Forecasting the consequences of accidental releases of radionuclides in the atmosphere from ensemble dispersion modelling

International Nuclear Information System (INIS)

Galmarini, S.; Bianconi, R.; Bellasio, R.; Graziani, G.

2001-01-01

The RTMOD system is presented as a tool for the intercomparison of long-range dispersion models as well as a system for support of decision making. RTMOD is an internet-based procedure that collects the results of more than 20 models used around the world to predict the transport and deposition of radioactive releases in the atmosphere. It allows the real-time acquisition of model results and their intercomparison. Taking advantage of the availability of several model results, the system can also be used as a tool to support decision making in case of emergency. The new concept of ensemble dispersion modelling is introduced which is the basis for the decision-making application of RTMOD. New statistical parameters are presented that allow gathering the results of several models to produce a single dispersion forecast. The devised parameters are presented and tested on the results of RTMOD exercises

Long-range forecasting of intermittent streamflow

Science.gov (United States)

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

2011-11-01

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

Long-range forecasting of intermittent streamflow

Directory of Open Access Journals (Sweden)

F. F. van Ogtrop

2011-11-01

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

Pollen Forecast and Dispersion Modelling

Science.gov (United States)

Costantini, Monica; Di Giuseppe, Fabio; Medaglia, Carlo Maria; Travaglini, Alessandro; Tocci, Raffaella; Brighetti, M. Antonia; Petitta, Marcello

2014-05-01

The aim of this study is monitoring, mapping and forecast of pollen distribution for the city of Rome using in-situ measurements of 10 species of common allergenic pollens and measurements of PM10. The production of daily concentration maps, associated to a mobile phone app, are innovative compared to existing dedicated services to people who suffer from respiratory allergies. The dispersal pollen is one of the most well-known causes of allergic disease that is manifested by disorders of the respiratory functions. Allergies are the third leading cause of chronic disease and it is estimated that tens millions of people in Italy suffer from it. Recent works reveal that during the last few years there was a progressive increase of affected subjects, especially in urban areas. This situation may depend: on the ability to transport of pollutants, on the ability to react between pollutants and pollen and from a combination of other irritants, existing in densely populated and polluted urban areas. The methodology used to produce maps is based on in-situ measurements time series relative to 2012, obtained from networks of air quality and pollen stations in the metropolitan area of Rome. The monitoring station aerobiological of University of Rome "Tor Vergata" is located at the Department of Biology. The instrument used to pollen monitoring is a volumetric sampler type Hirst (Hirst 1952), Model 2000 VPPS Lanzoni; the data acquisition is carried out as reported in Standard UNI 11008:2004 - "Qualità dell'aria - Metodo di campionamento e conteggio dei granuli pollinici e delle spore fungine aerodisperse" - the protocol that describes the procedure for measuring of the concentration of pollen grains and fungal spores dispersed into the atmosphere, and reported in the "Manuale di gestione e qualità della R.I.M.A" (Travaglini et. al. 2009). All 10 allergenic pollen are monitored since 1996. At Tor Vergata university is also operating a meteorological station (SP2000, CAE

Applications of the PUFF model to forecasts of volcanic clouds dispersal from Etna and Vesuvio

Science.gov (United States)

Daniele, P.; Lirer, L.; Petrosino, P.; Spinelli, N.; Peterson, R.

2009-05-01

PUFF is a numerical volcanic ash tracking model developed to simulate the behaviour of ash clouds in the atmosphere. The model uses wind field data provided by meteorological models and adds dispersion and sedimentation physics to predict the evolution of the cloud once it reaches thermodynamic equilibrium with the atmosphere. The software is intended for use in emergency response situations during an eruption to quickly forecast the position and trajectory of the ash cloud in the near (˜1-72 h) future. In this paper, we describe the first application of the PUFF model in forecasting volcanic ash dispersion from the Etna and Vesuvio volcanoes. We simulated the daily occurrence of an eruptive event of Etna utilizing ash cloud parameters describing the paroxysm of 22nd July 1998 and wind field data for the 1st September 2005-31st December 2005 time span from the Global Forecast System (GFS) model at the approximate location of the Etna volcano (38N 15E). The results show that volcanic ash particles are dispersed in a range of directions in response to changing wind field at various altitudes and that the ash clouds are mainly dispersed toward the east and southeast, although the exact trajectory is highly variable, and can change within a few hours. We tested the sensitivity of the model to the mean particle grain size and found that an increased concentration of ash particles in the atmosphere results when the mean grain size is decreased. Similarly, a dramatic variation in dispersion results when the logarithmic standard deviation of the particle-size distribution is changed. Additionally, we simulated the occurrence of an eruptive event at both Etna and Vesuvio, using the same parameters describing the initial volcanic plume, and wind field data recorded for 1st September 2005, at approximately 38N 15E for Etna and 41N 14E for Vesuvio. The comparison of the two simulations indicates that identical eruptions occurring at the same time at the two volcanic centres

Application of a mesoscale forecasting model (NMM) coupled to the CALMET to develop forecast meteorology to use with the CALPUFF air dispersion model

International Nuclear Information System (INIS)

Radonjic, Z.; Telenta, B.; Kirklady, J.; Chambers, D.; Kleb, H.

2006-01-01

An air quality assessment was undertaken as part of the Environmental Assessment for the Port Hope Area Initiative. The assessment predicted potential effects associated with the remediation efforts for historic low-level radioactive wastes and construction of Long-Term Waste Management Facilities (LTWMFs) for both the Port Hope and Port Granby Projects. A necessary element of air dispersion modelling is the development of suitable meteorological data. For the Port Hope and Port Granby Projects, a meteorological station was installed in close proximity to the location of the recommended LTWMF in Port Hope. The recommended location for the Port Granby LTWMF is approximately 10 km west of the Port Hope LTWMF. Concerns were raised regarding the applicability of data collected for the Port Hope meteorological station to the Port Granby Site. To address this concern, a new method for processing meteorological data, which coupled mesoscale meteorological forecasting data the U.S. EPA CALMET meteorological data processor, was applied. This methodology is possible because a new and advanced mesoscale forecasting modelling system enables extensive numerical calculations on personal computers. As a result of this advancement, mesoscale forecasting systems can now be coupled with the CALMET meteorological data processor and the CALPUFF air dispersion modelling system to facilitate wind field estimations and air dispersion analysis. (author)

Anthropogenic range contractions bias species climate change forecasts

Science.gov (United States)

Faurby, Søren; Araújo, Miguel B.

2018-03-01

Forecasts of species range shifts under climate change most often rely on ecological niche models, in which characterizations of climate suitability are highly contingent on the species range data used. If ranges are far from equilibrium under current environmental conditions, for instance owing to local extinctions in otherwise suitable areas, modelled environmental suitability can be truncated, leading to biased estimates of the effects of climate change. Here we examine the impact of such biases on estimated risks from climate change by comparing models of the distribution of North American mammals based on current ranges with ranges accounting for historical information on species ranges. We find that estimated future diversity, almost everywhere, except in coastal Alaska, is drastically underestimated unless the full historical distribution of the species is included in the models. Consequently forecasts of climate change impacts on biodiversity for many clades are unlikely to be reliable without acknowledging anthropogenic influences on contemporary ranges.

Short-range solar radiation forecasts over Sweden

Directory of Open Access Journals (Sweden)

T. Landelius

2018-04-01

Full Text Available In this article the performance for short-range solar radiation forecasts by the global deterministic and ensemble models from the European Centre for Medium-Range Weather Forecasts (ECMWF is compared with an ensemble of the regional mesoscale model HARMONIE-AROME used by the national meteorological services in Sweden, Norway and Finland. Note however that only the control members and the ensemble means are included in the comparison. The models resolution differs considerably with 18 km for the ECMWF ensemble, 9 km for the ECMWF deterministic model, and 2.5 km for the HARMONIE-AROME ensemble.The models share the same radiation code. It turns out that they all underestimate systematically the Direct Normal Irradiance (DNI for clear-sky conditions. Except for this shortcoming, the HARMONIE-AROME ensemble model shows the best agreement with the distribution of observed Global Horizontal Irradiance (GHI and DNI values. During mid-day the HARMONIE-AROME ensemble mean performs best. The control member of the HARMONIE-AROME ensemble also scores better than the global deterministic ECMWF model. This is an interesting result since mesoscale models have so far not shown good results when compared to the ECMWF models.Three days with clear, mixed and cloudy skies are used to illustrate the possible added value of a probabilistic forecast. It is shown that in these cases the mesoscale ensemble could provide decision support to a grid operator in terms of forecasts of both the amount of solar power and its probabilities.

Medium-Range Air Quality Forecast During the Beijing Olympic Games

Science.gov (United States)

Zhang, Y.; Smith, J.; Wang, Z.; Luo, L.; Wu, Q.

2008-12-01

Prior to the XXIX Olympiad in Beijing, air quality was a major concern for many athletes and visitors to the Games. In response to the need for enhanced air quality forecasts, we explored and tested the capability of medium-range air quality forecasting in a multimodel ensemble system. The system consists of the Weather Research and Forecasting Model with Chemistry module (WRF-Chem), the Fifth-Generation NCAR/PennState Mesoscale Model (MM5), and the Nested Air Quality Prediction Modeling System (NAQPMS) developed at the Institute of Atmospheric Physics (IAP). Both MM5 and NAQPMS have been in operational use to produce short-term air quality forecasts. WRFChem is the major addition to the multimodel system. Forced with the forecast from the NCEP Global Ensemble Forecast System (GENS) at the lateral boundary, the multimodel system makes ensemble air quality forecasts out to 16 days with emission scenarios that reflect measures for the Olympics, including the closing down of factories around the city and beyond, a traffic control program that reduced the number of automobiles around the city by about half and elimination of all construction activities. Analyses of two forecasts are presented in this study. They were made on 5 August 2008 and 8 August 2008, both covering the entire Olympic period. Each forecast consists of three ensemble members that were produced with the same regional model but were forced by the control and two 'extremes' of the GENS forecast. The two extreme members were hand-picked to represent the best and worst case scenarios. The forecasts are evaluated with observations taken during the Olympic Games that include satellite observations, in-situ meteorological stations, LIDAR and air quality observations at the IAP tower site, 1 km away from the 'Bird Nest'. The analyses show good model skill in the first 3 days and generally satisfactory after 96 hours, with a successful forecast of potential pollution episode on 20 August 2008. The challenge

Medium Range Ensembles Flood Forecasts for Community Level Applications

Science.gov (United States)

Fakhruddin, S.; Kawasaki, A.; Babel, M. S.; AIT

2013-05-01

Early warning is a key element for disaster risk reduction. In recent decades, there has been a major advancement in medium range and seasonal forecasting. These could provide a great opportunity to improve early warning systems and advisories for early action for strategic and long term planning. This could result in increasing emphasis on proactive rather than reactive management of adverse consequences of flood events. This can be also very helpful for the agricultural sector by providing a diversity of options to farmers (e.g. changing cropping pattern, planting timing, etc.). An experimental medium range (1-10 days) flood forecasting model has been developed for Bangladesh which provides 51 set of discharge ensembles forecasts of one to ten days with significant persistence and high certainty. This could help communities (i.e. farmer) for gain/lost estimation as well as crop savings. This paper describe the application of ensembles probabilistic flood forecast at the community level for differential decision making focused on agriculture. The framework allows users to interactively specify the objectives and criteria that are germane to a particular situation, and obtain the management options that are possible, and the exogenous influences that should be taken into account before planning and decision making. risk and vulnerability assessment was conducted through community consultation. The forecast lead time requirement, users' needs, impact and management options for crops, livestock and fisheries sectors were identified through focus group discussions, informal interviews and questionnaire survey.

Dispersion Models to Forecast Traffic-related Emissions in Urban Areas

Directory of Open Access Journals (Sweden)

Davide Scannapieco

2011-11-01

Full Text Available Down the centuries, a direct link had been developed between increase in mobility and increase in wealth. On the other hand, air emission of greenhouse gases (GHG due to vehicles equipped with internal combustion engines can be regarded as a negative pressure over the environment. In the coming decades, road transport is likely to remain a significant contributor to air pollution in cities. Many urban trips cover distances of less than 6 km. Since the effectiveness of catalytic converters in the initial minutes of engine operation is small, the average emission per distance driven is very high in urban areas. Also, poorly maintained vehicles that lack exhaust aftertreatment systems are responsible for a major part of pollutant emissions. Therefore in urban areas, where higher concentrations of vehicles can be easily found, air pollution represents a critical issue, being it related with both environment and human health protection: in truth, research in recent decades consistently indicates the adverse effects of outdoor air pollution on human health, and the evidence points to air pollution stemming from transport as an important contributor to these effects. Several institutions (EEA, USEPA, etc. focused their interest in dispersion models because of their potential effectiveness to forecast atmospheric pollution. Furthermore, air micropollutants such as Polycyclic Aromatic Compounds (PAH and Metallic Trace Elements (MTE are traffic-related and although very low concentrations their dispersion is a serious issue. However, dispersion models are usefully implemented to better manage this estimation problem. Nonetheless, policy makers and land managers have to deal with model selection, taking into account that several dispersion models are available, each one of them focused on specific goals (e.g., wind transport of pollutants, land morphology implementation, evaluation of micropollutants transport, etc.; a further aspect to be considered is

The Art and Science of Long-Range Space Weather Forecasting

Science.gov (United States)

Hathaway, David H.; Wilson, Robert M.

2006-01-01

Long-range space weather forecasts are akin to seasonal forecasts of terrestrial weather. We don t expect to forecast individual events but we do hope to forecast the underlying level of activity important for satellite operations and mission pl&g. Forecasting space weather conditions years or decades into the future has traditionally been based on empirical models of the solar cycle. Models for the shape of the cycle as a function of its amplitude become reliable once the amplitude is well determined - usually two to three years after minimum. Forecasting the amplitude of a cycle well before that time has been more of an art than a science - usually based on cycle statistics and trends. Recent developments in dynamo theory -the theory explaining the generation of the Sun s magnetic field and the solar activity cycle - have now produced models with predictive capabilities. Testing these models with historical sunspot cycle data indicates that these predictions may be highly reliable one, or even two, cycles into the future.

Short-range quantitative precipitation forecasting using Deep Learning approaches

Science.gov (United States)

Akbari Asanjan, A.; Yang, T.; Gao, X.; Hsu, K. L.; Sorooshian, S.

2017-12-01

Predicting short-range quantitative precipitation is very important for flood forecasting, early flood warning and other hydrometeorological purposes. This study aims to improve the precipitation forecasting skills using a recently developed and advanced machine learning technique named Long Short-Term Memory (LSTM). The proposed LSTM learns the changing patterns of clouds from Cloud-Top Brightness Temperature (CTBT) images, retrieved from the infrared channel of Geostationary Operational Environmental Satellite (GOES), using a sophisticated and effective learning method. After learning the dynamics of clouds, the LSTM model predicts the upcoming rainy CTBT events. The proposed model is then merged with a precipitation estimation algorithm termed Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) to provide precipitation forecasts. The results of merged LSTM with PERSIANN are compared to the results of an Elman-type Recurrent Neural Network (RNN) merged with PERSIANN and Final Analysis of Global Forecast System model over the states of Oklahoma, Florida and Oregon. The performance of each model is investigated during 3 storm events each located over one of the study regions. The results indicate the outperformance of merged LSTM forecasts comparing to the numerical and statistical baselines in terms of Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), RMSE and correlation coefficient especially in convective systems. The proposed method shows superior capabilities in short-term forecasting over compared methods.

Meteorological uncertainty of atmospheric dispersion model results (MUD)

International Nuclear Information System (INIS)

Havskov Soerensen, J.; Amstrup, B.; Feddersen, H.

2013-08-01

The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as possibilities for optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the 'most likely' dispersion scenario. However, recent developments in numerical weather prediction (NWP) include probabilistic forecasting techniques, which can be utilised also for long-range atmospheric dispersion models. The ensemble statistical methods developed and applied to NWP models aim at describing the inherent uncertainties of the meteorological model results. These uncertainties stem from e.g. limits in meteorological observations used to initialise meteorological forecast series. By perturbing e.g. the initial state of an NWP model run in agreement with the available observational data, an ensemble of meteorological forecasts is produced from which uncertainties in the various meteorological parameters are estimated, e.g. probabilities for rain. Corresponding ensembles of atmospheric dispersion can now be computed from which uncertainties of predicted radionuclide concentration and deposition patterns can be derived. (Author)

Medium Range Forecast (MRF) and Nested Grid Model (NGM)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Nested Grid Model (NGM) and Medium Range Forecast (MRF) Archive is historical digital data set DSI-6140, archived at the NOAA National Centers for Environmental...

Incorporating spatial autocorrelation into species distribution models alters forecasts of climate-mediated range shifts.

Science.gov (United States)

Crase, Beth; Liedloff, Adam; Vesk, Peter A; Fukuda, Yusuke; Wintle, Brendan A

2014-08-01

Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment-only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate

Using subseasonal-to-seasonal (S2S) extreme rainfall forecasts for extended-range flood prediction in Australia

Science.gov (United States)

White, C. J.; Franks, S. W.; McEvoy, D.

2015-06-01

Meteorological and hydrological centres around the world are looking at ways to improve their capacity to be able to produce and deliver skilful and reliable forecasts of high-impact extreme rainfall and flooding events on a range of prediction timescales (e.g. sub-daily, daily, multi-week, seasonal). Making improvements to extended-range rainfall and flood forecast models, assessing forecast skill and uncertainty, and exploring how to apply flood forecasts and communicate their benefits to decision-makers are significant challenges facing the forecasting and water resources management communities. This paper presents some of the latest science and initiatives from Australia on the development, application and communication of extreme rainfall and flood forecasts on the extended-range "subseasonal-to-seasonal" (S2S) forecasting timescale, with a focus on risk-based decision-making, increasing flood risk awareness and preparedness, capturing uncertainty, understanding human responses to flood forecasts and warnings, and the growing adoption of "climate services". The paper also demonstrates how forecasts of flood events across a range of prediction timescales could be beneficial to a range of sectors and society, most notably for disaster risk reduction (DRR) activities, emergency management and response, and strengthening community resilience. Extended-range S2S extreme flood forecasts, if presented as easily accessible, timely and relevant information are a valuable resource to help society better prepare for, and subsequently cope with, extreme flood events.

Determining the bounds of skilful forecast range for probabilistic prediction of system-wide wind power generation

Directory of Open Access Journals (Sweden)

Dirk Cannon

2017-06-01

Full Text Available State-of-the-art wind power forecasts beyond a few hours ahead rely on global numerical weather prediction models to forecast the future large-scale atmospheric state. Often they provide initial and boundary conditions for nested high resolution simulations. In this paper, both upper and lower bounds on forecast range are identified within which global ensemble forecasts provide skilful information for system-wide wind power applications. An upper bound on forecast range is associated with the limit of predictability, beyond which forecasts have no more skill than predictions based on climatological statistics. A lower bound is defined at the lead time beyond which the resolved uncertainty associated with estimating the future large-scale atmospheric state is larger than the unresolved uncertainty associated with estimating the system-wide wind power response to a given large-scale state.The bounds of skilful ensemble forecast range are quantified for three leading global forecast systems. The power system of Great Britain (GB is used as an example because independent verifying data is available from National Grid. The upper bound defined by forecasts of GB-total wind power generation at a specific point in time is found to be 6–8 days. The lower bound is found to be 1.4–2.4 days. Both bounds depend on the global forecast system and vary seasonally. In addition, forecasts of the probability of an extreme power ramp event were found to possess a shorter limit of predictability (4.5–5.5 days. The upper bound on this forecast range can only be extended by improving the global forecast system (outside the control of most users or by changing the metric used in the probability forecast. Improved downscaling and microscale modelling of the wind farm response may act to decrease the lower bound. The potential gain from such improvements have diminishing returns beyond the short-range (out to around 2 days.

Spatio-temporal behaviour of medium-range ensemble forecasts

Science.gov (United States)

Kipling, Zak; Primo, Cristina; Charlton-Perez, Andrew

2010-05-01

Using the recently-developed mean-variance of logarithms (MVL) diagram, together with the TIGGE archive of medium-range ensemble forecasts from nine different centres, we present an analysis of the spatio-temporal dynamics of their perturbations, and show how the differences between models and perturbation techniques can explain the shape of their characteristic MVL curves. We also consider the use of the MVL diagram to compare the growth of perturbations within the ensemble with the growth of the forecast error, showing that there is a much closer correspondence for some models than others. We conclude by looking at how the MVL technique might assist in selecting models for inclusion in a multi-model ensemble, and suggest an experiment to test its potential in this context.

Using subseasonal-to-seasonal (S2S extreme rainfall forecasts for extended-range flood prediction in Australia

Directory of Open Access Journals (Sweden)

C. J. White

2015-06-01

Full Text Available Meteorological and hydrological centres around the world are looking at ways to improve their capacity to be able to produce and deliver skilful and reliable forecasts of high-impact extreme rainfall and flooding events on a range of prediction timescales (e.g. sub-daily, daily, multi-week, seasonal. Making improvements to extended-range rainfall and flood forecast models, assessing forecast skill and uncertainty, and exploring how to apply flood forecasts and communicate their benefits to decision-makers are significant challenges facing the forecasting and water resources management communities. This paper presents some of the latest science and initiatives from Australia on the development, application and communication of extreme rainfall and flood forecasts on the extended-range "subseasonal-to-seasonal" (S2S forecasting timescale, with a focus on risk-based decision-making, increasing flood risk awareness and preparedness, capturing uncertainty, understanding human responses to flood forecasts and warnings, and the growing adoption of "climate services". The paper also demonstrates how forecasts of flood events across a range of prediction timescales could be beneficial to a range of sectors and society, most notably for disaster risk reduction (DRR activities, emergency management and response, and strengthening community resilience. Extended-range S2S extreme flood forecasts, if presented as easily accessible, timely and relevant information are a valuable resource to help society better prepare for, and subsequently cope with, extreme flood events.

Ensemble atmospheric dispersion calculations for decision support systems

International Nuclear Information System (INIS)

Borysiewicz, M.; Potempski, S.; Galkowski, A.; Zelazny, R.

2003-01-01

This document describes two approaches to long-range atmospheric dispersion of pollutants based on the ensemble concept. In the first part of the report some experiences related to the exercises undertaken under the ENSEMBLE project of the European Union are presented. The second part is devoted to the implementation of mesoscale numerical prediction models RAMS and atmospheric dispersion model HYPACT on Beowulf cluster and theirs usage for ensemble forecasting and long range atmospheric ensemble dispersion calculations based on available meteorological data from NCEO, NOAA (USA). (author)

Extended-range forecasting of Chinese summer surface air temperature and heat waves

Science.gov (United States)

Zhu, Zhiwei; Li, Tim

2018-03-01

Because of growing demand from agricultural planning, power management and activity scheduling, extended-range (5-30-day lead) forecasting of summer surface air temperature (SAT) and heat waves over China is carried out in the present study via spatial-temporal projection models (STPMs). Based on the training data during 1960-1999, the predictability sources are found to propagate from Europe, Northeast Asia, and the tropical Pacific, to influence the intraseasonal 10-80 day SAT over China. STPMs are therefore constructed using the projection domains, which are determined by these previous predictability sources. For the independent forecast period (2000-2013), the STPMs can reproduce EOF-filtered 30-80 day SAT at all lead times of 5-30 days over most part of China, and observed 30-80 and 10-80 day SAT at 25-30 days over eastern China. Significant pattern correlation coefficients account for more than 50% of total forecasts at all 5-30-day lead times against EOF-filtered and observed 30-80 day SAT, and at a 20-day lead time against observed 10-80 day SAT. The STPMs perform poorly in reproducing 10-30 day SAT. Forecasting for the first two modes of 10-30 day SAT only shows useful skill within a 15-day lead time. Forecasting for the third mode of 10-30 day SAT is useless after a 10-day lead time. The forecasted heat waves over China are determined by the reconstructed SAT which is the summation of the forecasted 10-80 day SAT and the lower frequency (longer than 80-day) climatological SAT. Over a large part of China, the STPMs can forecast more than 30% of heat waves within a 15-day lead time. In general, the STPMs demonstrate the promising skill for extended-range forecasting of Chinese summer SAT and heat waves.

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

Directory of Open Access Journals (Sweden)

J. Rhee

2016-06-01

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

Meteorological uncertainty of atmospheric dispersion model results (MUD)

Energy Technology Data Exchange (ETDEWEB)

Havskov Soerensen, J.; Amstrup, B.; Feddersen, H. [Danish Meteorological Institute, Copenhagen (Denmark)] [and others

2013-08-15

The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as possibilities for optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the 'most likely' dispersion scenario. However, recent developments in numerical weather prediction (NWP) include probabilistic forecasting techniques, which can be utilised also for long-range atmospheric dispersion models. The ensemble statistical methods developed and applied to NWP models aim at describing the inherent uncertainties of the meteorological model results. These uncertainties stem from e.g. limits in meteorological observations used to initialise meteorological forecast series. By perturbing e.g. the initial state of an NWP model run in agreement with the available observational data, an ensemble of meteorological forecasts is produced from which uncertainties in the various meteorological parameters are estimated, e.g. probabilities for rain. Corresponding ensembles of atmospheric dispersion can now be computed from which uncertainties of predicted radionuclide concentration and deposition patterns can be derived. (Author)

Towards a medium-range coastal station fog forecasting system

CSIR Research Space (South Africa)

Landman, S

2013-09-01

Full Text Available -1 29th Annual conference of South African Society for Atmospheric Sciences (SASAS) 2013 http://sasas.ukzn.ac.za/homepage.aspx Towards a Medium-Range Coastal Station Fog Forecasting System Stephanie Landman*1, Estelle Marx1, Willem A. Landman2...

Application of data assimilation to improve the forecasting capability of an atmospheric dispersion model for a radioactive plume

International Nuclear Information System (INIS)

Jeong, H.J.; Han, M.H.; Hwang, W.T.; Kim, E.H.

2008-01-01

Modeling an atmospheric dispersion of a radioactive plume plays an influential role in assessing the environmental impacts caused by nuclear accidents. The performance of data assimilation techniques combined with Gaussian model outputs and measurements to improve forecasting abilities are investigated in this study. Tracer dispersion experiments are performed to produce field data by assuming a radiological emergency. Adaptive neuro-fuzzy inference system (ANFIS) and linear regression filter are considered to assimilate the Gaussian model outputs with measurements. ANFIS is trained so that the model outputs are likely to be more accurate for the experimental data. Linear regression filter is designed to assimilate measurements similar to the ANFIS. It is confirmed that ANFIS could be an appropriate method for an improvement of the forecasting capability of an atmospheric dispersion model in the case of a radiological emergency, judging from the higher correlation coefficients between the measured and the assimilated ones rather than a linear regression filter. This kind of data assimilation method could support a decision-making system when deciding on the best available countermeasures for public health from among emergency preparedness alternatives

Dispersal Kernel Determines Symmetry of Spread and Geographical Range for an Insect

International Nuclear Information System (INIS)

Holland, J.D.

2009-01-01

The distance from a source patch that dispersing insects reach depends on the number of dispersers, or random draws from a probability density function called a dispersal kernel, and the shape of that kernel. This can cause asymmetrical dispersal between habitat patches that produce different numbers of dispersers. Spatial distributions based on these dynamics can explain several ecological patterns including mega populations and geographic range boundaries. I hypothesized that a locally extirpated long horned beetle, the sugar maple borer, has a new geographical range shaped primarily by probabilistic dispersal distances. I used data on occurrence from Ontario, Canada to construct a model of geographical range in Indiana, USA based on maximum dispersal distance scaled by habitat area. This model predicted the new range boundary within 500 m very accurately. This beetle may be an ideal organism for exploring spatial dynamics driven by dispersal.

Dispersion-tailored, low-loss photonic crystal fibers for the THz range

DEFF Research Database (Denmark)

Nielsen, Kristian; Rasmussen, Henrik K.; Adam, Aurèle J.L.

2009-01-01

We have fabricated a new type of photonic crystal fibers based on a cyclic olefin copolymer, transparent in the THz range. We characterize the propagation loss, dispersion, and spatial beam profile in fibers designed for low and high dispersion.......We have fabricated a new type of photonic crystal fibers based on a cyclic olefin copolymer, transparent in the THz range. We characterize the propagation loss, dispersion, and spatial beam profile in fibers designed for low and high dispersion....

On the skill of various ensemble spread estimators for probabilistic short range wind forecasting

Science.gov (United States)

Kann, A.

2012-05-01

A variety of applications ranging from civil protection associated with severe weather to economical interests are heavily dependent on meteorological information. For example, a precise planning of the energy supply with a high share of renewables requires detailed meteorological information on high temporal and spatial resolution. With respect to wind power, detailed analyses and forecasts of wind speed are of crucial interest for the energy management. Although the applicability and the current skill of state-of-the-art probabilistic short range forecasts has increased during the last years, ensemble systems still show systematic deficiencies which limit its practical use. This paper presents methods to improve the ensemble skill of 10-m wind speed forecasts by combining deterministic information from a nowcasting system on very high horizontal resolution with uncertainty estimates from a limited area ensemble system. It is shown for a one month validation period that a statistical post-processing procedure (a modified non-homogeneous Gaussian regression) adds further skill to the probabilistic forecasts, especially beyond the nowcasting range after +6 h.

Medium range forecasting of Hurricane Harvey flash flooding using ECMWF and social vulnerability data

Science.gov (United States)

Pillosu, F. M.; Jurlina, T.; Baugh, C.; Tsonevsky, I.; Hewson, T.; Prates, F.; Pappenberger, F.; Prudhomme, C.

2017-12-01

During hurricane Harvey the greater east Texas area was affected by extensive flash flooding. Their localised nature meant they were too small for conventional large scale flood forecasting systems to capture. We are testing the use of two real time forecast products from the European Centre for Medium-range Weather Forecasts (ECMWF) in combination with local vulnerability information to provide flash flood forecasting tools at the medium range (up to 7 days ahead). Meteorological forecasts are the total precipitation extreme forecast index (EFI), a measure of how the ensemble forecast probability distribution differs from the model-climate distribution for the chosen location, time of year and forecast lead time; and the shift of tails (SOT) which complements the EFI by quantifying how extreme an event could potentially be. Both products give the likelihood of flash flood generating precipitation. For hurricane Harvey, 3-day EFI and SOT products for the period 26th - 29th August 2017 were used, generated from the twice daily, 18 km, 51 ensemble member ECMWF Integrated Forecast System. After regridding to 1 km resolution the forecasts were combined with vulnerable area data to produce a flash flood hazard risk area. The vulnerability data were floodplains (EU Joint Research Centre), road networks (Texas Department of Transport) and urban areas (Census Bureau geographic database), together reflecting the susceptibility to flash floods from the landscape. The flash flood hazard risk area forecasts were verified using a traditional approach against observed National Weather Service flash flood reports, a total of 153 reported flash floods have been detected in that period. Forecasts performed best for SOT = 5 (hit ratio = 65%, false alarm ratio = 44%) and EFI = 0.7 (hit ratio = 74%, false alarm ratio = 45%) at 72 h lead time. By including the vulnerable areas data, our verification results improved by 5-15%, demonstrating the value of vulnerability information within

Use of medium-range numerical weather prediction model output to produce forecasts of streamflow

Science.gov (United States)

Clark, M.P.; Hay, L.E.

