine signal processing to machine learning methodologies by introducing a hybrid Ensemble Empirical Mode Decomposition (EEMD), Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) model in order to forecast the monthly and daily Euro (EUR)/United States Dollar (USD), USD/Japanese Yen (JPY), Australian Dollar (AUD)/Norwegian Krone (NOK), New Zealand Dollar (NZD)/Brazilian Real (BRL) and South African Rand (ZAR)/Philippine Peso (PHP) exchange rates. After th...

  14. 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.

  15. 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...

  16. monthly energy consumption forecasting using wavelet analysis and ...

    African Journals Online (AJOL)

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    paper, a wavelet transform and radial basis function neural network based energy forecast model is developed to .... These prop- erties lead to quicker learning in comparison to ..... Machine Learning and Cybernetics,. IEEE Transactions, 8: ...

  17. Testing efficacy of monthly forecast application in agrometeorology: Winter wheat phenology dynamic

    Science.gov (United States)

    Lalic, B.; Jankovic, D.; Dekic, Lj; Eitzinger, J.; Firanj Sremac, A.

    2017-02-01

    Use of monthly weather forecast as input meteorological data for agrometeorological forecasting, crop modelling and plant protection can foster promising applications in agricultural production. Operational use of monthly or seasonal weather forecast can help farmers to optimize field operations (fertilizing, irrigation) and protection measures against plant diseases and pests by taking full advantage of monthly forecast information in predicting plant development, pest and disease risks and yield potentials few weeks in advance. It can help producers to obtain stable or higher yield with the same inputs and to minimise losses caused by weather. In Central and South-Eastern Europe ongoing climate change lead to shifts of crops phenology dynamics (i.e. in Serbia 4-8 weeks earlier in 2016 than in previous years) and brings this subject in the front of agronomy science and practice. Objective of this study is to test efficacy of monthly forecast in predicting phenology dynamics of different winter wheat varieties, using phenological model developed by Forecasting and Warning Service of Serbia in plant protection. For that purpose, historical monthly forecast for four months (March 1, 2005 - June 30, 2005) was assimilated from ECMWF MARS archive for 50 ensemble members and control run. Impact of different agroecological conditions is tested by using observed and forecasted data for two locations - Rimski Sancevi (Serbia) and Groß-Enzersdorf (Austria).

  18. Improvement of ECMWF monthly forecasts of precipitation over France with an analog method

    Science.gov (United States)

    Berthelot, M.; Dubus, L.; Gailhard, J.

    2010-09-01

    Optimal operation of hydro-power plants requires accurate forecasts of precipitation which are then integrated into hydrological models to forecast river flows and water volumes in reservoirs. Precipitation is a difficult parameter to forecast, especially at long lead times (monthly and over) one of the reasons being the too coarse resolution of numerical weather prediction systems. In this study, we evaluate ECMWF's monthly forecasts of precipitation over 9 important basins in France. The deterministic approach shows that forecasts are useless over week 1. Using the probabilistic approach allows to get useful information for some events (lower and upper terciles for instance), up to week 3, but the overall scores are quite low, and hardly better than climatological scores. In a second step, EDF's analog method, currently used in operations for D+7 forecasts, has been adapted to ECWMF's monthly forecasts. It uses Z700 and Z1000 fields over North Atlantic and Europe to get local precipitations. For the nine catchments studied here and for the four weeks, results show an overall improvement of analog precipitation forecasts compared to raw forecasts. Improvement is also identified with respect to climatology in more than half of the catchments. The prediction skill is mostly pronounced for extreme events (low and heavy precipitations). The analog method thus presents significant performance, suited for operational use. Improvements can also be expected with some optimization of the method (mix of predictors, new similarity criterion…)

  19. STUDY OF THE EFFECTS OF REDUCING SYSTEMATIC ERRORS ON MONTHLY REGIONAL CLIMATE DYNAMICAL FORECAST

    Institute of Scientific and Technical Information of China (English)

    ZENG Xin-min; XI Chao-li

    2009-01-01

    A nested-model system is constructed by embedding the regional climate model RegCM3 into a general circulation model tbr monthly-scale regional climate forecast over East China. The systematic errors are formulated for the region on the basis of 10-yr (1991-2000) results of the nested-model system,and of the datasets of the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) and the temperature analysis of the National Meteorological Center (NMC),U.S.A.,which are then used for correcting the original forecast by the system for the period 2001-2005. After the assessment of the original and corrected forecasts for monthly precipitation and surthce air temperature,it is found that the corrected forecast is apparently better than the original,suggesting that the approach can be applied for improving monthly-scale regional climate dynamical lbrecast.

  20. The relative utility of persistence and medium-range dynamical forecasts of monthly mean 700 mb heights

    Science.gov (United States)

    Livezey, Robert E.; Schemm, Jae-Kyung E.

    1988-01-01

    The proposal of Harnack et al. (1986) that monthly 700-mb height anomalies can be predicted using simulated medium-range numerical forecasts is tested with actual numerical forecasts produced under strict operational conditions. These conditions included nonavailability of long model forecast histories from which to develop estimates of drift, frequent model changes over the period of the experiment, a requirement that each monthly forecast be made four days before the beginning of the month, and the use of only data which are available on the day of the forecast. The overall results and month-by-month results confirm the hypothesis of the Harnack group.

  1. Evaluating a coupled discrete wavelet transform and support vector regression for daily and monthly streamflow forecasting

    Science.gov (United States)

    Liu, Zhiyong; Zhou, Ping; Chen, Gang; Guo, Ledong

    2014-11-01

    This study investigated the performance and potential of a hybrid model that combined the discrete wavelet transform and support vector regression (the DWT-SVR model) for daily and monthly streamflow forecasting. Three key factors of the wavelet decomposition phase (mother wavelet, decomposition level, and edge effect) were proposed to consider for improving the accuracy of the DWT-SVR model. The performance of DWT-SVR models with different combinations of these three factors was compared with the regular SVR model. The effectiveness of these models was evaluated using the root-mean-squared error (RMSE) and Nash-Sutcliffe model efficiency coefficient (NSE). Daily and monthly streamflow data observed at two stations in Indiana, United States, were used to test the forecasting skill of these models. The results demonstrated that the different hybrid models did not always outperform the SVR model for 1-day and 1-month lead time streamflow forecasting. This suggests that it is crucial to consider and compare the three key factors when using the DWT-SVR model (or other machine learning methods coupled with the wavelet transform), rather than choosing them based on personal preferences. We then combined forecasts from multiple candidate DWT-SVR models using a model averaging technique based upon Akaike's information criterion (AIC). This ensemble prediction was superior to the single best DWT-SVR model and regular SVR model for both 1-day and 1-month ahead predictions. With respect to longer lead times (i.e., 2- and 3-day and 2-month), the ensemble predictions using the AIC averaging technique were consistently better than the best DWT-SVR model and SVR model. Therefore, integrating model averaging techniques with the hybrid DWT-SVR model would be a promising approach for daily and monthly streamflow forecasting. Additionally, we strongly recommend considering these three key factors when using wavelet-based SVR models (or other wavelet-based forecasting models).

  2. Comparison of performance of statistical models in forecasting monthly streamflow of Kizil River,China

    Institute of Scientific and Technical Information of China (English)

    Shalamu ABUDU; Chun-liang CUI; James Phillip KING; Kaiser ABUDUKADEER

    2010-01-01

    This paper presents the application of autoregressive integrated moving average(ARIMA),seasonal ARIMA(SARIMA),and Jordan-Elman artificial neural networks(ANN)models in forecasting the monthly streamflow of the Kizil River in Xinjiang,China.Two different types of monthly streamflow data(original and deseasonalized data)were used to develop time series and Jordan-Elman ANN models using previous flow conditions as predictors.The one-month-ahead forecasting performances of all models for the testing period(1998-2005)were compared using the average monthly flow data from the Kalabeili gaging station on the Kizil River.The Jordan-Elman ANN models,using previous flow conditions as inputs,resulted in no significant improvement over time series models in one-month-ahead forecasting.The results suggest that the simple time series models(ARIMA and SARIMA)can be used in one-month-ahead streamflow forecasting at the study site with a simple and explicit model structure and a model performance similar to the Jordan-Elman ANN models.

  3. Verification of Monthly Mean Forecasts for Fire Weather Elements in the Contiguous United States.

    Science.gov (United States)

    Klein, Willlam H.; Charney, Joseph J.; McCutchan, Morris H.; Benoit, John W.

    1996-12-01

    The authors first review a system for specifying monthly mean anomalies of midday temperature (T), dew-point (D), and wind speed (W) at a large network of surface stations across the United States. Multiple regression equations containing approximately three terms were derived for each element and month from concurrent fields of 700-mb height anomaly (H) observed over North America and vicinity, plus the previous month's observed local anomaly of T, D, or W, during the 20-year period 1964-1983. Results of testing this forecast system on prognostic values of H prepared twice a month at the National Weather Service from 1973 to 1990 and on observed values of H during the independent period from 1984 to 1990 are presented.The authors pooled the data for all months and 122 stations to compute Heidke skill scores (HSS) for two, three, four, and five classes for each weather element. All scores showed skill that dropped steadily as the number of classes increased. In all cases skill was highest for T and lowest for W, the same result as that obtained in the original derivation. In order to examine the distribution of skill, we computed the HSS separately for each class. The skill scores for the first and last classes were greater than those for the middle class for all elements and months. It is encouraging that forecasts above the 90th percentile, the most critical category for fire potential, consistently showed greater skill than forecasts for any other class. Geographical and seasonal variations of skill based on prognostic height was also examined. The results showed that skill is strongly dependent on location, month, and weather element. Mean annual scores were positive for each element in all parts of the country, while monthly scores were highest in January and lowest in October.Although the forecasts based on prognostic heights were more skillful than climatology for all weather elements, they improved over month-to-month persistence only for T and D. If the

  4. Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms

    Directory of Open Access Journals (Sweden)

    Jeng-Fung Chen

    2017-03-01

    Full Text Available Electricity demand forecasting plays an important role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate prediction of electricity demands is therefore vital. In this study, artificial neural networks (ANNs trained by different heuristic algorithms, including Gravitational Search Algorithm (GSA and Cuckoo Optimization Algorithm (COA, are utilized to estimate monthly electricity demands. The empirical data used in this study are the historical data affecting electricity demand, including rainy time, temperature, humidity, wind speed, etc. The proposed models are applied to Hanoi, Vietnam. Based on the performance indices calculated, the constructed models show high forecasting performances. The obtained results also compare with those of several well-known methods. Our study indicates that the ANN-COA model outperforms the others and provides more accurate forecasting than traditional methods.

  5. 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

  6. 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.

  7. A Wavelet Neural Network Hybrid Model for Monthly Ammonia Forecasting in River Water

    OpenAIRE

    2013-01-01

    Forecasting water quality is always an effective approach for water environmental management. This study presents a combined Wavelet transform (WA) and Artificial Neural Network (ANN) model for monthly ammonia nitrogen series prediction in river water. The WA decomposed original time series into different subseries, in which the most significant one was chosen as the training data instead of the original series. Compared to the traditional ANN, the WA-ANN models were found more accurate and r...

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

    Science.gov (United States)

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

    2014-05-01

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

  9. Predicting the Poaceae pollen season: six month-ahead forecasting and identification of relevant features

    Science.gov (United States)

    Navares, Ricardo; Aznarte, José Luis

    2017-04-01

    In this paper, we approach the problem of predicting the concentrations of Poaceae pollen which define the main pollination season in the city of Madrid. A classification-based approach, based on a computational intelligence model (random forests), is applied to forecast the dates in which risk concentration levels are to be observed. Unlike previous works, the proposal extends the range of forecasting horizons up to 6 months ahead. Furthermore, the proposed model allows to determine the most influential factors for each horizon, making no assumptions about the significance of the weather features. The performace of the proposed model proves it as a successful tool for allergy patients in preventing and minimizing the exposure to risky pollen concentrations and for researchers to gain a deeper insight on the factors driving the pollination season.

  10. Predicting the Poaceae pollen season: six month-ahead forecasting and identification of relevant features

    Science.gov (United States)

    Navares, Ricardo; Aznarte, José Luis

    2016-09-01

    In this paper, we approach the problem of predicting the concentrations of Poaceae pollen which define the main pollination season in the city of Madrid. A classification-based approach, based on a computational intelligence model (random forests), is applied to forecast the dates in which risk concentration levels are to be observed. Unlike previous works, the proposal extends the range of forecasting horizons up to 6 months ahead. Furthermore, the proposed model allows to determine the most influential factors for each horizon, making no assumptions about the significance of the weather features. The performace of the proposed model proves it as a successful tool for allergy patients in preventing and minimizing the exposure to risky pollen concentrations and for researchers to gain a deeper insight on the factors driving the pollination season.

  11. A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network

    Science.gov (United States)

    Humphrey, Greer B.; Gibbs, Matthew S.; Dandy, Graeme C.; Maier, Holger R.

    2016-09-01

    Monthly streamflow forecasts are needed to support water resources decision making in the South East of South Australia, where baseflow represents a significant proportion of the total streamflow and soil moisture and groundwater are important predictors of runoff. To address this requirement, the utility of a hybrid monthly streamflow forecasting approach is explored, whereby simulated soil moisture from the GR4J conceptual rainfall-runoff model is used to represent initial catchment conditions in a Bayesian artificial neural network (ANN) statistical forecasting model. To assess the performance of this hybrid forecasting method, a comparison is undertaken of the relative performances of the Bayesian ANN, the GR4J conceptual model and the hybrid streamflow forecasting approach for producing 1-month ahead streamflow forecasts at three key locations in the South East of South Australia. Particular attention is paid to the quantification of uncertainty in each of the forecast models and the potential for reducing forecast uncertainty by using the hybrid approach is considered. Case study results suggest that the hybrid models developed in this study are able to take advantage of the complementary strengths of both the ANN models and the GR4J conceptual models. This was particularly the case when forecasting high flows, where the hybrid models were shown to outperform the two individual modelling approaches in terms of the accuracy of the median forecasts, as well as reliability and resolution of the forecast distributions. In addition, the forecast distributions generated by the hybrid models were up to 8 times more precise than those based on climatology; thus, providing a significant improvement on the information currently available to decision makers.

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

    Science.gov (United States)

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

    2016-05-01

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

  13. A Wavelet Neural Network Hybrid Model for Monthly Ammonia Forecasting in River Water

    Directory of Open Access Journals (Sweden)

    Yi Wang

    2013-06-01

    Full Text Available Forecasting water quality is always an effective approach for water environmental management. This study presents a combined Wavelet transform (WA and Artificial Neural Network (ANN model for monthly ammonia nitrogen series prediction in river water. The WA decomposed original time series into different subseries, in which the most significant one was chosen as the training data instead of the original series. Compared to the traditional ANN, the WA-ANN models were found more accurate and reliable. The results of the study indicate that WA could remove the noise of the original datasets and the WA-ANN could help environment decision-maker manage water quality more effective.

  14. 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).

  15. Dynamical forecast vs Ensemble Streamflow Prediction (ESP): how sensitive are monthly and seasonal hydrological forecasts to the quality of rainfall drivers?

    Science.gov (United States)

    Tanguy, Maliko; Prudhomme, Christel; Harrigan, Shaun; Smith, Katie

    2017-04-01

    Seasonal forecasting of hydrological extremes is challenging for the hydro-meteorological modelling community, and the performance of hydrological forecasts at lead times over 1 month is still poor especially for catchments with limited hydrological memory. A considerable amount of effort is being invested within the meteorological community to improve dynamic meteorological forecasting which can then be used to drive hydrological models to produce physically-driven hydrological forecasts. However, currently for the UK, these meteorological forecasts are being produced at 1 month or seasonal time-step, whereas hydrological models often require daily or sub-daily time-steps. A simpler way to get seasonal forecasts is to use historical climate data to drive hydrological models using Ensemble Streamflow Prediction (ESP). This gives a range of possible future hydrological status given known initial conditions, but it does not contain any information on the future dynamic of the atmosphere. The error is highly dependent on the type of catchment, but ESP is an improvement compared to simply using climatology of river flows, especially in groundwater dominated catchments. The objective of this study is to find out how accurate the seasonal rainfall forecast has to be (in terms of total rainfall and temporal distribution) for the dynamical seasonal forecast to beat ESP. To this aim, we have looked at the sensitivity of hydrological models to the quality of driving rainfall input, proxy of 'best possible' forecasts. Study catchments representative of the range of UK's hydro-climatic conditions were selected. For these catchments, synthetic rainfall time series derived from observed data were created by increasingly degrading the data. The number of rainy days, their intensity and their sequencing were artificially modified to analyse which of these characteristics is most important to get a better hydrological forecast using a simple lumped hydrological model (GR4J), and

  16. 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.

  17. Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform

    Science.gov (United States)

    Kalteh, Aman Mohammad

    2013-04-01

    Reliable and accurate forecasts of river flow is needed in many water resources planning, design development, operation and maintenance activities. In this study, the relative accuracy of artificial neural network (ANN) and support vector regression (SVR) models coupled with wavelet transform in monthly river flow forecasting is investigated, and compared to regular ANN and SVR models, respectively. The relative performance of regular ANN and SVR models is also compared to each other. For this, monthly river flow data of Kharjegil and Ponel stations in Northern Iran are used. The comparison of the results reveals that both ANN and SVR models coupled with wavelet transform, are able to provide more accurate forecasting results than the regular ANN and SVR models. However, it is found that SVR models coupled with wavelet transform provide better forecasting results than ANN models coupled with wavelet transform. The results also indicate that regular SVR models perform slightly better than regular ANN models.

  18. Comparison of forecasts of mean monthly water level in the Paraguay River, Brazil, from two fractionally differenced models

    Science.gov (United States)

    Prass, Taiane S.; Bravo, Juan Martin; Clarke, Robin T.; Collischonn, Walter; Lopes, SíLvia R. C.

    2012-05-01

    The paper compares forecasts of mean monthly water levels up to six months ahead at Ladário, on the Upper Paraguay River, Brazil, estimated from two long-range dependence models. In one of them, the marked seasonal cycle was removed and a fractionally differenced model was fitted to the transformed series. In the other, a seasonal fractionally differenced model was fitted to water levels without transformation. Forecasts from both models for periods up to six months ahead were compared with forecasts given by simpler "short-range dependence" Box-Jenkins models, one fitted to the transformed series, the other a seasonal autoregressive moving average (ARMA) model. Estimates of parameters in the four models (two "long-range dependence", two "short-range dependence") were updated at six-monthly intervals over a 20 year period, and forecasts were compared using root mean square errors (rmse) between water-level forecasts and observed levels. As judged by rmse, performances of the two long-range dependence models, and of the ARMA (1,1) short-range dependence model, were very similar; all three out-performed the seasonal short-range dependence ARMA model. There was evidence that all models performed better during recession periods, than on the hydrograph rising limb.

  19. 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.

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

    Indian Academy of Sciences (India)

    Hamid Moeeni; Hossein Bonakdari; Isa Ebtehaj

    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) andgene 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 seriescaused 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 (R2=78.8, VAF=78.8, RMSE=0.89, MAPE=43.4, CRM=0.053) outperforms SARIMA and GEP and SARIMA– ANN (R2=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.

  1. 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

  2. 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.

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

    NARCIS (Netherlands)

    Candogan Yossef, N.A.N.N.; Beek, L.P.H. van; Kwadijk, J.C.J.; Bierkens, M.F.P.

    2012-01-01

    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 PCRGLOBWB in reproducing the occurrence of past extremes in monthly discharge on a global scale. Global terrestrial hydrology from 1958

  4. The ScaLIng Macroweather Model (SLIMM) and monthly and inter annual regional forecasting.

    Science.gov (United States)

    Lovejoy, S.; Del Rio Amador, L.; Sloman, L.

    2015-12-01

    By exploiting the sensitive dependence on initial conditions, GCM's can generate a statistical ensemble of future states in which the high frequency "weather" is treated as a driving noise. Following Hasselman, 1976, this has lead to stochastic models that directly generate the noise, and model the low frequencies using systems of integer ordered linear ordinary differential equations, the most well known are the linear inverse models (LIM). These have been presented as a benchmark for decadal surface temperature forecast. Using the LIM, hindcast skills comparable to and sometimes even better than the skill of (coupled) Global Circulation Models (GCM's) from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Nevertheless, the short range exponential temporal decorrelations implicit in the LIM models are unrealistic (the true decorrelations are closer to long range power laws), and - as a consequence - the useful limit to the forecast horizon is roughly one year: it enormously underestimates the memory of the system. In presentation, we make a scaling analogue of the LIM: ScaLIng Macroweather Model (SLIMM) that exploits the power law (scaling) behavior in time of the temperature field and consequently, make use of the long history dependence of the data to improve the skill. The results predicted analytically by the model have been tested by performing actual hindcasts in different 5º x 5º regions on the planet using the Twentieth Century Reanalysis as a reference datasets. As a first step, we removed the anthropogenic component of each time series based on its sensitivity to equivalent CO2 concentration for the last 130 years, the residues are our estimates of the natural variability that SLIMM predicts. This residues were treated as fractional Gaussian noise processes with scaling exponent H between -0.5 and 0. The value of H for each grid-point can be obtained directly from the data. We report maps of theoretical skill predicted by the model and we

  5. Forecasting monthly precipitation in Central Chile: a self-organizing map approach using filtered sea surface temperature

    Science.gov (United States)

    Rivera, Diego; Lillo, Mario; Uvo, Cintia B.; Billib, Max; Arumí, José Luis

    2012-01-01

    Western South America is subject to considerable inter-annual variability due to El Niño-Southern Oscillation (ENSO) so forecasting inter-annual variations associated with ENSO would provide an opportunity to tailor management decisions more appropriately to the season. On one hand, the self-organizing maps (SOM) method is a suitable technique to explore the association between sea surface temperature and precipitation fields. On the other hand, Wavelet transform is a filtering technique, which allows the identification of relevant frequencies in signals, and also allows localization on time. Taking advantage of both methods, we present a method to forecast monthly precipitation using the SOM trained with filtered SST anomalies. The use of the SOM to forecast precipitation for Chillan showed good agreement between forecasted and measured values, with correlation coefficients ( r 2) ranging from 0.72 to 0.91, making the combined use filtered SST fields and SOM a suitable tool to assist water management, for example in agricultural water management. The method can be easily tailored to be applied in other stations or to other variables.

  6. Forecasting, Forecasting

    Science.gov (United States)

    Michael A. Fosberg

    1987-01-01

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

  7. The First Six Months: PDEM Innovations in Forecasting Higher Education, January to July 1972.

    Science.gov (United States)

    Hoffman, Benjamin B.

    A Postsecondary Demand Survey was undertaken in 1972 to study the demand for postsecondary education in Manitoba, Canada, and to develop a system for forecasting enrollment at postsecondary institutions. The survey objective was to establish a profile of grade 12 students in 1972 to learn about their aspirations, plans, expectations after…

  8. The First Six Months: PDEM Innovations in Forecasting Higher Education, January to July 1972.

    Science.gov (United States)

    Hoffman, Benjamin B.

    A Postsecondary Demand Survey was undertaken in 1972 to study the demand for postsecondary education in Manitoba, Canada, and to develop a system for forecasting enrollment at postsecondary institutions. The survey objective was to establish a profile of grade 12 students in 1972 to learn about their aspirations, plans, expectations after…

  9. The use of artificial neural networks for forecasting the monthly mean soil temperatures in Adana, Turkey

    OpenAIRE

    BİLGİLİ, Mehmet

    2014-01-01

    The objective of this paper was to develop an artificial neural network (ANN) model in order to predict monthly mean soil temperature for the present month by using various previous monthly mean meteorological variables. For this purpose, the measured soil temperature and other meteorological data between the years of 2000 and 2007 at Adana meteorological station were used. The soil temperatures were measured at depths of 5, 10, 20, 50, and 100 cm below the ground level by the Turkish State M...

  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. Forecasting of monthly inflow and outflow currency using time series regression and ARIMAX: The Idul Fitri effect

    Science.gov (United States)

    Ahmad, Imam Safawi; Setiawan, Suhartono, Masun, Nunun Hilyatul

    2015-12-01

    Currency plays an important role in economic transactions of Indonesian society. In order to guarantee the availability of currency, Bank Indonesia needs to develop demand and supply planning of currency. The purpose of this study is to get model and predict inflow and outflow of currency in KPW BI Region IV (East Java) with ARIMA method, time series regression and ARIMAX. The data of monthly inflow and outflow is used of currency in KPW BI Surabaya, Malang, Kediri and Jember.The observation period starting from January 2003 to December 2014. Based on the smallest values of out-sample RMSE and SMAPE, ARIMA is the best model to predict the outflow of currency in KPW BI Surabaya and ARIMAX for KPW BI Malang, Kediri and Jember. The best forecasting model for inflow of currency in KPW BI Surabaya, Malang, Kediri and Jember chronologically as follows are calendar variation model, transfer function, ARIMA, and time series regression. These results indicates that the more complex models may not necessarily produce a more accurate forecast as the result of M3-Competition.

  12. 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.

    Long-range forecasting of summer monsoon rainfall was reported through linear models by Delsole and Shukla3. They showed that minimum number of predictors are sufficient for accurate forecasts. Recent studies4,5 reported long-range prediction...

  13. Forecasting Skill

    Science.gov (United States)

    1981-01-01

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

  14. Monthly evaporation forecasting using artificial neural networks and support vector machines

    Science.gov (United States)

    Tezel, Gulay; Buyukyildiz, Meral

    2016-04-01

    Evaporation is one of the most important components of the hydrological cycle, but is relatively difficult to estimate, due to its complexity, as it can be influenced by numerous factors. Estimation of evaporation is important for the design of reservoirs, especially in arid and semi-arid areas. Artificial neural network methods and support vector machines (SVM) are frequently utilized to estimate evaporation and other hydrological variables. In this study, usability of artificial neural networks (ANNs) (multilayer perceptron (MLP) and radial basis function network (RBFN)) and ɛ-support vector regression (SVR) artificial intelligence methods was investigated to estimate monthly pan evaporation. For this aim, temperature, relative humidity, wind speed, and precipitation data for the period 1972 to 2005 from Beysehir meteorology station were used as input variables while pan evaporation values were used as output. The Romanenko and Meyer method was also considered for the comparison. The results were compared with observed class A pan evaporation data. In MLP method, four different training algorithms, gradient descent with momentum and adaptive learning rule backpropagation (GDX), Levenberg-Marquardt (LVM), scaled conjugate gradient (SCG), and resilient backpropagation (RBP), were used. Also, ɛ-SVR model was used as SVR model. The models were designed via 10-fold cross-validation (CV); algorithm performance was assessed via mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2). According to the performance criteria, the ANN algorithms and ɛ-SVR had similar results. The ANNs and ɛ-SVR methods were found to perform better than the Romanenko and Meyer methods. Consequently, the best performance using the test data was obtained using SCG(4,2,2,1) with R 2 = 0.905.

  15. Can We Forecast 1-Month Span Aftershock Activity from the First Day Data after the Main Shock?

    Science.gov (United States)

    Omi, T.; Ogata, Y.; Hirata, Y.; Aihara, K.

    2014-12-01

    A large earthquake triggers persistent aftershock activity in and near the focal region. Thus, intermediate term forecasting of aftershocks at its earlier stage is important for mitigating seismic risks. A main difficulty for the early forecasting is the substantial incompleteness of early aftershock data. To deal with such incomplete data, we have developed a statistical model of the incomplete data, enabling us to obtain the immediate estimate of the forecasting models from incomplete data [1, 2]. Another difficulty for the intermediate term forecasting is that we have to determine the parameter values of the forecasting models with high accuracy, because even a small bias in the parameter values can lead to a significant bias of the forecasting in intermediate term. However such accurate estimation is quite difficult at the early stage, especially using the early and incomplete data. Here we present a Bayesian forecasting method by using the epidemic-type aftershock sequence (ETAS) model. The Bayesian forecasting considers not only the best parameter values such as the maximum likelihood estimates or maximum a posteriori estimates but also the estimation uncertainty of the parameter values. By analyzing aftershock sequences in Japan, we show the forecasting performances of the intermediate-term aftershocks can be significantly improved by considering the estimation uncertainty of the ETAS model [3]. Furthermore, we discuss the impact of the modeling of the magnitude frequency distribution of detected aftershocks within a day span on the forecasting of large aftershocks. [1] T. Omi, Y. Ogata, Y. Hirata and K. Aihara, "Forecasting large aftershocks within one day after the main shock", Scientific Reports 3, 2218 (2013). [2] T. Omi, Y. Ogata, Y. Hirata and K. Aihara, "Estimating the ETAS model from an early aftershock sequence", Geophysical Research Letters 41, 850 (2014). [3] T. Omi, Y. Ogata, Y. Hirata and K. Aihara, "Intermediate-term forecasting of aftershocks

  16. Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore

    Science.gov (United States)

    Shi, Yuan; Liu, Xu; Kok, Suet-Yheng; Rajarethinam, Jayanthi; Liang, Shaohong; Yap, Grace; Chong, Chee-Seng; Lee, Kim-Sung; Tan, Sharon S.Y.; Chin, Christopher Kuan Yew; Lo, Andrew; Kong, Waiming; Ng, Lee Ching; Cook, Alex R.

    2015-01-01

    Background: With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention. Objectives: We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak. Methods: We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore. Results: Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore’s dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response. Conclusions: Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue. Citation: Shi Y, Liu X, Kok SY, Rajarethinam J, Liang S, Yap G, Chong CS, Lee KS, Tan SS, Chin CK, Lo A, Kong W, Ng LC, Cook AR. 2016. Three-month real-time dengue forecast models: an early warning system for outbreak

  17. 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.

  18. 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.