2004-01-01

This paper examines an archive containing over 40 years of 8-day atmospheric forecasts over the contiguous United States from the NCEP reanalysis project to assess the possibilities for using medium-range numerical weather prediction model output for predictions of streamflow. This analysis shows the biases in the NCEP forecasts to be quite extreme. In many regions, systematic precipitation biases exceed 100% of the mean, with temperature biases exceeding 3??C. In some locations, biases are even higher. The accuracy of NCEP precipitation and 2-m maximum temperature forecasts is computed by interpolating the NCEP model output for each forecast day to the location of each station in the NWS cooperative network and computing the correlation with station observations. Results show that the accuracy of the NCEP forecasts is rather low in many areas of the country. Most apparent is the generally low skill in precipitation forecasts (particularly in July) and low skill in temperature forecasts in the western United States, the eastern seaboard, and the southern tier of states. These results outline a clear need for additional processing of the NCEP Medium-Range Forecast Model (MRF) output before it is used for hydrologic predictions. Techniques of model output statistics (MOS) are used in this paper to downscale the NCEP forecasts to station locations. Forecasted atmospheric variables (e.g., total column precipitable water, 2-m air temperature) are used as predictors in a forward screening multiple linear regression model to improve forecasts of precipitation and temperature for stations in the National Weather Service cooperative network. This procedure effectively removes all systematic biases in the raw NCEP precipitation and temperature forecasts. MOS guidance also results in substantial improvements in the accuracy of maximum and minimum temperature forecasts throughout the country. For precipitation, forecast improvements were less impressive. MOS guidance increases

Fitness declines towards range limits and local adaptation to climate affect dispersal evolution during climate‐induced range shifts

DEFF Research Database (Denmark)

Hargreaves, Anna; Bailey, Susan; Laird, Robert

2015-01-01

Dispersal ability will largely determine whether species track their climatic niches during climate change, a process especially important for populations at contracting (low-latitude/low-elevation) range limits that otherwise risk extinction. We investigate whether dispersal evolution....... We simulate a species distributed continuously along a temperature gradient using a spatially explicit, individual-based model. We compare range-wide dispersal evolution during climate stability vs. directional climate change, with uniform fitness vs. fitness that declines towards range limits (RLs...... at contracting range limits is facilitated by two processes that potentially enable edge populations to experience and adjust to the effects of climate deterioration before they cause extinction: (i) climate-induced fitness declines towards range limits and (ii) local adaptation to a shifting climate gradient...

Long-range dispersion interactions. I. Formalism for two heteronuclear atoms

International Nuclear Information System (INIS)

Zhang, J.-Y.; Mitroy, J.

2007-01-01

A general procedure for systematically evaluating the long-range dispersion interaction between two heteronuclear atoms in arbitrary states is outlined. The C 6 dispersion parameter can always be written in terms of sum rules involving oscillator strengths only and formulas for a number of symmetry cases are given. The dispersion coefficients for excited alkali-metal atoms interacting with the ground-state H and He are tabulated

Ensemble Forecasts with Useful Skill-Spread Relationships for African meningitis and Asia Streamflow Forecasting

Science.gov (United States)

Hopson, T. M.

2014-12-01

One potential benefit of an ensemble prediction system (EPS) is its capacity to forecast its own forecast error through the ensemble spread-error relationship. In practice, an EPS is often quite limited in its ability to represent the variable expectation of forecast error through the variable dispersion of the ensemble, and perhaps more fundamentally, in its ability to provide enough variability in the ensembles dispersion to make the skill-spread relationship even potentially useful (irrespective of whether the EPS is well-calibrated or not). In this paper we examine the ensemble skill-spread relationship of an ensemble constructed from the TIGGE (THORPEX Interactive Grand Global Ensemble) dataset of global forecasts and a combination of multi-model and post-processing approaches. Both of the multi-model and post-processing techniques are based on quantile regression (QR) under a step-wise forward selection framework leading to ensemble forecasts with both good reliability and sharpness. The methodology utilizes the ensemble's ability to self-diagnose forecast instability to produce calibrated forecasts with informative skill-spread relationships. A context for these concepts is provided by assessing the constructed ensemble in forecasting district-level humidity impacting the incidence of meningitis in the meningitis belt of Africa, and in forecasting flooding events in the Brahmaputra and Ganges basins of South Asia.

Meta-heuristic CRPS minimization for the calibration of short-range probabilistic forecasts

Science.gov (United States)

Mohammadi, Seyedeh Atefeh; Rahmani, Morteza; Azadi, Majid

2016-08-01

This paper deals with the probabilistic short-range temperature forecasts over synoptic meteorological stations across Iran using non-homogeneous Gaussian regression (NGR). NGR creates a Gaussian forecast probability density function (PDF) from the ensemble output. The mean of the normal predictive PDF is a bias-corrected weighted average of the ensemble members and its variance is a linear function of the raw ensemble variance. The coefficients for the mean and variance are estimated by minimizing the continuous ranked probability score (CRPS) during a training period. CRPS is a scoring rule for distributional forecasts. In the paper of Gneiting et al. (Mon Weather Rev 133:1098-1118, 2005), Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is used to minimize the CRPS. Since BFGS is a conventional optimization method with its own limitations, we suggest using the particle swarm optimization (PSO), a robust meta-heuristic method, to minimize the CRPS. The ensemble prediction system used in this study consists of nine different configurations of the weather research and forecasting model for 48-h forecasts of temperature during autumn and winter 2011 and 2012. The probabilistic forecasts were evaluated using several common verification scores including Brier score, attribute diagram and rank histogram. Results show that both BFGS and PSO find the optimal solution and show the same evaluation scores, but PSO can do this with a feasible random first guess and much less computational complexity.

Development of the ClearSky smoke dispersion forecast system for agricultural field burning in the Pacific Northwest

Science.gov (United States)

Jain, Rahul; Vaughan, Joseph; Heitkamp, Kyle; Ramos, Charleston; Claiborn, Candis; Schreuder, Maarten; Schaaf, Mark; Lamb, Brian

The post-harvest burning of agricultural fields is commonly used to dispose of crop residue and provide other desired services such as pest control. Despite careful regulation of burning, smoke plumes from field burning in the Pacific Northwest commonly degrade air quality, particularly for rural populations. In this paper, ClearSky, a numerical smoke dispersion forecast system for agricultural field burning that was developed to support smoke management in the Inland Pacific Northwest, is described. ClearSky began operation during the summer through fall burn season of 2002 and continues to the present. ClearSky utilizes Mesoscale Meteorological Model version 5 (MM5v3) forecasts from the University of Washington, data on agricultural fields, a web-based user interface for defining burn scenarios, the Lagrangian CALPUFF dispersion model and web-served animations of plume forecasts. The ClearSky system employs a unique hybrid source configuration, which treats the flaming portion of a field as a buoyant line source and the smoldering portion of the field as a buoyant area source. Limited field observations show that this hybrid approach yields reasonable plume rise estimates using source parameters derived from recent field burning emission field studies. The performance of this modeling system was evaluated for 2003 by comparing forecast meteorology against meteorological observations, and comparing model-predicted hourly averaged PM 2.5 concentrations against observations. Examples from this evaluation illustrate that while the ClearSky system can accurately predict PM 2.5 surface concentrations due to field burning, the overall model performance depends strongly on meteorological forecast error. Statistical evaluation of the meteorological forecast at seven surface stations indicates a strong relationship between topographical complexity near the station and absolute wind direction error with wind direction errors increasing from approximately 20° for sites in

Using demography and movement behavior to predict range expansion of the southern sea otter.

Science.gov (United States)

Tinker, M.T.; Doak, D.F.; Estes, J.A.

2008-01-01

In addition to forecasting population growth, basic demographic data combined with movement data provide a means for predicting rates of range expansion. Quantitative models of range expansion have rarely been applied to large vertebrates, although such tools could be useful for restoration and management of many threatened but recovering populations. Using the southern sea otter (Enhydra lutris nereis) as a case study, we utilized integro-difference equations in combination with a stage-structured projection matrix that incorporated spatial variation in dispersal and demography to make forecasts of population recovery and range recolonization. In addition to these basic predictions, we emphasize how to make these modeling predictions useful in a management context through the inclusion of parameter uncertainty and sensitivity analysis. Our models resulted in hind-cast (1989–2003) predictions of net population growth and range expansion that closely matched observed patterns. We next made projections of future range expansion and population growth, incorporating uncertainty in all model parameters, and explored the sensitivity of model predictions to variation in spatially explicit survival and dispersal rates. The predicted rate of southward range expansion (median = 5.2 km/yr) was sensitive to both dispersal and survival rates; elasticity analysis indicated that changes in adult survival would have the greatest potential effect on the rate of range expansion, while perturbation analysis showed that variation in subadult dispersal contributed most to variance in model predictions. Variation in survival and dispersal of females at the south end of the range contributed most of the variance in predicted southward range expansion. Our approach provides guidance for the acquisition of further data and a means of forecasting the consequence of specific management actions. Similar methods could aid in the management of other recovering populations.

Study on The Extended Range Weather Forecast of Low Frequency Signal Based on Period Analysis Method

Science.gov (United States)

Li, X.

2016-12-01

Although many studies have explored the MJO and its application for weather forecasting, low-frequency oscillation has been insufficiently studied for the extend range weather forecasting over middle and high latitudes. In China, low-frequency synoptic map is a useful tool for meteorological operation department to forecast extend range weather. It is therefore necessary to develop objective methods to serve the need for finding low-frequency signal, interpretation and application of this signal in the extend range weather forecasting. In this paper, method of Butterworth band pass filter was applied to get low-frequency height field at 500hPa from 1980 to 2014 by using NCEP/NCAR daily grid data. Then period analysis and optimal subset regression methods were used to process the low frequency data of 150 days before the first forecast day and extend the low frequency signal of 500hPa low-frequency high field to future 30 days in the global from June to August during 2011-2014. Finally, the results were test. The main results are as follows: (1) In general, the fitting effect of low frequency signals of 500hPa low-frequency height field by period analysis in the northern hemisphere was better than that in the southern hemisphere, and was better in the low latitudes than that in the high latitudes. The fitting accuracy gradually reduced with the increase of forecast time length, which tended to be stable during the late forecasting period. (2) The fitting effects over the 6 key regions in China showed that except filtering result over Xinjiang area in the first 10 days and 30 days, filtering results over the other 5 key regions throughout the whole period have passed reliability test with level more than 95%. (3) The center and scope of low and high low frequency systems can be fitted well by using the methods mentioned above, which is consist with the corresponding use of the low-frequency synoptic map for the prediction of the extended period. Application of the

Long-range dispersion interactions. III: Method for two homonuclear atoms

International Nuclear Information System (INIS)

Mitroy, J.; Zhang, J.-Y.

2007-01-01

A procedure for systematically evaluating the long-range dispersion interaction between two homonuclear atoms in arbitrary LS coupled states is outlined. The method is then used to generate dispersion coefficients for a number of the low-lying states of the Na and Mg dimers

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

Science.gov (United States)

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

2017-04-01

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

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

DEFF Research Database (Denmark)

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

2004-01-01

and is based on the simultaneous analysis of several model simulations by means of ad-hoc statistical treatments and parameters. The models considered in this study are operational long-range transport and dispersion models used to support decision making in various countries in case of accidental releases...

The European tracer experiment ETEX: a real-time long range atmospheric dispersion model exercise in different weather conditions

International Nuclear Information System (INIS)

Graziani, G.; )

1998-01-01

Two long-range tracer experiments were conducted. An inert, non-depositing tracer was being released at Rennes in France for 12 hours. The 168 sampling ground stations were run by the National Meteorological Services. Twenty-four institutions took part in the real-time forecasting of the cloud evolution using 28 long-range dispersion models. The horizontal projection of the cloud evolution over Europe was combined with real-time aerial chemical analysis. The results of the comparison indicate that a limited group of models (7-8) were capable of obtaining a good reproduction of the cloud movement throughout Europe for the first release. Large differences were, however, found in the predicted tracer concentration at a particular location. For the second release, there were large differences between the measured and calculated cloud, particularly after a front passage, which indicates that some efforts have still to be spent before consensus on the model reliability is achieved. (P.A.)

Monitoring and forecasting Etna volcanic plumes

Directory of Open Access Journals (Sweden)

S. Scollo

2009-09-01

Full Text Available In this paper we describe the results of a project ongoing at the Istituto Nazionale di Geofisica e Vulcanologia (INGV. The objective is to develop and implement a system for monitoring and forecasting volcanic plumes of Etna. Monitoring is based at present by multispectral infrared measurements from the Spin Enhanced Visible and Infrared Imager on board the Meteosat Second Generation geosynchronous satellite, visual and thermal cameras, and three radar disdrometers able to detect ash dispersal and fallout. Forecasting is performed by using automatic procedures for: i downloading weather forecast data from meteorological mesoscale models; ii running models of tephra dispersal, iii plotting hazard maps of volcanic ash dispersal and deposition for certain scenarios and, iv publishing the results on a web-site dedicated to the Italian Civil Protection. Simulations are based on eruptive scenarios obtained by analysing field data collected after the end of recent Etna eruptions. Forecasting is, hence, supported by plume observations carried out by the monitoring system. The system was tested on some explosive events occurred during 2006 and 2007 successfully. The potentiality use of monitoring and forecasting Etna volcanic plumes, in a way to prevent threats to aviation from volcanic ash, is finally discussed.

Evaluations of Extended-Range tropical Cyclone Forecasts in the Western North Pacific by using the Ensemble Reforecasts: Preliminary Results

Science.gov (United States)

Tsai, Hsiao-Chung; Chen, Pang-Cheng; Elsberry, Russell L.

2017-04-01

The objective of this study is to evaluate the predictability of the extended-range forecasts of tropical cyclone (TC) in the western North Pacific using reforecasts from National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) during 1996-2015, and from the Climate Forecast System (CFS) during 1999-2010. Tsai and Elsberry have demonstrated that an opportunity exists to support hydrological operations by using the extended-range TC formation and track forecasts in the western North Pacific from the ECMWF 32-day ensemble. To demonstrate this potential for the decision-making processes regarding water resource management and hydrological operation in Taiwan reservoir watershed areas, special attention is given to the skill of the NCEP GEFS and CFS models in predicting the TCs affecting the Taiwan area. The first objective of this study is to analyze the skill of NCEP GEFS and CFS TC forecasts and quantify the forecast uncertainties via verifications of categorical binary forecasts and probabilistic forecasts. The second objective is to investigate the relationships among the large-scale environmental factors [e.g., El Niño Southern Oscillation (ENSO), Madden-Julian Oscillation (MJO), etc.] and the model forecast errors by using the reforecasts. Preliminary results are indicating that the skill of the TC activity forecasts based on the raw forecasts can be further improved if the model biases are minimized by utilizing these reforecasts.

Prediction of kharif rice yield at Kharagpur using disaggregated extended range rainfall forecasts

Science.gov (United States)

Dhekale, B. S.; Nageswararao, M. M.; Nair, Archana; Mohanty, U. C.; Swain, D. K.; Singh, K. K.; Arunbabu, T.

2017-08-01

The Extended Range Forecasts System (ERFS) has been generating monthly and seasonal forecasts on real-time basis throughout the year over India since 2009. India is one of the major rice producer and consumer in South Asia; more than 50% of the Indian population depends on rice as staple food. Rice is mainly grown in kharif season, which contributed 84% of the total annual rice production of the country. Rice cultivation in India is rainfed, which depends largely on rains, so reliability of the rainfall forecast plays a crucial role for planning the kharif rice crop. In the present study, an attempt has been made to test the reliability of seasonal and sub-seasonal ERFS summer monsoon rainfall forecasts for kharif rice yield predictions at Kharagpur, West Bengal by using CERES-Rice (DSSATv4.5) model. These ERFS forecasts are produced as monthly and seasonal mean values and are converted into daily sequences with stochastic weather generators for use with crop growth models. The daily sequences are generated from ERFS seasonal (June-September) and sub-seasonal (July-September, August-September, and September) summer monsoon (June to September) rainfall forecasts which are considered as input in CERES-rice crop simulation model for the crop yield prediction for hindcast (1985-2008) and real-time mode (2009-2015). The yield simulated using India Meteorological Department (IMD) observed daily rainfall data is considered as baseline yield for evaluating the performance of predicted yields using the ERFS forecasts. The findings revealed that the stochastic disaggregation can be used to disaggregate the monthly/seasonal ERFS forecasts into daily sequences. The year to year variability in rice yield at Kharagpur is efficiently predicted by using the ERFS forecast products in hindcast as well as real time, and significant enhancement in the prediction skill is noticed with advancement in the season due to incorporation of observed weather data which reduces uncertainty of

Meteorological perspective on intermediate range atmospheric dispersion

International Nuclear Information System (INIS)

Van der Hoven, I.

1981-01-01

The intermediate range of atmospheric transport and diffusion is defined as those dispersion processes which take place at downwind distances of 10 to 100 kilometers from pollutant sources. Meteorologists often define this range as the mesoscale. It is the range of distances where certain environmental assessments are of concern such as the determination of significant deterioration of visibility, the effect of effluent releases from tall stacks, and the effect of pollutant sources in rural settings upon the more distant urban centers. Atmospheric diffusion theory is based on steady state conditions and spatial homogeniety. Techniques must be developed to measure the inhomogenieties, models must be devised to account for the complexities, and a data base consisting of appropriate measured meteorological parameters concurrent with tracer gas concentrations should be collected

Range expansion drives dispersal evolution in an equatorial three-species symbiosis.

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Léotard, Guillaume; Debout, Gabriel; Dalecky, Ambroise; Guillot, Sylvain; Gaume, Laurence; McKey, Doyle; Kjellberg, Finn

2009-01-01

Recurrent climatic oscillations have produced dramatic changes in species distributions. This process has been proposed to be a major evolutionary force, shaping many life history traits of species, and to govern global patterns of biodiversity at different scales. During range expansions selection may favor the evolution of higher dispersal, and symbiotic interactions may be affected. It has been argued that a weakness of climate fluctuation-driven range dynamics at equatorial latitudes has facilitated the persistence there of more specialized species and interactions. However, how much the biology and ecology of species is changed by range dynamics has seldom been investigated, particularly in equatorial regions. We studied a three-species symbiosis endemic to coastal equatorial rainforests in Cameroon, where the impact of range dynamics is supposed to be limited, comprised of two species-specific obligate mutualists--an ant-plant and its protective ant--and a species-specific ant parasite of this mutualism. We combined analyses of within-species genetic diversity and of phenotypic variation in a transect at the southern range limit of this ant-plant system. All three species present congruent genetic signatures of recent gradual southward expansion, a result compatible with available regional paleoclimatic data. As predicted, this expansion has been accompanied by the evolution of more dispersive traits in the two ant species. In contrast, we detected no evidence of change in lifetime reproductive strategy in the tree, nor in its investment in food resources provided to its symbiotic ants. Despite the decreasing investment in protective workers and the increasing investment in dispersing females by both the mutualistic and the parasitic ant species, there was no evidence of destabilization of the symbiosis at the colonization front. To our knowledge, we provide here the first evidence at equatorial latitudes that biological traits associated with dispersal are

A robust method to forecast volcanic ash clouds

Science.gov (United States)

Denlinger, Roger P.; Pavolonis, Mike; Sieglaff, Justin

2012-01-01

Ash clouds emanating from volcanic eruption columns often form trails of ash extending thousands of kilometers through the Earth's atmosphere, disrupting air traffic and posing a significant hazard to air travel. To mitigate such hazards, the community charged with reducing flight risk must accurately assess risk of ash ingestion for any flight path and provide robust forecasts of volcanic ash dispersal. In response to this need, a number of different transport models have been developed for this purpose and applied to recent eruptions, providing a means to assess uncertainty in forecasts. Here we provide a framework for optimal forecasts and their uncertainties given any model and any observational data. This involves random sampling of the probability distributions of input (source) parameters to a transport model and iteratively running the model with different inputs, each time assessing the predictions that the model makes about ash dispersal by direct comparison with satellite data. The results of these comparisons are embodied in a likelihood function whose maximum corresponds to the minimum misfit between model output and observations. Bayes theorem is then used to determine a normalized posterior probability distribution and from that a forecast of future uncertainty in ash dispersal. The nature of ash clouds in heterogeneous wind fields creates a strong maximum likelihood estimate in which most of the probability is localized to narrow ranges of model source parameters. This property is used here to accelerate probability assessment, producing a method to rapidly generate a prediction of future ash concentrations and their distribution based upon assimilation of satellite data as well as model and data uncertainties. Applying this method to the recent eruption of Eyjafjallajökull in Iceland, we show that the 3 and 6 h forecasts of ash cloud location probability encompassed the location of observed satellite-determined ash cloud loads, providing an

Home range size and breeding dispersal of a common buzzard (Buteo buteo

Directory of Open Access Journals (Sweden)

Väli Ülo

2017-12-01

Full Text Available Telemetric studies have provided ample information on threatened raptors, but still little is known about space use and dispersal of common species. Here I describe the home range and breeding dispersal of a GPS-tracked adult male common buzzard, studied in south-eastern Estonia in 2014–16. This buzzard’s home range covered 8.3 km2 (kernel 95% estimate with the core range being 2.1 km2 (kernel 50%. The home range increased in the course of the breeding season but decreased again before migration. Surprisingly, the nests in the two successive breeding years were located in the opposite margins of the home range, 1.7 km from each other.

An evaluation of dry deposition from the long range atmospheric dispersion

International Nuclear Information System (INIS)

Suh, K.S.; Kim, E.H.; Hwang, W.T.; Han, M.H.; Lee, H.S.; Lee, C.W.

2003-01-01

The dry deposition of pollutants released into the atmosphere must be evaluated to estimate the radiological dose of terrestrial plants and foodstuffs in the ecosystem. Especially, the atmospheric dispersion and dry deposition models have been widely developed to predict and minimize the radiological damage for the surrounding environment after the TMI-2 and the Chernobyl accidents. A Lagrangian particle model for the evaluation the long-range dispersion has been firstly developed in Korea since 2001. The particle tracking method was used for the estimation of the concentration distribution of the radioactive materials released into the atmosphere. The model is designed to estimate air concentration and ground deposition at distances up to some thousands of kilometers from the source point in the horizontal direction. The turbulent motion is considered to separate the treatment of particles within the mixing layer and above the mixing layer. Also, the dispersion model is designed to receive the results of the MM5 model being operated by KMA (Korea Meteorological Administration). The test run of the long-range dispersion model has been performed in the area which covered extends from 102.47deg E to 173.34deg E and from 12.27deg N to 53.72deg N in Northeast Asia. The release point of Cs-137 assumed in the east part of the China. The long range dispersion model has been firstly developed to estimate the radiological consequences against a nuclear accident. The model will be supplemented by the comparative study using the data of the ETEX experiments. (author)

Medium-range reference evapotranspiration forecasts for the contiguous United States based on multi-model numerical weather predictions

Science.gov (United States)

Medina, Hanoi; Tian, Di; Srivastava, Puneet; Pelosi, Anna; Chirico, Giovanni B.

2018-07-01

Reference evapotranspiration (ET0) plays a fundamental role in agronomic, forestry, and water resources management. Estimating and forecasting ET0 have long been recognized as a major challenge for researchers and practitioners in these communities. This work explored the potential of multiple leading numerical weather predictions (NWPs) for estimating and forecasting summer ET0 at 101 U.S. Regional Climate Reference Network stations over nine climate regions across the contiguous United States (CONUS). Three leading global NWP model forecasts from THORPEX Interactive Grand Global Ensemble (TIGGE) dataset were used in this study, including the single model ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (EC), the National Centers for Environmental Prediction Global Forecast System (NCEP), and the United Kingdom Meteorological Office forecasts (MO), as well as multi-model ensemble forecasts from the combinations of these NWP models. A regression calibration was employed to bias correct the ET0 forecasts. Impact of individual forecast variables on ET0 forecasts were also evaluated. The results showed that the EC forecasts provided the least error and highest skill and reliability, followed by the MO and NCEP forecasts. The multi-model ensembles constructed from the combination of EC and MO forecasts provided slightly better performance than the single model EC forecasts. The regression process greatly improved ET0 forecast performances, particularly for the regions involving stations near the coast, or with a complex orography. The performance of EC forecasts was only slightly influenced by the size of the ensemble members, particularly at short lead times. Even with less ensemble members, EC still performed better than the other two NWPs. Errors in the radiation forecasts, followed by those in the wind, had the most detrimental effects on the ET0 forecast performances.

Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

Science.gov (United States)

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

2009-04-01

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

Medium-Range Forecast Skill for Extraordinary Arctic Cyclones in Summer of 2008-2016

Science.gov (United States)

Yamagami, Akio; Matsueda, Mio; Tanaka, Hiroshi L.

2018-05-01

Arctic cyclones (ACs) are a severe atmospheric phenomenon that affects the Arctic environment. This study assesses the forecast skill of five leading operational medium-range ensemble forecasts for 10 extraordinary ACs that occurred in summer during 2008-2016. Average existence probability of the predicted ACs was >0.9 at lead times of ≤3.5 days. Average central position error of the predicted ACs was less than half of the mean radius of the 10 ACs (469.1 km) at lead times of 2.5-4.5 days. Average central pressure error of the predicted ACs was 5.5-10.7 hPa at such lead times. Therefore, the operational ensemble prediction systems generally predict the position of ACs within 469.1 km 2.5-4.5 days before they mature. The forecast skill for the extraordinary ACs is lower than that for midlatitude cyclones in the Northern Hemisphere but similar to that in the Southern Hemisphere.

Range expansion drives dispersal evolution in an equatorial three-species symbiosis.

Directory of Open Access Journals (Sweden)

Guillaume Léotard

Full Text Available Recurrent climatic oscillations have produced dramatic changes in species distributions. This process has been proposed to be a major evolutionary force, shaping many life history traits of species, and to govern global patterns of biodiversity at different scales. During range expansions selection may favor the evolution of higher dispersal, and symbiotic interactions may be affected. It has been argued that a weakness of climate fluctuation-driven range dynamics at equatorial latitudes has facilitated the persistence there of more specialized species and interactions. However, how much the biology and ecology of species is changed by range dynamics has seldom been investigated, particularly in equatorial regions.We studied a three-species symbiosis endemic to coastal equatorial rainforests in Cameroon, where the impact of range dynamics is supposed to be limited, comprised of two species-specific obligate mutualists--an ant-plant and its protective ant--and a species-specific ant parasite of this mutualism. We combined analyses of within-species genetic diversity and of phenotypic variation in a transect at the southern range limit of this ant-plant system. All three species present congruent genetic signatures of recent gradual southward expansion, a result compatible with available regional paleoclimatic data. As predicted, this expansion has been accompanied by the evolution of more dispersive traits in the two ant species. In contrast, we detected no evidence of change in lifetime reproductive strategy in the tree, nor in its investment in food resources provided to its symbiotic ants.Despite the decreasing investment in protective workers and the increasing investment in dispersing females by both the mutualistic and the parasitic ant species, there was no evidence of destabilization of the symbiosis at the colonization front. To our knowledge, we provide here the first evidence at equatorial latitudes that biological traits associated

Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation

KAUST Repository

Altaf, Muhammad

2013-08-01

This paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H∞ filter. By design, an H∞ filter is more robust than the common Kalman filter in the sense that the estimation error in the H∞ filter has, in general, a finite growth rate with respect to the uncertainties in assimilation. The computational hydrodynamical model used in this study is the Advanced Circulation (ADCIRC) model. The authors assimilate data obtained from Hurricanes Katrina and Ike as test cases. The results clearly show that the H∞-based SEIK filter provides more accurate short-range forecasts of storm surge compared to recently reported data assimilation results resulting from the standard SEIK filter.

Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation

KAUST Repository

Altaf, Muhammad; Butler, T.; Luo, X.; Dawson, C.; Mayo, T.; Hoteit, Ibrahim

2013-01-01

This paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H∞ filter. By design, an H∞ filter is more robust than the common Kalman filter in the sense that the estimation error in the H∞ filter has, in general, a finite growth rate with respect to the uncertainties in assimilation. The computational hydrodynamical model used in this study is the Advanced Circulation (ADCIRC) model. The authors assimilate data obtained from Hurricanes Katrina and Ike as test cases. The results clearly show that the H∞-based SEIK filter provides more accurate short-range forecasts of storm surge compared to recently reported data assimilation results resulting from the standard SEIK filter.

Recent developments in the applications of the Regional Atmospheric Modeling System (RAMS) for emergency response planning and operational forecasting at the Kennedy Space Center

International Nuclear Information System (INIS)

Lyons, W.A.; Tremback, C.J.

1996-01-01

The authors will summarize ten years of developing and applying the Regional Atmospheric Modeling System (RAMS) to emergency response and operational dispersion forecasting at the Kennedy Space Center (KSC). RAMS forms the core of two workstation-based operational systems, ERDAS (the Emergency Response Dose Assessment System) and PROWESS (Parallelized RAMS Operational Weather Simulation System) which are undergoing extensive operational testing prior to potential deployment as part of the range forecasting system at KSC. RAMS has been interfaced with HYPACT (the Hybrid Particle and Concentration Transport Model) to produce detailed 3-D dispersion forecasts from a variety of sources including cold spills, routine launch operations, and explosive conflagrations of launch vehicles

How Hydroclimate Influences the Effectiveness of Particle Filter Data Assimilation of Streamflow in Initializing Short- to Medium-range Streamflow Forecasts

Science.gov (United States)

Clark, E.; Wood, A.; Nijssen, B.; Clark, M. P.

2017-12-01

Short- to medium-range (1- to 7-day) streamflow forecasts are important for flood control operations and in issuing potentially life-save flood warnings. In the U.S., the National Weather Service River Forecast Centers (RFCs) issue such forecasts in real time, depending heavily on a manual data assimilation (DA) approach. Forecasters adjust model inputs, states, parameters and outputs based on experience and consideration of a range of supporting real-time information. Achieving high-quality forecasts from new automated, centralized forecast systems will depend critically on the adequacy of automated DA approaches to make analogous corrections to the forecasting system. Such approaches would further enable systematic evaluation of real-time flood forecasting methods and strategies. Toward this goal, we have implemented a real-time Sequential Importance Resampling particle filter (SIR-PF) approach to assimilate observed streamflow into simulated initial hydrologic conditions (states) for initializing ensemble flood forecasts. Assimilating streamflow alone in SIR-PF improves simulated streamflow and soil moisture during the model spin up period prior to a forecast, with consequent benefits for forecasts. Nevertheless, it only consistently limits error in simulated snow water equivalent during the snowmelt season and in basins where precipitation falls primarily as snow. We examine how the simulated initial conditions with and without SIR-PF propagate into 1- to 7-day ensemble streamflow forecasts. Forecasts are evaluated in terms of reliability and skill over a 10-year period from 2005-2015. The focus of this analysis is on how interactions between hydroclimate and SIR-PF performance impact forecast skill. To this end, we examine forecasts for 5 hydroclimatically diverse basins in the western U.S. Some of these basins receive most of their precipitation as snow, others as rain. Some freeze throughout the mid-winter while others experience significant mid-winter melt

Long-range dipolar order and dispersion forces in polar liquids

Science.gov (United States)

Besford, Quinn Alexander; Christofferson, Andrew Joseph; Liu, Maoyuan; Yarovsky, Irene

2017-11-01

Complex solvation phenomena, such as specific ion effects, occur in polar liquids. Interpretation of these effects in terms of structure and dispersion forces will lead to a greater understanding of solvation. Herein, using molecular dynamics, we probe the structure of polar liquids through specific dipolar pair correlation functions that contribute to the potential of mean force that is "felt" between thermally rotating dipole moments. It is shown that unique dipolar order exists at separations at least up to 20 Å for all liquids studied. When the structural order is compared with a dipolar dispersion force that arises from local co-operative enhancement of dipole moments, a strong agreement is found. Lifshitz theory of dispersion forces was compared with the structural order, where the theory is validated for all liquids that do not have significant local dipole correlations. For liquids that do have significant local dipole correlations, specifically liquid water, Lifshitz theory underestimates the dispersion force by a factor of 5-10, demonstrating that the force that leads to the increased structure in liquid water is missed by Lifshitz theory of van der Waals forces. We apply similar correlation functions to an ionic aqueous system, where long-range order between water's dipole moment and a single chloride ion is found to exist at 20 Å of separation, revealing a long-range perturbation of water's structure by an ion. Furthermore, we found that waters within the 1st, 2nd, and 3rd solvation shells of a chloride ion exhibit significantly enhanced dipolar interactions, particularly with waters at larger distances of separation. Our results provide a link between structures, dispersion forces, and specific ion effects, which may lead to a more robust understanding of solvation.