  19. 3个月平均气温距平的CCA预报方法%CCA FORECAST SCHEME OF 3-MONTH MEAN TEMPERATURE ANOMALY

    Institute of Scientific and Technical Information of China (English)

    余金波; 吴洪宝

    2001-01-01

    A statistical model is CCA-designed to forecast 3-month mean temperature anomaly in China,which is estimated by using cross-verification scheme,indicating that the skill decreases slowly with the increased leading time intervals;higher skills are found for quasi-global surface temperature as a predictor;it's easy to predict JAS temperature and hard to deal with OND analog.Some meaningful results are obtained from the forecast skill analysis.%用根据CCA方法设计的一个统计预报模式对我国3个月平均气温距平进行预报试验,并用交叉检验方法进行估计。结果表明:预报技巧随提前时间增长而减小得较少;用全球表面温度作预报因子有较高的预报技巧;7、8、9月3个月较易预报,而10、11、12月3个月较难预报。

  20. Prospects for Improved Forecasts of Weather and Short-Term Climate Variability on Subseasonal (2-Week to 2-Month) Times Scales

    Science.gov (United States)

    Schubert, Siegfried; Dole, Randall; vandenDool, Huug; Suarez, Max; Waliser, Duane

    2002-01-01

    This workshop, held in April 2002, brought together various Earth Sciences experts to focus on the subseasonal prediction problem. While substantial advances have occurred over the last few decades in both weather and seasonal prediction, progress in improving predictions on these intermediate time scales (time scales ranging from about two weeks to two months) has been slow. The goals of the workshop were to get an assessment of the "state of the art" in predictive skill on these time scales, to determine the potential sources of "untapped" predictive skill, and to make recommendations for a course of action that will accelerate progress in this area. One of the key conclusions of the workshop was that there is compelling evidence for predictability at forecast lead times substantially longer than two weeks. Tropical diabatic heating and soil wetness were singled out as particularly important processes affecting predictability on these time scales. Predictability was also linked to various low-frequency atmospheric "phenomena" such as the annular modes in high latitudes (including their connections to the stratosphere), the Pacific/North American (PNA) pattern, and the Madden Julian Oscillation (MJO). The latter, in particular, was highlighted as a key source of untapped predictability in the tropics and subtropics, including the Asian and Australian monsoon regions.

  1. 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.

  2. 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.

  3. 提高月度售电量预测精度的一种新方法%A new method to promote the forecasting accuracy of monthly electricity consumption

    Institute of Scientific and Technical Information of China (English)

    潘小辉; 刘丽萍; 李扬

    2013-01-01

      This paper finds the principles and characters of the development of electricity consumption by making data mining analysis on historical data, and puts forward a new method to fore⁃cast monthly electricity consumption. When forecasting the elec⁃tricity consumption of a month, the new method firstly forecasts the electricity consumption of the season that the month belongs to. Then use the Forecasting values of the season electricity consump⁃tion and season proportion to forecast the monthly electricity con⁃sumption. The new method has been applied to forecast monthly electricity consumption from April of 2005 to December of 2007 in a city of Jiangsu province. With 2.35% average relative error, the results show the validity of the new method.%  通过对地区售电量历史数据的研究分析,挖掘出其发展的规律和特点,并创新性地提出了一种月度售电量预测的新方法。该方法在预测某月售电量时,先预测出该月所在季度的季度售电量,再根据占季比和所在季度的季度售电量的预测值,预测出该月的售电量,若该月处于春节影响期将最后给予修正。利用该预测新方法对江苏某市2005年4月至2007年12月的月度售电量进行了预测,预测的平均相对误差为2.35%,预测精度得到了较大的提高,证明了该预测新方法的准确性和可靠性。

  4. Forecast Forecasts the Trend

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

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

  5. Forecast Forecasts the Trend

    Institute of Scientific and Technical Information of China (English)

    Wang Ting

    2009-01-01

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

  6. 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

  7. 长江上游月降水人工神经网络预测模型%ARTIFICIAL NEURAL NETWORK MODELS FOR FORECASTING MONTHLY PRECIPITATION IN THE UPPER YANGTZE RIVER

    Institute of Scientific and Technical Information of China (English)

    冯亚文; 任国玉; 张丽; 罗华超

    2011-01-01

    Monthly precipitation forecast for the upper Yangtze River is very essential to the water resources management for the entire Yangtze River basin. Three typical meteorological stations were selected respectively in three different climatic zones. All the selected stations contained nearly 60 years of monthly precipitation records in the upper Yangtze River. This paper estimated the month of precipitation and precipitation time delay parameter, and established monthly precipitation forecasting model using back-propagation neural network, radial basis function neural network, generalized regression neural network and multiple linear regression method respectively, to predict the precipitation of coming month. Then, the mean square error and coefficient of determination were used to verify the simulation accuracy of various models and the model simulation results. The results show that artificial neural network prediction model is superior to multiple linear regression in general. Especially, the performance of the back-propagation neural network is better than the others. It can be determined as an effective monthly precipitation methods for the upper Yangtze River after determining reasonable input variables and network structure.%长江上游月降水量预测对于三峡库区及整个长江流域水资源管理具有重要意义.根据长江上游不同气候区降水差异,选取玉树、九龙和宜宾3个代表性气象站点近60 a的月降水量数据,运用反向传播神经网络、径向基函数神经网络、广义回归神经网络和多元线性回归法,确定降水时滞和降水月份,建立月降水预测模型,来预测未来一个月的降水量,并采用均方误差和判定系数来验证和对比各种模型的模拟效果.结果显示:人工神经网络模型总体上优于多元线性回归,特别是反向传播种经网络的模拟结果各站表现较好,在确定合理的输入变量和网络结构后,可以尝试作为长江上游各站月降水预测模型.

  8. 17 CFR 210.11-03 - Presentation of financial forecast.

    Science.gov (United States)

    2010-04-01

    ... 17 Commodity and Securities Exchanges 2 2010-04-01 2010-04-01 false Presentation of financial forecast. 210.11-03 Section 210.11-03 Commodity and Securities Exchanges SECURITIES AND EXCHANGE COMMISSION... Information § 210.11-03 Presentation of financial forecast. (a) A financial forecast may be filed in lieu...

  9. Forecasting Using Random Subspace Methods

    NARCIS (Netherlands)

    T. Boot (Tom); D. Nibbering (Didier)

    2016-01-01

    textabstractRandom subspace methods are a novel approach to obtain accurate forecasts in high-dimensional regression settings. We provide a theoretical justification of the use of random subspace methods and show their usefulness when forecasting monthly macroeconomic variables. We focus on two appr

  10. Demand forecasting

    OpenAIRE

    Gregor, Belčec

    2011-01-01

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

  11. Innovation Forecasting

    Science.gov (United States)

    1997-11-01

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

  12. Earthquake forecasting and its verification

    Directory of Open Access Journals (Sweden)

    J. R. Holliday

    2005-01-01

    Full Text Available No proven method is currently available for the reliable short time prediction of earthquakes (minutes to months. However, it is possible to make probabilistic hazard assessments for earthquake risk. In this paper we discuss a new approach to earthquake forecasting based on a pattern informatics (PI method which quantifies temporal variations in seismicity. The output, which is based on an association of small earthquakes with future large earthquakes, is a map of areas in a seismogenic region ('hotspots'' where earthquakes are forecast to occur in a future 10-year time span. This approach has been successfully applied to California, to Japan, and on a worldwide basis. Because a sharp decision threshold is used, these forecasts are binary--an earthquake is forecast either to occur or to not occur. The standard approach to the evaluation of a binary forecast is the use of the relative (or receiver operating characteristic (ROC diagram, which is a more restrictive test and less subject to bias than maximum likelihood tests. To test our PI method, we made two types of retrospective forecasts for California. The first is the PI method and the second is a relative intensity (RI forecast based on the hypothesis that future large earthquakes will occur where most smaller earthquakes have occurred in the recent past. While both retrospective forecasts are for the ten year period 1 January 2000 to 31 December 2009, we performed an interim analysis 5 years into the forecast. The PI method out performs the RI method under most circumstances.

  13. Evaluation of forecast skill of monthly rainfall over Northeast China using multi-models%多模式对东北地区月降水预测性能对比评估

    Institute of Scientific and Technical Information of China (English)

    李永生; 段春锋; 王莹

    2016-01-01

    基于中国、美国、欧洲和日本的4种气候模式对1983—2010年东北地区降水的回报试验结果,利用2011—2014年东北地区业务应用的结果和国家气象信息中心提供的东北地区172个气象站的观测资料,采用距平相关系数(ACC)、趋势异常综合评分(Ps)和距平符号一致率(Pc)3种定量方法对比评估了4种模式对东北地区月降水的预测性能。结果表明:EC模式和CFSv 2模式与BCC模式和TCC模式相比,EC模式和CFSv 2模式对东北地区月降水的总体预测效果较好,具有一定的预测技巧。从空间上来看,CFSv 2模式各月Pc的分布存在较明显的差异,模式仍有较大的改进空间。CFSv 2模式对东北地区初夏典型旱涝年具有一定的预测能力,对典型涝年的预测效果优于典型旱年。%The prediction skill of four climate models for monthly rainfall over Northeast China was evaluated using three qualitative evaluation methods,i.e.,anomaly correlation coefficient (ACC),trend anomaly inspection evalu-ation (Ps)and anomaly symbol consistency rate (Pc).Many data were used in this study,including 172 meteoro-logical stations over Northeast China supplied by the National Meteorological Information Center,the hindcast ex-perimental results of rainfall over Northeast China from 1983 to 2010 according to four climate models from Chi-na,America,Japan and Europe,and the operational application results over Northeast China from 201 1 to 2014. The results indicate that the monthly rainfall prediction skills of EC (European Center for Medium-Range Weather Forecasts)and CFSv 2 (Coupled Forecast System Model Version 2)models are better than those of BCC (Bei-jing Climate Center)and TCC (Tokyo Climate Center)models.Looking at the spatial distribution,there is a sig-nificant difference in the distribution of each monthly Pc for CFSv 2 model,indicating that this model has a big space for its improvement.The CFSv 2 model has

  14. 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...

  15. 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...

  16. Skillful seasonal forecasts of Arctic sea ice retreat and advance dates in a dynamical forecast system

    Science.gov (United States)

    Sigmond, M.; Reader, M. C.; Flato, G. M.; Merryfield, W. J.; Tivy, A.

    2016-12-01

    The need for skillful seasonal forecasts of Arctic sea ice is rapidly increasing. Technology to perform such forecasts with coupled atmosphere-ocean-sea ice systems has only recently become available, with previous skill evaluations mainly limited to area-integrated quantities. Here we show, based on a large set of retrospective ensemble model forecasts, that a dynamical forecast system produces skillful seasonal forecasts of local sea ice retreat and advance dates - variables that are of great interest to a wide range of end users. Advance dates can generally be skillfully predicted at longer lead times ( 5 months on average) than retreat dates ( 3 months). The skill of retreat date forecasts mainly stems from persistence of initial sea ice anomalies, whereas advance date forecasts benefit from longer time scale and more predictable variability in ocean temperatures. These results suggest that further investments in the development of dynamical seasonal forecast systems may result in significant socioeconomic benefits.

  17. 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...

  18. General forecasting correcting formula

    OpenAIRE

    Harin, Alexander

    2009-01-01

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

  19. General forecasting correcting formula

    OpenAIRE

    2009-01-01

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

  20. Information Forecasting.

    Science.gov (United States)

    Hanneman, Gerhard J.

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

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

    Science.gov (United States)

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

    1995-05-01

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

  2. Seasonal hydrological ensemble forecasts over Europe

    Science.gov (United States)

    Arnal, Louise; Wetterhall, Fredrik; Pappenberger, Florian

    2015-04-01

    Seasonal forecasts have an important socio-economic value in hydro-meteorological forecasting. The applications are for example hydropower management, spring flood prediction and water resources management. The latter includes prediction of low flows, primordial for navigation, water quality assessment, droughts and agricultural water needs. Traditionally, seasonal hydrological forecasts are done using the observed discharge from previous years, so called Ensemble Streamflow Prediction (ESP). With the recent increasing development of seasonal meteorological forecasts, the incentive for developing and improving seasonal hydrological forecasts is great. In this study, a seasonal hydrological forecast, driven by the ECMWF's System 4 (SEA), was compared with an ESP of modelled discharge using observations. The hydrological model used for both forecasts was the LISFLOOD model, run over a European domain with a spatial resolution of 5 km. The forecasts were produced from 1990 until the present time, with a daily time step. They were issued once a month with a lead time of seven months. The SEA forecasts are constituted of 15 ensemble members, extended to 51 members every three months. The ESP forecasts comprise 20 ensembles and served as a benchmark for this comparative study. The forecast systems were compared using a diverse set of verification metrics, such as continuous ranked probability scores, ROC curves, anomaly correlation coefficients and Nash-Sutcliffe efficiency coefficients. These metrics were computed over several time-scales, ranging from a weekly to a six-months basis, for each season. The evaluation enabled the investigation of several aspects of seasonal forecasting, such as limits of predictability, timing of high and low flows, as well as exceedance of percentiles. The analysis aimed at exploring the spatial distribution and timely evolution of the limits of predictability.

  3. The Use of Interactive Graphics Processing in Short-Range Terminal Weather Forecasting: An Initial Assessment.

    Science.gov (United States)

    1983-03-31

    ingest individual forecasts into a data file through the use of formatted interrogation/ response messages via the AlcIDAS ’ keyboard /alpha- numeric CRT...to 10 min of observation time. The AlcIDAS forecaster has many computer analyses to aid him that are not available to forecasters relying on

  4. Forecasting global atmospheric CO2

    Directory of Open Access Journals (Sweden)

    A. Agustí-Panareda

    2014-05-01

    Full Text Available A new global atmospheric carbon dioxide (CO2 real-time forecast is now available as part of the pre-operational 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 CO2 forecasting system is that the land surface, including vegetation CO2 fluxes, is modelled online within the IFS. Other CO2 fluxes are prescribed from inventories and from off-line statistical and physical models. The CO2 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 CO2 on different temporal and spatial scales compared to observations. The modulation of the amplitude of the CO2 diurnal cycle by near-surface winds and boundary layer height is generally well represented in the forecast. The CO2 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 CO2 fluxes from vegetation compared to using equivalent monthly mean fluxes with a diurnal cycle. However, biases in the modelled CO2 fluxes also lead to accumulating errors in the CO2 forecast. These biases vary with season with an underestimation of the amplitude of the seasonal cycle both for the CO2 fluxes compared to total optimized fluxes and the atmospheric CO2 compared to observations. The largest biases in the atmospheric CO2 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 CO2 analyses based on the assimilation of CO2 satellite retrievals, as they

  5. 7 CFR 612.3 - Data collected and forecasts.

    Science.gov (United States)

    2010-01-01

    ... provide related drometeorological data, such as precipitation, temperature, humidity, solar tradiation, and wind. (b) Water supply forecasts in the western states area are generally made monthly...

  6. Intermediate-term forecasting of aftershocks from an early aftershock sequence: Bayesian and ensemble forecasting approaches

    Science.gov (United States)

    Omi, Takahiro; Ogata, Yosihiko; Hirata, Yoshito; Aihara, Kazuyuki

    2015-04-01

    Because aftershock occurrences can cause significant seismic risks for a considerable time after the main shock, prospective forecasting of the intermediate-term aftershock activity as soon as possible is important. The epidemic-type aftershock sequence (ETAS) model with the maximum likelihood estimate effectively reproduces general aftershock activity including secondary or higher-order aftershocks and can be employed for the forecasting. However, because we cannot always expect the accurate parameter estimation from incomplete early aftershock data where many events are missing, such forecasting using only a single estimated parameter set (plug-in forecasting) can frequently perform poorly. Therefore, we here propose Bayesian forecasting that combines the forecasts by the ETAS model with various probable parameter sets given the data. By conducting forecasting tests of 1 month period aftershocks based on the first 1 day data after the main shock as an example of the early intermediate-term forecasting, we show that the Bayesian forecasting performs better than the plug-in forecasting on average in terms of the log-likelihood score. Furthermore, to improve forecasting of large aftershocks, we apply a nonparametric (NP) model using magnitude data during the learning period and compare its forecasting performance with that of the Gutenberg-Richter (G-R) formula. We show that the NP forecast performs better than the G-R formula in some cases but worse in other cases. Therefore, robust forecasting can be obtained by employing an ensemble forecast that combines the two complementary forecasts. Our proposed method is useful for a stable unbiased intermediate-term assessment of aftershock probabilities.

  7. Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

    Science.gov (United States)

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

    2016-09-01

    Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France to provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. We apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the streamflow forecasts. The approaches are based on the linear scaling and the distribution mapping methods. A daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. We then evaluate the skill of raw (without bias correction) and bias-corrected precipitation and streamflow ensemble forecasts in 16 catchments in France. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy and overall performance. A reference prediction system, based on historical observed precipitation and catchment initial conditions at the time of forecast (i.e. ESP method) is used as benchmark in the computation of the skill. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. Two bias correction methods show the best performance for the studied catchments, each method being more successful in improving specific attributes of the forecasts: the simple linear scaling of monthly values contributes mainly to increasing forecast sharpness and accuracy, while the empirical distribution mapping

  8. kwmc Terminal Aerodrome Forecast

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    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...

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    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...

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    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...

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    Data.gov (United States)

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    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...

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    Data.gov (United States)

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  19. kpkb 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. kaus 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. kcar 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. kaeg 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. ksoa 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. paom 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. 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...

  6. khya 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. kjfk 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. kpie 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. kpns 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. ksat 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. kcid 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. ksny 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. klch 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. khnd 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. ksaf 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. kdec 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. ktvl 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. kttd 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. kluk 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. ksdf 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. krno 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. kwwr 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. 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...

  4. kcle 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. kcxp 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. kgpi 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. paun 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. keat 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. pasc 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. koun 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. klee 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. kiag 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. kbmg 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. 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...

  15. kmmh 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. kiln 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. kbmi 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. kcha 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. pahn 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. khon 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. kpeq 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. kden 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. 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...

  4. kmaf 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. khsa 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. kvis 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. 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...

  8. kmrb 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. kcub 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. 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...

  11. ptkk 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. kcsm 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. ksfo 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. kjhw 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. kdab 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. kecp 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. kmls 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. kroc 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. kfod 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. 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...

  1. pabe 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. ktop 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. ktrk 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. krvs 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. kgld 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. kama 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. kmgm 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. Ensemble forecasting of sub-seasonal to seasonal streamflow by a Bayesian joint probability modelling approach

    Science.gov (United States)

    Zhao, Tongtiegang; Schepen, Andrew; Wang, Q. J.

    2016-10-01

    The Bayesian joint probability (BJP) modelling approach is used operationally to produce seasonal (three-month-total) ensemble streamflow forecasts in Australia. However, water resource managers are calling for more informative sub-seasonal forecasts. Taking advantage of BJP's capability of handling multiple predictands, ensemble forecasting of sub-seasonal to seasonal streamflows is investigated for 23 catchments around Australia. Using antecedent streamflow and climate indices as predictors, monthly forecasts are developed for the three-month period ahead. Forecast reliability and skill are evaluated for the period 1982-2011 using a rigorous leave-five-years-out cross validation strategy. BJP ensemble forecasts of monthly streamflow volumes are generally reliable in ensemble spread. Forecast skill, relative to climatology, is positive in 74% of cases in the first month, decreasing to 57% and 46% respectively for streamflow forecasts for the final two months of the season. As forecast skill diminishes with increasing lead time, the monthly forecasts approach climatology. Seasonal forecasts accumulated from monthly forecasts are found to be similarly skilful to forecasts from BJP models based on seasonal totals directly. The BJP modelling approach is demonstrated to be a viable option for producing ensemble time-series sub-seasonal to seasonal streamflow forecasts.

  9. Business forecasting methods

    OpenAIRE

    Skribans, V.

    2002-01-01

    The paper comprises three parts: description of the forecasting conception, classification of forecasting methods, and the general forecasting model. The first part deals with the definition of business forecasting, the second classifies and deals in more detail with the most frequently practiced forecasting methods and comes up with recommendations how to apply them in specific situations. The third part present the analysis of newly established forecasting model.

  10. Optimising seasonal streamflow forecast lead time for operational decision making in Australia

    Science.gov (United States)

    Schepen, Andrew; Zhao, Tongtiegang; Wang, Q. J.; Zhou, Senlin; Feikema, Paul

    2016-10-01

    Statistical seasonal forecasts of 3-month streamflow totals are released in Australia by the Bureau of Meteorology and updated on a monthly basis. The forecasts are often released in the second week of the forecast period, due to the onerous forecast production process. The current service relies on models built using data for complete calendar months, meaning the forecast production process cannot begin until the first day of the forecast period. Somehow, the bureau needs to transition to a service that provides forecasts before the beginning of the forecast period; timelier forecast release will become critical as sub-seasonal (monthly) forecasts are developed. Increasing the forecast lead time to one month ahead is not considered a viable option for Australian catchments that typically lack any predictability associated with snowmelt. The bureau's forecasts are built around Bayesian joint probability models that have antecedent streamflow, rainfall and climate indices as predictors. In this study, we adapt the modelling approach so that forecasts have any number of days of lead time. Daily streamflow and sea surface temperatures are used to develop predictors based on 28-day sliding windows. Forecasts are produced for 23 forecast locations with 0-14- and 21-day lead time. The forecasts are assessed in terms of continuous ranked probability score (CRPS) skill score and reliability metrics. CRPS skill scores, on average, reduce monotonically with increase in days of lead time, although both positive and negative differences are observed. Considering only skilful forecast locations, CRPS skill scores at 7-day lead time are reduced on average by 4 percentage points, with differences largely contained within +5 to -15 percentage points. A flexible forecasting system that allows for any number of days of lead time could benefit Australian seasonal streamflow forecast users by allowing more time for forecasts to be disseminated, comprehended and made use of prior to

  11. Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model

    Science.gov (United States)

    Bennett, James C.; Wang, Q. J.; Li, Ming; Robertson, David E.; Schepen, Andrew

    2016-10-01

    We present a new streamflow forecasting system called forecast guided stochastic scenarios (FoGSS). FoGSS makes use of ensemble seasonal precipitation forecasts from a coupled ocean-atmosphere general circulation model (CGCM). The CGCM forecasts are post-processed with the method of calibration, bridging and merging (CBaM) to produce ensemble precipitation forecasts over river catchments. CBaM corrects biases and removes noise from the CGCM forecasts, and produces highly reliable ensemble precipitation forecasts. The post-processed CGCM forecasts are used to force the Wapaba monthly rainfall-runoff model. Uncertainty in the hydrological modeling is accounted for with a three-stage error model. Stage 1 applies the log-sinh transformation to normalize residuals and homogenize their variance; Stage 2 applies a conditional bias-correction to correct biases and help remove negative forecast skill; Stage 3 applies an autoregressive model to improve forecast accuracy at short lead-times and propagate uncertainty through the forecast. FoGSS generates ensemble forecasts in the form of time series for the coming 12 months. In a case study of two catchments, FoGSS produces reliable forecasts at all lead-times. Forecast skill with respect to climatology is evident to lead-times of about 3 months. At longer lead-times, forecast skill approximates that of climatology forecasts; that is, forecasts become like stochastic scenarios. Because forecast skill is virtually never negative at long lead-times, forecasts of accumulated volumes can be skillful. Forecasts of accumulated 12 month streamflow volumes are significantly skillful in several instances, and ensembles of accumulated volumes are reliable. We conclude that FoGSS forecasts could be highly useful to water managers.

  12. Method for Water Management Considering Long-term Probabilistic Forecasts

    Science.gov (United States)

    Hwang, J.; Kang, J.; Suh, A. S.

    2015-12-01

    This research is aimed at predicting the monthly inflow of the Andong-dam basin in South Korea using long-term probabilistic forecasts to apply long-term forecasts to water management. Forecasted Cumulative Distribution Functions (CDFs) of monthly precipitation are plotted by combining the range of monthly precipitation based on proper Probability Density Function (PDF) in past data with probabilistic forecasts in each category. Ensembles of inflow are estimated by entering generated ensembles of precipitation based on the CDFs into the 'abcd' water budget model. The bias and RMSE between averages in past data and observed inflow are compared to them in forecasted ensembles. In our results, the bias and RMSE of average precipitation in the forecasted ensemble are bigger than in past data, whereas the average inflow in the forecasted ensemble is smaller than in past data. This result could be used for reference data to apply long-term forecasts to water management, because of the limit in the number of forecasted data for verification and differences between the Andong-dam basin and the forecasted regions. This research has significance by suggesting a method of applying probabilistic information in climate variables from long-term forecasts to water management in Korea. Original data of a climate model, which produces long-term probabilistic forecasts should be verified directly as input data of a water budget model in the future, so that a more scientific response in water management against uncertainty of climate change could be reached.

  13. Extreme precipitation event over North China in August 2010: observations, monthly forecasting, and link to intra-seasonal variability of the Silk-Road wave-train across Eurasia

    Science.gov (United States)

    Orsolini, Yvan; Zhang, Ling; Peters, Dieter; Fraedrich, Klaus

    2014-05-01

    Forecast of regional precipitation events at the sub-seasonal timescale remains a big challenge for operational global prediction systems. Over the Far East in summer, climate and precipitation are strongly influenced by the fluctuating western Pacific subtropical high (WPSH) and strong precipitation is often associated with southeasterly low-level wind that brings moist-laden air from the southern China seas. The WPSH variability is partly influenced by quasi-stationary wave-trains propagating eastwards from Europe across Asia along the two westerly jets: the Silk-Road wave-train along the Asian jet at mid-latitudes and, on a more northern route, the polar wave-train along the sub-polar jet. While the Silk-Road wave-train appears as a robust, internal mode of variability in seasonal predictions models, its predictability is very low on the sub-seasonal to seasonal time scale. A case in point is the unusual summer of 2010, when China experienced its worst seasonal flooding for a decade, triggered by unusually prolonged and severe monsoonal rains. In addition that summer was also characterized by record-breaking heat wave over Eastern Europe and Russia as well as catastrophic monsoonal floods in Pakistan 2010. The impact of the latter circulation anomalies on the precipitation further east over China, has been little explored. Here, we examine the role and the actual predictability of the Silk-Road wave-train, and its impact on precipitation over Northeastern China throughout August 2010, using the high-resolution IFS forecast model of ECMWF, realistic initialized and run in an ensemble mode. We demonstrate that the forecast failure with regard to flooding and extreme precipitation over Northeastern China in August 2010 is linked to the failure to represent intra-seasonal variations of the Silk-Road wave-train and the associated intensification of the WPSH.

  14. Seasonal streamflow forecasting with the global hydrological forecasting system FEWS-World

    Science.gov (United States)

    Candogan Yossef, N.; Van Beek, L. P.; Winsemius, H.; Bierkens, M. F.

    2011-12-01

    The year-to-year variability of river discharge brings about risks and opportunities in water resources management. Reliable hydrological forecasts and effective communication allow several sectors to make more informed management decisions. In many developing regions of the world, there are no efficient hydrological forecasting systems. For these regions, a global forecasting system which indicates increased probabilities of streamflow excesses or shortages over long lead-times can be of great value. FEWS-World is developed for this purpose. The system incorporates the global hydrological model PCR-GLOBWB and delivers streamflow forecasts on a global scale. This study investigates the skill and value of FEWS-World. Skill is defined as the ability of the system to forecast discharge extremes; and value is its usefulness for possible users and ultimately for affected populations. Skill is assessed in historical simulation mode as well as retroactive forecasting mode. The eventual goal is to transfer FEWS-World to operational forecasting mode, where the system will use operational seasonal forecasts from the European Center for Medium-Range Weather Forecasts (ECMWF). The results will be disseminated on the internet to provide valuable information for users in data and model-poor regions of the world. The preliminary skill assessment of PCR-GLOBWB in reproducing flow extremes is carried out for a selection of 20 large rivers of the world. The model is run for a historical period, with a meteorological forcing data set based on observations from the Climate Research Unit of the University of East Anglia, and the ERA-40 reanalysis of ECMWF. Model skill in reproducing monthly anomalies as well as floods and droughts is assessed by applying verification measures developed for deterministic meteorological forecasts. The results of this preliminary analysis shows that even where the simulated hydrographs are biased, higher skills can be attained in reproducing monthly

  15. 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)

  16. Probabilistic Forecasting of the Wave Energy Flux

    DEFF Research Database (Denmark)

    Pinson, Pierre; Reikard, G.; Bidlot, J.-R.

    2012-01-01

    markets. A methodology for the probabilistic forecasting of the wave energy flux is introduced, based on a log-Normal assumption for the shape of predictive densities. It uses meteorological forecasts (from the European Centre for Medium-range Weather Forecasts – ECMWF) and local wave measurements......Wave energy will certainly have a significant role to play in the deployment of renewable energy generation capacities. As with wind and solar, probabilistic forecasts of wave power over horizons of a few hours to a few days are required for power system operation as well as trading in electricity...... as input. The parameters of the models involved are adaptively and recursively estimated. The methodology is evaluated for 13 locations around North-America over a period of 15months. The issued probabilistic forecasts substantially outperform the various benchmarks considered, with improvements between 6...

  17. Seasonal UK hydrological forecasts using rainfall forecasts - what level of skill?

    Science.gov (United States)

    Bell, Victoria; Davies, Helen; Kay, Alison; Scaife, Adam

    2017-04-01

    Skilful winter seasonal predictions for the North Atlantic circulation and Northern Europe, including the UK have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. The Hydrological Outlook UK (HOUK: www.hydoutuk.net) is the first operational hydrological forecast system for the UK that delivers monthly outlooks of the water situation for both river flow and groundwater levels. The output from the HOUK are publicly available and used each month by government agencies, practitioners and academics alongside other sources of information such as flood warnings and meteorological forecasts. The HOUK brings together information on current and forecast weather conditions, and river flows, and uses several modelling approaches to explore possible future hydrological conditions. One of the techniques combines ensembles of monthly-resolution seasonal rainfall forecasts provided by the Met Office GloSea5 forecast system with hydrological modelling tools to provide estimates of river flows up to a few months ahead. The approach combines a high resolution, spatially distributed hydrological initial condition (HIC) provided by a hydrological model (Grid-to-Grid) driven by weather observations up to the forecast time origin. Considerable efforts have been made to accommodate the temporal and spatial resolution of the GloSea5 rainfall forecasts (monthly time-step and national-scale) in a spatially distributed forecasting system, leading to the development of a monthly resolution water balance model (WBM) to forecast regional mean river flows for the next 1 and 3 months ahead. The work presented here provides the first assessment of the skill in the HOUK national-scale flow forecasts using an ensemble of rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009). The skill in the combined modelling system has been assessed for different seasons and regions of Britain, and compared to what might be achieved using

  18. Long-Range Forecasting of Surface Air Temperature and Precipitation for the Korean Peninsula

    Science.gov (United States)

    2013-03-01

    climate variations causing floods and droughts in the U.S. and on global-scale impacts of ENLN. The CAB does not produce forecasts specifically for...each month and predictand. Gillies (2012) used a lagged average ensemble approach (Hoffman and Kalnay 1983). He created cumulative forecasts that...included all forecast members available at the time of the forecast issuance. His lagged average ensembles represented the forecasts from multiple

  19. 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.