Operational hydrological forecasting in Bavaria. Part I: Forecast uncertainty

Science.gov (United States)

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

2009-04-01

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

Statistical Short-Range Guidance for Peak Wind Speed Forecasts on Kennedy Space Center/Cape Canaveral Air Force Station: Phase I Results

Science.gov (United States)

Lambert, Winifred C.; Merceret, Francis J. (Technical Monitor)

2002-01-01

This report describes the results of the ANU's (Applied Meteorology Unit) Short-Range Statistical Forecasting task for peak winds. The peak wind speeds are an important forecast element for the Space Shuttle and Expendable Launch Vehicle programs. The Keith Weather Squadron and the Spaceflight Meteorology Group indicate that peak winds are challenging to forecast. The Applied Meteorology Unit was tasked to develop tools that aid in short-range forecasts of peak winds at tower sites of operational interest. A 7 year record of wind tower data was used in the analysis. Hourly and directional climatologies by tower and month were developed to determine the seasonal behavior of the average and peak winds. In all climatologies, the average and peak wind speeds were highly variable in time. This indicated that the development of a peak wind forecasting tool would be difficult. Probability density functions (PDF) of peak wind speed were calculated to determine the distribution of peak speed with average speed. These provide forecasters with a means of determining the probability of meeting or exceeding a certain peak wind given an observed or forecast average speed. The climatologies and PDFs provide tools with which to make peak wind forecasts that are critical to safe operations.

Dispersal Range of Anopheles sinensis in Yongcheng City, China by Mark-Release-Recapture Methods

Science.gov (United States)

Guo, Yuhong; Ren, Dongsheng; Zheng, Canjun; Wu, Haixia; Yang, Shuran; Liu, Jingli; Li, Hongsheng; Li, Huazhong; Li, Qun; Yang, Weizhong; Chu, Cordia

2012-01-01

Background Studying the dispersal range of Anopheles sinensis is of major importance for understanding the transition from malaria control to elimination. However, no data are available regarding the dispersal range of An. sinensis in China. The aim of the present study was to study the dispersal range of An. sinensis and provide the scientific basis for the development of effective control measures for malaria elimination in China. Methodology/Principal Findings Mark-Release-Recapture (MRR) experiments were conducted with 3000 adult wild An. sinensis in 2010 and 3000 newly emerged wild An. sinensis in 2011 in two villages of Yongcheng City in Henan Province. Marked An. sinensis were recaptured daily for ten successive days using light traps. The overall recapture rates were 0.83% (95% CI, 0.50%∼1.16%) in 2010 and 1.33% (95% CI, 0.92%∼1.74%) in 2011. There was no significant difference in the recapture rates of wild An. sinensis and newly emerged An. sinensis. The majority of An. sinensis were captured due east at study site I compared with most in the west at study site II. Eighty percent and 90% of the marked An. sinensis were recaptured within a radius of 100 m from the release point in study site I and II, respectively, with a maximum dispersal range of 400 m within the period of this study. Conclusions/Significance Our results indicate that local An. sinensis may have limited dispersal ranges. Therefore, control efforts should target breeding and resting sites in proximity of the villages. PMID:23226489

Very-short range forecasting system for 2018 Pyeonchang Winter Olympic and Paralympic games

Science.gov (United States)

Nam, Ji-Eun; Park, Kyungjeen; Kim, Minyou; Kim, Changhwan; Joo, Sangwon

2016-04-01

The 23rd Olympic Winter and the 13th Paralympic Winter Games will be held in Pyeongchang, Republic of Korea respectively from 9 to 25 February 2018 and from 9 to 18 February 2018. The Korea Meteorological Administration (KMA) and the National Institute for Meteorological Science (NIMS) have the responsibility to provide weather information for the management of the Games and the safety of the public. NIMS will carry out a Forecast Demonstration Project (FDP) and a Research and Development Project (RDP) which will be called ICE-POP 2018. These projects will focus on intensive observation campaigns to understand severe winter weathers over the Pyeongchang region, and the research results from the RDP will be used to improve the accuracy of nowcasting and very short-range forecast systems during the Games. To support these projects, NIMS developed Very-short range Data Assimilation and Prediction System (VDAPS), which is run in real time with 1 hour cycling interval and up to 12 hour forecasts. The domain is covering Korean Peninsular and surrounding seas with 1.5km horizontal resolution. AWS, windprofiler, buoy, sonde, aircraft, scatwinds, and radar radial winds are assimilated by 3DVAR on 3km resolution inner domain. The rain rate is converted into latent heat and initialized via nudging. The visibility data are also assimilated with the addition of aerosol control variable. The experiments results show the improvement in rainfall over south sea of Korean peninsula. In order to reduce excessive rainfalls during first 2 hours due to the reduced cycling interval, the data assimilation algorithm is optimized.

Teaching ocean wave forecasting using computer-generated visualization and animation—Part 2: swell forecasting

Science.gov (United States)

Whitford, Dennis J.

2002-05-01

This paper, the second of a two-part series, introduces undergraduate students to ocean wave forecasting using interactive computer-generated visualization and animation. Verbal descriptions and two-dimensional illustrations are often insufficient for student comprehension. Fortunately, the introduction of computers in the geosciences provides a tool for addressing this problem. Computer-generated visualization and animation, accompanied by oral explanation, have been shown to be a pedagogical improvement to more traditional methods of instruction. Cartographic science and other disciplines using geographical information systems have been especially aggressive in pioneering the use of visualization and animation, whereas oceanography has not. This paper will focus on the teaching of ocean swell wave forecasting, often considered a difficult oceanographic topic due to the mathematics and physics required, as well as its interdependence on time and space. Several MATLAB ® software programs are described and offered to visualize and animate group speed, frequency dispersion, angular dispersion, propagation, and wave height forecasting of deep water ocean swell waves. Teachers may use these interactive visualizations and animations without requiring an extensive background in computer programming.

The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change.

Science.gov (United States)

Valladares, Fernando; Matesanz, Silvia; Guilhaumon, François; Araújo, Miguel B; Balaguer, Luis; Benito-Garzón, Marta; Cornwell, Will; Gianoli, Ernesto; van Kleunen, Mark; Naya, Daniel E; Nicotra, Adrienne B; Poorter, Hendrik; Zavala, Miguel A

2014-11-01

Species are the unit of analysis in many global change and conservation biology studies; however, species are not uniform entities but are composed of different, sometimes locally adapted, populations differing in plasticity. We examined how intraspecific variation in thermal niches and phenotypic plasticity will affect species distributions in a warming climate. We first developed a conceptual model linking plasticity and niche breadth, providing five alternative intraspecific scenarios that are consistent with existing literature. Secondly, we used ecological niche-modeling techniques to quantify the impact of each intraspecific scenario on the distribution of a virtual species across a geographically realistic setting. Finally, we performed an analogous modeling exercise using real data on the climatic niches of different tree provenances. We show that when population differentiation is accounted for and dispersal is restricted, forecasts of species range shifts under climate change are even more pessimistic than those using the conventional assumption of homogeneously high plasticity across a species' range. Suitable population-level data are not available for most species so identifying general patterns of population differentiation could fill this gap. However, the literature review revealed contrasting patterns among species, urging greater levels of integration among empirical, modeling and theoretical research on intraspecific phenotypic variation. © 2014 The Authors. Ecology Letters published by John Wiley & Sons Ltd and CNRS.

Flow time, flow velocity and longitudinal dispersion in Moselle and Weser rivers; Fliesszeit, Fliessgeschwindigkeit und Longitudinale Dispersion in Mosel und Weser

Energy Technology Data Exchange (ETDEWEB)

Krause, W.J.; Krinitzky, T.; Cremer, M. [Bundesanstalt fuer Gewaesserkunde (BfG), Koblenz (Germany)

2003-07-01

Since 1980, the Federal Institute of Hydrology has performed dispersion investigations with tritium as a tracer on Federal Waterways. The aim was to establish dispersion prognoses, i.e. forecasts of the longitudinal dispersion of concentrations of noxious substances in the water column. Characteristic parameters like discharge-relevant flow velocities, dispersion and elimination constants of emittent sites and selected river sections will be determined. They will serve as basis for a mathematical model permitting to forecast discharge-relevant flow velocities, expected impact times, concentration maxima and the duration of critical concentration increases. In the following, the results obtained at the Moselle river and the investigations carried out on the Weser river will be shortly described. (orig.)

Extended-range forecast for the temporal distribution of clustering tropical cyclogenesis over the western North Pacific

Science.gov (United States)

Zhu, Zhiwei; Li, Tim; Bai, Long; Gao, Jianyun

2017-11-01

Based on outgoing longwave radiation (OLR), an index for clustering tropical cyclogenesis (CTC) over the western North Pacific (WNP) was defined. Around 76 % of total CTC events were generated during the active phase of the CTC index, and 38 % of the total active phase was concurrent with CTC events. For its continuous property, the CTC index was used as the representative predictand for extended-range forecasting the temporal distribution of CTC events. The predictability sources for CTC events were detected via correlation analyses of the previous 35-5-day lead atmospheric fields against the CTC index. The results showed that the geopotential height at different levels and the 200 hPa zonal wind over the global tropics possessed large predictability sources, whereas the predictability sources of other variables, e.g., OLR, zonal wind, and relatively vorticity at 850 hPa and relatively humility at 700 hPa, were mainly confined to the tropical Indian Ocean and western Pacific Ocean. Several spatial-temporal projection model (STPM) sets were constructed to carry out the extended-range forecast for the CTC index. By combining the output of STPMs separately conducted for the two dominant modes of intraseasonal variability, e.g., the 10-30 and the 30-80 day mode, useful forecast skill could be achieved for a 30-day lead time. The combined output successfully captured both the 10-30 and 30-80 day mode at least 10 days in advance. With a relatively low rate of false alarm, the STPM achieved hits for 80 % (69 %) of 54 CTC events during 2003-2014 at the 10-day (20-day) lead time, suggesting a practical value of the STPM for real-time forecasting WNP CTC events at an extended range.

Ensemble atmospheric dispersion modeling for emergency response consequence assessments

International Nuclear Information System (INIS)

Addis, R.P.; Buckley, R.L.

2003-01-01

models. This provides a better understanding of the atmosphere and plume behavior than would a single model output. Atmospheric models often give the impression of greater accuracy than the science is capable of delivering. The ensemble approach is a powerful way to reassert the concept of having a family of equally valid solutions, while enabling outliers to be identified. The U.S. Department of Energy's Savannah River Technology Center (SRTC) has participated in RTMOD and ENSEMBLE. SRTC uses the Regional Atmospheric Modeling System (RAMS) and Lagrangian Particle Dispersion Model (LPDM) to provide plume forecasts in real-time for the European grid as described in the figure. The NOAA northern hemispheric model, Global Forecast System (a combination of the medium range forecast and aviation forecast models), is used to provide the initial and boundary conditions for RAMS. The model plume forecast data are sent to the ENSEMBLE WEB page in real-time where they may be compared with other model outputs. SRTC has participated in all the ENSEMBLE exercises in real-time. An example of the ensemble output is shown in the figure, which shows an overlay of the SRTC (crosshatched) initial 60-hour forecast for the plume overlaid on an ensemble of 5 other model outputs. The plume shadings show the level of consensus for a minimum threshold, enabling modelers to determine consensus between models and identify possible outliers. The traditional approach to provide atmospheric consequence assessment tools to aid decision-makers in response to a release from a nuclear facility is to provide a plume output from a particular model. However, the non-unique nature of solutions to the non-linear equations that govern the atmosphere, and the sensitivity of such equations to perturbations in the initial and boundary conditions, results in any single model output being simply one of many viable solutions. As such, the traditional approach does a disservice to decision-makers by inferring greater

Long Range Financial Forecasting for School Districts.

Science.gov (United States)

Baker, Michael E.

Public school systems infrequently project their financial outlook beyond the coming year. Yet, financial projections over a multiyear period are necessary if the financial "crises" that frequently occur in public organizations are to be avoided. This paper discusses the importance of financial forecasting and planning, the development of…

How uncertain are day-ahead wind forecasts?

Energy Technology Data Exchange (ETDEWEB)

Grimit, E. [3TIER Environmental Forecast Group, Seattle, WA (United States)

2006-07-01

Recent advances in the combination of weather forecast ensembles with Bayesian statistical techniques have helped to address uncertainties in wind forecasting. Weather forecast ensembles are a collection of numerical weather predictions. The combination of several equally-skilled forecasts typically results in a consensus forecast with greater accuracy. The distribution of forecasts also provides an estimate of forecast inaccuracy. However, weather forecast ensembles tend to be under-dispersive, and not all forecast uncertainties can be taken into account. In order to address these issues, a multi-variate linear regression approach was used to correct the forecast bias for each ensemble member separately. Bayesian model averaging was used to provide a predictive probability density function to allow for multi-modal probability distributions. A test location in eastern Canada was used to demonstrate the approach. Results of the test showed that the method improved wind forecasts and generated reliable prediction intervals. Prediction intervals were much shorter than comparable intervals based on a single forecast or on historical observations alone. It was concluded that the approach will provide economic benefits to both wind energy developers and investors. refs., tabs., figs.

Adaptive Blending of Model and Observations for Automated Short-Range Forecasting: Examples from the Vancouver 2010 Olympic and Paralympic Winter Games

Science.gov (United States)

Bailey, Monika E.; Isaac, George A.; Gultepe, Ismail; Heckman, Ivan; Reid, Janti

2014-01-01

An automated short-range forecasting system, adaptive blending of observations and model (ABOM), was tested in real time during the 2010 Vancouver Olympic and Paralympic Winter Games in British Columbia. Data at 1-min time resolution were available from a newly established, dense network of surface observation stations. Climatological data were not available at these new stations. This, combined with output from new high-resolution numerical models, provided a unique and exciting setting to test nowcasting systems in mountainous terrain during winter weather conditions. The ABOM method blends extrapolations in time of recent local observations with numerical weather predictions (NWP) model predictions to generate short-range point forecasts of surface variables out to 6 h. The relative weights of the model forecast and the observation extrapolation are based on performance over recent history. The average performance of ABOM nowcasts during February and March 2010 was evaluated using standard scores and thresholds important for Olympic events. Significant improvements over the model forecasts alone were obtained for continuous variables such as temperature, relative humidity and wind speed. The small improvements to forecasts of variables such as visibility and ceiling, subject to discontinuous changes, are attributed to the persistence component of ABOM.

Submesoscale dispersion in the vicinity of the Deepwater Horizon spill

OpenAIRE

Poje, Andrew C.; Özgökmen, Tamay M.; Lipphardt, Bruce L.; Haus, Brian K.; Ryan, Edward H.; Haza, Angelique C.; Jacobs, Gregg A.; Reniers, A. J. H. M.; Olascoaga, Maria Josefina; Novelli, Guillaume; Griffa, Annalisa; Beron-Vera, Francisco J.; Chen, Shuyi S.; Coelho, Emanuel; Hogan, Patrick J.

2014-01-01

Reliable forecasts for the dispersion of oceanic contamination are important for coastal ecosystems, society and the economy as evidenced by the Deepwater Horizon oil spill in the Gulf of Mexico in 2010 and the Fukushima nuclear plant incident in the Pacific Ocean in 2011. Accurate prediction of pollutant pathways and concentrations at the ocean surface requires understanding ocean dynamics over a broad range of spatial scales. Fundamental questions concerning the structure of the velocity fi...

Incorporating Medium-Range Weather Forecasts in Seasonal Crop Scenarios over the Greater Horn of Africa to Support National/Regional/Local Decision Makers

Science.gov (United States)

Shukla, S.; Husak, G. J.; Funk, C. C.; Verdin, J. P.

2015-12-01

The USAID's Famine Early Warning Systems Network (FEWS NET) provides seasonal assessments of crop conditions over the Greater Horn of Africa (GHA) and other food insecure regions. These assessments and current livelihood, nutrition, market conditions and conflicts are used to generate food security scenarios that help national, regional and local decision makers target their resources and mitigate socio-economic losses. Among the various tools that FEWS NET uses is the FAO's Water Requirement Satisfaction Index (WRSI). The WRSI is a simple yet powerful crop assessment model that incorporates current moisture conditions (at the time of the issuance of forecast), precipitation scenarios, potential evapotranspiration and crop parameters to categorize crop conditions into different classes ranging from "failure" to "very good". The WRSI tool has been shown to have a good agreement with local crop yields in the GHA region. At present, the precipitation scenarios used to drive the WRSI are based on either a climatological forecast (that assigns equal chances of occurrence to all possible scenarios and has no skill over the forecast period) or a sea-surface temperature anomaly based scenario (which at best have skill at the seasonal scale). In both cases, the scenarios fail to capture the skill that can be attained by initial atmospheric conditions (i.e., medium-range weather forecasts). During the middle of a cropping season, when a week or two of poor rains can have a devastating effect, two weeks worth of skillful precipitation forecasts could improve the skill of the crop scenarios. With this working hypothesis, we examine the value of incorporating medium-range weather forecasts in improving the skill of crop scenarios in the GHA region. We use the NCEP's Global Ensemble Forecast system (GEFS) weather forecasts and examine the skill of crop scenarios generated using the GEFS weather forecasts with respect to the scenarios based solely on the climatological forecast

Selfing ability and dispersal are positively related, but not affected by range position: a multispecies study on southern African Asteraceae.

Science.gov (United States)

de Waal, C; Rodger, J G; Anderson, B; Ellis, A G

2014-05-01

Dispersal and breeding system traits are thought to affect colonization success. As species have attained their present distribution ranges through colonization, these traits may vary geographically. Although several theories predict associations between dispersal ability, selfing ability and the relative position of a population within its geographic range, there is little theoretical or empirical consensus on exactly how these three variables are related. We investigated relationships between dispersal ability, selfing ability and range position across 28 populations of 13 annual, wind-dispersed Asteraceae species from the Namaqualand region of South Africa. Controlling for phylogeny, relative dispersal ability--assessed from vertical fall time of fruits--was positively related to an index of autofertility--determined from hand-pollination experiments. These findings support the existence of two discrete syndromes: high selfing ability associated with good dispersal and obligate outcrossing associated with lower dispersal ability. This is consistent with the hypothesis that selection for colonization success drives the evolution of an association between these traits. However, no general effect of range position on dispersal or breeding system traits was evident. This suggests selection on both breeding system and dispersal traits acts consistently across distribution ranges. © 2014 The Authors. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Tailoring of the free spectral range and geometrical cavity dispersion of a microsphere by a coating layer.

Science.gov (United States)

Ristić, Davor; Mazzola, Maurizio; Chiappini, Andrea; Rasoloniaina, Alphonse; Féron, Patrice; Ramponi, Roberta; Righini, Giancarlo C; Cibiel, Gilles; Ivanda, Mile; Ferrari, Maurizio

2014-09-01

The modal dispersion of a whispering gallery mode (WGM) resonator is a very important parameter for use in all nonlinear optics applications. In order to tailor the WGM modal dispersion of a microsphere, we have coated a silica microsphere with a high-refractive-index coating in order to study its effect on the WGM modal dispersion. We used Er(3+) ions as a probe for a modal dispersion assessment. We found that, by varying the coating thickness, the geometrical cavity dispersion can be used to shift overall modal dispersion in a very wide range in both the normal and anomalous dispersion regime.

Longitudinal dispersion of radioactive substances in Federal waterways

Energy Technology Data Exchange (ETDEWEB)

Krause, W.J. [Bundesanstalt fuer Gewaesserkunde (BfG), Koblenz (Germany); Speer, W.; Luellwitz, T.; Cremer, M.; Tolksdorf, W.

2007-08-15

In the context of radioactivity monitoring in German Federal Waterways (BWStr) by the Federal Institute of Hydrology (BfG) according to the Precautionary Radiation Protection Act (StrVG), the prediction of the dispersion of radioactive substances in water is one of the key tasks. The aim is the forecasting of the longitudinal dispersion of concentrations of soluble hazardous substances in flowing water. These predictions are based on the so-called dispersion tests with tritium as a tracer that the BfG has performed since 1980. Characteristic parameters like discharge-dependent flow velocities, dispersion and elimination constants related to emission sources or selected river sections are determined. They will serve as basis for a mathematical model to forecast discharge-dependent flow velocities, expected impact times, concentration maxima, and the duration of critical increases in concentrations. In the following, the results obtained till now from three investigation campaigns on the River Weser and its source rivers Werra and Fulda are described. (orig.)

Robust Approaches to Forecasting

OpenAIRE

Jennifer Castle; David Hendry; Michael P. Clements

2014-01-01

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

Long-range forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time forecast for the year 2015

Science.gov (United States)

Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.

2016-11-01

All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.

Dispersal limitation at the expanding range margin of an evergreen tree in urban habitats?

DEFF Research Database (Denmark)

Møller, Linda Agerbo; Skou, Anne-Marie Thonning; Kollmann, Johannes Christian

2012-01-01

Dispersal limitations contribute to shaping plant distribution patterns and thus are significant for biodiversity conservation and urban ecology. In fleshy-fruited plants, for example, any preference of frugivorous birds affects dispersal capacities of certain fruit species. We conducted a removal...... landscapes. The results should be included in urban forestry and planting of potentially invasive ornamental species. © 2011 Elsevier GmbH. All rights reserved....... experiment with fruits of Ilex aquifolium, a species that is currently expanding its range margin in northern Europe in response to climate change. The species is also a popular ornamental tree and naturalization has been observed in many parts of its range. Fruits of native I. aquifolium and of three...

Probabilistic forecasts of wind power generation accounting for geographically dispersed information

DEFF Research Database (Denmark)

Tastu, Julija; Pinson, Pierre; Trombe, Pierre-Julien

2014-01-01

be optimized by accounting for spatio-temporal effects that are so far merely considered. The way these effects may be included in relevant models is described for the case of both parametric and nonparametric approaches to generating probabilistic forecasts. The resulting predictions are evaluated on the real...... of the first order moments of predictive densities. The best performing approach, based on adaptive quantile regression, using spatially corrected point forecasts as input, consistently outperforms the state-of-theartbenchmark based on local information only, by 1.5%-4.6%, depending upon the lead time....

The Lagrangian particle dispersion model FLEXPART-WRF VERSION 3.1

Energy Technology Data Exchange (ETDEWEB)

Brioude, J.; Arnold, D.; Stohl, A.; Cassiani, M.; Morton, Don; Seibert, P.; Angevine, W. M.; Evan, S.; Dingwell, A.; Fast, Jerome D.; Easter, Richard C.; Pisso, I.; Bukhart, J.; Wotawa, G.

2013-11-01

The Lagrangian particle dispersion model FLEXPART was originally designed for cal- culating long-range and mesoscale dispersion of air pollutants from point sources, such as after an accident in a nuclear power plant. In the meantime FLEXPART has evolved into a comprehensive tool for atmospheric transport modeling and analysis at different scales. This multiscale need from the modeler community has encouraged new developments in FLEXPART. In this document, we present a version that works with the Weather Research and Forecasting (WRF) mesoscale meteoro- logical model. Simple procedures on how to run FLEXPART-WRF are presented along with special options and features that differ from its predecessor versions. In addition, test case data, the source code and visualization tools are provided to the reader as supplementary material.

Objectives for next generation of practical short-range atmospheric dispersion models

International Nuclear Information System (INIS)

Olesen, H.R.; Mikkelsen, T.

1992-01-01

The proceedings contains papers from the workshop ''Objectives for Next Generation of Practical Short-Range Atmospheric Dispersion Models''. They deal with two types of models, namely models for regulatory purposes and models for real-time applications. The workshop was the result of an action started in 1991 for increased cooperation and harmonization within atmospheric dispersion modelling. The focus of the workshop was on the management of model development and the definition of model objectives, rather than on detailed model contents. It was the intention to identify actions that can be taken in order to improve the development and use of atmospheric dispersion models. The papers in the proceedings deal with various topics within the broad spectrum of matters related to up-to-date practical models, such as their scientific basis, requirements for model input and output, meteorological preprocessing, standardisation within modelling, electronic information exchange as a potentially useful tool, model evaluation and data bases for model evaluation. In addition to the papers, the proceedings contain summaries of the discussions at the workshop. These summaries point to a number of recommended actions which can be taken in order to improve ''modelling culture''. (AB)

Boundary layer heights and surface fluxes of momentum and heat derived from ECMWF data for use in pollutant dispersion models - problems with data accuracy

Energy Technology Data Exchange (ETDEWEB)

Wotawa, G. [Univ. of Agricultural Sciences, Inst. of Meteorology and Physics, Vienna (Austria); Stohl, A. [Ludwig-Maximilians-Univ. Muenchen, Munich (Germany)

1997-10-01

Certain boundary layer parameters, especially boundary layer heights, are very important for pollutant dispersion modelling. On the regional scale (>- 100 km), data of the numerical weather prediction model of the European Centre for Medium-Range Weather Forecasts are often used for that purpose. Based on ECMWF data, the meteorological preprocessor FLEXTRA for Lagrangian air quality simulation models and the Lagrangian particle diffusion model FLEXPART have been developed. Using analyses and short term forecasts, a temporal resolution of three hours can be achieved. Some alternative methods to obtain boundary layer parameters can be applied, producing different results which affect all subsequent calculations, for instance the calculation of boundary layer trajectories and the dispersion of air pollutants. (au)

Long-Range Corrected Hybrid Density Functionals with Damped Atom-Atom Dispersion Corrections

Energy Technology Data Exchange (ETDEWEB)

Chai, Jeng-Da; Head-Gordon, Martin

2008-06-14

We report re-optimization of a recently proposed long-range corrected (LC) hybrid density functionals [J.-D. Chai and M. Head-Gordon, J. Chem. Phys. 128, 084106 (2008)] to include empirical atom-atom dispersion corrections. The resulting functional, {omega}B97X-D yields satisfactory accuracy for thermochemistry, kinetics, and non-covalent interactions. Tests show that for non-covalent systems, {omega}B97X-D shows slight improvement over other empirical dispersion-corrected density functionals, while for covalent systems and kinetics, it performs noticeably better. Relative to our previous functionals, such as {omega}B97X, the new functional is significantly superior for non-bonded interactions, and very similar in performance for bonded interactions.

Flood forecasting and uncertainty of precipitation forecasts

International Nuclear Information System (INIS)

Kobold, Mira; Suselj, Kay

2004-01-01

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

The Use of Principal Components in Long-Range Forecasting

Science.gov (United States)

Chern, Jonq-Gong

Large-scale modes of the global sea surface temperatures and the Northern Hemisphere tropospheric circulation are described by principal component analysis. The first and the second SST components well describe the El Nino episodes, and the El Nino index (ENI), suggested in this study, is consistent with the winter Southern Oscillation index (SOI), where this ENI is a composite component of the weighted first and second SST components. The large-scale interactive modes of the coupling ocean-atmosphere system are identified by cross-correlation analysis The result shows that the first SST component is strongly correlated with the first component of geopotential height in lead time of 6 months. In the El Nino-Southern Oscillation (ENSO) evolution, the El Nino mode strongly influences the winter tropospheric circulation in the mid -latitudes for up to three leading seasons. The regional long-range variation of climate is investigated with these major components of the SST and the tropospheric circulation. In the mid-latitude, the climate of the central United States shows a weak linkage with these large-scale circulations, and the climate of the western United States appears to be consistently associated with the ENSO modes. These El Nino modes also show a dominant influence on Eastern Asia as evidenced in Taiwan Mei-Yu patterns. Possible regional long-range forecasting schemes, utilizing the complementary characteristics of the winter El Nino mode and SST anomalies, are examined with the Taiwan Mei-Yu.

Analysis of stable components in the extended-range forecast for the coming 10–30 days in winter 2010 and 2011

International Nuclear Information System (INIS)

Wang Kuo; Zeng Yu-Xing; Wang Xu-Jia; Feng Guo-Lin

2013-01-01

In this paper we try to extract stable components in the extended-range forecast for the coming 10–30 days by using empirical orthogonal function (EOF) analysis, similarity coefficient, and some other methods based on the National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis daily data. The comparisons of the coefficient of variance of climatological background field and truth data in winter between 2010 and 2011 are made. The method of extracting stable components and climatological background field can be helpful to increase forecasting skill. The forecasting skill improvement of air temperature is better than geopotential height at 500 hPa. Moreover, this method improves the predictability better in the Pacific Ocean. In China, the forecast in winter in Northeast China is more uncertain than in the other parts. (geophysics, astronomy, and astrophysics)

On the reliability of seasonal climate forecasts

Science.gov (United States)

Weisheimer, A.; Palmer, T. N.

2014-01-01

Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1–5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that ‘goodness’ should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a ‘5’ should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of ‘goodness’ rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching ‘5’ across all regions and variables in 30 years time. PMID:24789559

Universal dispersion model for characterization of optical thin films over wide spectral range: Application to magnesium fluoride

Science.gov (United States)

Franta, Daniel; Nečas, David; Giglia, Angelo; Franta, Pavel; Ohlídal, Ivan

2017-11-01

Optical characterization of magnesium fluoride thin films is performed in a wide spectral range from far infrared to extreme ultraviolet (0.01-45 eV) utilizing the universal dispersion model. Two film defects, i.e. random roughness of the upper boundaries and defect transition layer at lower boundary are taken into account. An extension of universal dispersion model consisting in expressing the excitonic contributions as linear combinations of Gaussian and truncated Lorentzian terms is introduced. The spectral dependencies of the optical constants are presented in a graphical form and by the complete set of dispersion parameters that allows generating tabulated optical constants with required range and step using a simple utility in the newAD2 software package.

Error sensitivity analysis in 10-30-day extended range forecasting by using a nonlinear cross-prediction error model

Science.gov (United States)

Xia, Zhiye; Xu, Lisheng; Chen, Hongbin; Wang, Yongqian; Liu, Jinbao; Feng, Wenlan

2017-06-01

Extended range forecasting of 10-30 days, which lies between medium-term and climate prediction in terms of timescale, plays a significant role in decision-making processes for the prevention and mitigation of disastrous meteorological events. The sensitivity of initial error, model parameter error, and random error in a nonlinear crossprediction error (NCPE) model, and their stability in the prediction validity period in 10-30-day extended range forecasting, are analyzed quantitatively. The associated sensitivity of precipitable water, temperature, and geopotential height during cases of heavy rain and hurricane is also discussed. The results are summarized as follows. First, the initial error and random error interact. When the ratio of random error to initial error is small (10-6-10-2), minor variation in random error cannot significantly change the dynamic features of a chaotic system, and therefore random error has minimal effect on the prediction. When the ratio is in the range of 10-1-2 (i.e., random error dominates), attention should be paid to the random error instead of only the initial error. When the ratio is around 10-2-10-1, both influences must be considered. Their mutual effects may bring considerable uncertainty to extended range forecasting, and de-noising is therefore necessary. Second, in terms of model parameter error, the embedding dimension m should be determined by the factual nonlinear time series. The dynamic features of a chaotic system cannot be depicted because of the incomplete structure of the attractor when m is small. When m is large, prediction indicators can vanish because of the scarcity of phase points in phase space. A method for overcoming the cut-off effect ( m > 4) is proposed. Third, for heavy rains, precipitable water is more sensitive to the prediction validity period than temperature or geopotential height; however, for hurricanes, geopotential height is most sensitive, followed by precipitable water.