  20. Utilizing Climate Forecasts for Improving Water and Power Systems Coordination

    Science.gov (United States)

    Arumugam, S.; Queiroz, A.; Patskoski, J.; Mahinthakumar, K.; DeCarolis, J.

    2016-12-01

    Climate forecasts, typically monthly-to-seasonal precipitation forecasts, are commonly used to develop streamflow forecasts for improving reservoir management. Irrespective of their high skill in forecasting, temperature forecasts in developing power demand forecasts are not often considered along with streamflow forecasts for improving water and power systems coordination. In this study, we consider a prototype system to analyze the utility of climate forecasts, both precipitation and temperature, for improving water and power systems coordination. The prototype system, a unit-commitment model that schedules power generation from various sources, is considered and its performance is compared with an energy system model having an equivalent reservoir representation. Different skill sets of streamflow forecasts and power demand forecasts are forced on both water and power systems representations for understanding the level of model complexity required for utilizing monthly-to-seasonal climate forecasts to improve coordination between these two systems. The analyses also identify various decision-making strategies - forward purchasing of fuel stocks, scheduled maintenance of various power systems and tradeoff on water appropriation between hydropower and other uses - in the context of various water and power systems configurations. Potential application of such analyses for integrating large power systems with multiple river basins is also discussed.

  1. General correcting formula of forecasting?

    OpenAIRE

    2009-01-01

    A general correcting formula of forecasting (as a framework for long-use and standardized forecasts) is proposed. The formula provides new forecasting resources and areas of application including economic forecasting.

  2. General correcting formula of forecasting?

    OpenAIRE

    Harin, Alexander

    2009-01-01

    A general correcting formula of forecasting (as a framework for long-use and standardized forecasts) is proposed. The formula provides new forecasting resources and areas of application including economic forecasting.

  3. Monthly errors

    Data.gov (United States)

    U.S. Environmental Protection Agency — The 2006 monthly average statistical metrics for 2m Q (g kg-1) domain-wide for the base and MODIS WRF simulations against MADIS observations. This dataset is...

  4. Optimal lead time for dengue forecast.

    Directory of Open Access Journals (Sweden)

    Yien Ling Hii

    Full Text Available BACKGROUND: A dengue early warning system aims to prevent a dengue outbreak by providing an accurate prediction of a rise in dengue cases and sufficient time to allow timely decisions and preventive measures to be taken by local authorities. This study seeks to identify the optimal lead time for warning of dengue cases in Singapore given the duration required by a local authority to curb an outbreak. METHODOLOGY AND FINDINGS: We developed a Poisson regression model to analyze relative risks of dengue cases as functions of weekly mean temperature and cumulative rainfall with lag times of 1-5 months using spline functions. We examined the duration of vector control and cluster management in dengue clusters > = 10 cases from 2000 to 2010 and used the information as an indicative window of the time required to mitigate an outbreak. Finally, we assessed the gap between forecast and successful control to determine the optimal timing for issuing an early warning in the study area. Our findings show that increasing weekly mean temperature and cumulative rainfall precede risks of increasing dengue cases by 4-20 and 8-20 weeks, respectively. These lag times provided a forecast window of 1-5 months based on the observed weather data. Based on previous vector control operations, the time needed to curb dengue outbreaks ranged from 1-3 months with a median duration of 2 months. Thus, a dengue early warning forecast given 3 months ahead of the onset of a probable epidemic would give local authorities sufficient time to mitigate an outbreak. CONCLUSIONS: Optimal timing of a dengue forecast increases the functional value of an early warning system and enhances cost-effectiveness of vector control operations in response to forecasted risks. We emphasize the importance of considering the forecast-mitigation gaps in respective study areas when developing a dengue forecasting model.

  5. 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.

  6. Seasonal hydrological ensemble forecasts over Europe

    Science.gov (United States)

    Arnal, Louise; Wetterhall, Fredrik; Stephens, Elisabeth; Cloke, Hannah; Pappenberger, Florian

    2016-04-01

    This study investigates the limits of predictability in dynamical seasonal discharge forecasting, in both space and time, over Europe. Seasonal forecasts have an important socioeconomic value. Applications are numerous and cover hydropower management, spring flood prediction, low flow prediction for navigation and agricultural water demands. Additionally, the constant increase in NWP skill for longer lead times and the predicted increase in the intensity and frequency of hydro-meteorological extremes, have amplified the incentive to promote and further improve hydrological forecasts on sub-seasonal to seasonal timescales. In this study, seasonal hydrological forecasts (SEA), driven by the ECMWF's System 4 in hindcast mode, were analysed against an Ensemble Streamflow Prediction (ESP) benchmark. The ESP was forced with an ensemble of resampled historical meteorological observations and started with perfect initial conditions. Both forecasts were produced by the LISFLOOD model, run on the pan-European scale with a spatial resolution of 5 by 5 km. The forecasts were issued monthly on a daily time step, from 1990 until the current time, up to a lead time of 7 months. The seasonal discharge forecasts were analysed against the ESP on a catchment scale in terms of their accuracy, skill and sharpness, using a diverse set of verification metrics (e.g. KGE, CRPSS and ROC). Additionally, a reverse-ESP was constructed by forcing the LISFLOOD model with a single perfect meteorological set of observations and initiated from an ensemble of resampled historical initial conditions. The comparison of the ESP with the reverse-ESP approach enabled the identification of the respective contribution of meteorological forcings and hydrologic initial conditions errors to seasonal discharge forecasting uncertainties in Europe. These results could help pinpoint target elements of the forecasting chain which, after being improved, could lead to substantial increase in discharge predictability

  7. Demand Forecasting Errors

    OpenAIRE

    Mackie, Peter; Nellthorp, John; Laird, James

    2005-01-01

    Demand forecasts form a key input to the economic appraisal. As such any errors present within the demand forecasts will undermine the reliability of the economic appraisal. The minimization of demand forecasting errors is therefore important in the delivery of a robust appraisal. This issue is addressed in this note by introducing the key issues, and error types present within demand fore...

  8. Benchmarking Judgmentally Adjusted Forecasts

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); L.P. de Bruijn (Bert)

    2017-01-01

    textabstractMany publicly available macroeconomic forecasts are judgmentally adjusted model-based forecasts. In practice, usually only a single final forecast is available, and not the underlying econometric model, nor are the size and reason for adjustment known. Hence, the relative weights given

  9. Benchmarking judgmentally adjusted forecasts

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); L.P. de Bruijn (Bert)

    2015-01-01

    markdownabstractMany publicly available macroeconomic forecasts are judgmentally-adjusted model-based forecasts. In practice usually only a single final forecast is available, and not the underlying econometric model, nor are the size and reason for adjustment known. Hence, the relative weights

  10. Benchmarking judgmentally adjusted forecasts

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); L.P. de Bruijn (Bert)

    2015-01-01

    markdownabstractMany publicly available macroeconomic forecasts are judgmentally-adjusted model-based forecasts. In practice usually only a single final forecast is available, and not the underlying econometric model, nor are the size and reason for adjustment known. Hence, the relative weights give

  11. 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...

  12. Evaluating probability forecasts

    CERN Document Server

    Lai, Tze Leung; Shen, David Bo; 10.1214/11-AOS902

    2012-01-01

    Probability forecasts of events are routinely used in climate predictions, in forecasting default probabilities on bank loans or in estimating the probability of a patient's positive response to treatment. Scoring rules have long been used to assess the efficacy of the forecast probabilities after observing the occurrence, or nonoccurrence, of the predicted events. We develop herein a statistical theory for scoring rules and propose an alternative approach to the evaluation of probability forecasts. This approach uses loss functions relating the predicted to the actual probabilities of the events and applies martingale theory to exploit the temporal structure between the forecast and the subsequent occurrence or nonoccurrence of the event.

  13. Inaccuracy in traffic forecasts

    DEFF Research Database (Denmark)

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

    2006-01-01

    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...

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

    Directory of Open Access Journals (Sweden)

    T. Sinha

    2012-04-01

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

  15. 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.

  16. Forecasting Sales in a Sugar Factory

    Directory of Open Access Journals (Sweden)

    Vassilios ASSIMAKOPOULOS

    2011-12-01

    Full Text Available Beets’ cultivation and sugar production represent one of the most important parts of Greek agricultural economy. A careful and well-organized planning of the production as well as the determination of an accurate safety stock is important for sugar industry, as for many other companies and organizations, in order to define the production quantity which leads to maximum revenues and profits. Forecasting, and especially widely used statistical forecasting techniques, is the best way for policymakers to organize their activities and company’s production and make the appropriate adjustments. Apparently, management information systems and forecasting support packages play a leading role in this area, since the amount of data under process is usually quite large and demands an automated procedure to effectively produce and evaluate forecasts. In this case study, “Pythia”, an expert forecasting platform developed by the Forecasting and Strategy Unit of the National Technical University of Athens, was implemented on a monthly data series regarding sugar sales of a Greek sugar factory for the years 2000-2005, bringing theory and practice together. Additionally, the methods or combinations of methods which are well suited for this time series are highlighted based on three error indices. Finally, the results of the study and conclusions are considered and perspectives of progress and development in the field of forecasting are contemplated.

  17. 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 impact

  18. Objective Lightning Probability Forecast Tool Phase II

    Science.gov (United States)

    Lambert, Winnie

    2007-01-01

    This presentation describes the improvement of a set of lightning probability forecast equations that are used by the 45th Weather Squadron forecasters for their daily 1100 UTC (0700 EDT) weather briefing during the warm season months of May-September. This information is used for general scheduling of operations at Cape Canaveral Air Force Station and Kennedy Space Center. Forecasters at the Spaceflight Meteorology Group also make thunderstorm forecasts during Shuttle flight operations. Five modifications were made by the Applied Meteorology Unit: increased the period of record from 15 to 17 years, changed the method of calculating the flow regime of the day, calculated a new optimal layer relative humidity, used a new smoothing technique for the daily climatology, and used a new valid area. The test results indicated that the modified equations showed and increase in skill over the current equations, good reliability, and an ability to distinguish between lightning and non-lightning days.

  19. Forecasting tourist arrivals in South Africa

    Directory of Open Access Journals (Sweden)

    Andrea Saayman

    2010-12-01

    Full Text Available Purpose: The aim of this paper is to model and forecast tourism to South Africa from the country's main intercontinental tourism markets. These include Great Britain, Germany, the Netherlands, the United States of America and France. Problem investigated: Tourism to South Africa has grown substantially since the first democratic elections in 1994. It is currently the third largest industry in the country and a vital source of foreign exchange earnings. Tourist arrivals continue to grow annually, and have shown some resilience to a number of emerging market crises, including the terrorist attacks in the USA. Business success, marketing decisions, government's investment policy as well as macroeconomic policy are influenced by the accuracy of tourism forecasts, since the tourism product comprises a number of services that cannot be accumulated. Accurate forecasts of tourism demand are paramount to ensure the availability of such services when demanded. In addition, the seasonal nature of tourism leads to a pattern of excess capacity followed by shortage in capacity. Method: Since univariate time series modelling has proved to be a very successful method for forecasting tourist arrivals, it is also the method employed in this paper. The naïve model is tested against a standard ARIMA model, as well as the Holt-Winters exponential smoothing and seasonal-non-seasonal ARIMA models. Forecasting accuracy is assessed using the mean absolute percentage error, root mean square error and Theill's U of the various models. Monthly tourist arrivals from 1994 to 2006 are used in the analysis, and arrivals are forecasted for 2007. Findings: The results show that seasonal ARIMA models deliver the most accurate predictions of arrivals over three time horizons, namely three months, six months and 12 months. Value: This paper is the first tourist arrivals forecast using South African data for the country as a whole, and therefore it forms an interesting case study

  20. Lessons from the first generation of marine ecological forecast products

    NARCIS (Netherlands)

    Payne, Mark R.; Hobday, Alistair J.; MacKenzie, Brian R.; Tommasi, Desiree; Dempsey, Danielle P.; Fassler, Sascha; Haynie, Alan C.; Ji, Rubao; Liu, Gang; Lynch, Patrick D.; Matei, Daniela; Miesner, Anna K.; Mills, Katherine E.; Strand, Kjersti O.; Villarino, Ernesto

    2017-01-01

    Recent years have seen a rapid expansion in the ability of earth system models to describe and predict the physical state of the ocean. Skilful forecasts ranging from seasonal (3 months) to decadal (5-10 years) time scales are now a reality. With the advance of these forecasts of ocean physics, the

  1. 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...

  2. Previsão da temperatura média mensal de Uberlândia, MG, com modelos de séries temporais Forecast of monthly mean temperatures in Uberlândia, Minas Gerais, Brazil using time series models

    Directory of Open Access Journals (Sweden)

    Maria I. S. Silva

    2008-10-01

    Full Text Available Modelos de séries temporais têm sido amplamente usados no estudo de variáveis climatológicas, como temperatura e precipitação. Diversos são os objetivos traçados neste trabalho a fim de se analisar a série de temperatura média mensal da cidade de Uberlândia, MG, descrevendo seus componentes, e fazer previsões para períodos subseqüentes através de modelos ajustados para a série. A análise permitiu identificar, na série, a presença dos componentes, tendência e sazonalidade. Modelos do tipo SARIMA foram ajustados e, por meio dos critérios AIC (Akaike Information Criterion, BIC (Bayesian Information Criterion e MSE (Mean Square Error foi selecionado o modelo SARIMA (3,1,0(0,1,1 para fins de previsão.Time series models have been used for climatological variables such as temperature and rainfall. In this paper, time series of monthly mean temperatures for the municipality of Uberlândia, Minas Gerais, were analyzed describing their components and making forecasts using the SARIMA models. The analysis showed trend and seasonality components. The SARIMA model was adjusted by AIC (Akaike Information Criterion, BIC (Bayesian Information Criterion and MSE (Mean Square Error criterion. The results showed that the SARIMA (3,1,0(0,1,1 was a good model for forecasting.

  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...... have improved over time, as often claimed by forecasters, this does not show in the data. For nine out of ten rail projects, passenger forecasts are overestimated; average overestimation is 106%. For 72% of rail projects, forecasts are overestimated by more than two-thirds. For 50% of road projects...... 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. 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...

  5. Forecaster priorities for improving probabilistic flood forecasts

    Science.gov (United States)

    Wetterhall, Fredrik; Pappenberger, Florian; Alfieri, Lorenzo; Cloke, Hannah; Thielen, Jutta

    2014-05-01

    Hydrological ensemble prediction systems (HEPS) have in recent years been increasingly used for the operational forecasting of floods by European hydrometeorological agencies. The most obvious advantage of HEPS is that more of the uncertainty in the modelling system can be assessed. In addition, ensemble prediction systems generally have better skill than deterministic systems both in the terms of the mean forecast performance and the potential forecasting of extreme events. Research efforts have so far mostly been devoted to the improvement of the physical and technical aspects of the model systems, such as increased resolution in time and space and better description of physical processes. Developments like these are certainly needed; however, in this paper we argue that there are other areas of HEPS that need urgent attention. This was also the result from a group exercise and a survey conducted to operational forecasters within the European Flood Awareness System (EFAS) to identify the top priorities of improvement regarding their own system. They turned out to span a range of areas, the most popular being to include verification of an assessment of past forecast performance, a multi-model approach for hydrological modelling, to increase the forecast skill on the medium range (>3 days) and more focus on education and training on the interpretation of forecasts. In light of limited resources, we suggest a simple model to classify the identified priorities in terms of their cost and complexity to decide in which order to tackle them. This model is then used to create an action plan of short-, medium- and long-term research priorities with the ultimate goal of an optimal improvement of EFAS in particular and to spur the development of operational HEPS in general.

  6. Towards reliable seasonal ensemble streamflow forecasts for ephemeral rivers

    Science.gov (United States)

    Bennett, James; Wang, Qj; Li, Ming; Robertson, David

    2016-04-01

    Despite their inherently variable nature, ephemeral rivers are an important water resource in many dry regions. Water managers are likely benefit considerably from even mildly skilful ensemble forecasts of streamflow in ephemeral rivers. As with any ensemble forecast, forecast uncertainty - i.e., the spread of the ensemble - must be reliably quantified to allow users of the forecasts to make well-founded decisions. Correctly quantifying uncertainty in ephemeral rivers is particularly challenging because of the high incidence of zero flows, which are difficult to handle with conventional statistical techniques. Here we apply a seasonal streamflow forecasting system, the model for generating Forecast Guided Stochastic Scenarios (FoGSS), to 26 Australian ephemeral rivers. FoGSS uses post-processed ensemble rainfall forecasts from a coupled ocean-atmosphere prediction system to force an initialised monthly rainfall runoff model, and then applies a staged hydrological error model to describe and propagate hydrological uncertainty in the forecast. FoGSS produces 12-month streamflow forecasts; as forecast skill declines with lead time, the forecasts are designed to transit seamlessly to stochastic scenarios. The ensemble rainfall forecasts used in FoGSS are known to be unbiased and reliable, and we concentrate here on the hydrological error model. The FoGSS error model has several features that make it well suited to forecasting ephemeral rivers. First, FoGSS models the error after data is transformed with a log-sinh transformation. The log-sinh transformation is able to normalise even highly skewed data and homogenise its variance, allowing us to assume that errors are Gaussian. Second, FoGSS handles zero values using data censoring. Data censoring allows streamflow in ephemeral rivers to be treated as a continuous variable, rather than having to model the occurrence of non-zero values and the distribution of non-zero values separately. This greatly simplifies parameter

  7. Skill of global hydrological forecasting system FEWS GLOWASIS using climatic ESP forecasts

    Science.gov (United States)

    Weerts, A. H.; Candogan, N.; Winsemius, H. C.; van Beek, R.; Westerhoff, R.

    2012-04-01

    Forecasting of water availability and scarcity is a prerequisite for the management of hydropower reservoirs, basin-scale management of water resources, agriculture and disaster relief. The EU 7th Framework Program project Global Water Scarcity Information Service (GLOWASIS) aims to pre-validate a service that provides real-time global-scale information on water scarcity. In this contribution, we demonstrate what skill (compared to a climatology) may be reached with a global seasonal ensemble forecasting system consisting of: a) a global hydrological model PCR-GLOBWB; b) an updating procedure for initial forecasting states, based on the best-guess global rainfall information. As best guess, a combination of ERA-Interim Reanalysis rainfall and Global Precipitation Climatology Project (GPCP) observations is being used; c) a forecast based on Ensemble Streamflow Prediction (ESP)procedure and reverse ESP procedure (Wood and Lettenmaier, 2008). In the ESP procedure, a meteorological forecast ensemble is generated based on past years of observation series (i.e. climatological observations). As observations, the combination of ERA-Interim and GPCP is used. In reverse ESP, an ensemble is generated by using a set of initial states from a large sample of updates at the specific month of interest, and forecasts are produced using one observed set. This analysis allows us to measure how much skill may be expected from hydrological inertia and climatology alone, leaving aside for the moment potential skill improvement by using seasonal meteorological forecasts. In future work, we will measure how much skill improvement compared to the forecasts mentioned above may be reached, when ECMWF Seasonal forecasts are used. This will allow us to measure the contributions to skill of each component (initial state inertia, hydrology and meteorological inputs) of the forecast system.

  8. Discrete post-processing of total cloud cover ensemble forecasts

    Science.gov (United States)

    Hemri, Stephan; Haiden, Thomas; Pappenberger, Florian

    2017-04-01

    This contribution presents an approach to post-process ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical post-processing of ensemble predictions are tested. The first approach is based on multinomial logistic regression, the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on station-wise post-processing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model. Reference Hemri, S., Haiden, T., & Pappenberger, F. (2016). Discrete post-processing of total cloud cover ensemble forecasts. Monthly Weather Review 144, 2565-2577.

  9. Downscaling of GCM forecasts to streamflow over Scandinavia

    DEFF Research Database (Denmark)

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

    2008-01-01

    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......-western Norway. The physical interpretation of the forecasting skill is that stations close to the Norwegian coast are directly exposed to prevailing winds from the Atlantic ocean, which constitute the principal source of predictive information from the atmosphere on the seasonal timescale....... 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...

  10. The GOCF/AWAP system - forecasting temperature extremes

    Energy Technology Data Exchange (ETDEWEB)

    Fawcett, Robert [National Climate Centre, Australian Bureau of Meteorology, Docklands, Victoria 3008 (Australia); Hume, Timothy, E-mail: r.fawcett@bom.gov.a, E-mail: t.hume@bom.gov.a [Centre for Australian Weather and Climate Research, Australian Bureau of Meteorology, Docklands, Victoria 3008 (Australia)

    2010-08-15

    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 {sup 0}C and standard deviations.

  11. The GOCF/AWAP system - forecasting temperature extremes

    Science.gov (United States)

    Fawcett, Robert; Hume, Timothy

    2010-08-01

    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 °C and standard deviations.

  12. 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...... evidence of forecaster herding. On the contrary, forecasters anti-herd and, thereby, tend to intentionally scatter their forecasts around the consensus forecast. The extent of anti-herding seems to vary over time.We also found that more pronounced anti-herding leads to less accurate forecasts....

  13. 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.

  14. 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.

  15. 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...

  16. Towards custom made seasonal/decadal forecasting

    Science.gov (United States)

    Mahlstein, Irina; Spirig, Christoph; Liniger, Mark

    2014-05-01

    Climate indices offer the possibility to deliver information to the end user that can be easily applied to their field of work. For instance, a 3-monthly mean average temperature does not say much about the Heating Degree Days of a season, or how many frost days there are to be expected. Hence, delivering aggregated climate information can be more useful to the consumer than just raw data. In order to ensure that the end-users actually get what they need, the providers need to know what exactly they need to deliver. Hence, the specific user-needs have to be identified. In the framework of EUPORIAS, interviews with the end-user were conducted in order to learn more about the types of information that are needed. But also to investigate what knowledge exists among the users about seasonal/decadal forecasting and in what way uncertainties are taken into account. It is important that we gain better knowledge of how forecasts/predictions are applied by the end-user to their specific situation and business. EUPORIAS, which is embedded in the framework of EU FP7, aims exactly to improve that knowledge and deliver very specific forecasts that are custom made. Here we present examples of seasonal forecasts and their skill of several climate impact indices with direct relevance for specific economic sectors, such as energy. The results are compared to the visualization of conventional depiction of seasonal forecasts, such as 3 monthly average temperature tercile probabilities and the differences are highlighted.

  17. Towards seasonal forecasting of malaria in India.

    Science.gov (United States)

    Lauderdale, Jonathan M; Caminade, Cyril; Heath, Andrew E; Jones, Anne E; MacLeod, David A; Gouda, Krushna C; Murty, Upadhyayula Suryanarayana; Goswami, Prashant; Mutheneni, Srinivasa R; Morse, Andrew P

    2014-08-10

    Malaria presents public health challenge despite extensive intervention campaigns. A 30-year hindcast of the climatic suitability for malaria transmission in India is presented, using meteorological variables from a state of the art seasonal forecast model to drive a process-based, dynamic disease model. The spatial distribution and seasonal cycles of temperature and precipitation from the forecast model are compared to three observationally-based meteorological datasets. These time series are then used to drive the disease model, producing a simulated forecast of malaria and three synthetic malaria time series that are qualitatively compared to contemporary and pre-intervention malaria estimates. The area under the Relative Operator Characteristic (ROC) curve is calculated as a quantitative metric of forecast skill, comparing the forecast to the meteorologically-driven synthetic malaria time series. The forecast shows probabilistic skill in predicting the spatial distribution of Plasmodium falciparum incidence when compared to the simulated meteorologically-driven malaria time series, particularly where modelled incidence shows high seasonal and interannual variability such as in Orissa, West Bengal, and Jharkhand (North-east India), and Gujarat, Rajastan, Madhya Pradesh and Maharashtra (North-west India). Focusing on these two regions, the malaria forecast is able to distinguish between years of "high", "above average" and "low" malaria incidence in the peak malaria transmission seasons, with more than 70% sensitivity and a statistically significant area under the ROC curve. These results are encouraging given that the three month forecast lead time used is well in excess of the target for early warning systems adopted by the World Health Organization. This approach could form the basis of an operational system to identify the probability of regional malaria epidemics, allowing advanced and targeted allocation of resources for combatting malaria in India.

  18. Forecasting with Colonel Blotto

    NARCIS (Netherlands)

    Peeters, R.J.A.P.; Wolk, K.L.

    2015-01-01

    In this paper we design and test a competitive forecasting mechanism based on the Colonel Blotto game. In the game, forecasters allocate a fixed number of resources to different 'battlefields'. Each field is realized with a probability that is determined by a stochastic process. Subjects learn about

  19. Housing price forecastability

    DEFF Research Database (Denmark)

    Bork, Lasse; Møller, Stig Vinther

    2012-01-01

    We examine US housing price forecastability using a common factor approach based on a large panel of 122 economic time series. We …nd that a simple three-factor model generates an explanatory power of about 50% in one-quarter ahead in-sample forecasting regressions. The predictive power of the mo...

  20. Congressional Election Forecasting.

    Science.gov (United States)

    Lewis-Beck, Michael S.; Rice, Tom W.

    1988-01-01

    Reviews the growing literature on the forecasting of elections, providing an example in the form of 1988 congressional election predictions. Briefly discusses the history of election outcome prediction and outlines two scientific forecasting models which, the authors state, are appropriate for use in the classroom. (GEA)

  1. Earthquake forecast enrichment scores

    Directory of Open Access Journals (Sweden)

    Christine Smyth

    2012-03-01

    Full Text Available The Collaboratory for the Study of Earthquake Predictability (CSEP is a global project aimed at testing earthquake forecast models in a fair environment. Various metrics are currently used to evaluate the submitted forecasts. However, the CSEP still lacks easily understandable metrics with which to rank the universal performance of the forecast models. In this research, we modify a well-known and respected metric from another statistical field, bioinformatics, to make it suitable for evaluating earthquake forecasts, such as those submitted to the CSEP initiative. The metric, originally called a gene-set enrichment score, is based on a Kolmogorov-Smirnov statistic. Our modified metric assesses if, over a certain time period, the forecast values at locations where earthquakes have occurred are significantly increased compared to the values for all locations where earthquakes did not occur. Permutation testing allows for a significance value to be placed upon the score. Unlike the metrics currently employed by the CSEP, the score places no assumption on the distribution of earthquake occurrence nor requires an arbitrary reference forecast. In this research, we apply the modified metric to simulated data and real forecast data to show it is a powerful and robust technique, capable of ranking competing earthquake forecasts.

  2. 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

  3. 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. Qua

  4. 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

  5. Confidence in Coastal Forecasts

    NARCIS (Netherlands)

    Baart, F.

    2013-01-01

    This thesis answers the question "How can we show and improve our confidence in coastal forecasts?", by providing four examples of common coastal forecasts. The first example shows how to improve the estimate of the one in ten thousand year storm-surge level. The three dimensional reconstruction,

  6. 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.

  7. Aviation Forecasting in ICAO

    Science.gov (United States)

    Mcmahon, J.

    1972-01-01

    Opinions or plans of qualified experts in the field are used for forecasting future requirements for air navigational facilities and services of international civil aviation. ICAO periodically collects information from Stators and operates on anticipated future operations, consolidates this information, and forecasts the future level of activity at different airports.

  8. Lessons from the First Generation of Marine Ecological Forecast Products

    Directory of Open Access Journals (Sweden)

    Mark R. Payne

    2017-09-01

    Full Text Available Recent years have seen a rapid expansion in the ability of earth system models to describe and predict the physical state of the ocean. Skilful forecasts ranging from seasonal (3 months to decadal (5–10 years time scales are now a reality. With the advance of these forecasts of ocean physics, the first generation of marine ecological forecasts has started to emerge. Such forecasts are potentially of great value in the management of living marine resources and for all of those who are dependent on the ocean for both nutrition and their livelihood; however, this is still a field in its infancy. We review the state of the art in this emerging field and identify the lessons that can be learnt and carried forward from these pioneering efforts. The majority of this first wave of products are forecasts of spatial distributions, possibly reflecting the inherent suitability of this response variable to the task of forecasting. Promising developments are also seen in forecasting fish-stock recruitment where, despite well-recognized challenges in understanding and predicting this response, new process knowledge and model approaches that could form a basis for forecasting are becoming available. Forecasts of phenology and coral-bleaching events are also being applied to monitoring and industry decisions. Moving marine ecological forecasting forward will require striking a balance between what is feasible and what is useful. We propose here a set of criteria to quickly identify “low-hanging fruit” that can potentially be predicted; however, ensuring the usefulness of forecast products also requires close collaboration with actively engaged end-users. Realizing the full potential of marine ecological forecasting will require bridging the gaps between marine ecology and climatology on the one-hand, and between science and end-users on the other. Nevertheless, the successes seen thus far and the potential to develop further products suggest that the field

  9. Skill improvement of seasonal Arctic sea ice forecasts using bias-correction and ensemble calibration

    Science.gov (United States)

    Krikken, Folmer; Hazeleger, Wilco; Vlot, Willem; Schmeits, Maurice; Guemas, Virginie

    2016-04-01

    We explore the standard error and skill of dynamical seasonal sea ice forecasts of the Arctic using different bias-correction and ensemble calibration methods. The latter is often used in weather forecasting, but so far has not been applied to Arctic sea ice forecasts. We use seasonal predictions of Arctic sea ice of a 5-member ensemble forecast using the fully coupled GCM EC-Earth, with model initial states obtained by nudging towards ORAS4 and ERA-Interim. The raw model forecasts contain large biases in total sea ice area, especially during the summer months. This is mainly caused by a difference in average seasonal cycle between EC-Earth and observations, which translates directly into the forecasts yielding large biases. Further errors are introduced by the differences in long term trend between the observed sea ice, and the uninitialised EC-earth simulation. We find that extended logistic regression (ELR) and heteroscedastic extended logistic regression (HELR) both prove viable ensemble calibration methods, and improve the forecasts substantially compared to standard bias correction techniques. No clear distinction between ELR and HELR is found. Forecasts starting in May have higher skill (CRPSS > 0 up to 5 months lead time) than forecasts starting in August (2-3 months) and November (2-3 months), with trend-corrected climatology as reference. Analysis of regional skill in the Arctic shows distinct differences, where mainly the Arctic ocean and the Kara and Barents sea prove to be one of the more predictable regions with skilful forecasts starting in May up to 5-6 months lead time. Again, forecasts starting in August and November show much lower regional skill. Overall, it is still difficult to beat relative simple statistical forecasts, but by using ELR and HELR we are getting reasonably close to skilful seasonal forecasts up to 12 months lead time. These results show there is large potential, and need, for using ensemble calibration in seasonal forecasts of

  10. 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...