Switching between bistable states in a discrete nonlinear model with long-range dispersion

DEFF Research Database (Denmark)

Johansson, Magnus; Gaididei, Yuri B.; Christiansen, Peter Leth

1998-01-01

In the framework of a discrete nonlinear Schrodinger equation with long-range dispersion, we propose a general mechanism for obtaining a controlled switching between bistable localized excitations. We show that the application of a spatially symmetric kick leads to the excitation of an internal...

Can the confidence in long range atmospheric transport models be increased? The pan European experience of ensemble

International Nuclear Information System (INIS)

Galmarini, S.; Bianconi, R.; Mikkelsen, T.

2003-01-01

Full text: In the unfortunate event of an accidental release of radioactive material to the environment, the first concern for early-phase emergency response is atmospheric dispersion. For this purpose, several countries worldwide use operational Long Range Atmospheric Transport (LRAT) models to produce predictions of the event evolution over the continental scale to determine whether, when and how the radioactive cloud is going to hit their country. While presenting the multi-model ensemble dispersion forecast system (ENSEMBLE), the paper seeks to answer the following questions: is atmospheric dispersion forecasting an important asset of the early-phase emergency response management?; Is there a 'Perfect Atmospheric Dispersion Model'?; Is there a way to make the results of dispersion models more reliable and trustworthy? Several activities conducted during the 1990's, sought to estimate quantitatively the capability of LRAT models to forecast the atmospheric dispersion of radionuclides in the atmosphere. The results obtained clearly demonstrated that: the predictions of the various operational LRAT models used worldwide do not systematically agree (mainly due to conceptual differences in model structure and differences in the meteorological forecasts used to simulate the dispersion); none of the models used in the various countries is better than others under all circumstances and therefore there is no objective indication that shows one or few models to be the 'perfect model/s'. Given the realistic scenario that an accident can take place any time, any national authority is however faced with the practical need of managing the emergency and therefore with the dilemma: 'shall one rely an a LRAT model or only an the now cast provided by a monitoring network?' and 'to what extent are a model predictions going to be deceptive in the decision making process?' Since it goes without saying that even a vague idea an the future evolution of a dispersion process is better

The ability to use FLEXPART in simulation of the long-range radioactive materials dispersed from nuclear power plants near Vietnam border

International Nuclear Information System (INIS)

Pham Kim Long; Pham Duy Hien; Nguyen Hao Quang; Do Xuan Anh; Duong Duc Thang; Doan Quang Tuyen

2016-01-01

FLEXPART is a Lagrangian transport and dispersion model suitable for the simulation of a large range of atmospheric transport processes. FLEXPART has been researched and applied in simulation of the long-range dispersion of radioactive materials. It can be applicable to the problem of radioactive materials released from the nuclear power plants impact on Vietnam. This report presents simulation of radioactive dispersion from the accident assumed Fangchenggang and Changjiang nuclear power plants in China with the FLEXPART, using meteorological data from the National Centers for Environmental Prediction (NCEP). The results of simulations and analyzing showed good applicability of FLEXPART for a long-range radioactive materials dispersion. The preliminary simulation results show that the impact of the radioactive material dispersion in Vietnam varies by the well-known characteristics of the monsoon of our country. Winter is the time when the dominant northeast winds up radioactive dispersion most towards our country, its sphere of influence extends from the Northeast (Quang Ninh) to North Central (Da Nang). (author)

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...

Pair-Wise and Many-Body Dispersive Interactions Coupled to an Optimally Tuned Range-Separated Hybrid Functional.

Science.gov (United States)

Agrawal, Piyush; Tkatchenko, Alexandre; Kronik, Leeor

2013-08-13

We propose a nonempirical, pair-wise or many-body dispersion-corrected, optimally tuned range-separated hybrid functional. This functional retains the advantages of the optimal-tuning approach in the prediction of the electronic structure. At the same time, it gains accuracy in the prediction of binding energies for dispersively bound systems, as demonstrated on the S22 and S66 benchmark sets of weakly bound dimers.

A model for short and medium range dispersion of radionuclides released to the atmosphere

International Nuclear Information System (INIS)

Clarke, R.H.

1979-09-01

A Working Group was established to give practical guidance on the estimation of the dispersion of radioactive releases to the atmosphere. The dispersion is estimated in the short and medium range, that is from about 100 m to a few tens of kilometres from the source, and is based upon a Gaussian plume model. A scheme is presented for categorising atmospheric conditions and values of the associated dispersion parameters are given. Typical results are presented for releases in specific meteorological conditions and a scheme is included to allow for durations of release of up to 24 hours. Consideration has also been given to predicting longer term average concentrations, typically annual averages, and results are presented which facilitate site specific calculations. The results of the models are extended to 100 km from the source, but the increasing uncertainty with which results may be predicted beyond a few tens of kilometres from the source is emphasised. Three technical appendices provide some of the rationale behind the decisions made in adopting the various models in the proposed dispersion scheme. (author)

Impacts of Amazonia biomass burning aerosols assessed from short-range weather forecasts

Directory of Open Access Journals (Sweden)

S. R. Kolusu

2015-11-01

Full Text Available The direct radiative impacts of biomass burning aerosols (BBA on meteorology are investigated using short-range forecasts from the Met Office Unified Model (MetUM over South America during the South American Biomass Burning Analysis (SAMBBA. The impacts are evaluated using a set of three simulations: (i no aerosols, (ii with monthly mean aerosol climatologies and (iii with prognostic aerosols modelled using the Coupled Large-scale Aerosol Simulator for Studies In Climate (CLASSIC scheme. Comparison with observations show that the prognostic CLASSIC scheme provides the best representation of BBA. The impacts of BBA are quantified over central and southern Amazonia from the first and second day of 2-day forecasts during 14 September–3 October 2012. On average, during the first day of the forecast, including prognostic BBA reduces the clear-sky net radiation at the surface by 15 ± 1 W m−2 and reduces net top-of-atmosphere (TOA radiation by 8 ± 1 W m−2, with a direct atmospheric warming of 7 ± 1 W m−2. BBA-induced reductions in all-sky radiation are smaller in magnitude: 9.0 ± 1 W m−2 at the surface and 4.0 ± 1 W m−2 at TOA. In this modelling study the BBA therefore exert an overall cooling influence on the Earth–atmosphere system, although some levels of the atmosphere are directly warmed by the absorption of solar radiation. Due to the reduction of net radiative flux at the surface, the mean 2 m air temperature is reduced by around 0.1 ± 0.02 °C. The BBA also cools the boundary layer (BL but warms air above by around 0.2 °C due to the absorption of shortwave radiation. The overall impact is to reduce the BL depth by around 19 ± 8 m. These differences in heating lead to a more anticyclonic circulation at 700 hPa, with winds changing by around 0.6 m s−1. Inclusion of climatological or prognostic BBA in the MetUM makes a small but significant improvement in forecasts of temperature and relative humidity, but improvements were

Atmospheric aerosol dispersion models and their applications to environmental risk assessment

Directory of Open Access Journals (Sweden)

Andrzej Mazur

2014-03-01

Full Text Available Introduction. Numerical models of dispersion of atmospheric pollutants are widely used to forecast the spread of contaminants in the air and to analyze the effects of this phenomenon. The aim of the study is to investigate the possibilities and the quality of diagnosis and prediction of atmospheric transport of aerosols in the air using the dispersion model of atmospheric pollutants, developed at the Institute of Meteorology and Water Management (IMWM in Warsaw. Material and methods. A model of the dispersion of atmospheric pollutants, linked with meteorological models in a diagnostic mode, was used to simulate the transport of the cloud of aerosols released during the crash near the town of Ożydiw (Ukraine and of volcanic ash – during the volcanic eruption of Eyjafjallajökull in Iceland. Results. Possible directions of dispersion of pollutants in the air and its concentration in the atmosphere and deposition to the soil were assessed. The analysis of temporal variability of concentrations of aerosols in the atmosphere confirmed that the model developed at IMWM is an effective tool for diagnosis of air quality in the area of Poland as well as for determination of exposure duration to the aerosol clouds for different weather scenarios. Conclusions. The results are a confirmation of the thesis, that because in the environmental risk assessment, an important element is not only current information on the level of pollution concentrations, but also the time of exposure to pollution and forecast of these elements, and consequently the predicted effects on man or the environment in general; so it is necessary to use forecasting tools, similar to presented application. The dispersion model described in the paper is an operational tool for description, analysis and forecasting of emergency situations in case of emissions of hazardous substances.

Development of scheme for predicting atmospheric dispersion of radionuclides during nuclear emergency by using atmospheric dynamic model

Energy Technology Data Exchange (ETDEWEB)

Nagai, Haruyasu; Chino, Masamichi; Yamazawa, Hiromi (Japan Atomic Energy Research Inst., Tokyo (Japan))

1999-07-01

The meteorological forecast models are critically important for the accuracy of predicting the atmospheric dispersion of radionuclides discharged into atmosphere during nuclear emergencies. Thus, this paper describes a new scheme for predicting environmental impacts due to accidental release of radionuclides by using an atmospheric dynamic model PHYSIC. The advantages of introducing PHYSIC are, (1) three-dimensional local meteorological forecasts can be conducted, (2) synoptic meteorological changes can be considered by inputting grid data of synoptic forecasts from Japan Meteorological Agency to PHYSIC as initial and boundary conditions, (3) forecasts can be improved by nudging method using local meteorological observations, and (4) atmospheric dispersion model can consider the variation of the mixed layer. (author)

Development of scheme for predicting atmospheric dispersion of radionuclides during nuclear emergency by using atmospheric dynamic model

International Nuclear Information System (INIS)

Nagai, Haruyasu; Chino, Masamichi; Yamazawa, Hiromi

1999-01-01

The meteorological forecast models are critically important for the accuracy of predicting the atmospheric dispersion of radionuclides discharged into atmosphere during nuclear emergencies. Thus, this paper describes a new scheme for predicting environmental impacts due to accidental release of radionuclides by using an atmospheric dynamic model PHYSIC. The advantages of introducing PHYSIC are, (1) three-dimensional local meteorological forecasts can be conducted, (2) synoptic meteorological changes can be considered by inputting grid data of synoptic forecasts from Japan Meteorological Agency to PHYSIC as initial and boundary conditions, (3) forecasts can be improved by nudging method using local meteorological observations, and (4) atmospheric dispersion model can consider the variation of the mixed layer. (author)

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

Science.gov (United States)

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

2017-11-01

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

How disturbance, competition and dispersal interact to prevent tree range boundaries from keeping pace with climate change

Science.gov (United States)

Liang, Y.; Duveneck, M.; Gustafson, E. J.; Serra-Diaz, J. M.; Thompson, J. R.

2017-12-01

Climate change is expected to cause geographic shifts in tree species' ranges, but such shifts may not keep pace with climate changes because seed dispersal distances are often limited and competition-induced changes in community composition can be relatively slow. Disturbances may speed changes in community composition, but the interactions among climate change, disturbance and competitive interactions to produce range shifts are poorly understood. We used a physiologically-based mechanistic landscape model to study these interactions in the northeastern United States. We designed a series of disturbance scenarios to represent varied disturbance regimes in terms of both disturbance extent and intensity. We simulated forest succession by incorporating climate change under a high emissions future, disturbances, seed dispersal, and competition using the landscape model parameterized with forest inventory data. Tree species range boundary shifts in the next century were quantified as the change in the location of the 5th (the trailing edge) and 95th (the leading edge) percentiles of the spatial distribution of simulated species. Simulated tree species range boundary shifts in New England over the next century were far below (usually Disturbances may expedite species` recruitment into new sites, but they had little effect on the velocity of simulated range boundary shifts. Range shifts at the trailing edge tended to be associated with photosynthetic capacity, competitive ability for light and seed dispersal ability, whereas shifts at the leading edge were associated only with photosynthetic capacity and competition for light. This study underscores the importance of understanding the role of interspecific competition and disturbance when studying tree range shifts.

Short-term wind power combined forecasting based on error forecast correction

International Nuclear Information System (INIS)

Liang, Zhengtang; Liang, Jun; Wang, Chengfu; Dong, Xiaoming; Miao, Xiaofeng

2016-01-01

Highlights: • The correlation relationships of short-term wind power forecast errors are studied. • The correlation analysis method of the multi-step forecast errors is proposed. • A strategy selecting the input variables for the error forecast models is proposed. • Several novel combined models based on error forecast correction are proposed. • The combined models have improved the short-term wind power forecasting accuracy. - Abstract: With the increasing contribution of wind power to electric power grids, accurate forecasting of short-term wind power has become particularly valuable for wind farm operators, utility operators and customers. The aim of this study is to investigate the interdependence structure of errors in short-term wind power forecasting that is crucial for building error forecast models with regression learning algorithms to correct predictions and improve final forecasting accuracy. In this paper, several novel short-term wind power combined forecasting models based on error forecast correction are proposed in the one-step ahead, continuous and discontinuous multi-step ahead forecasting modes. First, the correlation relationships of forecast errors of the autoregressive model, the persistence method and the support vector machine model in various forecasting modes have been investigated to determine whether the error forecast models can be established by regression learning algorithms. Second, according to the results of the correlation analysis, the range of input variables is defined and an efficient strategy for selecting the input variables for the error forecast models is proposed. Finally, several combined forecasting models are proposed, in which the error forecast models are based on support vector machine/extreme learning machine, and correct the short-term wind power forecast values. The data collected from a wind farm in Hebei Province, China, are selected as a case study to demonstrate the effectiveness of the proposed

Comparative toxicity of two oil dispersants, superdispersant-25 and corexit 9527, to a range of coastal species.

Science.gov (United States)

Scarlett, Alan; Galloway, Tamara S; Canty, Martin; Smith, Emma L; Nilsson, Johanna; Rowland, Steven J

2005-05-01

The acute toxicity of the oil dispersant Corexit 9527 reported in the literature is highly variable. No peer-reviewed data exist for Superdispersant-25 (SD-25). This study compares the toxicity of the two dispersants to a range of marine species representing different phyla occupying a wide range of niches: The marine sediment-dwelling amphipod Corophium volutator (Pallas), the common mussel Mytilus edulis (L.), the symbiotic snakelocks anemone Anemonia viridis (Forskål), and the seagrass Zostera marina (L.). Organisms were exposed to static dispersant concentrations for 48-h and median lethal concentration (LC50), median effect concentration (EC50), and lowest-observable-effect concentration (LOEC) values obtained. The sublethal effects of 48-h exposures and the ability of species to recover for up to 72 h after exposure were quantified relative to the 48-h endpoints. Results indicated that the anemone lethality test was the most sensitive with LOECs of 20 ppm followed by mussel feeding rate, seagrass photosynthetic index and amphipod lethality, with mussel lethality being the least sensitive with LOECs of 250 ppm for both dispersants. The results were consistent with current theory that dispersants act physically and irreversibly on the respiratory organs and reversibly, depending on exposure time, on the nervous system. Superdispersant-25 was found overall to be less toxic than Corexit 9527 and its sublethal effects more likely to be reversible following short-term exposure.

When mechanism matters: Bayesian forecasting using models of ecological diffusion

Science.gov (United States)

Hefley, Trevor J.; Hooten, Mevin B.; Russell, Robin E.; Walsh, Daniel P.; Powell, James A.

2017-01-01

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

An Early-Warning System for Volcanic Ash Dispersal: The MAFALDA Procedure

Science.gov (United States)

Barsotti, S.; Nannipieri, L.; Neri, A.

2006-12-01

Forecasts of the dispersal of volcanic ash is a fundamental goal in order to mitigate its potential impact on urbanized areas and transport routes surrounding explosive volcanoes. To this aim we developed an early- warning procedure named MAFALDA (Modeling And Forecasting Ash Loading and Dispersal in the Atmosphere). Such tool is able to quantitatively forecast the atmospheric concentration of ash as well as the ground deposition as a function of time over a 3D spatial domain.\\The main features of MAFALDA are: (1) the use of the hybrid Lagrangian-Eulerian code VOL-CALPUFF able to describe both the rising column phase and the atmospheric dispersal as a function of weather conditions, (2) the use of high-resolution weather forecasting data, (3) the short execution time that allows to analyse a set of scenarios and (4) the web-based CGI software application (written in Perl programming language) that shows the results in a standard graphical web interface and makes it suitable as an early-warning system during volcanic crises.\\MAFALDA is composed by a computational part that simulates the ash cloud dynamics and a graphical interface for visualizing the modelling results. The computational part includes the codes for elaborating the meteorological data, the dispersal code and the post-processing programs. These produces hourly 2D maps of aerial ash concentration at several vertical levels, extension of "threat" area on air and 2D maps of ash deposit on the ground, in addition to graphs of hourly variations of column height.\\The processed results are available on the web by the graphical interface and the users can choose, by drop-down menu, which data to visualize. \\A first partial application of the procedure has been carried out for Mt. Etna (Italy). In this case, the procedure simulates four volcanological scenarios characterized by different plume intensities and uses 48-hrs weather forecasting data with a resolution of 7 km provided by the Italian Air Force.

New Developments in Wildfire Pollution Forecasting at the Canadian Meteorological Centre

Science.gov (United States)

Pavlovic, Radenko; Chen, Jack; Munoz-Alpizar, Rodrigo; Davignon, Didier; Beaulieu, Paul-Andre; Landry, Hugo; Menard, Sylvain; Gravel, Sylvie; Moran, Michael

2017-04-01

Environment and Climate Change Canada's air quality forecast system with near-real-time wildfire emissions, named FireWork, was developed in 2012 and has been run by the Canadian Meteorological Centre Operations division (CMCO) since 2013. In June 2016 this system was upgraded to operational status and wildfire smoke forecasts for North America are now available to the general public. FireWork's ability to model the transport and diffusion of wildfire smoke plumes has proved to be valuable to regional air quality forecasters and emergency first responders. Some of the most challenging issues with wildfire pollution modelling concern the production of wildfire emission estimates and near-source dispersion within the air quality model. As a consequence, FireWork is undergoing constant development. During the massive Fort McMurray wildfire event in western Canada in May 2016, for example, different wildfire emissions processing approaches and wildfire emissions injection and dispersion schemes were tested within the air quality model. Work on various FireWork components will continue in order to deliver a new operational version of the forecasting system for the 2017 wildfire season. Some of the proposed improvements will be shown in this presentation along with current and planned FireWork post-processing products.

Ensemble dispersion forecasting - Part 2. Application and evaluation

DEFF Research Database (Denmark)

Galmarini, S.; Bianconi, R.; Addis, R.

2004-01-01

of the dispersion of ETEX release 1 and the model ensemble is compared with the monitoring data. The scope of the comparison is to estimate to what extent the ensemble analysis is an improvement with respect to the single model results and represents a superior analysis of the process evolution. (C) 2004 Elsevier...

Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch

KAUST Repository

Xie, Le

2014-01-01

We propose a novel statistical wind power forecast framework, which leverages the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind farms. Critical assessment of the performance of spatio-temporal wind power forecast is performed using realistic wind farm data from West Texas. It is shown that spatio-temporal wind forecast models are numerically efficient approaches to improving forecast quality. By reducing uncertainties in near-term wind power forecasts, the overall cost benefits on system dispatch can be quantified. We integrate the improved forecast with an advanced robust look-ahead dispatch framework. This integrated forecast and economic dispatch framework is tested in a modified IEEE RTS 24-bus system. Numerical simulation suggests that the overall generation cost can be reduced by up to 6% using a robust look-ahead dispatch coupled with spatio-temporal wind forecast as compared with persistent wind forecast models. © 2013 IEEE.

Diverse range dynamics and dispersal routes of plants on the Tibetan Plateau during the late Quaternary.

Directory of Open Access Journals (Sweden)

Haibin Yu

Full Text Available Phylogeographical studies have suggested that several plant species on the Tibetan Plateau (TP underwent recolonization during the Quaternary and may have had distinct range dynamics in response to the last glacial. To further test this hypothesis and locate the possible historical dispersal routes, we selected 20 plant species from different parts of the TP and modeled their geographical distributions over four time periods using species distribution models (SDMs. Furthermore, we applied the least-cost path method together with SDMs and shared haplotypes to estimate their historical dispersal corridors. We identified three general scenarios of species distribution change during the late Quaternary: the 'contraction-expansion' scenario for species in the northeastern TP, the 'expansion-contraction' scenario for species in the southeast and the 'stable' scenario for widespread species. During the Quaternary, we identified that these species were likely to recolonize along the low-elevation valleys, huge mountain ranges and flat plateau platform (e.g. the Yarlung Zangbo Valley and the Himalaya. We inferred that Quaternary cyclic glaciations along with the various topographic and climatic conditions of the TP could have resulted in the diverse patterns of range shift and dispersal of Tibetan plant species. Finally, we believe that this study would provide valuable insights for the conservation of alpine species under future climate change.

How disturbance, competition, and dispersal interact to prevent tree range boundaries from keeping pace with climate change.

Science.gov (United States)

Liang, Yu; Duveneck, Matthew J; Gustafson, Eric J; Serra-Diaz, Josep M; Thompson, Jonathan R

2018-01-01

Climate change is expected to cause geographic shifts in tree species' ranges, but such shifts may not keep pace with climate changes because seed dispersal distances are often limited and competition-induced changes in community composition can be relatively slow. Disturbances may speed changes in community composition, but the interactions among climate change, disturbance and competitive interactions to produce range shifts are poorly understood. We used a physiologically based mechanistic landscape model to study these interactions in the northeastern United States. We designed a series of disturbance scenarios to represent varied disturbance regimes in terms of both disturbance extent and intensity. We simulated forest succession by incorporating climate change under a high-emissions future, disturbances, seed dispersal, and competition using the landscape model parameterized with forest inventory data. Tree species range boundary shifts in the next century were quantified as the change in the location of the 5th (the trailing edge) and 95th (the leading edge) percentiles of the spatial distribution of simulated species. Simulated tree species range boundary shifts in New England over the next century were far below (usually change (usually more than 110 km over 100 years) under a high-emissions scenario. Simulated species` ranges shifted northward at both the leading edge (northern boundary) and trailing edge (southern boundary). Disturbances may expedite species' recruitment into new sites, but they had little effect on the velocity of simulated range boundary shifts. Range shifts at the trailing edge tended to be associated with photosynthetic capacity, competitive ability for light and seed dispersal ability, whereas shifts at the leading edge were associated only with photosynthetic capacity and competition for light. This study underscores the importance of understanding the role of interspecific competition and disturbance when studying tree range

High Resolution Trajectory-Based Smoke Forecasts Using VIIRS Aerosol Optical Depth and NUCAPS Carbon Monoxide Retrievals

Science.gov (United States)

Pierce, R. B.; Smith, N.; Barnet, C.; Barnet, C. D.; Kondragunta, S.; Davies, J. E.; Strabala, K.

2016-12-01

We use Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Aerosol Optical Depth (AOD) and combined Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) NOAA-Unique CrIS-ATMS Processing System (NUCAPS) carbon monoxide (CO) retrievals to initialize trajectory-based, high spatial resolution North American smoke dispersion forecasts during the May 2016 Fort McMurray wildfire in northern Alberta and the July 2016 Soberanes Fire in Northern California. These two case studies illustrate how long range transport of wild fire smoke can adversely impact surface air quality thousands of kilometers downwind and how local topographic flow can lead to complex transport patterns near the wildfire source region. The NUCAPS CO retrievals are shown to complement the high resolution VIIRS AOD retrievals by providing retrievals in partially cloudy scenes and also providing information on the vertical distribution of the wildfire smoke. This work addresses the need for low latency, web-based, high resolution forecasts of smoke dispersion for use by NWS Incident Meteorologists (IMET) to support on-site decision support services for fire incident management teams. The primary user community for the IDEA-I smoke forecasts is the Western regions of the NWS and US EPA due to the significant impacts of wildfires in these regions. Secondary users include Alaskan NWS offices and Western State and Local air quality management agencies such as the Western Regional Air Partnership (WRAP).

Using Temperature Forecasts to Improve Seasonal Streamflow Forecasts in the Colorado and Rio Grande Basins

Science.gov (United States)

Lehner, F.; Wood, A.; Llewellyn, D.; Blatchford, D. B.; Goodbody, A. G.; Pappenberger, F.

2017-12-01

Recent studies have documented the influence of increasing temperature on streamflow across the American West, including snow-melt driven rivers such as the Colorado or Rio Grande. At the same time, some basins are reporting decreasing skill in seasonal streamflow forecasts, termed water supply forecasts (WSFs), over the recent decade. While the skill in seasonal precipitation forecasts from dynamical models remains low, their skill in predicting seasonal temperature variations could potentially be harvested for WSFs to account for non-stationarity in regional temperatures. Here, we investigate whether WSF skill can be improved by incorporating seasonal temperature forecasts from dynamical forecasting models (from the North American Multi Model Ensemble and the European Centre for Medium-Range Weather Forecast System 4) into traditional statistical forecast models. We find improved streamflow forecast skill relative to traditional WSF approaches in a majority of headwater locations in the Colorado and Rio Grande basins. Incorporation of temperature into WSFs thus provides a promising avenue to increase the robustness of current forecasting techniques in the face of continued regional warming.

Operational mesoscale atmospheric dispersion prediction using high performance parallel computing cluster for emergency response

International Nuclear Information System (INIS)

Srinivas, C.V.; Venkatesan, R.; Muralidharan, N.V.; Das, Someshwar; Dass, Hari; Eswara Kumar, P.

2005-08-01

An operational atmospheric dispersion prediction system is implemented on a cluster super computer for 'Online Emergency Response' for Kalpakkam nuclear site. The numerical system constitutes a parallel version of a nested grid meso-scale meteorological model MM5 coupled to a random walk particle dispersion model FLEXPART. The system provides 48 hour forecast of the local weather and radioactive plume dispersion due to hypothetical air borne releases in a range of 100 km around the site. The parallel code was implemented on different cluster configurations like distributed and shared memory systems. Results of MM5 run time performance for 1-day prediction are reported on all the machines available for testing. A reduction of 5 times in runtime is achieved using 9 dual Xeon nodes (18 physical/36 logical processors) compared to a single node sequential run. Based on the above run time results a cluster computer facility with 9-node Dual Xeon is commissioned at IGCAR for model operation. The run time of a triple nested domain MM5 is about 4 h for 24 h forecast. The system has been operated continuously for a few months and results were ported on the IMSc home page. Initial and periodic boundary condition data for MM5 are provided by NCMRWF, New Delhi. An alternative source is found to be NCEP, USA. These two sources provide the input data to the operational models at different spatial and temporal resolutions and using different assimilation methods. A comparative study on the results of forecast is presented using these two data sources for present operational use. Slight improvement is noticed in rainfall, winds, geopotential heights and the vertical atmospheric structure while using NCEP data probably because of its high spatial and temporal resolution. (author)

Linking long-range weather forecasts and heat consumption as a determining factor when buying fuel chips for town heating plants

International Nuclear Information System (INIS)

Rolev, A.-M.

1991-12-01

The aim of this study is to test whether long-range weather forecasts from the meteorological services can be used as a determining factor when buying fuel chips. In the study the fuel consumption of heating plants and the factors determining the monthly consumption are mentioned. Degree-day statistics in Denmark for the last 30 years are explained as well as the difficulties in conjunction with the prediction of long-range weather conditions. This study compares degree days in 1989-1990 month by month with the actual and theoretic chip consumption in three different heating plants the same year. The theoretic chip consumption is calculated on the basis of degree days in a ''standard year'' and the annual chip consumption of the heating plant, among other things. Furthermore, on the basis of degree-day statistics the report makes it possible to estimate the monthly chip consumption of a heating plant in a ''standard year'', in an extremely cold year (maximum degree days), and in an extremely warm year (minimum degree days). However, not everything can be predicted, and it is not yet possible to predict reliable weather forecasts for more than 5 days ahead. The study concludes that long-range weather forecasts cannot be used as a determining factor when buying fuel chips for heating plants. When buying fuel chips one must still use statistics and degree days, supplimented by figures based on experience from actual chip consumption in the individual heating plant. These figures take into consideration the different types of heating plants, as well as heat supply, chip-supplier, storing facilities, other fuels, etc. (au)

Assessing flood forecast uncertainty with fuzzy arithmetic

Directory of Open Access Journals (Sweden)

de Bruyn Bertrand

2016-01-01

Full Text Available Providing forecasts for flow rates and water levels during floods have to be associated with uncertainty estimates. The forecast sources of uncertainty are plural. For hydrological forecasts (rainfall-runoff performed using a deterministic hydrological model with basic physics, two main sources can be identified. The first obvious source is the forcing data: rainfall forecast data are supplied in real time by meteorological forecasting services to the Flood Forecasting Service within a range between a lowest and a highest predicted discharge. These two values define an uncertainty interval for the rainfall variable provided on a given watershed. The second source of uncertainty is related to the complexity of the modeled system (the catchment impacted by the hydro-meteorological phenomenon, the number of variables that may describe the problem and their spatial and time variability. The model simplifies the system by reducing the number of variables to a few parameters. Thus it contains an intrinsic uncertainty. This model uncertainty is assessed by comparing simulated and observed rates for a large number of hydro-meteorological events. We propose a method based on fuzzy arithmetic to estimate the possible range of flow rates (and levels of water making a forecast based on possible rainfalls provided by forcing and uncertainty model. The model uncertainty is here expressed as a range of possible values. Both rainfall and model uncertainties are combined with fuzzy arithmetic. This method allows to evaluate the prediction uncertainty range. The Flood Forecasting Service of Oise and Aisne rivers, in particular, monitors the upstream watershed of the Oise at Hirson. This watershed’s area is 310 km2. Its response time is about 10 hours. Several hydrological models are calibrated for flood forecasting in this watershed and use the rainfall forecast. This method presents the advantage to be easily implemented. Moreover, it permits to be carried out

Forecasting long-range atmospheric transport episodes of polychlorinated biphenyls using FLEXPART

Science.gov (United States)

Halse, Anne Karine; Eckhardt, Sabine; Schlabach, Martin; Stohl, Andreas; Breivik, Knut

2013-06-01

The analysis of concentrations of persistent organic pollutants (POPs) in ambient air is costly and can only be done for a limited number of samples. It is thus beneficial to maximize the information content of the samples analyzed via a targeted observation strategy. Using polychlorinated biphenyls (PCBs) as an example, a forecasting system to predict and evaluate long-range atmospheric transport (LRAT) episodes of POPs at a remote site in southern Norway has been developed. The system uses the Lagrangian particle transport model FLEXPART, and can be used for triggering extra ("targeted") sampling when LRAT episodes are predicted to occur. The system was evaluated by comparing targeted samples collected over 12-25 h during individual LRAT episodes with monitoring samples regularly collected over one day per week throughout a year. Measured concentrations in all targeted samples were above the 75th percentile of the concentrations obtained from the regular monitoring program and included the highest measured values of all samples. This clearly demonstrates the success of the targeted sampling strategy.

Skill forecasting from different wind power ensemble prediction methods

International Nuclear Information System (INIS)

Pinson, Pierre; Nielsen, Henrik A; Madsen, Henrik; Kariniotakis, George

2007-01-01

This paper presents an investigation on alternative approaches to the providing of uncertainty estimates associated to point predictions of wind generation. Focus is given to skill forecasts in the form of prediction risk indices, aiming at giving a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the dispersion of ensemble members for a single prediction horizon, or over a set of successive look-ahead times. It is shown on the test case of a Danish offshore wind farm how prediction risk indices may be related to several levels of forecast uncertainty (and energy imbalances). Wind power ensemble predictions are derived from the transformation of ECMWF and NCEP ensembles of meteorological variables to power, as well as by a lagged average approach alternative. The ability of risk indices calculated from the various types of ensembles forecasts to resolve among situations with different levels of uncertainty is discussed

Three-dimensional visualization of ensemble weather forecasts - Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

Science.gov (United States)

Rautenhaus, M.; Grams, C. M.; Schäfler, A.; Westermann, R.