  11. 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)

  12. Judgmental Forecasting of Operational Capabilities

    DEFF Research Database (Denmark)

    Hallin, Carina Antonia; Tveterås, Sigbjørn; Andersen, Torben Juul

    This paper explores a new judgmental forecasting indicator, the Employee Sensed Operational Capabilities (ESOC). The purpose of the ESOC is to establish a practical prediction tool that can provide early signals about changes in financial performance by gauging frontline employees’ sensing...... can predict financial performance. Monthly data were collected from frontline employees in three different companies during an 18-month period, and the initial results indicate that the ESOChas predictive power....... of changes in the firm’s operational capabilities. We present the first stage of the development of ESOC by applying a formative measurement approach to test the index in relation to financial performance and against an organizational commitment scale. We use distributed lag models to test whether the ESOC...

  13. NCEP-ECPC monthly to seasonal US fire danger forecasts

    Science.gov (United States)

    J. Roads; P. Tripp; H. Juang; J. Wang; F. Fujioka; S. Chen

    2010-01-01

    Five National Fire Danger Rating System indices (including the Ignition Component, Energy Release Component, Burning Index, Spread Component, and the Keetch–Byram Drought Index) and the Fosberg Fire Weather Index are used to characterise US fire danger. These fire danger indices and input meteorological variables, including temperature, relative humidity, precipitation...

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

    Science.gov (United States)

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

    2017-09-01

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

  15. 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

  16. Combining forecasts in short term load forecasting: Empirical analysis and identification of robust forecaster

    Indian Academy of Sciences (India)

    YOGESH K BICHPURIYA; S A SOMAN; A SUBRAMANYAM

    2016-10-01

    We present an empirical analysis to show that combination of short term load forecasts leads to better accuracy. We also discuss other aspects of combination, i.e.,distribution of weights, effect of variation in the historical window and distribution of forecast errors. The distribution of forecast errors is analyzed in order to get a robust forecast. We define a robust forecaster as one which has consistency in forecast accuracy, lesser shocks (outliers) and lower standard deviation in the distribution of forecast errors. We propose a composite ranking (CRank) scheme based on a composite score which considers three performance measures—standard deviation, kurtosis of distribution of forecast errors and accuracy of forecasts. The CRank helps in identification of a robust forecasts given a choice of individual and combined forecaster. The empirical analysis has been done with the real life data sets of two distribution companies in India.

  17. 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

  18. 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.

  19. Artificial Neural Network for Monthly Rainfall Rate Prediction

    Science.gov (United States)

    Purnomo, H. D.; Hartomo, K. D.; Prasetyo, S. Y. J.

    2017-03-01

    Rainfall rate forecasting plays an important role in various human activities. Rainfall forecasting is a challenging task due to the uncertainty of natural phenomena. In this paper, two neural network models are proposed for monthly rainfall rate forecasting. The performance of the proposed model is assesses based on monthly rainfall rate in Ampel, Boyolali, from 2001-2013. The experiment results show that the accuracy of the first model is much better than the accuracy of the second model. Its average accuracy is just above 98%, while the accuracy of the second model is approximately 75%. In additional, both models tend to perform better when the fluctuation of rainfall is low.

  20. 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.

  1. Forecasting for dynamic line rating

    DEFF Research Database (Denmark)

    Michiorri, Andrea; Nguyen, Huu-Minh; Alessandrini, Stefano

    2015-01-01

    . In order to facilitate the integration of DLR into power system operations, research has been launched into DLR forecasting, following a similar avenue to IRS production forecasting, i.e. based on a mix of statistical methods and meteorological forecasts. The development of reliable DLR forecasts...

  2. An experimental seasonal hydrological forecasting system over the Yellow River basin - Part 2: The added value from climate forecast models

    Science.gov (United States)

    Yuan, Xing

    2016-06-01

    This is the second paper of a two-part series on introducing an experimental seasonal hydrological forecasting system over the Yellow River basin in northern China. While the natural hydrological predictability in terms of initial hydrological conditions (ICs) is investigated in a companion paper, the added value from eight North American Multimodel Ensemble (NMME) climate forecast models with a grand ensemble of 99 members is assessed in this paper, with an implicit consideration of human-induced uncertainty in the hydrological models through a post-processing procedure. The forecast skill in terms of anomaly correlation (AC) for 2 m air temperature and precipitation does not necessarily decrease over leads but is dependent on the target month due to a strong seasonality for the climate over the Yellow River basin. As there is more diversity in the model performance for the temperature forecasts than the precipitation forecasts, the grand NMME ensemble mean forecast has consistently higher skill than the best single model up to 6 months for the temperature but up to 2 months for the precipitation. The NMME climate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing model regionalized over the Yellow River basin to produce forecasts of soil moisture, runoff and streamflow. And the NMME/VIC forecasts are compared with the Ensemble Streamflow Prediction method (ESP/VIC) through 6-month hindcast experiments for each calendar month during 1982-2010. As verified by the VIC offline simulations, the NMME/VIC is comparable to the ESP/VIC for the soil moisture forecasts, and the former has higher skill than the latter only for the forecasts at long leads and for those initialized in the rainy season. The forecast skill for runoff is lower for both forecast approaches, but the added value from NMME/VIC is more obvious, with an increase of the average AC by 0.08-0.2. To compare with the observed

  3. Forecast Master Program case studies: Final report

    Energy Technology Data Exchange (ETDEWEB)

    Engle, R.; Granger, C.; Ramanathan, R. (ed.)

    1987-04-01

    This report presents a number of case studies using the computer software package FORECAST MASTER (FM). The series studied and forecast are, aggregate monthly California Electricity Sales, system energy demand data from Ontario Hydro, peak demand data for the residential and commercial customers of Georgia Power Company, Massachusetts Electric commercial sales, Narragansett Electric commercial sales, average and peak demand using Georgia Power Company data. A variety of methods have been studied by each of the contributing authors; trend line fitting, exponential smoothing, Box-Jenkins univariate forecasting, vector autoregression, state space modeling, dynamic econometric models including time-varying parameters and general order serial correlation corrections. Thus both the data sets and the modeling/forecasting methodologies are varied. A number of conclusions emerge from these case studies: FM provides a powerful set of tools to aid a utility forecaster, a great deal of caution should be exercised in pre-processing the data; it can have unintended side effects, diagnostic tests are very useful in econometric models, the Akaike Information Criterion is a useful measure for selecting the best state space model, and state space and econometric approaches both need equal amounts of care in model analysis and presentation.

  4. 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.

  5. Econometrics 101: forecasting demystified

    Energy Technology Data Exchange (ETDEWEB)

    Crow, R.T.

    1980-05-01

    Forecasting by econometric modeling is described in a commonsense way which omits much of the technical jargon. A trend of continuous growth is no longer an adequate forecasting tool. Today's forecasters must consider rapid changes in price, policies, regulations, capital availability, and the cost of being wrong. A forecasting model is designed by identifying future influences on electricity purchases and quantifying their relationships to each other. A record is produced which can be evaluated and used to make corrections in the models. Residential consumption is used to illustrate how this works and to demonstrate how power consumption is also related to the purchase and use of equipment. While models can quantify behavioral relationships, they cannot account for the impacts of non-price factors because of limited data. (DCK)

  6. Intelligent energy demand forecasting

    CERN Document Server

    Hong, Wei-Chiang

    2013-01-01

    This book offers approaches and methods to calculate optimal electric energy allocation, using evolutionary algorithms and intelligent analytical tools to improve the accuracy of demand forecasting. Focuses on improving the drawbacks of existing algorithms.

  7. 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...

  8. Financial Analysts’ Forecasts

    DEFF Research Database (Denmark)

    Stæhr, Simone

    in the financial forecasts on which they base investment decisions they may end up losing money as a consequence of biased forecasts. Thus, relying primarily on decision theories such as social comparison theory and theories on confirmation bias this thesis investigates how and why pronounced biases in financial......This thesis is broadly concentrated on decision making under uncertainty. It seeks to investigate how agents in financial markets make decisions at the individual level and how these decisions can sometimes be affected by personal traits and cognitive biases rather than being perfectly rational....... 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...

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

    Directory of Open Access Journals (Sweden)

    J. Schaake

    2007-04-01

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

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

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

  10. Value assessment of a global hydrological forecasting system

    Science.gov (United States)

    Candogan Yossef, N.; Winsemius, H.; van Beek, L. P. H.; van Beek, E.; Bierkens, M. F. P.

    2012-04-01

    investigated over lead times from short-range through medium-range to monthly and seasonal. These sectors are disaster management agencies and aid organizations, dam operators, hydro-power companies, irrigation agencies and agricultural sector, water supply companies, tourism sector and wildlife agencies, navigation sector, insurance companies. Most sectors benefit from seasonal forecasts in managing the risks and opportunities brought by the inter-annual variability of streamflow. Many sectors need forecasts on a lead time of several months to prepare for appropriate response, and others can make use of seasonal hydrological forecasts for planning purposes. A global forecasting system would be valuable for those parts of the world where there are no available local systems, provided that information on forecast reliability is properly communicated. The availability of a forecast along with an indication of its reliability offers water managers the choice of using the forecasts in decision-making. The real value of a forecast is specific to the user. It is to be determined on the basis of the the user's requirements, and by evaluating the benefits and costs of possible actions that can be taken in response to the forecast.

  11. 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...

  12. 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...

  13. An overview of health forecasting.

    Science.gov (United States)

    Soyiri, Ireneous N; Reidpath, Daniel D

    2013-01-01

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

  14. 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

  15. 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.

  16. Forecasting the Brazilian real and the Mexican peso: Asymmetric loss, forecast rationality, and forecaster herding

    DEFF Research Database (Denmark)

    Stadtmann, Georg; Fritsche, Ulrich; Pierdzioch, Christian

    2015-01-01

    Using forecasts of exchange rates of the Brazilian real and the Mexican peso against the US dollar, we analyze the symmetry of the loss function of exchange-rate forecasters and the rationality of their forecasts. Symmetry of the loss function can be rejected for some forecasters but not all. Even...... when allowing for asymmetric loss functions, the predictions of some forecasters do not fit the traditional definition of rational forecasts. We interpret our results in terms of recent research on forecaster (anti-)herding....

  17. 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...

  18. Increased Accuracy in Statistical Seasonal Hurricane Forecasting

    Science.gov (United States)

    Nateghi, R.; Quiring, S. M.; Guikema, S. D.

    2012-12-01

    Hurricanes are among the costliest and most destructive natural hazards in the U.S. Accurate hurricane forecasts are crucial to optimal preparedness and mitigation decisions in the U.S. where 50 percent of the population lives within 50 miles of the coast. We developed a flexible statistical approach to forecast annual number of hurricanes in the Atlantic region during the hurricane season. Our model is based on the method of Random Forest and captures the complex relationship between hurricane activity and climatic conditions through careful variable selection, model testing and validation. We used the National Hurricane Center's Best Track hurricane data from 1949-2011 and sixty-one candidate climate descriptors to develop our model. The model includes information prior to the hurricane season, i.e., from the last three months of the previous year (Oct. through Dec.) and the first five months of the current year (January through May). Our forecast errors are substantially lower than other leading forecasts such as that of the National Oceanic and Atmospheric Administration (NOAA).

  19. Long-range forecasting of intermittent streamflow

    Directory of Open Access Journals (Sweden)

    F. F. van Ogtrop

    2011-01-01

    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 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 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 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.

  20. Forecasting electricity usage using univariate time series models

    Science.gov (United States)

    Hock-Eam, Lim; Chee-Yin, Yip

    2014-12-01

    Electricity is one of the important energy sources. A sufficient supply of electricity is vital to support a country's development and growth. Due to the changing of socio-economic characteristics, increasing competition and deregulation of electricity supply industry, the electricity demand forecasting is even more important than before. It is imperative to evaluate and compare the predictive performance of various forecasting methods. This will provide further insights on the weakness and strengths of each method. In literature, there are mixed evidences on the best forecasting methods of electricity demand. This paper aims to compare the predictive performance of univariate time series models for forecasting the electricity demand using a monthly data of maximum electricity load in Malaysia from January 2003 to December 2013. Results reveal that the Box-Jenkins method produces the best out-of-sample predictive performance. On the other hand, Holt-Winters exponential smoothing method is a good forecasting method for in-sample predictive performance.

  1. Advances in Business and Management Forecasting

    CERN Document Server

    Lawrence, Kenneth D

    2011-01-01

    The topics within Advances in Business and Management Forecasting will normally include sales and marketing, forecasting, new product forecasting, judgmentally-based forecasting, the application of surveys to forecasting, forecasting for strategic business decisions, improvements in forecasting accuracy, and sales response models.

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

    OpenAIRE

    Rhee, J; Im, J.; Park, S.

    2016-01-01

    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 Precipitatio...

  3. 基于月度数据的景区客流预测优化研究——以黄山风景区为例%Research on Forecasting Optimization of Tourist Arrivals in Scenic Areas Based on Monthly Data——A Case Study of Huangshan Scenic Areas

    Institute of Scientific and Technical Information of China (English)

    余向洋; 朱国兴; 沙润; 胡善风; 汪丽

    2012-01-01

    Based on 24 years’monthly data of tourist arrivals in Huangshan Scenic Areas,9 forecasting methods including widely used time series prediction methods,Least Square Support Vector Machine(LSSVM),recently popular conbined prediction method,and etal.,are adopted to predict tourist arrivals of 2-year time scale,and optimized by Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE) and Theil Inequality Coefficient(TIC) in order that the proper method is chosed to help provide decision support for managers.The result shows autoregressive integrated moving average ARIMA perform best in RMSE,MAE,and TIC,LSSVM perform best in MAPE,but other prediction methods don’t present obvious advantage.%采用当前使用极为广泛的9种方法,利用黄山风景区24年(1987年1月—2010年12月)的月度客流数据进行2年时间尺度的预测,并采用均方根误差(RMSE)、平均绝对误差(MAE)、平均绝对百分误差(MAPE)和希尔不等系数(TIC)等4个预测性能指标对预测模型进行比较优化,发挥旅游客流预报对于景区管理者的决策支持作用。研究结果表明:ARIMA方法在预测性能4个指标中有3个最优,LS-SVM方法的MAPE表现最优。而其他方法在本研究中则未表现出明显的优势。

  4. Short-term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

    Science.gov (United States)

    Hebert, David A.; Allard, Richard A.; Metzger, E. Joseph; Posey, Pamela G.; Preller, Ruth H.; Wallcraft, Alan J.; Phelps, Michael W.; Smedstad, Ole Martin

    2015-12-01

    In this study the forecast skill of the U.S. Navy operational Arctic sea ice forecast system, the Arctic Cap Nowcast/Forecast System (ACNFS), is presented for the period February 2014 to June 2015. ACNFS is designed to provide short term, 1-7 day forecasts of Arctic sea ice and ocean conditions. Many quantities are forecast by ACNFS; the most commonly used include ice concentration, ice thickness, ice velocity, sea surface temperature, sea surface salinity, and sea surface velocities. Ice concentration forecast skill is compared to a persistent ice state and historical sea ice climatology. Skill scores are focused on areas where ice concentration changes by ±5% or more, and are therefore limited to primarily the marginal ice zone. We demonstrate that ACNFS forecasts are skilful compared to assuming a persistent ice state, especially beyond 24 h. ACNFS is also shown to be particularly skilful compared to a climatologic state for forecasts up to 102 h. Modeled ice drift velocity is compared to observed buoy data from the International Arctic Buoy Programme. A seasonal bias is shown where ACNFS is slower than IABP velocity in the summer months and faster in the winter months. In February 2015, ACNFS began to assimilate a blended ice concentration derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Interactive Multisensor Snow and Ice Mapping System (IMS). Preliminary results show that assimilating AMSR2 blended with IMS improves the short-term forecast skill and ice edge location compared to the independently derived National Ice Center Ice Edge product.

  5. 32 CFR 536.69 - Retention of file.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 3 2010-07-01 2010-07-01 true Retention of file. 536.69 Section 536.69 National... UNITED STATES Investigation and Processing of Claims § 536.69 Retention of file. After final action has been taken, the settlement authority will retain the file until at least one month after either...

  6. Identifiable Data Files - Denominator File

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Denominator File combines Medicare beneficiary entitlement status information from administrative enrollment records with third-party payer information and GHP...

  7. Low flow forecasting with a lead time of 14 days for navigation and energy supply in the Rhine River

    NARCIS (Netherlands)

    Demirel, M.C.; Booij, Martijn J.

    2011-01-01

    Low flow forecasting, days or even months in advance, is particularly important to the efficient operation of power plants and freight shipment. This study presents a low flow forecasting model with a lead time of 14 days for the Rhine River. The forecasts inherit uncertainty sources mainly because

  8. Computer files.

    Science.gov (United States)

    Malik, M

    1995-02-01

    From what has been said, several recommendations can be made for users of small personal computers regardless of which operating system they use. If your computer has a large hard disk not specially required by any single application, organize the disk into a small number of volumes. You will then be using the computer as if it had several smaller disks, which will help you to create a logical file structure. The size of individual volumes has to be selected carefully with respect to the files kept in each volume. Otherwise, it may be that you will have too much space in one volume and not enough in another. In each volume, organize the structure of directories and subdirectories logically so that they correspond to the logic of your file content. Be aware of the fact that the directories suggested as default when installing new software are often not the optimum. For instance, it is better to put different graphics packages under a common subdirectory rather than to install them at the same level as all other packages including statistics, text processors, etc. Create a special directory for each task you use the computer. Note that it is a bad practice to keep many different and logically unsorted files in the root directory of any of your volumes. Only system and important service files should be kept there. Although any file may be written all over the disk, access to it will be faster if it is written over the minimum number of cylinders. From time to time, use special programs that reorganize your files in this way.(ABSTRACT TRUNCATED AT 250 WORDS)

  9. 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

  10. 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.

  11. 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

  12. Forecasting with Universal Approximators and a Learning Algorithm

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl

    This paper applies three universal approximators for forecasting. They are the Artificial Neural Networks, the Kolmogorov- Gabor polynomials, as well as the Elliptic Basis Function Networks. Even though forecast combination has a long history in econometrics focus has not been on proving loss....... The use of universal approximators along with a combination scheme for which explicit loss bounds exist should give a solid theoretical foundation to the way the forecasts are performed. The practical performance will be investigated by considering various monthly postwar macroeconomic data sets for the G...

  13. Documentation for the NOAA Atlantic Real-Time Ocean Forecast System (RTOFS) models from the NOAA National Centers for Environmental Prediction (NCEP) (NODC Accession 0026419)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This NODC accession contains documentation files that apply to all data collections archived at NODC relating to the NOAA Atlantic Real-Time Ocean Forecast System...

  14. Drought forecasting in Luanhe River basin involving climatic indices

    Science.gov (United States)

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

    2016-09-01

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

  15. Time Series Forecasting: A Nonlinear Dynamics Approach

    OpenAIRE

    Sello, Stefano

    1999-01-01

    The problem of prediction of a given time series is examined on the basis of recent nonlinear dynamics theories. Particular attention is devoted to forecast the amplitude and phase of one of the most common solar indicator activity, the international monthly smoothed sunspot number. It is well known that the solar cycle is very difficult to predict due to the intrinsic complexity of the related time behaviour and to the lack of a succesful quantitative theoretical model of the Sun magnetic cy...

  16. Probability Forecast of Regional Landslide Based on Numerical Weather Forecast

    Institute of Scientific and Technical Information of China (English)

    GAO Kechang; WEI Fangqiang; CUI Peng; HU Kaiheng; XU Jing; ZHANG Guoping; BI Baogui

    2006-01-01

    The regional forecast of landslide is one of the key points of hazard mitigation. It is also a hot and difficult point in research field. To solve this problem has become urgent task along with Chinese economy fast development. This paper analyzes the principle of regional landslide forecast and the factors for forecasting. The method of a combination of Information Value Model and Extension Model has been put forward to be as the forecast model. Using new result of Numerical Weather Forecast Research and that combination model, we discuss the implementation feasibility of regional landslide forecast. Finally, with the help of Geographic Information System, an operation system for southwest of China landslide forecast has been developed. It can carry out regional landslide forecast daily and has been pilot run in NMC. Since this is the first time linking theoretical research with meteorological service, further works are needed to enhance it.

  17. 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.

  18. 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.

  19. Seasonal forecasting of fire over Kalimantan, Indonesia

    Directory of Open Access Journals (Sweden)

    A. C. Spessa

    2014-08-01

    Full Text Available 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 up-to-date and long series observations on burnt area and 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 area with several months lead time explaining at least 70% of the variance between rainfall and with burnt 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.

  20. 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.

  1. Probabilistic load forecasting via Quantile Regression Averaging on sister forecasts

    OpenAIRE

    Bidong Liu; Jakub Nowotarski; Tao Hong; Rafal Weron

    2015-01-01

    Majority of the load forecasting literature has been on point forecasting, which provides the expected value for each step throughout the forecast horizon. In the smart grid era, the electricity demand is more active and less predictable than ever before. As a result, probabilistic load forecasting, which provides additional information on the variability and uncertainty of future load values, is becoming of great importance to power systems planning and operations. This paper proposes a prac...

  2. Post-processing GCM daily rainfall and temperature forecasts for applications in water management and agriculture

    Science.gov (United States)

    Schepen, Andrew; Wang, Qj; Everingham, Yvette; Zhao, Tongtiegang

    2017-04-01

    Ensemble time series forecasts of rainfall and temperature up to six months ahead are sought for applications in water management and agricultural production. Raw GCM forecasts are generally not suitable for direct use in hydrological models or agricultural production simulators and must be post-processed first, to ensure they are reliable, as skilful as possible, and have realistic temporal patterns. In this study, we test two post-processing approaches to produce daily forecasts for cropping regions and water supply catchments in Australia. In the first approach, we apply the calibration, bridging and merging (CBaM) method to produce statistically reliable monthly forecasts based on GCM outputs of rainfall, temperature and sea surface temperatures. We then disaggregate the monthly forecasts to obtain realistic daily time series forecasts that can be used as inputs to crop and hydrological models. In the second approach, we develop a method for directly post-processing daily GCM forecasts using a Bayesian joint probability (BJP) model. We demonstrate and evaluate the two approaches through a case study for the Tully sugar region in north-eastern Australia. The daily post-processed forecasts will benefit applications in streamflow forecasting and crop yield forecasting.

  3. 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

  4. Forecasting Turbine Icing Events

    DEFF Research Database (Denmark)

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

    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...

  5. Are demand forecasting techniques applicable to libraries?

    OpenAIRE

    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.

  6. Are demand forecasting techniques applicable to libraries?

    OpenAIRE

    M S Sridhar

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

    Science.gov (United States)

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

    2017-04-01

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

  8. 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...

  9. 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 asp......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...

  10. Persistent forecasting of disruptive technologies

    National Research Council Canada - National Science Library

    Committee on Forecasting Future Disruptive Technologies; National Research Council

    ...) and the Defense Intelligence Agency (DIA) tasked the Committee for Forecasting Future Disruptive Technologies with providing guidance and insight on how to build a persistent forecasting system to predict, analyze, and reduce the impact...

  11. Improved sub-seasonal meteorological forecast skill using weighted multi-model ensemble simulations

    Science.gov (United States)

    Wanders, Niko; Wood, Eric F.

    2016-09-01

    Sub-seasonal to seasonal weather and hydrological forecasts have the potential to provide vital information for a variety of water-related decision makers. Here, we investigate the skill of four sub-seasonal forecast models from phase-2 of the North American Multi-Model Ensemble using reforecasts for the period 1982-2012. Two weighted multi-model ensemble means from the models have been developed for predictions of both sub-seasonal precipitation and temperature. By combining models through optimal weights, the multi-model forecast skill is significantly improved compared to a ‘standard’ equally weighted multi-model forecast mean. We show that optimal model weights are robust and the forecast skill is maintained for increased length of time and regions with a low initial forecast skill show significant skill after optimal weighting of the individual model forecast. The sub-seasonal model forecasts models show high skill over the tropics, approximating their skill at monthly resolution. Using the weighted approach, a significant increase is found in the forecast skill for dry, wet, cold and warm extreme events. The weighted mean approach brings significant advances to sub-seasonal forecasting due to its reduced uncertainty in the forecasts with a gain in forecast skill. This significantly improves their value for end-user applications and our ability to use them to prepare for upcoming extreme conditions, like floods and droughts.

  12. A Forecasting Decision Support System

    OpenAIRE

    Sayed, Hanaa E.; Hossam A. Gabbar; Fouad, Soheir A.; Ahmed, Khalil M.; Miyazaki, Shigeji

    2008-01-01

    Nowadays forecasting is needed in many fields such as weather forecasting, population estimation, industry demand forecasting, and many others. As complexity and factors increase, it becomes impossible for a human being to do the prediction operation without support of computer system. A Decision support system is needed to model all demand factors and combine with expert opinions to enhance forecasting accuracy. In this research work, we present a decision support system using winters', simp...

  13. Medium range flood forecasts at global scale

    Science.gov (United States)

    Voisin, N.; Wood, A. W.; Lettenmaier, D. P.; Wood, E. F.

    2006-12-01

    While weather and climate forecast methods have advanced greatly over the last two decades, this capability has yet to be evidenced in mitigation of water-related natural hazards (primarily floods and droughts), especially in the developing world. Examples abound of extreme property damage and loss of life due to floods in the underdeveloped world. For instance, more than 4.5 million people were affected by the July 2000 flooding of the Mekong River and its tributaries in Cambodia, Vietnam, Laos and Thailand. The February- March 2000 floods in the Limpopo River of Mozambique caused extreme disruption to that country's fledgling economy. Mitigation of these events through advance warning has typically been modest at best. Despite the above noted improvement in weather and climate forecasts, there is at present no system for forecasting of floods globally, notwithstanding that the potential clearly exists. We describe a methodology that is eventually intended to generate global flood predictions routinely. It draws heavily from the experimental North American Land Data Assimilation System (NLDAS) and the companion Global Land Data Assimilation System (GLDAS) for development of nowcasts, and the University of Washington Experimental Hydrologic Prediction System to develop ensemble hydrologic forecasts based on Numerical Weather Prediction (NWP) models which serve both as nowcasts (and hence reduce the need for in situ precipitation and other observations in parts of the world where surface networks are critically deficient) and provide forecasts for lead times as long as fifteen days. The heart of the hydrologic modeling system is the University of Washington/Princeton University Variable Infiltration Capacity (VIC) macroscale hydrology model. In the prototype (tested using retrospective data), VIC is driven globally up to the time of forecast with daily ERA40 precipitation (rescaled on a monthly basis to a station-based global climatology), ERA40 wind, and ERA40

  14. Bias correcting precipitation forecasts for extended-range skilful seasonal streamflow predictions

    Science.gov (United States)

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

    2016-04-01

    Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to also benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France in order to provide insights into the way bias correcting seasonal precipitation forecasts can contribute to maintain skill of seasonal flow predictions at extended lead times. First, we evaluate the skill of raw (i.e., without bias correction) seasonal precipitation ensemble forecasts for streamflow forecasting in sixteen French catchments. A lumped daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. A reference prediction system based on historic observed precipitation and watershed initial conditions at the time of forecast (i.e., ESP method) is used as benchmark. In a second step, we apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the flow forecasts. The approaches were based on the linear scaling and the distribution mapping methods. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy, and overall performance. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, reliability is an attribute that is not significantly improved. Forecast skill is generally improved when applying bias correction. Two bias correction methods showed the best performance for the studied catchments, with, however, each method being more successful in improving specific attributes of forecast quality: the simple linear scaling of monthly values contributed mainly to increase forecast

  15. 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.

  16. Evaluation of seasonal ensemble forecasts in Norway

    Science.gov (United States)

    Tore Sinnes, Svein; Engeland, Kolbjørn; Langsholt, Elin; Roar Sælthun, Nils

    2017-04-01

    Throughout the winter and spring season, seasonal forecasts are used by the Norwegian Water Resources and Energy Directorate (NVE) in order to assess the probability for sever floods or for low seasonal runoff volumes. The latter is especially important for hydropower production. The seasonal forecasts are generated by a set of 145 lumped, elevation distributed HBV models distributed all over Norway. The observed weather is used to establish the initial snow cover, soil moisture and groundwater levels in the HBV model. Subsequently, scenarios are created by using time series of observed weather the previous 50 years, creating a total of 50 ensembles. The predictability of this seasonal forecasting system depends therefore on the importance of the initial conditions, and in Norway the seasonal snow cover is especially important. The aim of this study is to evaluate the performance of the seasonal forecasts of flood peaks and seasonal runoff volumes and especially to evaluate of the predictability depends on (i) catchment climatology and (ii) issue dates and lead times. For achieving these aims, evaluation criterions assessing reliability and sharpness were used. The results shows that the predictability is the highest for catchments where the spring runoff is dominated by snow melt. The predictability is the highest for the shortest lead times (up to 1 months ahead).The predictive performance is higher for runoff volumes than for the flood peaks.

  17. Evolving forecasting classifications and applications in health forecasting

    Science.gov (United States)

    Soyiri, Ireneous N; Reidpath, Daniel D

    2012-01-01

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

  18. Weighting of NMME temperature and precipitation forecasts across Europe

    Science.gov (United States)

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

    2017-09-01

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

  19. On forecasting SETAR processes

    NARCIS (Netherlands)

    de Gooijer, J.G.; de Bruin, P.T.

    1998-01-01

    Suppose a time series {Yt} is generated by a first-order stationary self-exciting threshold autoregressive (SETAR) model with Gaussian innovations. The minimum mean squared error h-step ahead forecast ~(h)=E[Yt+h]g; s<~t] for h > 2 involves a sequence of complicated numerical integrations and closed

  20. Federal Forecasters Directory, 1995.

    Science.gov (United States)

    National Center for Education Statistics (ED), Washington, DC.

    This directory lists employees of the federal government who are involved in forecasting for policy formation and trend prediction purposes. Job title, agency, business address, phone or e-mail number, and specialty areas are listed for each employee. Employees are listed for the following agencies: (1) Bureau of the Census; (2) Bureau of Economic…