2015-07-01

We present the application of interactive three-dimensional (3-D) visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX - North Atlantic Waveguide and Downstream Impact Experiment) campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs) has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF) ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off).

NKS NordRisk. Atlas of long-range atmospheric dispersion and deposition of radionuclides from selected risk sites in the Northern Hemisphere

International Nuclear Information System (INIS)

Havskov Soerensen, J.; Baklanov, A.; Mahura, A.; Lauritzen, Bent; Mikkelsen, Torben

2008-07-01

Within the NKS NordRisk project, 'Nuclear risk from atmospheric dispersion in Northern Europe', the NKS NordRisk Atlas has been developed. The atlas describes risks from hypothetical long-range atmospheric dispersion and deposition of radionuclides from selected nuclear risk sites in the Northern Hemisphere. A number of case studies of long-term long-range atmospheric transport and deposition of radionuclides has been developed, based on two years of meteorological data. Radionuclide concentrations in air and radionuclide depositions have been evaluated and examples of long-term averages of the dispersion and deposition and of the variability around these mean values are provided. (au)

Lagrangian Stochastic Dispersion Model IMS Model Suite and its Validation against Experimental Data

International Nuclear Information System (INIS)

Bartok, J.

2010-01-01

The dissertation presents IMS Lagrangian Dispersion Model, which is a 'new generation' Slovak dispersion model of long-range transport, developed by MicroStep-MIS. It solves trajectory equation for a vast number of Lagrangian 'particles' and stochastic equation that simulates the effects of turbulence. Model contains simulation of radioactive decay (full decay chains of more than 300 nuclides), and dry and wet deposition. Model was integrated into IMS Model Suite, a system in which several models and modules can run and cooperate, e.g. LAM model WRF preparing fine resolution meteorological data for dispersion. The main theme of the work is validation of dispersion model against large scale international campaigns CAPTEX and ETEX, which are two of the largest tracer experiments. Validation addressed treatment of missing data, data interpolation into comparable temporal and spatial representation. The best model results were observed for ETEX I, standard results for CAPTEXes and worst results for ETEX II, known in modelling community for its meteorological conditions that can be hardly resolved by models. The IMS Lagrangian Dispersion Model was identified as capable long range dispersion model for slowly- or nonreacting chemicals and radioactive matter. Influence of input data on simulation quality is discussed within the work. Additional modules were prepared according to praxis requirement: a) Recalculation of concentrations of radioactive pollutant into effective doses form inhalation, immersion in the plume and deposition. b) Dispersion of mineral dust was added and tested in desert locality, where wind and soil moisture were firstly analysed and forecast by WRF. The result was qualitatively verified in case study against satellite observations. (author)

The role of central bank transparency for guiding private sector forecasts

OpenAIRE

Ehrmann, Michael; Eijffinger, Sylvester C W; Fratzscher, Marcel

2009-01-01

There is a broad consensus in the literature that costs of information processing and acquisition may generate costly disagreements in expectations among economic agents, and that central banks may play a central role in reducing such dispersion in expectations. This paper analyses empirically whether enhanced central bank transparency lowers dispersion among professional forecasters of key economic variables, using a large set of proxies for central bank transparency in 12 advanced economies...

Underestimated risks of recurrent long-range ash dispersal from northern Pacific Arc volcanoes.

Science.gov (United States)

Bourne, A J; Abbott, P M; Albert, P G; Cook, E; Pearce, N J G; Ponomareva, V; Svensson, A; Davies, S M

2016-07-21

Widespread ash dispersal poses a significant natural hazard to society, particularly in relation to disruption to aviation. Assessing the extent of the threat of far-travelled ash clouds on flight paths is substantially hindered by an incomplete volcanic history and an underestimation of the potential reach of distant eruptive centres. The risk of extensive ash clouds to aviation is thus poorly quantified. New evidence is presented of explosive Late Pleistocene eruptions in the Pacific Arc, currently undocumented in the proximal geological record, which dispersed ash up to 8000 km from source. Twelve microscopic ash deposits or cryptotephra, invisible to the naked eye, discovered within Greenland ice-cores, and ranging in age between 11.1 and 83.7 ka b2k, are compositionally matched to northern Pacific Arc sources including Japan, Kamchatka, Cascades and Alaska. Only two cryptotephra deposits are correlated to known high-magnitude eruptions (Towada-H, Japan, ca 15 ka BP and Mount St Helens Set M, ca 28 ka BP). For the remaining 10 deposits, there is no evidence of age- and compositionally-equivalent eruptive events in regional volcanic stratigraphies. This highlights the inherent problem of under-reporting eruptions and the dangers of underestimating the long-term risk of widespread ash dispersal for trans-Pacific and trans-Atlantic flight routes.

Automated system for smoke dispersion prediction due to wild fires in Alaska

Science.gov (United States)

Kulchitsky, A.; Stuefer, M.; Higbie, L.; Newby, G.

2007-12-01

Community climate models have enabled development of specific environmental forecast systems. The University of Alaska (UAF) smoke group was created to adapt a smoke forecast system to the Alaska region. The US Forest Service (USFS) Missoula Fire Science Lab had developed a smoke forecast system based on the Weather Research and Forecasting (WRF) Model including chemistry (WRF/Chem). Following the successful experience of USFS, which runs their model operationally for the contiguous U.S., we develop a similar system for Alaska in collaboration with scientists from the USFS Missoula Fire Science Lab. Wildfires are a significant source of air pollution in Alaska because the climate and vegetation favor annual summer fires that burn huge areas. Extreme cases occurred in 2004, when an area larger than Maryland (more than 25000~km2) burned. Small smoke particles with a diameter less than 10~μm can penetrate deep into lungs causing health problems. Smoke also creates a severe restriction to air transport and has tremendous economical effect. The smoke dispersion and forecast system for Alaska was developed at the Geophysical Institute (GI) and the Arctic Region Supercomputing Center (ARSC), both at University of Alaska Fairbanks (UAF). They will help the public and plan activities a few days in advance to avoid dangerous smoke exposure. The availability of modern high performance supercomputers at ARSC allows us to create and run high-resolution, WRF-based smoke dispersion forecast for the entire State of Alaska. The core of the system is a Python program that manages the independent pieces. Our adapted Alaska system performs the following steps \\begin{itemize} Calculate the medium-resolution weather forecast using WRF/Met. Adapt the near real-time satellite-derived wildfire location and extent data that are received via direct broadcast from UAF's "Geographic Information Network of Alaska" (GINA) Calculate fuel moisture using WRF forecasts and National Fire Danger

NKS NordRisk. Atlas of long-range atmospheric dispersion and deposition of radionuclides from selected risk sites in the Northern Hemisphere

Energy Technology Data Exchange (ETDEWEB)

Havskov Soerensen, J.; Baklanov, A.; Mahura, A. (Danish Meteorological Institute, Copenhagen (Denmark)); Lauritzen, Bent; Mikkelsen, Torben (Technical Univ. of Denmark, Risoe National Lab. for Sustainable Energy, Roskilde (Denmark))

2008-07-15

Within the NKS NordRisk project, 'Nuclear risk from atmospheric dispersion in Northern Europe', the NKS NordRisk Atlas has been developed. The atlas describes risks from hypothetical long-range atmospheric dispersion and deposition of radionuclides from selected nuclear risk sites in the Northern Hemisphere. A number of case studies of long-term long-range atmospheric transport and deposition of radionuclides has been developed, based on two years of meteorological data. Radionuclide concentrations in air and radionuclide depositions have been evaluated and examples of long-term averages of the dispersion and deposition and of the variability around these mean values are provided. (au)

Combining forecast weights: Why and how?

Science.gov (United States)

Yin, Yip Chee; Kok-Haur, Ng; Hock-Eam, Lim

2012-09-01

This paper proposes a procedure called forecast weight averaging which is a specific combination of forecast weights obtained from different methods of constructing forecast weights for the purpose of improving the accuracy of pseudo out of sample forecasting. It is found that under certain specified conditions, forecast weight averaging can lower the mean squared forecast error obtained from model averaging. In addition, we show that in a linear and homoskedastic environment, this superior predictive ability of forecast weight averaging holds true irrespective whether the coefficients are tested by t statistic or z statistic provided the significant level is within the 10% range. By theoretical proofs and simulation study, we have shown that model averaging like, variance model averaging, simple model averaging and standard error model averaging, each produces mean squared forecast error larger than that of forecast weight averaging. Finally, this result also holds true marginally when applied to business and economic empirical data sets, Gross Domestic Product (GDP growth rate), Consumer Price Index (CPI) and Average Lending Rate (ALR) of Malaysia.

Forecasting winds over nuclear power plants statistics

International Nuclear Information System (INIS)

Marais, Ch.

1997-01-01

In the event of an accident at nuclear power plant, it is essential to forecast the wind velocity at the level where the efflux occurs (about 100 m). At present meteorologists refine the wind forecast from the coarse grid of numerical weather prediction (NWP) models. The purpose of this study is to improve the forecasts by developing a statistical adaptation method which corrects the NWP forecasts by using statistical comparisons between wind forecasts and observations. The Multiple Linear Regression method is used here to forecast the 100 m wind at 12 and 24 hours range for three Electricite de France (EDF) sites. It turns out that this approach gives better forecasts than the NWP model alone and is worthy of operational use. (author)

Numerical Weather Forecasting at the Savannah River Site

International Nuclear Information System (INIS)

Buckley, R.L.

1999-01-01

Facilities such as the Savannah River Site (SRS), which contain the potential for hazardous atmospheric releases, rely on the predictive capabilities of dispersion models to assess possible emergency response actions. The operational design in relation to domain size and forecast time is presented, along with verification of model results over extended time periods with archived surface observations

Worldwide satellite market demand forecast

Science.gov (United States)

Bowyer, J. M.; Frankfort, M.; Steinnagel, K. M.

1981-01-01

The forecast is for the years 1981 - 2000 with benchmark years at 1985, 1990 and 2000. Two typs of markets are considered for this study: Hardware (worldwide total) - satellites, earth stations and control facilities (includes replacements and spares); and non-hardware (addressable by U.S. industry) - planning, launch, turnkey systems and operations. These markets were examined for the INTELSAT System (international systems and domestic and regional systems using leased transponders) and domestic and regional systems. Forecasts were determined for six worldwide regions encompassing 185 countries using actual costs for existing equipment and engineering estimates of costs for advanced systems. Most likely (conservative growth rate estimates) and optimistic (mid range growth rate estimates) scenarios were employed for arriving at the forecasts which are presented in constant 1980 U.S. dollars. The worldwide satellite market demand forecast predicts that the market between 181 and 2000 will range from $35 to $50 billion. Approximately one-half of the world market, $16 to $20 billion, will be generated in the United States.

Ensemble forecasting of species distributions.

Science.gov (United States)

Araújo, Miguel B; New, Mark

2007-01-01

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

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...

ECMWF Extreme Forecast Index for water vapor transport: A forecast tool for atmospheric rivers and extreme precipitation

Science.gov (United States)

Lavers, David A.; Pappenberger, Florian; Richardson, David S.; Zsoter, Ervin

2016-11-01

In winter, heavy precipitation and floods along the west coasts of midlatitude continents are largely caused by intense water vapor transport (integrated vapor transport (IVT)) within the atmospheric river of extratropical cyclones. This study builds on previous findings that showed that forecasts of IVT have higher predictability than precipitation, by applying and evaluating the European Centre for Medium-Range Weather Forecasts Extreme Forecast Index (EFI) for IVT in ensemble forecasts during three winters across Europe. We show that the IVT EFI is more able (than the precipitation EFI) to capture extreme precipitation in forecast week 2 during forecasts initialized in a positive North Atlantic Oscillation (NAO) phase; conversely, the precipitation EFI is better during the negative NAO phase and at shorter leads. An IVT EFI example for storm Desmond in December 2015 highlights its potential to identify upcoming hydrometeorological extremes, which may prove useful to the user and forecasting communities.

Three-dimensional visualization of ensemble weather forecasts – Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

Directory of Open Access Journals (Sweden)

M. Rautenhaus

2015-07-01

Full Text Available We present the application of interactive three-dimensional (3-D visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off.

Dispersal in the sub-Antarctic: king penguins show remarkably little population genetic differentiation across their range.

Science.gov (United States)

Clucas, Gemma V; Younger, Jane L; Kao, Damian; Rogers, Alex D; Handley, Jonathan; Miller, Gary D; Jouventin, Pierre; Nolan, Paul; Gharbi, Karim; Miller, Karen J; Hart, Tom

2016-10-13

Seabirds are important components of marine ecosystems, both as predators and as indicators of ecological change, being conspicuous and sensitive to changes in prey abundance. To determine whether fluctuations in population sizes are localised or indicative of large-scale ecosystem change, we must first understand population structure and dispersal. King penguins are long-lived seabirds that occupy a niche across the sub-Antarctic zone close to the Polar Front. Colonies have very different histories of exploitation, population recovery, and expansion. We investigated the genetic population structure and patterns of colonisation of king penguins across their current range using a dataset of 5154 unlinked, high-coverage single nucleotide polymorphisms generated via restriction site associated DNA sequencing (RADSeq). Despite breeding at a small number of discrete, geographically separate sites, we find only very slight genetic differentiation among colonies separated by thousands of kilometers of open-ocean, suggesting migration among islands and archipelagos may be common. Our results show that the South Georgia population is slightly differentiated from all other colonies and suggest that the recently founded Falkland Island colony is likely to have been established by migrants from the distant Crozet Islands rather than nearby colonies on South Georgia, possibly as a result of density-dependent processes. The observed subtle differentiation among king penguin colonies must be considered in future conservation planning and monitoring of the species, and demographic models that attempt to forecast extinction risk in response to large-scale climate change must take into account migration. It is possible that migration could buffer king penguins against some of the impacts of climate change where colonies appear panmictic, although it is unlikely to protect them completely given the widespread physical changes projected for their Southern Ocean foraging grounds

Atmospheric dispersion modeling: Challenges of the Fukushima Daiichi response

Energy Technology Data Exchange (ETDEWEB)

Sugiyama, Gayle [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Nasstrom, John [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Pobanz, Brenda [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Foster, Kevin [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Simpson, Matthew [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Vogt, Phil [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Aluzzi, Fernando [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Homann, Steve [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2012-05-01

In this research, the U.S. Department of Energy’s (DOE) National Atmospheric Release Advisory Center (NARAC) provided a wide range of predictions and analyses as part of the response to the Fukushima Daiichi Nuclear Power Plant accident including: daily Japanese weather forecasts and atmospheric transport predictions to inform planning for field monitoring operations and to provide U.S. government agencies with ongoing situational awareness of meteorological conditions; estimates of possible dose in Japan based on hypothetical U.S. Nuclear Regulatory Commission scenarios of potential radionuclide releases to support protective action planning for U.S. citizens; predictions of possible plume arrival times and dose levels at U.S. locations; and source estimation and plume model refinement based on atmospheric dispersion modeling and available monitoring data.

Contrasting growth forecasts across the geographical range of Scots pine due to altitudinal and latitudinal differences in climatic sensitivity.

Science.gov (United States)

Matías, Luis; Linares, Juan C; Sánchez-Miranda, Ángela; Jump, Alistair S

2017-10-01

Ongoing changes in global climate are altering ecological conditions for many species. The consequences of such changes are typically most evident at the edge of a species' geographical distribution, where differences in growth or population dynamics may result in range expansions or contractions. Understanding population responses to different climatic drivers along wide latitudinal and altitudinal gradients is necessary in order to gain a better understanding of plant responses to ongoing increases in global temperature and drought severity. We selected Scots pine (Pinus sylvestris L.) as a model species to explore growth responses to climatic variability (seasonal temperature and precipitation) over the last century through dendrochronological methods. We developed linear models based on age, climate and previous growth to forecast growth trends up to year 2100 using climatic predictions. Populations were located at the treeline across a latitudinal gradient covering the northern, central and southernmost populations and across an altitudinal gradient at the southern edge of the distribution (treeline, medium and lower elevations). Radial growth was maximal at medium altitude and treeline of the southernmost populations. Temperature was the main factor controlling growth variability along the gradients, although the timing and strength of climatic variables affecting growth shifted with latitude and altitude. Predictive models forecast a general increase in Scots pine growth at treeline across the latitudinal distribution, with southern populations increasing growth up to year 2050, when it stabilizes. The highest responsiveness appeared at central latitude, and moderate growth increase is projected at the northern limit. Contrastingly, the model forecasted growth declines at lowland-southern populations, suggesting an upslope range displacement over the coming decades. Our results give insight into the geographical responses of tree species to climate change

NKS NordRisk II: Atlas of long-range atmospheric dispersion and deposition of radionuclides from selected risk sites in the Northern Hemisphere

DEFF Research Database (Denmark)

Smith Korsholm, Ulrik; Astrup, Poul; Lauritzen, Bent

The present atlas has been developed within the NKS/NordRisk-II project "Nuclear risk from atmospheric dispersion in Northern Europe". The atlas describes risks from hypothetical long-range dispersion and deposition of radionuclides from 16 nuclear risk sites on the Northern Hemisphere...... spanning the climate variability associated with the North Atlantic Oscillation, and corresponding time evolution of the ensemble mean atmospheric dispersion....

Can the confidence in long range atmospheric transport models be increased? The Pan-European experience of ENSEMBLE

DEFF Research Database (Denmark)

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

2004-01-01

Is atmospheric dispersion forecasting an important asset of the early-phase nuclear emergency response management? Is there a 'perfect atmospheric dispersion model'? Is there a way to make the results of dispersion models more reliable and trustworthy? While seeking to answer these questions the ...

Long-Range Socio-Economic Forecasting of World Development in the Works by IMEMO RAS

Directory of Open Access Journals (Sweden)

Suslov D. V.

2011-12-01

Full Text Available A brief overview is given of papers by the Institute of World Economy and International Relations, Russian Academy of Sciences (IMEMO RAS on long-term socio-economic forecasting of global development. The forecasting methodology is shown, its capabilities and limitations, as well as the structure, main results and characteristics of the forecasts made by IMEMO RAS since early 2000s. The «Strategic Global Outlook for 2030» has acquired features of an interdisciplinary research, and has been developed based on a system analysis of objective socio-economic indicators, long-term global and regional socio-demographic trends, and expert assessment of the future dynamics of the political situation in individual countries and in intergovernmental relations. This methodology allowed the focus to be placed primarily on the stable trends of development in the world economy and the system of international relations, their actors, structures and institutions

Forecasting interest rates with shifting endpoints

DEFF Research Database (Denmark)

Van Dijk, Dick; Koopman, Siem Jan; Wel, Michel van der

2014-01-01

We consider forecasting the term structure of interest rates with the assumption that factors driving the yield curve are stationary around a slowly time-varying mean or ‘shifting endpoint’. The shifting endpoints are captured using either (i) time series methods (exponential smoothing) or (ii......) long-range survey forecasts of either interest rates or inflation and output growth, or (iii) exponentially smoothed realizations of these macro variables. Allowing for shifting endpoints in yield curve factors provides substantial and significant gains in out-of-sample predictive accuracy, relative...... to stationary and random walk benchmarks. Forecast improvements are largest for long-maturity interest rates and for long-horizon forecasts....

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

A composite stability index for dichotomous forecast of thunderstorms

Science.gov (United States)

Chaudhuri, Sutapa; Middey, Anirban

2012-12-01

Thunderstorms are the perennial feature of Kolkata (22° 32' N, 88° 20' E), India during the premonsoon season (April-May). Precise forecast of these thunderstorms is essential to mitigate the associated catastrophe due to lightning flashes, strong wind gusts, torrential rain, and occasional hail and tornadoes. The present research provides a composite stability index for forecasting thunderstorms. The forecast quality detection parameters are computed with the available indices during the period from 1997 to 2006 to select the most relevant indices with threshold ranges for the prevalence of such thunderstorms. The analyses reveal that the lifted index (LI) within the range of -5 to -12 °C, convective inhibition energy (CIN) within the range of 0-150 J/kg and convective available potential energy (CAPE) within the ranges of 2,000 to 7,000 J/kg are the most pertinent indices for the prevalence thunderstorms over Kolkata during the premonsoon season. A composite stability index, thunderstorm prediction index (TPI) is formulated with LI, CIN, and CAPE. The statistical skill score analyses show that the accuracy in forecasting such thunderstorms with TPI is 99.67 % with lead time less than 12 h during training the index whereas the accuracies are 89.64 % with LI, 60 % with CIN and 49.8 % with CAPE. The performance diagram supports that TPI has better forecast skill than its individual components. The forecast with TPI is validated with the observation of the India Meteorological Department during the period from 2007 to 2009. The real-time forecast of thunderstorms with TPI is provided for the year 2010.

Adjusting particle-size distributions to account for aggregation in tephra-deposit model forecasts

Science.gov (United States)

Mastin, Larry G.; Van Eaton, Alexa; Durant, A.J.

2016-01-01

Volcanic ash transport and dispersion (VATD) models are used to forecast tephra deposition during volcanic eruptions. Model accuracy is limited by the fact that fine-ash aggregates (clumps into clusters), thus altering patterns of deposition. In most models this is accounted for by ad hoc changes to model input, representing fine ash as aggregates with density ρagg, and a log-normal size distribution with median μagg and standard deviation σagg. Optimal values may vary between eruptions. To test the variance, we used the Ash3d tephra model to simulate four deposits: 18 May 1980 Mount St. Helens; 16–17 September 1992 Crater Peak (Mount Spurr); 17 June 1996 Ruapehu; and 23 March 2009 Mount Redoubt. In 192 simulations, we systematically varied μagg and σagg, holding ρagg constant at 600 kg m−3. We evaluated the fit using three indices that compare modeled versus measured (1) mass load at sample locations; (2) mass load versus distance along the dispersal axis; and (3) isomass area. For all deposits, under these inputs, the best-fit value of μagg ranged narrowly between  ∼  2.3 and 2.7φ (0.20–0.15 mm), despite large variations in erupted mass (0.25–50 Tg), plume height (8.5–25 km), mass fraction of fine ( discrete process that is insensitive to eruptive style or magnitude. This result offers the potential for a simple, computationally efficient parameterization scheme for use in operational model forecasts. Further research may indicate whether this narrow range also reflects physical constraints on processes in the evolving cloud.

The role of central bank transparency for guiding private sector forecasts

NARCIS (Netherlands)

Eijffinger, S.C.W.; Ehrmann, M.; Fratzscher, M.

2012-01-01

Central banks have become remarkably more transparent over the last few decades. In this paper, we study the effects of this evolution, focusing on whether enhanced central bank transparency lowers dispersion among professional forecasters of key economic variables. We use a large set of proxies for

Forecasting wildland fire behavior using high-resolution large-eddy simulations

Science.gov (United States)

Munoz-Esparza, D.; Kosovic, B.; Jimenez, P. A.; Anderson, A.; DeCastro, A.; Brown, B.

2017-12-01

Wildland fires are responsible for large socio-economic impacts. Fires affect the environment, damage structures, threaten lives, cause health issues, and involve large suppression costs. These impacts can be mitigated via accurate fire spread forecast to inform the incident management team. To this end, the state of Colorado is funding the development of the Colorado Fire Prediction System (CO-FPS). The system is based on the Weather Research and Forecasting (WRF) model enhanced with a fire behavior module (WRF-Fire). Realistic representation of wildland fire behavior requires explicit representation of small scale weather phenomena to properly account for coupled atmosphere-wildfire interactions. Moreover, transport and dispersion of biomass burning emissions from wildfires is controlled by turbulent processes in the atmospheric boundary layer, which are difficult to parameterize and typically lead to large errors when simplified source estimation and injection height methods are used. Therefore, we utilize turbulence-resolving large-eddy simulations at a resolution of 111 m to forecast fire spread and smoke distribution using a coupled atmosphere-wildfire model. This presentation will describe our improvements to the level-set based fire-spread algorithm in WRF-Fire and an evaluation of the operational system using 12 wildfire events that occurred in Colorado in 2016, as well as other historical fires. In addition, the benefits of explicit representation of turbulence for smoke transport and dispersion will be demonstrated.

Intelligent launch and range operations virtual testbed (ILRO-VTB)

Science.gov (United States)

Bardina, Jorge; Rajkumar, Thirumalainambi

2003-09-01

Intelligent Launch and Range Operations Virtual Test Bed (ILRO-VTB) is a real-time web-based command and control, communication, and intelligent simulation environment of ground-vehicle, launch and range operation activities. ILRO-VTB consists of a variety of simulation models combined with commercial and indigenous software developments (NASA Ames). It creates a hybrid software/hardware environment suitable for testing various integrated control system components of launch and range. The dynamic interactions of the integrated simulated control systems are not well understood. Insight into such systems can only be achieved through simulation/emulation. For that reason, NASA has established a VTB where we can learn the actual control and dynamics of designs for future space programs, including testing and performance evaluation. The current implementation of the VTB simulates the operations of a sub-orbital vehicle of mission, control, ground-vehicle engineering, launch and range operations. The present development of the test bed simulates the operations of Space Shuttle Vehicle (SSV) at NASA Kennedy Space Center. The test bed supports a wide variety of shuttle missions with ancillary modeling capabilities like weather forecasting, lightning tracker, toxic gas dispersion model, debris dispersion model, telemetry, trajectory modeling, ground operations, payload models and etc. To achieve the simulations, all models are linked using Common Object Request Broker Architecture (CORBA). The test bed provides opportunities for government, universities, researchers and industries to do a real time of shuttle launch in cyber space.

Model-free aftershock forecasts constructed from similar sequences in the past

Science.gov (United States)

van der Elst, N.; Page, M. T.

2017-12-01

The basic premise behind aftershock forecasting is that sequences in the future will be similar to those in the past. Forecast models typically use empirically tuned parametric distributions to approximate past sequences, and project those distributions into the future to make a forecast. While parametric models do a good job of describing average outcomes, they are not explicitly designed to capture the full range of variability between sequences, and can suffer from over-tuning of the parameters. In particular, parametric forecasts may produce a high rate of "surprises" - sequences that land outside the forecast range. Here we present a non-parametric forecast method that cuts out the parametric "middleman" between training data and forecast. The method is based on finding past sequences that are similar to the target sequence, and evaluating their outcomes. We quantify similarity as the Poisson probability that the observed event count in a past sequence reflects the same underlying intensity as the observed event count in the target sequence. Event counts are defined in terms of differential magnitude relative to the mainshock. The forecast is then constructed from the distribution of past sequences outcomes, weighted by their similarity. We compare the similarity forecast with the Reasenberg and Jones (RJ95) method, for a set of 2807 global aftershock sequences of M≥6 mainshocks. We implement a sequence-specific RJ95 forecast using a global average prior and Bayesian updating, but do not propagate epistemic uncertainty. The RJ95 forecast is somewhat more precise than the similarity forecast: 90% of observed sequences fall within a factor of two of the median RJ95 forecast value, whereas the fraction is 85% for the similarity forecast. However, the surprise rate is much higher for the RJ95 forecast; 10% of observed sequences fall in the upper 2.5% of the (Poissonian) forecast range. The surprise rate is less than 3% for the similarity forecast. The similarity

A preliminary study of the impact of the ERS 1 C band scatterometer wind data on the European Centre for Medium-Range Weather Forecasts global data assimilation system

Science.gov (United States)

Hoffman, Ross N.

1993-01-01

A preliminary assessment of the impact of the ERS 1 scatterometer wind data on the current European Centre for Medium-Range Weather Forecasts analysis and forecast system has been carried out. Although the scatterometer data results in changes to the analyses and forecasts, there is no consistent improvement or degradation. Our results are based on comparing analyses and forecasts from assimilation cycles. The two sets of analyses are very similar except for the low level wind fields over the ocean. Impacts on the analyzed wind fields are greater over the southern ocean, where other data are scarce. For the most part the mass field increments are too small to balance the wind increments. The effect of the nonlinear normal mode initialization on the analysis differences is quite small, but we observe that the differences tend to wash out in the subsequent 6-hour forecast. In the Northern Hemisphere, analysis differences are very small, except directly at the scatterometer locations. Forecast comparisons reveal large differences in the Southern Hemisphere after 72 hours. Notable differences in the Northern Hemisphere do not appear until late in the forecast. Overall, however, the Southern Hemisphere impacts are neutral. The experiments described are preliminary in several respects. We expect these data to ultimately prove useful for global data assimilation.

Short-range forecast of Shershnevskoie (South Ural) water-storage algal blooms: preliminary results of predictors' choosing and membership functions' construction

Science.gov (United States)

Gayazova, Anna; Abdullaev, Sanjar

2014-05-01

Short-range forecasting of algal blooms in drinking water reservoirs and other waterbodies is an actual element of water treatment system. Particularly, Shershnevskoie reservoir - the source of drinking water for Chelyabinsk city (South Ural region of Russia) - is exposed to interannual, seasonal and short-range fluctuations of blue-green alga Aphanizomenon flos-aquae and other dominant species abundance, which lead to technological problems and economic costs and adversely affect the water treatment quality. Whereas the composition, intensity and the period of blooms affected not only by meteorological seasonal conditions but also by ecological specificity of waterbody, that's important to develop object-oriented forecasting, particularly, search for an optimal number of predictors for such forecasting. Thereby, firstly fuzzy logic and fuzzy artificial neural network patterns for blue-green alga Microcystis aeruginosa (M. aeruginosa) blooms prediction in nearby undrained Smolino lake were developed. These results subsequently served as the base to derive membership functions for Shernevskoie reservoir forecasting patterns. Time series with the total lenght about 138-159 days of dominant species seasonal abundance, water temperature, cloud cover, wind speed, mineralization, phosphate and nitrate concentrations were obtained through field observations held at Lake Smolino (Chelyabinsk) in the warm season of 2009 and 2011 with time resolution of 2-7 days. The cross-correlation analysis of the data revealed the potential predictors of M. aeruginosa abundance quasi-periodic oscillations: green alga Pediastrum duplex (P. duplex) abundance and mineralization for 2009, P. duplex abundance, water temperature and concentration of nitrates for 2011. According to the results of cross-correlation analysis one membership function "P. duplex abundance" and one rule linking M. aeruginosa and P. duplex abundances were set up for database of 2009. Analogically, for database of 2011

Introduction to time series analysis and forecasting

CERN Document Server

Montgomery, Douglas C; Kulahci, Murat

2008-01-01

An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data. Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts.

Long-range transmission of pollutants simulated by a two-dimensional pseudospectral dispersion model

International Nuclear Information System (INIS)

Prahm, L.P.; Christensen, O.

1977-01-01

The pseudospectral dispersion model (Christensen and Prahm, 1976) is adapted for simulation of the long-range transmission of sulphur pollutants in the European region, covering an area of about 4000 km x 4000 km. Regional ''background'' concentrations of sulphur oxides are found to be highly dependent on distant sources and to correlate poorly with local source strength during the considered three- and four-day episodes. The simulation is based on emission data, given in squares of about 50 km x 50 km and on synoptic wind fields derived from observed wind velocities of the 850 mb level and the surface level. The two-dimensional model includes a constant vertical mixing depth. Appropriate values for the deposition and the transformation rates of SO 2 and SO/sup 4 are used. The concentration of pollutants computed from the two-dimensional pseudospectral dispersion model reflects the variable meteorological conditions. Computed concentrations are compared with measurements, giving spatial correlations between 0.4 and 0.8 for more than 400 ground-based 24 h mean values, and a spatial correlation of 0.9 for eight aircraft samples averaged over approx.30 min. A discussion of the influence of different sources of error in the model simulation is given. The high numerical accuracy of the pseudospectral model is combined with a modest consumption of CPU computer time. This study is the first application of the pseudospectral dispersion model which compares computed concentrations with measured field data. The model has possible applications as a tool for assessment of the impact of both national and international emission regulation strategies

NKS NordRisk II: Atlas of long-range atmospheric dispersion and deposition of radionuclides from selected risk sites in the Northern Hemisphere

International Nuclear Information System (INIS)

Smith Korsholm, U.; Havskov Soerensen, J.; Astrup, P.; Lauritzen, B.

2011-04-01

The present atlas has been developed within the NKS/NordRisk-II project 'Nuclear risk from atmospheric dispersion in Northern Europe'. The atlas describes risks from hypothetical long-range dispersion and deposition of radionuclides from 16 nuclear risk sites on the Northern Hemisphere. The atmospheric dispersion model calculations cover a period of 30 days following each release to ensure almost complete deposition of the dispersed material. The atlas contains maps showing the total deposition and time-integrated air concentration of Cs-137 and I-131 based on three years of meteorological data spanning the climate variability associated with the North Atlantic Oscillation, and corresponding time evolution of the ensemble mean atmospheric dispersion. (Author)

NKS NordRisk II: Atlas of long-range atmospheric dispersion and deposition of radionuclides from selected risk sites in the Northern Hemisphere

Energy Technology Data Exchange (ETDEWEB)

Smith Korsholm, U.; Havskov Soerensen, J. (Danish Meteorological Institute (DMI), Copenhagen (Denmark)); Astrup, P.; Lauritzen, B. (Technical Univ. of Denmark, Risoe National Lab. for Sustainable Energy. Radiation Research Div., Roskilde (Denmark))

2011-04-15

The present atlas has been developed within the NKS/NordRisk-II project 'Nuclear risk from atmospheric dispersion in Northern Europe'. The atlas describes risks from hypothetical long-range dispersion and deposition of radionuclides from 16 nuclear risk sites on the Northern Hemisphere. The atmospheric dispersion model calculations cover a period of 30 days following each release to ensure almost complete deposition of the dispersed material. The atlas contains maps showing the total deposition and time-integrated air concentration of Cs-137 and I-131 based on three years of meteorological data spanning the climate variability associated with the North Atlantic Oscillation, and corresponding time evolution of the ensemble mean atmospheric dispersion. (Author)

Solid low-level waste forecasting guide

International Nuclear Information System (INIS)

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

1995-03-01

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

Long-Range Lightning Products for Short Term Forecasting of Tropical Cyclogenesis

Science.gov (United States)

Businger, S.; Pessi, A.; Robinson, T.; Stolz, D.

2010-12-01

This paper will describe innovative graphical products derived in real time from long-range lightning data. The products have been designed to aid in short-term forecasting of tropical cyclone development for the Tropical Cyclone Structure Experiment 2010 (TCS10) held over the western Pacific Ocean from 17 August to 17 October 2010 and are available online at http://www.soest.hawaii.edu/cgi-bin/pacnet/tcs10.pl. The long-range lightning data are from Vaisala’s Global Lightning Data 360 (GLD360) network and include time, location, current strength, polarity, and data quality indication. The products currently provided in real time include i. Infrared satellite imagery overlaid with lighting flash locations, with color indication of current strength and polarity (shades of blue for negative to ground and red for positive to ground). ii. A 15x15 degree storm-centered tile of IR imagery overlaid with lightning data as in i). iii. A pseudo reflectivity product showing estimates of radar reflectivity based on lightning rate - rain rate conversion derived from TRMM and PacNet data. iv. A lightning history product that plots each hour of lightning flash locations in a different color for a 12-hour period. v. Graphs of lightning counts within 50 or 300 km radius, respectively, of the storm center vs storm central sea-level pressure. vi. A 2-D graphic showing storm core lightning density along the storm track. The first three products above can be looped to gain a better understanding of the evolution of the lightning and storm structure. Examples of the graphics and their utility will be demonstrated and discussed. Histogram of lightning counts within 50 km of the storm center and graph of storm central pressure as a function of time.

Operational foreshock forecasting: Fifteen years after

Science.gov (United States)

Ogata, Y.

2010-12-01

We are concerned with operational forecasting of the probability that events are foreshocks of a forthcoming earthquake that is significantly larger (mainshock). Specifically, we define foreshocks as the preshocks substantially smaller than the mainshock by a magnitude gap of 0.5 or larger. The probability gain of foreshock forecast is extremely high compare to long-term forecast by renewal processes or various alarm-based intermediate-term forecasts because of a large event’s low occurrence rate in a short period and a narrow target region. Thus, it is desired to establish operational foreshock probability forecasting as seismologists have done for aftershocks. When a series of earthquakes occurs in a region, we attempt to discriminate foreshocks from a swarm or mainshock-aftershock sequence. Namely, after real time identification of an earthquake cluster using methods such as the single-link algorithm, the probability is calculated by applying statistical features that discriminate foreshocks from other types of clusters, by considering the events' stronger proximity in time and space and tendency towards chronologically increasing magnitudes. These features were modeled for probability forecasting and the coefficients of the model were estimated in Ogata et al. (1996) for the JMA hypocenter data (M≧4, 1926-1993). Currently, fifteen years has passed since the publication of the above-stated work so that we are able to present the performance and validation of the forecasts (1994-2009) by using the same model. Taking isolated events into consideration, the probability of the first events in a potential cluster being a foreshock vary in a range between 0+% and 10+% depending on their locations. This conditional forecasting performs significantly better than the unconditional (average) foreshock probability of 3.7% throughout Japan region. Furthermore, when we have the additional events in a cluster, the forecast probabilities range more widely from nearly 0% to

Improving short-range ensemble Kalman storm surge forecasting using robust adaptive inflation

NARCIS (Netherlands)

Altaf, M.U.; Butler, T.; Luo, X.; Dawson, C.; Mayo, T.; Hoteit, I.

2013-01-01

This paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H? filter. By design, an H? filter is more robust than the common Kalman filter in the sense

Development of a sales forecasting model for canopy windows

OpenAIRE

2014-01-01

M.Com. (Business Management) Forecasting is an important function used in a wide range of business planning or decision-making situations. The purpose ofthis study was to build a sales forecasting model that would be practical and cost effective, from the various forecasting methods and techniques available. Various forecast models, methods and techniques are outlined in the initial part of this study by the author. The author has outlined some of the fundamentals and limitations that unde...

Numerical simulation and variational data assimilation for atmospheric dispersion of pollutants

International Nuclear Information System (INIS)

Quelo, Denis

2004-01-01

This work has led to the development of a three-dimensional chemistry-transport model Polair3D which simulates photochemistry. Model-to-data comparison of ozone and nitrogen oxides measurements over Lille in 1998 has proven its reliability at regional scale. 4 D-var data assimilation has been implemented. It relies on the adjoint model of Polair3D obtained through automatic differentiation. An application of inverse modelling of emissions over Lille with real measurements has been performed. It has proven that the inversion of temporal parameters of nitrogen oxides emissions leads to a significant improvement of forecasts. The so-called second-order sensitivity allows to study the sensitivity of the inversion with respect to the data assimilation system itself by computing its conditioning. This is illustrated by two test cases: short-range dispersion of radionuclides and gas-phase atmospheric chemistry characterized by a wide range of timescales. (author) [fr

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

Directory of Open Access Journals (Sweden)

Hugo Carrão

2018-06-01

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

A system for using the air radioactivity measurements in a long range model to forecast cloud evolution

Energy Technology Data Exchange (ETDEWEB)

Galmarini, S.; Graziani, G. (Commission of the European Communities, Ispra (Italy). Joint Research Centre); Grippa, G.; De Cort, M. (Maind srl, Milan (Italy))

1993-01-01

A procedure was developed in the past to reduce uncertainties in long range transport model predictions mainly due to inputing windfield data to atmospheric transport models which are the result of the forecasts of global or regional circulation models. Measurements available in real-time of the air concentrations from national monitoring grids have been used to reduce the uncertainties. The system is based on a long range transport model which can run using a limited amount of meteorological information, and an interpolation routine which generates a new area source from the air measurements, available in real-time, at ground level. The procedure has now been fully automated and is available on a PC, with graphical output, to ease its use in emergency situations. The system requires a connection to the ECMWF network for meteorological input data and to a radiological data bank (ECURIE) or national monitoring networks for monitoring data. (author).

Gambling scores for earthquake predictions and forecasts

Science.gov (United States)

Zhuang, Jiancang

2010-04-01

This paper presents a new method, namely the gambling score, for scoring the performance earthquake forecasts or predictions. Unlike most other scoring procedures that require a regular scheme of forecast and treat each earthquake equally, regardless their magnitude, this new scoring method compensates the risk that the forecaster has taken. Starting with a certain number of reputation points, once a forecaster makes a prediction or forecast, he is assumed to have betted some points of his reputation. The reference model, which plays the role of the house, determines how many reputation points the forecaster can gain if he succeeds, according to a fair rule, and also takes away the reputation points betted by the forecaster if he loses. This method is also extended to the continuous case of point process models, where the reputation points betted by the forecaster become a continuous mass on the space-time-magnitude range of interest. We also calculate the upper bound of the gambling score when the true model is a renewal process, the stress release model or the ETAS model and when the reference model is the Poisson model.

How disturbance, competition, and dispersal interact to prevent tree range boundaries from keeping pace with climate change

Science.gov (United States)

Yu Liang; Matthew J. Duveneck; Eric J. Gustafson; Josep M. Serra-Diaz; Jonathan R. Thompson

2018-01-01

Climate change is expected to cause geographic shifts in tree species' ranges, but such shifts may not keep pace with climate changes because seed dispersal distances are often limited and competition-induced changes in community composition can be relatively slow. Disturbances may speed changes in community composition, but the interactions among climate change,...

House Price Forecasts, Forecaster Herding, and the Recent Crisis

DEFF Research Database (Denmark)

Stadtmann, Georg; Pierdzioch; Ruelke

2013-01-01

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

Forecasting global atmospheric CO2

International Nuclear Information System (INIS)

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

2014-01-01

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

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.

Major Risks, Uncertain Outcomes: Making Ensemble Forecasts Work for Multiple Audiences

Science.gov (United States)

Semmens, K. A.; Montz, B.; Carr, R. H.; Maxfield, K.; Ahnert, P.; Shedd, R.; Elliott, J.

2017-12-01

When extreme river levels are possible in a community, effective communication of weather and hydrologic forecasts is critical to protect life and property. Residents, emergency personnel, and water resource managers need to make timely decisions about how and when to prepare. Uncertainty in forecasting is a critical component of this decision-making, but often poses a confounding factor for public and professional understanding of forecast products. In 2016 and 2017, building on previous research about the use of uncertainty forecast products, and with funding from NOAA's CSTAR program, East Carolina University and Nurture Nature Center (a non-profit organization with a focus on flooding issues, based in Easton, PA) conducted a research project to understand how various audiences use and interpret ensemble forecasts showing a range of hydrologic forecast possibilities. These audiences include community residents, emergency managers and water resource managers. The research team held focus groups in Jefferson County, WV and Frederick County, MD, to test a new suite of products from the National Weather Service's Hydrologic Ensemble Forecast System (HEFS). HEFS is an ensemble system that provides short and long-range forecasts, ranging from 6 hours to 1 year, showing uncertainty in hydrologic forecasts. The goal of the study was to assess the utility of the HEFS products, identify the barriers to proper understanding of the products, and suggest modifications to product design that could improve the understandability and accessibility for residential, emergency managers, and water resource managers. The research team worked with the Sterling, VA Weather Forecast Office and the Middle Atlantic River Forecast center to develop a weather scenario as the basis of the focus group discussions, which also included pre and post session surveys. This presentation shares the findings from those focus group discussions and surveys, including recommendations for revisions to

A multidisciplinary system for monitoring and forecasting Etna volcanic plumes

Science.gov (United States)

Coltelli, Mauro; Prestifilippo, Michele; Spata, Gaetano; Scollo, Simona; Andronico, Daniele

2010-05-01

One of the most active volcanoes in the world is Mt. Etna, in Italy, characterized by frequent explosive activity from the central craters and from fractures opened along the volcano flanks which, during the last years, caused several damages to aviation and forced the closure of the Catania International Airport. To give precise warning to the aviation authorities and air traffic controller and to assist the work of VAACs, a novel system for monitoring and forecasting Etna volcanic plumes, was developed at the Istituto Nazionale di Geofisica e Vulcanologia, sezione di Catania, the managing institution for the surveillance of Etna volcano. Monitoring is carried out using multispectral infrared measurements from the Spin Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation geosynchronous satellite able to track the volcanic plume with a high time resolution, visual and thermal cameras used to monitor the explosive activity, three continuous wave X-band disdrometers which detect ash dispersal and fallout, sounding balloons used to evaluate the atmospheric fields, and finally field data collected after the end of the eruptive event needed to extrapolate important features of explosive activity. Forecasting is carried out daily using automatic procedures which download weather forecast data obtained by meteorological mesoscale models from the Italian Air Force national Meteorological Office and from the hydrometeorological service of ARPA-SIM; run four different tephra dispersal models using input parameters obtained by the analysis of the deposits collected after few hours since the eruptive event similar to 22 July 1998, 21-24 July 2001 and 2002-03 Etna eruptions; plot hazard maps on ground and in air and finally publish them on a web-site dedicated to the Italian Civil Protection. The system has been already tested successfully during several explosive events occurring at Etna in 2006, 2007 and 2008. These events produced eruption

3-D visualization of ensemble weather forecasts - Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

Science.gov (United States)

Rautenhaus, M.; Grams, C. M.; Schäfler, A.; Westermann, R.

2015-02-01

We present the application of interactive 3-D visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the ECMWF ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and forecast wind field resolution. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (three to seven days before take-off).

Use and Communication of Probabilistic Forecasts.

Science.gov (United States)

Raftery, Adrian E

2016-12-01

Probabilistic forecasts are becoming more and more available. How should they be used and communicated? What are the obstacles to their use in practice? I review experience with five problems where probabilistic forecasting played an important role. This leads me to identify five types of potential users: Low Stakes Users, who don't need probabilistic forecasts; General Assessors, who need an overall idea of the uncertainty in the forecast; Change Assessors, who need to know if a change is out of line with expectatations; Risk Avoiders, who wish to limit the risk of an adverse outcome; and Decision Theorists, who quantify their loss function and perform the decision-theoretic calculations. This suggests that it is important to interact with users and to consider their goals. The cognitive research tells us that calibration is important for trust in probability forecasts, and that it is important to match the verbal expression with the task. The cognitive load should be minimized, reducing the probabilistic forecast to a single percentile if appropriate. Probabilities of adverse events and percentiles of the predictive distribution of quantities of interest seem often to be the best way to summarize probabilistic forecasts. Formal decision theory has an important role, but in a limited range of applications.

Use and Communication of Probabilistic Forecasts

Science.gov (United States)

Raftery, Adrian E.

2015-01-01

Probabilistic forecasts are becoming more and more available. How should they be used and communicated? What are the obstacles to their use in practice? I review experience with five problems where probabilistic forecasting played an important role. This leads me to identify five types of potential users: Low Stakes Users, who don’t need probabilistic forecasts; General Assessors, who need an overall idea of the uncertainty in the forecast; Change Assessors, who need to know if a change is out of line with expectatations; Risk Avoiders, who wish to limit the risk of an adverse outcome; and Decision Theorists, who quantify their loss function and perform the decision-theoretic calculations. This suggests that it is important to interact with users and to consider their goals. The cognitive research tells us that calibration is important for trust in probability forecasts, and that it is important to match the verbal expression with the task. The cognitive load should be minimized, reducing the probabilistic forecast to a single percentile if appropriate. Probabilities of adverse events and percentiles of the predictive distribution of quantities of interest seem often to be the best way to summarize probabilistic forecasts. Formal decision theory has an important role, but in a limited range of applications. PMID:28446941

Rebuttal of "Polar bear population forecasts: a public-policy forecasting audit"

Science.gov (United States)

Amstrup, Steven C.; Caswell, Hal; DeWeaver, Eric; Stirling, Ian; Douglas, David C.; Marcot, Bruce G.; Hunter, Christine M.

2009-01-01

Observed declines in the Arctic sea ice have resulted in a variety of negative effects on polar bears (Ursus maritimus). Projections for additional future declines in sea ice resulted in a proposal to list polar bears as a threatened species under the United States Endangered Species Act. To provide information for the Department of the Interior's listing-decision process, the US Geological Survey (USGS) produced a series of nine research reports evaluating the present and future status of polar bears throughout their range. In response, Armstrong et al. [Armstrong, J. S., K. C. Green, W. Soon. 2008. Polar bear population forecasts: A public-policy forecasting audit. Interfaces 38(5) 382–405], which we will refer to as AGS, performed an audit of two of these nine reports. AGS claimed that the general circulation models upon which the USGS reports relied were not valid forecasting tools, that USGS researchers were not objective or lacked independence from policy decisions, that they did not utilize all available information in constructing their forecasts, and that they violated numerous principles of forecasting espoused by AGS. AGS (p. 382) concluded that the two USGS reports were "unscientific and inconsequential to decision makers." We evaluate the AGS audit and show how AGS are mistaken or misleading on every claim. We provide evidence that general circulation models are useful in forecasting future climate conditions and that corporate and government leaders are relying on these models to do so. We clarify the strict independence of the USGS from the listing decision. We show that the allegations of failure to follow the principles of forecasting espoused by AGS are either incorrect or are based on misconceptions about the Arctic environment, polar bear biology, or statistical and mathematical methods. We conclude by showing that the AGS principles of forecasting are too ambiguous and subjective to be used as a reliable basis for auditing scientific

Forecasting space weather: Can new econometric methods improve accuracy?

Science.gov (United States)

Reikard, Gordon

2011-06-01

Space weather forecasts are currently used in areas ranging from navigation and communication to electric power system operations. The relevant forecast horizons can range from as little as 24 h to several days. This paper analyzes the predictability of two major space weather measures using new time series methods, many of them derived from econometrics. The data sets are the A p geomagnetic index and the solar radio flux at 10.7 cm. The methods tested include nonlinear regressions, neural networks, frequency domain algorithms, GARCH models (which utilize the residual variance), state transition models, and models that combine elements of several techniques. While combined models are complex, they can be programmed using modern statistical software. The data frequency is daily, and forecasting experiments are run over horizons ranging from 1 to 7 days. Two major conclusions stand out. First, the frequency domain method forecasts the A p index more accurately than any time domain model, including both regressions and neural networks. This finding is very robust, and holds for all forecast horizons. Combining the frequency domain method with other techniques yields a further small improvement in accuracy. Second, the neural network forecasts the solar flux more accurately than any other method, although at short horizons (2 days or less) the regression and net yield similar results. The neural net does best when it includes measures of the long-term component in the data.

A successful forecast of an El Nino winter

International Nuclear Information System (INIS)

Kerr, R.A.

1992-01-01

This year, for the first time, weather forecasters used signs of a warming in the tropical Pacific as the basis for a long-range prediction of winter weather patterns across the United States. Now forecasters are talking about the next step: stretching the lead time for such forecasts by a year or more. That seems feasible because although this Pacific warming was unmistakable by the time forecasters at the National Weather Service's Climate Analysis Center (CAC) in Camp Springs, Maryland, issued their winter forecast, the El Nino itself had been predicted almost 2 years in advance by a computer model. Next time around, the CAC may well be listening to the modelers and predicting El Nino-related patterns of warmth and flooding seasons in advance

Ensemble-based simultaneous emission estimates and improved forecast of radioactive pollution from nuclear power plant accidents: application to ETEX tracer experiment

International Nuclear Information System (INIS)

Zhang, X.L.; Li, Q.B.; Su, G.F.; Yuan, M.Q.

2015-01-01

The accidental release of radioactive materials from nuclear power plant leads to radioactive pollution. We apply an augmented ensemble Kalman filter (EnKF) with a chemical transport model to jointly estimate the emissions of Perfluoromethylcyclohexane (PMCH), a tracer substitute for radionuclides, from a point source during the European Tracer Experiment, and to improve the forecast of its dispersion downwind. We perturb wind fields to account for meteorological uncertainties. We expand the state vector of PMCH concentrations through continuously adding an a priori emission rate for each succeeding assimilation cycle. We adopt a time-correlated red noise to simulate the temporal emission fluctuation. The improved EnKF system rapidly updates (and reduces) the excessively large initial first-guess emissions, thereby significantly improves subsequent forecasts (r = 0.83, p < 0.001). It retrieves 94% of the total PMCH released and substantially reduces transport error (>80% average reduction of the normalized mean square error). - Highlights: • EnKF is augmented for estimating emission and improving dispersion forecast. • The improved system retrieves 94% of the actual total tracer release in ETEX. • The system substantially improves the 3-h forecast of the tracer dispersion. • The method is robust and insensitive to the first-guess emissions. • The meteorological uncertainties exert strong influence on the performance

Numerical simulation and variational data assimilation for atmospheric dispersion of pollutants

International Nuclear Information System (INIS)

Quelo, D.

2004-12-01

This work has led to the development of a three-dimensional chemistry-transport model Polair3D which simulates photochemistry. Model-to-data comparison of ozone and nitrogen oxides measurements over the city of Lille in 1998 has proven its reliability at regional scale. 4-dimensional-variational data assimilation has been implemented. It relies on the adjoint model of Polair3D obtained through automatic differentiation. An application of inverse modelling of emissions over Lille city with real measurements has been performed. It has proven that the inversion of temporal parameters of nitrogen oxides emissions leads to a significant improvement of forecasts. The so-called second-order sensitivity allows the study of the sensitivity of the inversion with respect to the data assimilation system itself by computing its conditioning. This is illustrated by two test cases: short-range dispersion of radionuclides and gas-phase atmospheric chemistry characterized by a wide range of timescales. (author)

Forecasting Canadian nuclear power station construction costs

International Nuclear Information System (INIS)

Keng, C.W.K.

1985-01-01

Because of the huge volume of capital required to construct a modern electric power generating station, investment decisions have to be made with as complete an understanding of the consequences of the decision as possible. This understanding must be provided by the evaluation of future situations. A key consideration in an evaluation is the financial component. This paper attempts to use an econometric method to forecast the construction costs escalation of a standard Canadian nuclear generating station (NGS). A brief review of the history of Canadian nuclear electric power is provided. The major components of the construction costs of a Canadian NGS are studied and summarized. A database is built and indexes are prepared. Based on these indexes, an econometric forecasting model is constructed using an apparently new econometric methodology of forecasting modelling. Forecasts for a period of 40 years are generated and applications (such as alternative scenario forecasts and range forecasts) to uncertainty assessment and/or decision-making are demonstrated. The indexes, the model, and the forecasts and their applications, to the best of the author's knowledge, are the first for Canadian NGS constructions. (author)

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

Science.gov (United States)

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

2017-12-01

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

International Workshop on Industry Practices for Forecasting

CERN Document Server

Poggi, Jean-Michel; Brossat, Xavier

2015-01-01

The chapters in this volume stress the need for advances in theoretical understanding to go hand-in-hand with the widespread practical application of forecasting in industry. Forecasting and time series prediction have enjoyed considerable attention over the last few decades, fostered by impressive advances in observational capabilities and measurement procedures. On June 5-7, 2013, an international Workshop on Industry Practices for FORecasting was held in Paris, France, organized and supported by the OSIRIS Department of Electricité de France Research and Development Division. In keeping with tradition, both theoretical statistical results and practical contributions on this active field of statistical research and on forecasting issues in a rapidly evolving industrial environment are presented. The volume reflects the broad spectrum of the conference, including 16 articles contributed by specialists in various areas. The material compiled is broad in scope and ranges from new findings on forecasting in in...

Decadal climate predictions improved by ocean ensemble dispersion filtering

Science.gov (United States)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-06-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.Plain Language SummaryDecadal predictions aim to predict the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. The ocean memory due to its heat capacity holds big potential skill. In recent years, more precise initialization techniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions. Ensembles are another important aspect. Applying slightly perturbed predictions to trigger the famous butterfly effect results in an ensemble. Instead of evaluating one prediction, but the whole ensemble with its

Six rules for accurate effective forecasting.

Science.gov (United States)

Saffo, Paul

2007-01-01

The primary goal of forecasting is to identify the full range of possibilities facing a company, society, or the world at large. In this article, Saffo demythologizes the forecasting process to help executives become sophisticated and participative consumers of forecasts, rather than passive absorbers. He illustrates how to use forecasts to at once broaden understanding of possibilities and narrow the decision space within which one must exercise intuition. The events of 9/11, for example, were a much bigger surprise than they should have been. After all, airliners flown into monuments were the stuff of Tom Clancy novels in the 1990s, and everyone knew that terrorists had a very personal antipathy toward the World Trade Center. So why was 9/11 such a surprise? What can executives do to avoid being blind-sided by other such wild cards, be they radical shifts in markets or the seemingly sudden emergence of disruptive technologies? In describing what forecasters are trying to achieve, Saffo outlines six simple, commonsense rules that smart managers should observe as they embark on a voyage of discovery with professional forecasters. Map a cone of uncertainty, he advises, look for the S curve, embrace the things that don't fit, hold strong opinions weakly, look back twice as far as you look forward, and know when not to make a forecast.

Visualizing uncertainty : Towards a better understanding of weather forecasts

NARCIS (Netherlands)

Toet, A.; Tak, S.; Erp, J.B.F. van

2016-01-01

Uncertainty visualizations are increasingly used in communications to the general public. A well-known example is the weather forecast. Rather than providing an exact temperature value, weather forecasts often show the range in which the temperature will lie. But uncertainty visualizations are also

Coupling meteorological and hydrological models for flood forecasting

Directory of Open Access Journals (Sweden)

Bartholmes

2005-01-01

Full Text Available This paper deals with the problem of analysing the coupling of meteorological meso-scale quantitative precipitation forecasts with distributed rainfall-runoff models to extend the forecasting horizon. Traditionally, semi-distributed rainfall-runoff models have been used for real time flood forecasting. More recently, increased computer capabilities allow the utilisation of distributed hydrological models with mesh sizes from tenths of metres to a few kilometres. On the other hand, meteorological models, providing the quantitative precipitation forecast, tend to produce average values on meshes ranging from slightly less than 10 to 200 kilometres. Therefore, to improve the quality of flood forecasts, the effects of coupling the meteorological and the hydrological models at different scales were analysed. A distributed hydrological model (TOPKAPI was developed and calibrated using a 1x1 km mesh for the case of the river Po closed at Ponte Spessa (catchment area c. 37000 km2. The model was then coupled with several other European meteorological models ranging from the Limited Area Models (provided by DMI and DWD with resolutions from 0.0625° * 0.0625°, to the ECMWF ensemble predictions with a resolution of 1.85° * 1.85°. Interesting results, describing the coupled model behaviour, are available for a meteorological extreme event in Northern Italy (Nov. 1994. The results demonstrate the poor reliability of the quantitative precipitation forecasts produced by meteorological models presently available; this is not resolved using the Ensemble Forecasting technique, when compared with results obtainable with measured rainfall.

The Henetus wave forecast system in the Adriatic Sea

Directory of Open Access Journals (Sweden)

L. Bertotti

2011-11-01

Full Text Available We describe the Henetus wave forecast system in the Adriatic Sea. Operational since 1996, the system is continuously upgraded, especially through the correction of the input ECMWF wind fields. As these fields are of progressively improved quality with the increasing resolution of the meteorological model, the correction needs to be correspondingly updated. This ensures a practically constant quality of the Henetus results in the Adriatic Sea since 1996. After suitable and extended validation of the quality of the results at different forecast ranges, the operational range has been recently extended to five days. The Henetus results are used also to improve the tidal forecast on the Venetian coasts and the Venice lagoon, particularly during the most severe events. Extensive statistics on the model performance are provided, both as analysis and forecast, by comparing the model results versus both satellite and buoy data.

Multicomponent ensemble models to forecast induced seismicity

Science.gov (United States)

Király-Proag, E.; Gischig, V.; Zechar, J. D.; Wiemer, S.

2018-01-01

In recent years, human-induced seismicity has become a more and more relevant topic due to its economic and social implications. Several models and approaches have been developed to explain underlying physical processes or forecast induced seismicity. They range from simple statistical models to coupled numerical models incorporating complex physics. We advocate the need for forecast testing as currently the best method for ascertaining if models are capable to reasonably accounting for key physical governing processes—or not. Moreover, operational forecast models are of great interest to help on-site decision-making in projects entailing induced earthquakes. We previously introduced a standardized framework following the guidelines of the Collaboratory for the Study of Earthquake Predictability, the Induced Seismicity Test Bench, to test, validate, and rank induced seismicity models. In this study, we describe how to construct multicomponent ensemble models based on Bayesian weightings that deliver more accurate forecasts than individual models in the case of Basel 2006 and Soultz-sous-Forêts 2004 enhanced geothermal stimulation projects. For this, we examine five calibrated variants of two significantly different model groups: (1) Shapiro and Smoothed Seismicity based on the seismogenic index, simple modified Omori-law-type seismicity decay, and temporally weighted smoothed seismicity; (2) Hydraulics and Seismicity based on numerically modelled pore pressure evolution that triggers seismicity using the Mohr-Coulomb failure criterion. We also demonstrate how the individual and ensemble models would perform as part of an operational Adaptive Traffic Light System. Investigating seismicity forecasts based on a range of potential injection scenarios, we use forecast periods of different durations to compute the occurrence probabilities of seismic events M ≥ 3. We show that in the case of the Basel 2006 geothermal stimulation the models forecast hazardous levels

Long-range dispersion interactions. II. Alkali-metal and rare-gas atoms

International Nuclear Information System (INIS)

Mitroy, J.; Zhang, J.-Y.

2007-01-01

The dispersion coefficients for the van der Waals interactions between the rare gases Ne, Ar, Kr, and Xe and the low-lying states of Li, Na, K, and Rb are estimated using a combination of ab initio and semiempirical methods. The rare-gas oscillator strength distributions for the quadrupole and octupole transitions were derived by using high-quality calculations of rare-gas polarizabilities and dispersion coefficients to tune Hartree-Fock single-particle energies and expectation values

Forecast of auroral activity

International Nuclear Information System (INIS)

Lui, A.T.Y.

2004-01-01

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

Statistical methods for forecasting

CERN Document Server

Abraham, Bovas

2009-01-01

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

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

Science.gov (United States)

Slater, L. J.; Villarini, G.

2017-12-01

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

Assessing Hourly Precipitation Forecast Skill with the Fractions Skill Score

Science.gov (United States)

Zhao, Bin; Zhang, Bo

2018-02-01

Statistical methods for category (yes/no) forecasts, such as the Threat Score, are typically used in the verification of precipitation forecasts. However, these standard methods are affected by the so-called "double-penalty" problem caused by slight displacements in either space or time with respect to the observations. Spatial techniques have recently been developed to help solve this problem. The fractions skill score (FSS), a neighborhood spatial verification method, directly compares the fractional coverage of events in windows surrounding the observations and forecasts. We applied the FSS to hourly precipitation verification by taking hourly forecast products from the GRAPES (Global/Regional Assimilation Prediction System) regional model and quantitative precipitation estimation products from the National Meteorological Information Center of China during July and August 2016, and investigated the difference between these results and those obtained with the traditional category score. We found that the model spin-up period affected the assessment of stability. Systematic errors had an insignificant role in the fraction Brier score and could be ignored. The dispersion of observations followed a diurnal cycle and the standard deviation of the forecast had a similar pattern to the reference maximum of the fraction Brier score. The coefficient of the forecasts and the observations is similar to the FSS; that is, the FSS may be a useful index that can be used to indicate correlation. Compared with the traditional skill score, the FSS has obvious advantages in distinguishing differences in precipitation time series, especially in the assessment of heavy rainfall.

Forecast Combinations

OpenAIRE

Timmermann, Allan G

2005-01-01

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

Application of Medium and Seasonal Flood Forecasts for Agriculture Damage Assessment

Science.gov (United States)

Fakhruddin, Shamsul; Ballio, Francesco; Menoni, Scira

2015-04-01

Early warning is a key element for disaster risk reduction. In recent decades, major advancements have been made in medium range and seasonal flood forecasting. This progress provides a great opportunity to reduce agriculture damage and improve advisories for early action and planning for flood hazards. This approach can facilitate proactive rather than reactive management of the adverse consequences of floods. In the agricultural sector, for instance, farmers can take a diversity of options such as changing cropping patterns, applying fertilizer, irrigating and changing planting timing. An experimental medium range (1-10 day) and seasonal (20-25 days) flood forecasting model has been developed for Thailand and Bangladesh. It provides 51 sets of discharge ensemble forecasts of 1-10 days with significant persistence and high certainty and qualitative outlooks for 20-25 days. This type of forecast could assist farmers and other stakeholders for differential preparedness activities. These ensembles probabilistic flood forecasts have been customized based on user-needs for community-level application focused on agriculture system. The vulnerabilities of agriculture system were calculated based on exposure, sensitivity and adaptive capacity. Indicators for risk and vulnerability assessment were conducted through community consultations. The forecast lead time requirement, user-needs, impacts and management options for crops were identified through focus group discussions, informal interviews and community surveys. This paper illustrates potential applications of such ensembles for probabilistic medium range and seasonal flood forecasts in a way that is not commonly practiced globally today.

Forecaster Behaviour and Bias in Macroeconomic Forecasts

OpenAIRE

Roy Batchelor

2007-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2011-08-15

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

Development of numerical dispersion model for radioactive nuclei including resuspension processes

International Nuclear Information System (INIS)

Chiba, Masaru; Kurita, Susumu; Sasaki, Hidetaka

2003-01-01

Global-scale and local-scale dispersion model are developed combining to global and local scale meteorological forecasting model. By applying this system to another miner constituent such as mineral dust blowing by strong wind in arid region, this system shows very good performance to watch and predict the distribution of it. (author)

Forecast combinations

OpenAIRE

Aiolfi, Marco; Capistrán, Carlos; Timmermann, Allan

2010-01-01

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

A Bayesian method to rank different model forecasts of the same volcanic ash cloud: Chapter 24

Science.gov (United States)

Denlinger, Roger P.; Webley, P.; Mastin, Larry G.; Schwaiger, Hans F.

2012-01-01

Volcanic eruptions often spew fine ash high into the atmosphere, where it is carried downwind, forming long ash clouds that disrupt air traffic and pose a hazard to air travel. To mitigate such hazards, the community studying ash hazards must assess risk of ash ingestion for any flight path and provide robust and accurate forecasts of volcanic ash dispersal. We provide a quantitative and objective method to evaluate the efficacy of ash dispersal estimates from different models, using Bayes theorem to assess the predictions that each model makes about ash dispersal. We incorporate model and measurement uncertainty and produce a posterior probability for model input parameters. The integral of the posterior over all possible combinations of model inputs determines the evidence for each model and is used to compare models. We compare two different types of transport models, an Eulerian model (Ash3d) and a Langrangian model (PUFF), as applied to the 2010 eruptions of Eyjafjallajökull volcano in Iceland. The evidence for each model benefits from common physical characteristics of ash dispersal from an eruption column and provides a measure of how well each model forecasts cloud transport. Given the complexity of the wind fields, we find that the differences between these models depend upon the differences in the way the models disperse ash into the wind from the source plume. With continued observation, the accuracy of the estimates made by each model increases, increasing the efficacy of each model’s ability to simulate ash dispersal.

Results from the second year of a collaborative effort to forecast influenza seasons in the United States.

Science.gov (United States)

Biggerstaff, Matthew; Johansson, Michael; Alper, David; Brooks, Logan C; Chakraborty, Prithwish; Farrow, David C; Hyun, Sangwon; Kandula, Sasikiran; McGowan, Craig; Ramakrishnan, Naren; Rosenfeld, Roni; Shaman, Jeffrey; Tibshirani, Rob; Tibshirani, Ryan J; Vespignani, Alessandro; Yang, Wan; Zhang, Qian; Reed, Carrie

2018-02-24

Accurate forecasts could enable more informed public health decisions. Since 2013, CDC has worked with external researchers to improve influenza forecasts by coordinating seasonal challenges for the United States and the 10 Health and Human Service Regions. Forecasted targets for the 2014-15 challenge were the onset week, peak week, and peak intensity of the season and the weekly percent of outpatient visits due to influenza-like illness (ILI) 1-4 weeks in advance. We used a logarithmic scoring rule to score the weekly forecasts, averaged the scores over an evaluation period, and then exponentiated the resulting logarithmic score. Poor forecasts had a score near 0, and perfect forecasts a score of 1. Five teams submitted forecasts from seven different models. At the national level, the team scores for onset week ranged from <0.01 to 0.41, peak week ranged from 0.08 to 0.49, and peak intensity ranged from <0.01 to 0.17. The scores for predictions of ILI 1-4 weeks in advance ranged from 0.02-0.38 and was highest 1 week ahead. Forecast skill varied by HHS region. Forecasts can predict epidemic characteristics that inform public health actions. CDC, state and local health officials, and researchers are working together to improve forecasts. Published by Elsevier B.V.

Does money matter in inflation forecasting?

Science.gov (United States)

Binner, J. M.; Tino, P.; Tepper, J.; Anderson, R.; Jones, B.; Kendall, G.

2010-11-01

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two nonlinear techniques, namely, recurrent neural networks and kernel recursive least squares regression-techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naïve random walk model. The best models were nonlinear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. Beyond its economic findings, our study is in the tradition of physicists’ long-standing interest in the interconnections among statistical mechanics, neural networks, and related nonparametric statistical methods, and suggests potential avenues of extension for such studies.

Inverse problems using ANN in long range atmospheric dispersion with signature analysis picked scattered numerical sensors from CFD

International Nuclear Information System (INIS)

Sharma, Pavan K.; Gera, B.; Ghosh, A.K.; Kushwaha, H.S.

2010-01-01

Scalar dispersion in the atmosphere is an important area wherein different approaches are followed in development of good analytical model. The analyses based on Computational Fluid Dynamics (CFD) codes offer an opportunity of model development based on first principles of physics and hence such models have an edge over the existing models. Both forward and backward calculation methods are being developed for atmospheric dispersion around NPPs at BARC Forward modeling methods, which describe the atmospheric transport from sources to receptors, use forward-running transport and dispersion models or computational fluid dynamics models which are run many times, and the resulting dispersion field is compared to observations from multiple sensors. Backward or inverse modeling methods use only one model run in the reverse direction from the receptors to estimate the upwind sources. Inverse modeling methods include adjoint and tangent linear models, Kalman filters, and variational data assimilation, and neural network. The present paper is aimed at developing a new approach where the identified specific signatures at receptor points form the basis for source estimation or inversions. This approach is expected to reduce the large transient data sets to reduced and meaningful data sets. In fact this reduces the inherently transient data set into a time independent mean data set. Forward computation were carried out with CFD code for various case to generate a large set of data to train the ANN. Specific signature analysis was carried out to find the parameters of interest for ANN training like peak concentration, time to reach peak concentration and time to fall, the ANN was trained with data and source strength and location were predicted from ANN. Inverse problem was performed using ANN approach in long range atmospheric dispersion. An illustration of application of CFD code for atmospheric dispersion studies for a hypothetical case is also included in the paper. (author)

Improving weather forecasts for wind energy applications

Science.gov (United States)

Kay, Merlinde; MacGill, Iain

2010-08-01

Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms-1 and around 25 ms-1. A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

Improving weather forecasts for wind energy applications

International Nuclear Information System (INIS)

Kay, Merlinde; MacGill, Iain

2010-01-01

Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms -1 and around 25 ms -1 . A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

Overland movement in African clawed frogs (Xenopus laevis: empirical dispersal data from within their native range

Directory of Open Access Journals (Sweden)

F. André De Villiers

2017-11-01

Full Text Available Dispersal forms are an important component of the ecology of many animals, and reach particular importance for predicting ranges of invasive species. African clawed frogs (Xenopus laevis move overland between water bodies, but all empirical studies are from invasive populations with none from their native southern Africa. Here we report on incidents of overland movement found through a capture-recapture study carried out over a three year period in Overstrand, South Africa. The maximum distance moved was 2.4 km with most of the 91 animals, representing 5% of the population, moving ∼150 m. We found no differences in distances moved by males and females, despite the former being smaller. Fewer males moved overland, but this was no different from the sex bias found in the population. In laboratory performance trials, we found that males outperformed females, in both distance moved and time to exhaustion, when corrected for size. Overland movement occurred throughout the year, but reached peaks in spring and early summer when temporary water bodies were drying. Despite permanent impoundments being located within the study area, we found no evidence for migrations of animals between temporary and permanent water bodies. Our study provides the first dispersal kernel for X. laevis and suggests that it is similar to many non-pipid anurans with respect to dispersal.

Method for forecasting an earthquake from precursor signals

International Nuclear Information System (INIS)

Farnworth, D.F.

1996-01-01

A method for forecasting an earthquake from precursor signals by employing characteristic first electromagnetic signals, second, seismically induced electromagnetic signals, seismically induced mechanical signals, and infrasonic acoustic signals which have been observed to precede an earthquake. From a first electromagnetic signal, a magnitude, depth beneath the surface of the earth, distance, latitude, longitude, and first and second forecasts of the time of occurrence of the impending earthquake may be derived. From a second, seismically induced electromagnetic signal and the mechanical signal, third and fourth forecasts of the time of occurrence of an impending earthquake determined from the analysis above, a magnitude, depth beneath the surface of the earth and fourth and fifth forecasts of the time of occurrence of the impending earthquake may be derived. The forecasts of time available from the above analyses range from up to five weeks to substantially within one hour in advance of the earthquake. (author)

The potential value of seasonal forecasts in a changing climate

CSIR Research Space (South Africa)

Winsemius, HC

2013-12-01

Full Text Available -range Weather Forecasts (ECMWF) seasonal forecasting system. The focus is on the frequency of dry spells as well as the frequency of heat stress conditions expressed in the Temperature Heat Index. In areas where their frequency of occurrence increases...

Evaluation of medium-range ensemble flood forecasting based on calibration strategies and ensemble methods in Lanjiang Basin, Southeast China

Science.gov (United States)

Liu, Li; Gao, Chao; Xuan, Weidong; Xu, Yue-Ping

2017-11-01

Ensemble flood forecasts by hydrological models using numerical weather prediction products as forcing data are becoming more commonly used in operational flood forecasting applications. In this study, a hydrological ensemble flood forecasting system comprised of an automatically calibrated Variable Infiltration Capacity model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated. The hydrological model is optimized by the parallel programmed ε-NSGA II multi-objective algorithm. According to the solutions by ε-NSGA II, two differently parameterized models are determined to simulate daily flows and peak flows at each of the three hydrological stations. Then a simple yet effective modular approach is proposed to combine these daily and peak flows at the same station into one composite series. Five ensemble methods and various evaluation metrics are adopted. The results show that ε-NSGA II can provide an objective determination on parameter estimation, and the parallel program permits a more efficient simulation. It is also demonstrated that the forecasts from ECMWF have more favorable skill scores than other Ensemble Prediction Systems. The multimodel ensembles have advantages over all the single model ensembles and the multimodel methods weighted on members and skill scores outperform other methods. Furthermore, the overall performance at three stations can be satisfactory up to ten days, however the hydrological errors can degrade the skill score by approximately 2 days, and the influence persists until a lead time of 10 days with a weakening trend. With respect to peak flows selected by the Peaks Over Threshold approach, the ensemble means from single models or multimodels are generally underestimated, indicating that the ensemble mean can bring overall improvement in forecasting of flows. For

Fuel cycle forecasting - there are forecasts and there are forecasts

International Nuclear Information System (INIS)

Puechl, K.H.

1975-01-01

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

Fuel cycle forecasting - there are forecasts and there are forecasts

Energy Technology Data Exchange (ETDEWEB)

Puechl, K H

1975-12-01

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

Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change

DEFF Research Database (Denmark)

Diniz-Filho, José Alexandre F.; Bini, Luis Mauricio; Rangel, Thiago Fernando

2009-01-01

Forecasts of species range shifts under climate change are fraught with uncertainties and ensemble forecasting may provide a framework to deal with such uncertainties. Here, a novel approach to partition the variance among modeled attributes, such as richness or turnover, and map sources of uncer......Forecasts of species range shifts under climate change are fraught with uncertainties and ensemble forecasting may provide a framework to deal with such uncertainties. Here, a novel approach to partition the variance among modeled attributes, such as richness or turnover, and map sources...... of uncertainty in ensembles of forecasts is presented. We model the distributions of 3837 New World birds and project them into 2080. We then quantify and map the relative contribution of different sources of uncertainty from alternative methods for niche modeling, general circulation models (AOGCM......), and emission scenarios. The greatest source of uncertainty in forecasts of species range shifts arises from using alternative methods for niche modeling, followed by AOGCM, and their interaction. Our results concur with previous studies that discovered that projections from alternative models can be extremely...

Can decadal climate predictions be improved by ocean ensemble dispersion filtering?

Science.gov (United States)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-12-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years inadvance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-termweather forecasts represent an initial value problem and long-term climate projections represent a boundarycondition problem, the decadal climate prediction falls in-between these two time scales. The ocean memorydue to its heat capacity holds big potential skill on the decadal scale. In recent years, more precise initializationtechniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions.Ensembles are another important aspect. Applying slightly perturbed predictions results in an ensemble. Insteadof using and evaluating one prediction, but the whole ensemble or its ensemble average, improves a predictionsystem. However, climate models in general start losing the initialized signal and its predictive skill from oneforecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improvedby a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. Wefound that this procedure, called ensemble dispersion filter, results in more accurate results than the standarddecadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions showan increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with largerensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from oceanensemble dispersion filtering toward the ensemble mean. This study is part of MiKlip (fona-miklip.de) - a major project on decadal climate prediction in Germany.We focus on the Max-Planck-Institute Earth System Model using the low-resolution version (MPI-ESM-LR) andMiKlip's basic initialization strategy as in 2017 published decadal climate forecast: http://www.fona-miklip.de/decadal-forecast

A non-dispersive infrared analyser for analysis of HF in the PPM range

International Nuclear Information System (INIS)

Kartha, V.B.; Patel, N.D.; Venkateswaran, S.

1985-01-01

The determination of trace amounts of HF is of importance in many industrial processes, and in nuclear industry in the production of UF 6 . Mass spectrometric and gas chromatographic methods are difficult to use for this purpose, because of the highly corrosive nature of the samples. Infrared Spectroscopy can be conveniently used with the required sensitivity and it has the added advantage that continuous non-destructive on-line monitoring can be conveniently done. A non-dispersive infrared analyser for determination of HF in UF 6 in the concentration range of 50-2000 ppm has been fabricated and tested. The instrument has been shown to possess high sensitivity and good long term stability, so that it can be used as on-line monitor for non-destructive, continuous analysis. (author)

Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast

Directory of Open Access Journals (Sweden)

Jinyin Ye

2016-01-01

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

Canadian nuclear power plant construction cost forecast and analysis

International Nuclear Information System (INIS)

Keng, C.W.K.

1985-01-01

Because of the huge volume of capital required to construct a modern electric power generating station, investment decisions have to be made with as complete an understanding of the consequence of the decision as possible. This understanding must be provided by the evaluation of the situation to take place in the future. This paper attempts to use an econometric method to forecast the construction costs escalation of a standard Canadian nuclear generating station (NGS). A review of the history of Canadian nuclear electric power is provided. The major components of the construction costs of a Canadian NGS are studied and summarized. A data base is built and indexes are prepared. Based on these indexes an econometric forecasting model is constructed using an apparently new econometric methodology of forecasting modelling. Forecasts for a period of forty years are generated and applications of alternative scenario forecasts and range forecasts to uncertainty assessment are demonstrated. The indexes, the model, and the forecasts and their applications, to the best of the author's knowledge, are the very first ever done for Canadian NGS constructions

The new Met Office strategy for seasonal forecasts

Science.gov (United States)

Hewson, T. D.

2012-04-01

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

Mixing height derived from the DMI-HIRLAM NWP model, and used for ETEX dispersion modelling

Energy Technology Data Exchange (ETDEWEB)

Soerensen, J.H.; Rasmussen, A. [Danish Meteorological Inst., Copenhagen (Denmark)

1997-10-01

For atmospheric dispersion modelling it is of great significance to estimate the mixing height well. Mesoscale and long-range diffusion models using output from numerical weather prediction (NWP) models may well use NWP model profiles of wind, temperature and humidity in computation of the mixing height. This is dynamically consistent, and enables calculation of the mixing height for predicted states of the atmosphere. In autumn 1994, the European Tracer Experiment (ETEX) was carried out with the objective to validate atmospheric dispersion models. The Danish Meteorological Institute (DMI) participates in the model evaluations with the Danish Emergency Response Model of the Atmosphere (DERMA) using NWP model data from the DMI version of the High Resolution Limited Area Model (HIRLAM) as well as from the global model of the European Centre for Medium-Range Weather Forecast (ECMWF). In DERMA, calculation of mixing heights are performed based on a bulk Richardson number approach. Comparing with tracer gas measurements for the first ETEX experiment, a sensitivity study is performed for DERMA. Using DMI-HIRLAM data, the study shows that optimum values of the critical bulk Richardson number in the range 0.15-0.35 are adequate. These results are in agreement with recent mixing height verification studies against radiosonde data. The fairly large range of adequate critical values is a signature of the robustness of the method. Direct verification results against observed missing heights from operational radio-sondes released under the ETEX plume are presented. (au) 10 refs.

A Wind Forecasting System for Energy Application

Science.gov (United States)

Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

2010-05-01

Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated

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...

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

Science.gov (United States)

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

2009-04-01

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

Forecasting freight flows

DEFF Research Database (Denmark)

Lyk-Jensen, Stéphanie

2011-01-01

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

Net load forecasting for high renewable energy penetration grids

International Nuclear Information System (INIS)

Kaur, Amanpreet; Nonnenmacher, Lukas; Coimbra, Carlos F.M.

2016-01-01

We discuss methods for net load forecasting and their significance for operation and management of power grids with high renewable energy penetration. Net load forecasting is an enabling technology for the integration of microgrid fleets with the macrogrid. Net load represents the load that is traded between the grids (microgrid and utility grid). It is important for resource allocation and electricity market participation at the point of common coupling between the interconnected grids. We compare two inherently different approaches: additive and integrated net load forecast models. The proposed methodologies are validated on a microgrid with 33% annual renewable energy (solar) penetration. A heuristics based solar forecasting technique is proposed, achieving skill of 24.20%. The integrated solar and load forecasting model outperforms the additive model by 10.69% and the uncertainty range for the additive model is larger than the integrated model by 2.2%. Thus, for grid applications an integrated forecast model is recommended. We find that the net load forecast errors and the solar forecasting errors are cointegrated with a common stochastic drift. This is useful for future planning and modeling because the solar energy time-series allows to infer important features of the net load time-series, such as expected variability and uncertainty. - Highlights: • Net load forecasting methods for grids with renewable energy generation are discussed. • Integrated solar and load forecasting outperforms the additive model by 10.69%. • Net load forecasting reduces the uncertainty between the interconnected grids.

Robust forecast comparison

OpenAIRE

Jin, Sainan; Corradi, Valentina; Swanson, Norman

2015-01-01

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

Verification of Space Weather Forecasts using Terrestrial Weather Approaches

Science.gov (United States)

Henley, E.; Murray, S.; Pope, E.; Stephenson, D.; Sharpe, M.; Bingham, S.; Jackson, D.

2015-12-01

The Met Office Space Weather Operations Centre (MOSWOC) provides a range of 24/7 operational space weather forecasts, alerts, and warnings, which provide valuable information on space weather that can degrade electricity grids, radio communications, and satellite electronics. Forecasts issued include arrival times of coronal mass ejections (CMEs), and probabilistic forecasts for flares, geomagnetic storm indices, and energetic particle fluxes and fluences. These forecasts are produced twice daily using a combination of output from models such as Enlil, near-real-time observations, and forecaster experience. Verification of forecasts is crucial for users, researchers, and forecasters to understand the strengths and limitations of forecasters, and to assess forecaster added value. To this end, the Met Office (in collaboration with Exeter University) has been adapting verification techniques from terrestrial weather, and has been working closely with the International Space Environment Service (ISES) to standardise verification procedures. We will present the results of part of this work, analysing forecast and observed CME arrival times, assessing skill using 2x2 contingency tables. These MOSWOC forecasts can be objectively compared to those produced by the NASA Community Coordinated Modelling Center - a useful benchmark. This approach cannot be taken for the other forecasts, as they are probabilistic and categorical (e.g., geomagnetic storm forecasts give probabilities of exceeding levels from minor to extreme). We will present appropriate verification techniques being developed to address these forecasts, such as rank probability skill score, and comparing forecasts against climatology and persistence benchmarks. As part of this, we will outline the use of discrete time Markov chains to assess and improve the performance of our geomagnetic storm forecasts. We will also discuss work to adapt a terrestrial verification visualisation system to space weather, to help

Regional corrections and checking the reliability of geomagnetic forecasts

International Nuclear Information System (INIS)

Afanas'eva, V.I.; Shevnin, A.D.

1978-01-01

Regional corrections of the K index mark estimate with respect to the Moskva observatory are reviewed in order to improve the short-range forecast of the geomagnetic activity and to promote it within the aqua area. The forecasts of the storms of all categories and weak perturbations have been verified for the predominant days in the catalogue of the magnetic storms family. It is shown that the adopted methods of forecasts yield considerably good results for weak perturbations as well as for weak and moderate magnetic storms. Strong and very strong storms are less predictable

The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

Energy Technology Data Exchange (ETDEWEB)

Finley, Cathy [WindLogics, St. Paul, MN (United States)

2014-04-30

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the

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

Science.gov (United States)

Ayscue, Emily P.

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

Long range dependency and forecasting of housing price index and mortgage market rate: evidence of subprime crisis

Directory of Open Access Journals (Sweden)

Nadhem Selmi

2015-05-01

Full Text Available In this paper, we examine and forecast the House Price Index (HPI and mortgage market rate in terms of the description of the subprime crisis. We use a semi-parametric local polynomial Whittle estimator proposed by Shimotsu et al. (2005 [Shimotsu, K., & Phillips, P.C.B. (2005, Exact local Whittle estimation of fractional integration. The Annals of Statistics, 33(4, 1890-1933.] in a long memory parameter time series. Empirical investigation of HPI and mortgage market rate shows that these variables are more persistent when the d estimates are found on the Shimotsu method than on the one of Künsch (1987 [Künsch, H.R. (1987. Statistical aspects of self-similar processes. In Y. Prokhorov and V.V. Sazanov (eds., Proceedings of the First World Congress of the Bernoulli Society, VNU Science Press, Utrecht, 67-74.]. The estimating forecast values are more realistic and they strongly reflect the present US economy actuality in the two series as indicated by the forecast evaluation topics.

Objective Identification of Environmental Patterns Related to Tropical Cyclone Track Forecast Errors

National Research Council Canada - National Science Library

Sanabia, Elizabeth R

2006-01-01

The increase in skill of numerical model guidance and the use of consensus forecast techniques have led to significant improvements in the accuracy of tropical cyclone track forecasts at ranges beyond 72 hours...

An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting

Directory of Open Access Journals (Sweden)

F. Anctil

2009-11-01

Full Text Available Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap.

In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS, especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.

Evaluation of ensemble atmospheric simulations in oil dispersion models at Itaguai Port region; Avaliacao do uso de resultados numericos de previsao atmosferica por conjunto na modelagem da dispersao de oleo na regiao do Porto de Itaguai

Energy Technology Data Exchange (ETDEWEB)

Santos, Renato Goncalves dos; Silva, Mariana P.R.; Silva, Ricardo Marcelo da; Torres Junior, Audalio R. [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Lab. de Modelagem de Processos Marinhos e Atmosfericos (LAMMA); Landau, Luiz [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Lab. de Metodos Computacinais em Engenharia (LAMCE); Sa, Reginaldo Ventura de; Hochleitner, Fabio; Correa, Eduardo Barbosa [AQUAMET Meteorologia e Projeto de Sistemas, Rio de Janeiro, RJ (Brazil)

2008-07-01

This work discusses the use of numerical prediction using ensemble as boundary condition in pollutants dispersion models, applied in a hypothetical case of an oil spill occurrence in Itaguai Port. The Princeton Ocean Model (POM) has been used to simulate hydrodynamics and NICOIL Eulerian model to forecast oil spill dispersion, and ensemble wind forecast from Global Forecast System (GFS), aiming to assess the importance of this parameter variability in oil dispersion at sea. The wind scenarios using ensemble members has showed significant dispersion when compared to control simulation, demonstrating that the uncertainty in the atmospheric modeling can generate considerable variations in the placement of the final spot of oil. The region of interest was the Sepetiba Bay, located on the southern coast of the Rio de Janeiro state; because of port operations carried out around the Port of Itaguai where they can, eventually, oil leaks occur. (author)

Forecasting Skill

Science.gov (United States)

1981-01-01

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

Evaluating information in multiple horizon forecasts. The DOE's energy price forecasts

International Nuclear Information System (INIS)

Sanders, Dwight R.; Manfredo, Mark R.; Boris, Keith

2009-01-01

The United States Department of Energy's (DOE) quarterly price forecasts for energy commodities are examined to determine the incremental information provided at the one-through four-quarter forecast horizons. A direct test for determining information content at alternative forecast horizons, developed by Vuchelen and Gutierrez [Vuchelen, J. and Gutierrez, M.-I. 'A Direct Test of the Information Content of the OECD Growth Forecasts.' International Journal of Forecasting. 21(2005):103-117.], is used. The results suggest that the DOE's price forecasts for crude oil, gasoline, and diesel fuel do indeed provide incremental information out to three-quarters ahead, while natural gas and electricity forecasts are informative out to the four-quarter horizon. In contrast, the DOE's coal price forecasts at two-, three-, and four-quarters ahead provide no incremental information beyond that provided for the one-quarter horizon. Recommendations of how to use these results for making forecast adjustments is also provided. (author)

Verification of different forecasts of Hungarian Meteorological Service

Science.gov (United States)

Feher, B.

2009-09-01

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

Skill prediction of local weather forecasts based on the ECMWF ensemble

Directory of Open Access Journals (Sweden)

C. Ziehmann

2001-01-01

Full Text Available Ensemble Prediction has become an essential part of numerical weather forecasting. In this paper we investigate the ability of ensemble forecasts to provide an a priori estimate of the expected forecast skill. Several quantities derived from the local ensemble distribution are investigated for a two year data set of European Centre for Medium-Range Weather Forecasts (ECMWF temperature and wind speed ensemble forecasts at 30 German stations. The results indicate that the population of the ensemble mode provides useful information for the uncertainty in temperature forecasts. The ensemble entropy is a similar good measure. This is not true for the spread if it is simply calculated as the variance of the ensemble members with respect to the ensemble mean. The number of clusters in the C regions is almost unrelated to the local skill. For wind forecasts, the results are less promising.

Frost Monitoring and Forecasting Using MODIS Land Surface Temperature Data and a Numerical Weather Prediction Model Forecasts for Eastern Africa

Science.gov (United States)

Kabuchanga, Eric; Flores, Africa; Malaso, Susan; Mungai, John; Sakwa, Vincent; Shaka, Ayub; Limaye, Ashutosh

2014-01-01

Frost is a major challenge across Eastern Africa, severely impacting agricultural farms. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72-hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.

Forecasting power plant effects on the coastal zone. EG and G final report number B-4441

International Nuclear Information System (INIS)

1976-06-01

Field methods, data analyses, and calculation are presented exemplifying procedures for oceanic dispersion prediction as a tool for forecasting power plant effects on the coastal zone. Measurements were made of dye, drogues and temperatures near Pilgrim Station's discharge (Plymouth, Massachusetts), and of currents and other variables across Massachusetts Bay. Analysis of current data illustrates separation of tidal, wind-driven and inertial constituents and their significance for dispersion. Dye and temperature dispersion are compared with the currents study, and diffusion coefficients estimated. Current data from coastal sites (New Jersey and Massachusetts) are analyzed to determine field requirements for dispersion estimates. Methods to calculate expected precision of estimates based on brief current records are developed. Model calculations predicting dispersion based on observed ocean currents are described. Formulae are derived to estimate the spatial distribution of impact from a discharge. A numerical model to calculate discharge dispersion in more detail is discussed and used to study time variations of discharge effects. Model predictions are compared with field observations

Evaluation of streamflow forecast for the National Water Model of U.S. National Weather Service

Science.gov (United States)

Rafieeinasab, A.; McCreight, J. L.; Dugger, A. L.; Gochis, D.; Karsten, L. R.; Zhang, Y.; Cosgrove, B.; Liu, Y.

2016-12-01

The National Water Model (NWM), an implementation of the community WRF-Hydro modeling system, is an operational hydrologic forecasting model for the contiguous United States. The model forecasts distributed hydrologic states and fluxes, including soil moisture, snowpack, ET, and ponded water. In particular, the NWM provides streamflow forecasts at more than 2.7 million river reaches for three forecast ranges: short (15 hr), medium (10 days), and long (30 days). In this study, we verify short and medium range streamflow forecasts in the context of the verification of their respective quantitative precipitation forecasts/forcing (QPF), the High Resolution Rapid Refresh (HRRR) and the Global Forecast System (GFS). The streamflow evaluation is performed for summer of 2016 at more than 6,000 USGS gauges. Both individual forecasts and forecast lead times are examined. Selected case studies of extreme events aim to provide insight into the quality of the NWM streamflow forecasts. A goal of this comparison is to address how much streamflow bias originates from precipitation forcing bias. To this end, precipitation verification is performed over the contributing areas above (and between assimilated) USGS gauge locations. Precipitation verification is based on the aggregated, blended StageIV/StageII data as the "reference truth". We summarize the skill of the streamflow forecasts, their skill relative to the QPF, and make recommendations for improving NWM forecast skill.

National Forecast Charts

Science.gov (United States)

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

Utilities for high performance dispersion model PHYSIC

International Nuclear Information System (INIS)

Yamazawa, Hiromi

1992-09-01

The description and usage of the utilities for the dispersion calculation model PHYSIC were summarized. The model was developed in the study of developing high performance SPEEDI with the purpose of introducing meteorological forecast function into the environmental emergency response system. The procedure of PHYSIC calculation consists of three steps; preparation of relevant files, creation and submission of JCL, and graphic output of results. A user can carry out the above procedure with the help of the Geographical Data Processing Utility, the Model Control Utility, and the Graphic Output Utility. (author)

Adapting National Water Model Forecast Data to Local Hyper-Resolution H&H Models During Hurricane Irma

Science.gov (United States)

Singhofen, P.

2017-12-01

The National Water Model (NWM) is a remarkable undertaking. The foundation of the NWM is a 1 square kilometer grid which is used for near real-time modeling and flood forecasting of most rivers and streams in the contiguous United States. However, the NWM falls short in highly urbanized areas with complex drainage infrastructure. To overcome these shortcomings, the presenter proposes to leverage existing local hyper-resolution H&H models and adapt the NWM forcing data to them. Gridded near real-time rainfall, short range forecasts (18-hour) and medium range forecasts (10-day) during Hurricane Irma are applied to numerous detailed H&H models in highly urbanized areas of the State of Florida. Coastal and inland models are evaluated. Comparisons of near real-time rainfall data are made with observed gaged data and the ability to predict flooding in advance based on forecast data is evaluated. Preliminary findings indicate that the near real-time rainfall data is consistently and significantly lower than observed data. The forecast data is more promising. For example, the medium range forecast data provides 2 - 3 days advanced notice of peak flood conditions to a reasonable level of accuracy in most cases relative to both timing and magnitude. Short range forecast data provides about 12 - 14 hours advanced notice. Since these are hyper-resolution models, flood forecasts can be made at the street level, providing emergency response teams with valuable information for coordinating and dispatching limited resources.

Forecasting telecommunication new service demand by analogy method and combined forecast

Directory of Open Access Journals (Sweden)

Lin Feng-Jenq

2005-01-01

Full Text Available In the modeling forecast field, we are usually faced with the more difficult problems of forecasting market demand for a new service or product. A new service or product is defined as that there is absence of historical data in this new market. We hardly use models to execute the forecasting work directly. In the Taiwan telecommunication industry, after liberalization in 1996, there are many new services opened continually. For optimal investment, it is necessary that the operators, who have been granted the concessions and licenses, forecast this new service within their planning process. Though there are some methods to solve or avoid this predicament, in this paper, we will propose one forecasting procedure that integrates the concept of analogy method and the idea of combined forecast to generate new service forecast. In view of the above, the first half of this paper describes the procedure of analogy method and the approach of combined forecast, and the second half provides the case of forecasting low-tier phone demand in Taiwan to illustrate this procedure's feasibility.

Exploring the interactions between forecast accuracy, risk perception and perceived forecast reliability in reservoir operator's decision to use forecast

Science.gov (United States)

Shafiee-Jood, M.; Cai, X.

2017-12-01

Advances in streamflow forecasts at different time scales offer a promise for proactive flood management and improved risk management. Despite the huge potential, previous studies have found that water resources managers are often not willing to incorporate streamflow forecasts information in decisions making, particularly in risky situations. While low accuracy of forecasts information is often cited as the main reason, some studies have found that implementation of streamflow forecasts sometimes is impeded by institutional obstacles and behavioral factors (e.g., risk perception). In fact, a seminal study by O'Connor et al. (2005) found that risk perception is the strongest determinant of forecast use while managers' perception about forecast reliability is not significant. In this study, we aim to address this issue again. However, instead of using survey data and regression analysis, we develop a theoretical framework to assess the user-perceived value of streamflow forecasts. The framework includes a novel behavioral component which incorporates both risk perception and perceived forecast reliability. The framework is then used in a hypothetical problem where reservoir operator should react to probabilistic flood forecasts with different reliabilities. The framework will allow us to explore the interactions among risk perception and perceived forecast reliability, and among the behavioral components and information accuracy. The findings will provide insights to improve the usability of flood forecasts information through better communication and education.

Retrospective Evaluation of the Long-Term CSEP-Italy Earthquake Forecasts

Science.gov (United States)

Werner, M. J.; Zechar, J. D.; Marzocchi, W.; Wiemer, S.

2010-12-01

On 1 August 2009, the global Collaboratory for the Study of Earthquake Predictability (CSEP) launched a prospective and comparative earthquake predictability experiment in Italy. The goal of the CSEP-Italy experiment is to test earthquake occurrence hypotheses that have been formalized as probabilistic earthquake forecasts over temporal scales that range from days to years. In the first round of forecast submissions, members of the CSEP-Italy Working Group presented eighteen five-year and ten-year earthquake forecasts to the European CSEP Testing Center at ETH Zurich. We considered the twelve time-independent earthquake forecasts among this set and evaluated them with respect to past seismicity data from two Italian earthquake catalogs. Here, we present the results of tests that measure the consistency of the forecasts with the past observations. Besides being an evaluation of the submitted time-independent forecasts, this exercise provided insight into a number of important issues in predictability experiments with regard to the specification of the forecasts, the performance of the tests, and the trade-off between the robustness of results and experiment duration.

Review and forecast: Making hay

International Nuclear Information System (INIS)

Curran, R.

1997-01-01

Oil and natural gas industry prospects for 1997 were reviewed. By way of providing the foundation for a very favorable forecast, a wide range of indicators of a banner year in 1996 were assembled and provided in tabular form. Some 28 tables of statistical data provide insight into the reasons for an optimistic forecast for 1997. Statistics on oil and gas production, industry expenditures, exploratory well completions, costs per barrel of oil, estimates of supply and demand for petroleum products, gas liquid production, petrochemical and fertilizer production, sulfur production, drilling statistics, natural gas sales, gross production revenues and land sales, all attest to a record year in 1996, and provide reasons for a rosy outlook for 1997. 28 tabs

Surface drag effects on simulated wind fields in high-resolution atmospheric forecast model

Energy Technology Data Exchange (ETDEWEB)

Lim, Kyo Sun; Lim, Jong Myoung; Ji, Young Yong [Environmental Radioactivity Assessment Team,Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Shin, Hye Yum [NOAA/Geophysical Fluid Dynamics Laboratory, Princeton (United States); Hong, Jin Kyu [Yonsei University, Seoul (Korea, Republic of)

2017-04-15

It has been reported that the Weather Research and Forecasting (WRF) model generally shows a substantial over prediction bias at low to moderate wind speeds and winds are too geostrophic (Cheng and Steenburgh 2005), which limits the application of WRF model in the area that requires the accurate surface wind estimation such as wind-energy application, air-quality studies, and radioactive-pollutants dispersion studies. The surface drag generated by the subgrid-scale orography is represented by introducing a sink term in the momentum equation in their studies. The purpose of our study is to evaluate the simulated meteorological fields in the high-resolution WRF framework, that includes the parameterization of subgrid-scale orography developed by Mass and Ovens (2010), and enhance the forecast skill of low-level wind fields, which plays an important role in transport and dispersion of air pollutants including radioactive pollutants. The positive bias in 10-m wind speed is significantly alleviated by implementing the subgrid-scale orography parameterization, while other meteorological fields including 10-m wind direction are not changed. Increased variance of subgrid- scale orography enhances the sink of momentum and further reduces the bias in 10-m wind speed.

Wave ensemble forecast system for tropical cyclones in the Australian region

Science.gov (United States)

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

2018-05-01

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

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

Science.gov (United States)

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

2017-04-01

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

Forecasting daily patient volumes in the emergency department.

Science.gov (United States)

Jones, Spencer S; Thomas, Alun; Evans, R Scott; Welch, Shari J; Haug, Peter J; Snow, Gregory L

2008-02-01

Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. This study confirms the widely held belief that daily demand for ED services is characterized by

Assimilating concentration observations for transport and dispersion modeling in a meandering wind field

Science.gov (United States)

Haupt, Sue Ellen; Beyer-Lout, Anke; Long, Kerrie J.; Young, George S.

Assimilating concentration data into an atmospheric transport and dispersion model can provide information to improve downwind concentration forecasts. The forecast model is typically a one-way coupled set of equations: the meteorological equations impact the concentration, but the concentration does not generally affect the meteorological field. Thus, indirect methods of using concentration data to influence the meteorological variables are required. The problem studied here involves a simple wind field forcing Gaussian dispersion. Two methods of assimilating concentration data to infer the wind direction are demonstrated. The first method is Lagrangian in nature and treats the puff as an entity using feature extraction coupled with nudging. The second method is an Eulerian field approach akin to traditional variational approaches, but minimizes the error by using a genetic algorithm (GA) to directly optimize the match between observations and predictions. Both methods show success at inferring the wind field. The GA-variational method, however, is more accurate but requires more computational time. Dynamic assimilation of a continuous release modeled by a Gaussian plume is also demonstrated using the genetic algorithm approach.

Unsupervised/supervised learning concept for 24-hour load forecasting

Energy Technology Data Exchange (ETDEWEB)

Djukanovic, M [Electrical Engineering Inst. ' Nikola Tesla' , Belgrade (Yugoslavia); Babic, B [Electrical Power Industry of Serbia, Belgrade (Yugoslavia); Sobajic, D J; Pao, Y -H [Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Electrical Engineering and Computer Science

1993-07-01

An application of artificial neural networks in short-term load forecasting is described. An algorithm using an unsupervised/supervised learning concept and historical relationship between the load and temperature for a given season, day type and hour of the day to forecast hourly electric load with a lead time of 24 hours is proposed. An additional approach using functional link net, temperature variables, average load and last one-hour load of previous day is introduced and compared with the ANN model with one hidden layer load forecast. In spite of limited available weather variables (maximum, minimum and average temperature for the day) quite acceptable results have been achieved. The 24-hour-ahead forecast errors (absolute average) ranged from 2.78% for Saturdays and 3.12% for working days to 3.54% for Sundays. (Author)

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

Science.gov (United States)

Bergs, Jochen; Heerinckx, Philipe; Verelst, Sandra

2014-04-01

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

One-level modeling for diagnosing surface winds over complex terrain. II - Applicability to short-range forecasting

Science.gov (United States)

Alpert, P.; Getenio, B.; Zak-Rosenthal, R.

1988-01-01

The Alpert and Getenio (1988) modification of the Mass and Dempsey (1985) one-level sigma-surface model was used to study four synoptic events that included two winter cases (a Cyprus low and a Siberian high) and two summer cases. Results of statistical verification showed that the model is not only capable of diagnosing many details of surface mesoscale flow, but might also be useful for various applications which require operative short-range prediction of the diurnal changes of high-resolution surface flow over complex terrain, for example, in locating wildland fires, determining the dispersion of air pollutants, and predicting changes in wind energy or of surface wind for low-level air flights.

How accurate are forecasts of costs of energy? A methodological contribution

International Nuclear Information System (INIS)

Siddons, Craig; Allan, Grant; McIntyre, Stuart

2015-01-01

Forecasts of the cost of energy are typically presented as point estimates; however forecasts are seldom accurate, which makes it important to understand the uncertainty around these point estimates. The scale of the differences between forecasts and outturns (i.e. contemporary estimates) of costs may have important implications for government decisions on the appropriate form (and level) of support, modelling energy scenarios or industry investment appraisal. This paper proposes a methodology to assess the accuracy of cost forecasts. We apply this to levelised costs of energy for different generation technologies due to the availability of comparable forecasts and contemporary estimates, however the same methodology could be applied to the components of levelised costs, such as capital costs. The estimated “forecast errors” capture the accuracy of previous forecasts and can provide objective bounds to the range around current forecasts for such costs. The results from applying this method are illustrated using publicly available data for on- and off-shore wind, Nuclear and CCGT technologies, revealing the possible scale of “forecast errors” for these technologies. - Highlights: • A methodology to assess the accuracy of forecasts of costs of energy is outlined. • Method applied to illustrative data for four electricity generation technologies. • Results give an objective basis for sensitivity analysis around point estimates.

A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting

Energy Technology Data Exchange (ETDEWEB)

Haupt, Sue Ellen [National Center for Atmospheric Research, Boulder, CO (United States)

2016-04-19

The National Center for Atmospheric Research (NCAR) is pleased to have led a partnership to advance the state-of-the-science of solar power forecasting by designing, developing, building, deploying, testing, and assessing the SunCast™ Solar Power Forecasting System. The project has included cutting edge research, testing in several geographically- and climatologically-diverse high penetration solar utilities and Independent System Operators (ISOs), and wide dissemination of the research results to raise the bar on solar power forecasting technology. The partners include three other national laboratories, six universities, and industry partners. This public-private-academic team has worked in concert to perform use-inspired research to advance solar power forecasting through cutting-edge research to advance both the necessary forecasting technologies and the metrics for evaluating them. The project has culminated in a year-long, full-scale demonstration of provide irradiance and power forecasts to utilities and ISOs to use in their operations. The project focused on providing elements of a value chain, beginning with the weather that causes a deviation from clear sky irradiance and progresses through monitoring and observations, modeling, forecasting, dissemination and communication of the forecasts, interpretation of the forecast, and through decision-making, which produces outcomes that have an economic value. The system has been evaluated using metrics developed specifically for this project, which has provided rich information on model and system performance. Research was accomplished on the very short range (0-6 hours) Nowcasting system as well as on the longer term (6-72 hour) forecasting system. The shortest range forecasts are based on observations in the field. The shortest range system, built by Brookhaven National Laboratory (BNL) and based on Total Sky Imagers (TSIs) is TSICast, which operates on the shortest time scale with a latency of only a few

Development and testing of improved statistical wind power forecasting methods.

Energy Technology Data Exchange (ETDEWEB)

Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J. (Decision and Information Sciences); (INESC Porto)

2011-12-06

Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highly dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios

Interevent times in a new alarm-based earthquake forecasting model

Science.gov (United States)

Talbi, Abdelhak; Nanjo, Kazuyoshi; Zhuang, Jiancang; Satake, Kenji; Hamdache, Mohamed

2013-09-01

This study introduces a new earthquake forecasting model that uses the moment ratio (MR) of the first to second order moments of earthquake interevent times as a precursory alarm index to forecast large earthquake events. This MR model is based on the idea that the MR is associated with anomalous long-term changes in background seismicity prior to large earthquake events. In a given region, the MR statistic is defined as the inverse of the index of dispersion or Fano factor, with MR values (or scores) providing a biased estimate of the relative regional frequency of background events, here termed the background fraction. To test the forecasting performance of this proposed MR model, a composite Japan-wide earthquake catalogue for the years between 679 and 2012 was compiled using the Japan Meteorological Agency catalogue for the period between 1923 and 2012, and the Utsu historical seismicity records between 679 and 1922. MR values were estimated by sampling interevent times from events with magnitude M ≥ 6 using an earthquake random sampling (ERS) algorithm developed during previous research. Three retrospective tests of M ≥ 7 target earthquakes were undertaken to evaluate the long-, intermediate- and short-term performance of MR forecasting, using mainly Molchan diagrams and optimal spatial maps obtained by minimizing forecasting error defined by miss and alarm rate addition. This testing indicates that the MR forecasting technique performs well at long-, intermediate- and short-term. The MR maps produced during long-term testing indicate significant alarm levels before 15 of the 18 shallow earthquakes within the testing region during the past two decades, with an alarm region covering about 20 per cent (alarm rate) of the testing region. The number of shallow events missed by forecasting was reduced by about 60 per cent after using the MR method instead of the relative intensity (RI) forecasting method. At short term, our model succeeded in forecasting the

Advancing solar energy forecasting through the underlying physics

Science.gov (United States)

Yang, H.; Ghonima, M. S.; Zhong, X.; Ozge, B.; Kurtz, B.; Wu, E.; Mejia, F. A.; Zamora, M.; Wang, G.; Clemesha, R.; Norris, J. R.; Heus, T.; Kleissl, J. P.

2017-12-01

As solar power comprises an increasingly large portion of the energy generation mix, the ability to accurately forecast solar photovoltaic generation becomes increasingly important. Due to the variability of solar power caused by cloud cover, knowledge of both the magnitude and timing of expected solar power production ahead of time facilitates the integration of solar power onto the electric grid by reducing electricity generation from traditional ancillary generators such as gas and oil power plants, as well as decreasing the ramping of all generators, reducing start and shutdown costs, and minimizing solar power curtailment, thereby providing annual economic value. The time scales involved in both the energy markets and solar variability range from intra-hour to several days ahead. This wide range of time horizons led to the development of a multitude of techniques, with each offering unique advantages in specific applications. For example, sky imagery provides site-specific forecasts on the minute-scale. Statistical techniques including machine learning algorithms are commonly used in the intra-day forecast horizon for regional applications, while numerical weather prediction models can provide mesoscale forecasts on both the intra-day and days-ahead time scale. This talk will provide an overview of the challenges unique to each technique and highlight the advances in their ongoing development which come alongside advances in the fundamental physics underneath.

Forecasting urban water demand: A meta-regression analysis.

Science.gov (United States)

Sebri, Maamar

2016-12-01

Water managers and planners require accurate water demand forecasts over the short-, medium- and long-term for many purposes. These range from assessing water supply needs over spatial and temporal patterns to optimizing future investments and planning future allocations across competing sectors. This study surveys the empirical literature on the urban water demand forecasting using the meta-analytical approach. Specifically, using more than 600 estimates, a meta-regression analysis is conducted to identify explanations of cross-studies variation in accuracy of urban water demand forecasting. Our study finds that accuracy depends significantly on study characteristics, including demand periodicity, modeling method, forecasting horizon, model specification and sample size. The meta-regression results remain robust to different estimators employed as well as to a series of sensitivity checks performed. The importance of these findings lies in the conclusions and implications drawn out for regulators and policymakers and for academics alike. Copyright © 2016. Published by Elsevier Ltd.

Dispersal of sticky particles

Science.gov (United States)

Reddy, Ramana; Kumar, Sanjeev

2007-12-01

In this paper, we show through simulations that when sticky particles are broken continually, particles are dispersed into fine dust only if they are present in a narrow range of volume fractions. The upper limit of this range is 0.20 in the 2D and 0.10 in the 3D space. An increase in the dimensionality of space reduces the upper limit nearly by a factor of two. This scaling holds for dispersal of particles in hyperdimensional space of dimensions up to ten, the maximum dimension studied in this work. The maximum values of volume fractions obtained are significantly lower than those required for close packing and random packing of discs in 2D and spheres in 3D space. These values are also smaller than those required for critical phenomena of cluster percolation. The results obtained are attributed to merger cascades of sticky particles, triggered by breakup events. A simple theory that incorporates this cascade is developed to quantitatively explain the observed scaling of the upper limit with the dimensionality of space. The theory also captures the dynamics of the dispersal process in the corresponding range of particle volume fractions. The theory suggests that cascades of order one and two predominantly decide the upper limit for complete dispersal of particles.

Verification of ECMWF and ECMWF/MACC's global and direct irradiance forecasts with respect to solar electricity production forecasts

Directory of Open Access Journals (Sweden)

M. Schroedter-Homscheidt

2017-02-01

Full Text Available The successful electricity grid integration of solar energy into day-ahead markets requires at least hourly resolved 48 h forecasts. Technologies as photovoltaics and non-concentrating solar thermal technologies make use of global horizontal irradiance (GHI forecasts, while all concentrating technologies both from the photovoltaic and the thermal sector require direct normal irradiances (DNI. The European Centre for Medium-Range Weather Forecasts (ECMWF has recently changed towards providing direct as well as global irradiances. Additionally, the MACC (Monitoring Atmospheric Composition & Climate near-real time services provide daily analysis and forecasts of aerosol properties in preparation of the upcoming European Copernicus programme. The operational ECMWF/IFS (Integrated Forecast System forecast system will in the medium term profit from the Copernicus service aerosol forecasts. Therefore, within the MACC‑II project specific experiment runs were performed allowing for the assessment of the performance gain of these potential future capabilities. Also the potential impact of providing forecasts with hourly output resolution compared to three-hourly resolved forecasts is investigated. The inclusion of the new aerosol climatology in October 2003 improved both the GHI and DNI forecasts remarkably, while the change towards a new radiation scheme in 2007 only had minor and partly even unfavourable impacts on the performance indicators. For GHI, larger RMSE (root mean square error values are found for broken/overcast conditions than for scattered cloud fields. For DNI, the findings are opposite with larger RMSE values for scattered clouds compared to overcast/broken cloud situations. The introduction of direct irradiances as an output parameter in the operational IFS version has not resulted in a general performance improvement with respect to biases and RMSE compared to the widely used Skartveit et al. (1998 global to direct irradiance

Inflation Forecast Contracts

OpenAIRE

Gersbach, Hans; Hahn, Volker

2012-01-01

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

Nuclear risk from atmospheric dispersion in Northern Europe

International Nuclear Information System (INIS)

Lauritzen, B.

2007-04-01

The aim of the 2005-06 NKS-B NordRisk project has been to present practical methods for probabilistic risk assessment from long-range atmospheric transport and deposition of radioactive material. In this project an atlas of long-range atmospheric dispersion and deposition patterns derived from archived numerical weather prediction (NWP) model data coupled to an atmospheric dispersion model has been produced, and a PC-based software tool has been developed, based on a simplified description of the long-term, long-range atmospheric dispersion and deposition. The atlas and the software tool may allow for a rapid, first assessment of the risks following a nuclear emergency, when detailed information on the long-range atmospheric dispersion and deposition is not available. (au)

Dispersion Forces

CERN Document Server

Buhmann, Stefan Yoshi

2012-01-01

In this book, a modern unified theory of dispersion forces on atoms and bodies is presented which covers a broad range of advanced aspects and scenarios. Macroscopic quantum electrodynamics is shown to provide a powerful framework for dispersion forces which allows for discussing general properties like their non-additivity and the relation between microscopic and macroscopic interactions. It is demonstrated how the general results can be used to obtain dispersion forces on atoms in the presence of bodies of various shapes and materials. Starting with a brief recapitulation of volume I, this volume II deals especially with bodies of irregular shapes, universal scaling laws, dynamical forces on excited atoms, enhanced forces in cavity quantum electrodynamics, non-equilibrium forces in thermal environments and quantum friction. The book gives both the specialist and those new to the field a thorough overview over recent results in the field. It provides a toolbox for studying dispersion forces in various contex...

Non-seismic tsunamis: filling the forecast gap

Science.gov (United States)

Moore, C. W.; Titov, V. V.; Spillane, M. C.

2015-12-01

Earthquakes are the generation mechanism in over 85% of tsunamis. However, non-seismic tsunamis, including those generated by meteorological events, landslides, volcanoes, and asteroid impacts, can inundate significant area and have a large far-field effect. The current National Oceanographic and Atmospheric Administration (NOAA) tsunami forecast system falls short in detecting these phenomena. This study attempts to classify the range of effects possible from these non-seismic threats, and to investigate detection methods appropriate for use in a forecast system. Typical observation platforms are assessed, including DART bottom pressure recorders and tide gauges. Other detection paths include atmospheric pressure anomaly algorithms for detecting meteotsunamis and the early identification of asteroids large enough to produce a regional hazard. Real-time assessment of observations for forecast use can provide guidance to mitigate the effects of a non-seismic tsunami.

Sea Level Forecasts Aggregated from Established Operational Systems

Directory of Open Access Journals (Sweden)

Andy Taylor

2017-08-01

Full Text Available A system for providing routine seven-day forecasts of sea level observable at tide gauge locations is described and evaluated. Forecast time series are aggregated from well-established operational systems of the Australian Bureau of Meteorology; although following some adjustments these systems are only quasi-complimentary. Target applications are routine coastal decision processes under non-extreme conditions. The configuration aims to be relatively robust to operational realities such as version upgrades, data gaps and metadata ambiguities. Forecast skill is evaluated against hourly tide gauge observations. Characteristics of the bias correction term are demonstrated to be primarily static in time, with time varying signals showing regional coherence. This simple approach to exploiting existing complex systems can offer valuable levels of skill at a range of Australian locations. The prospect of interpolation between observation sites and exploitation of lagged-ensemble uncertainty estimates could be meaningfully pursued. Skill characteristics define a benchmark against which new operational sea level forecasting systems can be measured. More generally, an aggregation approach may prove to be optimal for routine sea level forecast services given the physically inhomogeneous processes involved and ability to incorporate ongoing improvements and extensions of source systems.

A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting

Directory of Open Access Journals (Sweden)

Chengshi Tian

2018-03-01

Full Text Available Short-term load forecasting plays an indispensable role in electric power systems, which is not only an extremely challenging task but also a concerning issue for all society due to complex nonlinearity characteristics. However, most previous combined forecasting models were based on optimizing weight coefficients to develop a linear combined forecasting model, while ignoring that the linear combined model only considers the contribution of the linear terms to improving the model’s performance, which will lead to poor forecasting results because of the significance of the neglected and potential nonlinear terms. In this paper, a novel nonlinear combined forecasting system, which consists of three modules (improved data pre-processing module, forecasting module and the evaluation module is developed for short-term load forecasting. Different from the simple data pre-processing of most previous studies, the improved data pre-processing module based on longitudinal data selection is successfully developed in this system, which further improves the effectiveness of data pre-processing and then enhances the final forecasting performance. Furthermore, the modified support vector machine is developed to integrate all the individual predictors and obtain the final prediction, which successfully overcomes the upper drawbacks of the linear combined model. Moreover, the evaluation module is incorporated to perform a scientific evaluation for the developed system. The half-hourly electrical load data from New South Wales are employed to verify the effectiveness of the developed forecasting system, and the results reveal that the developed nonlinear forecasting system can be employed in the dispatching and planning for smart grids.

Advances in electric power and energy systems load and price forecasting

CERN Document Server

2017-01-01

A comprehensive review of state-of-the-art approaches to power systems forecasting from the most respected names in the field, internationally. Advances in Electric Power and Energy Systems is the first book devoted exclusively to a subject of increasing urgency to power systems planning and operations. Written for practicing engineers, researchers, and post-grads concerned with power systems planning and forecasting, this book brings together contributions from many of the world’s foremost names in the field who address a range of critical issues, from forecasting power system load to power system pricing to post-storm service restoration times, river flow forecasting, and more. In a time of ever-increasing energy demands, mounting concerns over the environmental impacts of power generation, and the emergence of new, smart-grid technologies, electricity price forecasting has assumed a prominent role within both the academic and industrial ar nas. Short-run forecasting of electricity prices has become nece...

Dispersion stability of thermal nanofluids

Directory of Open Access Journals (Sweden)

Fan Yu

2017-10-01

Full Text Available Thermal nanofluids, the engineered fluids with dispersed functional nanoparticles, have exhibited extraordinary thermophysical properties and added functionalities, and thus have enabled a broad range of important applications. The poor dispersion stability of thermal nanofluids, however, has been considered as a long-existing issue that limits their further development and practical application. This review overviews the recent efforts and progresses in improving the dispersion stability of thermal nanofluids such as mechanistic understanding of dispersion behavior of nanofluids, examples of both water-based and oil-based nanofluids, strategies to stabilize nanofluids, and characterization techniques for dispersion behavior of nanofluids. Finally, on-going research needs, and possible solutions to research challenges and future research directions in exploring stably dispersed thermal nanofluids are discussed. Keywords: Thermal nanofluids, Dispersion, Aggregation, Electrostatic stabilization, Steric stabilization

Magnetogram Forecast: An All-Clear Space Weather Forecasting System

Science.gov (United States)

Barghouty, Nasser; Falconer, David

2015-01-01

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

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...

Are Forecast Updates Progressive?

NARCIS (Netherlands)

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

2010-01-01

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

Simultaneous calibration of ensemble river flow predictions over an entire range of lead times

Science.gov (United States)

Hemri, S.; Fundel, F.; Zappa, M.

2013-10-01

Probabilistic estimates of future water levels and river discharge are usually simulated with hydrologic models using ensemble weather forecasts as main inputs. As hydrologic models are imperfect and the meteorological ensembles tend to be biased and underdispersed, the ensemble forecasts for river runoff typically are biased and underdispersed, too. Thus, in order to achieve both reliable and sharp predictions statistical postprocessing is required. In this work Bayesian model averaging (BMA) is applied to statistically postprocess ensemble runoff raw forecasts for a catchment in Switzerland, at lead times ranging from 1 to 240 h. The raw forecasts have been obtained using deterministic and ensemble forcing meteorological models with different forecast lead time ranges. First, BMA is applied based on mixtures of univariate normal distributions, subject to the assumption of independence between distinct lead times. Then, the independence assumption is relaxed in order to estimate multivariate runoff forecasts over the entire range of lead times simultaneously, based on a BMA version that uses multivariate normal distributions. Since river runoff is a highly skewed variable, Box-Cox transformations are applied in order to achieve approximate normality. Both univariate and multivariate BMA approaches are able to generate well calibrated probabilistic forecasts that are considerably sharper than climatological forecasts. Additionally, multivariate BMA provides a promising approach for incorporating temporal dependencies into the postprocessed forecasts. Its major advantage against univariate BMA is an increase in reliability when the forecast system is changing due to model availability.

Computation of probabilistic hazard maps and source parameter estimation for volcanic ash transport and dispersion

Energy Technology Data Exchange (ETDEWEB)

Madankan, R. [Department of Mechanical and Aerospace Engineering, University at Buffalo (United States); Pouget, S. [Department of Geology, University at Buffalo (United States); Singla, P., E-mail: psingla@buffalo.edu [Department of Mechanical and Aerospace Engineering, University at Buffalo (United States); Bursik, M. [Department of Geology, University at Buffalo (United States); Dehn, J. [Geophysical Institute, University of Alaska, Fairbanks (United States); Jones, M. [Center for Computational Research, University at Buffalo (United States); Patra, A. [Department of Mechanical and Aerospace Engineering, University at Buffalo (United States); Pavolonis, M. [NOAA-NESDIS, Center for Satellite Applications and Research (United States); Pitman, E.B. [Department of Mathematics, University at Buffalo (United States); Singh, T. [Department of Mechanical and Aerospace Engineering, University at Buffalo (United States); Webley, P. [Geophysical Institute, University of Alaska, Fairbanks (United States)

2014-08-15

Volcanic ash advisory centers are charged with forecasting the movement of volcanic ash plumes, for aviation, health and safety preparation. Deterministic mathematical equations model the advection and dispersion of these plumes. However initial plume conditions – height, profile of particle location, volcanic vent parameters – are known only approximately at best, and other features of the governing system such as the windfield are stochastic. These uncertainties make forecasting plume motion difficult. As a result of these uncertainties, ash advisories based on a deterministic approach tend to be conservative, and many times over/under estimate the extent of a plume. This paper presents an end-to-end framework for generating a probabilistic approach to ash plume forecasting. This framework uses an ensemble of solutions, guided by Conjugate Unscented Transform (CUT) method for evaluating expectation integrals. This ensemble is used to construct a polynomial chaos expansion that can be sampled cheaply, to provide a probabilistic model forecast. The CUT method is then combined with a minimum variance condition, to provide a full posterior pdf of the uncertain source parameters, based on observed satellite imagery. The April 2010 eruption of the Eyjafjallajökull volcano in Iceland is employed as a test example. The puff advection/dispersion model is used to hindcast the motion of the ash plume through time, concentrating on the period 14–16 April 2010. Variability in the height and particle loading of that eruption is introduced through a volcano column model called bent. Output uncertainty due to the assumed uncertain input parameter probability distributions, and a probabilistic spatial-temporal estimate of ash presence are computed.

Cellular automata-based forecasting of the impact of accidental fire and toxic dispersion in process industries

International Nuclear Information System (INIS)

Sarkar, Chinmoy; Abbasi, S.A.

2006-01-01

The strategies to prevent accidents from occurring in a process industry, or to minimize the harm if an accident does take place, always revolve around forecasting the likely accidents and their impacts. Based on the likely frequency and severity of the accidents, resources are committed towards preventing the accidents. Nearly all techniques of ranking hazardous units, be it the hazard and operability studies, fault tree analysis, hazard indice, etc. - qualitative as well as quantitative - depend essentially on the assessment of the likely frequency and the likely harm accidents in different units may cause. This fact makes it exceedingly important that the forecasting the accidents and their likely impact is done as accurately as possible. In the present study we introduce a new approach to accident forecasting based on the discrete modeling paradigm of cellular automata. In this treatment an accident is modeled as a self-evolving phenomena, the impact of which is strongly influenced by the size, nature, and position of the environmental components which lie in the vicinity of the accident site. The outward propagation of the mass, energy and momentum from the accident epicenter is modeled as a fast diffusion process occurring in discrete space-time coordinates. The quantum of energy and material that would flow into each discrete space element (cell) due to the accidental release is evaluated and the degree of vulnerability posed to the receptors if present in the cell is measured at the end of each time element. This approach is able to effectively take into account the modifications in the flux of energy and material which occur as a result of the heterogeneous environment prevailing between the accident epicenter and the receptor. Consequently, more realistic accident scenarios are generated than possible with the prevailing techniques. The efficacy of the approach has been illustrated with case studies

Load forecasting

International Nuclear Information System (INIS)

Mak, H.

1995-01-01

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

Relating Tropical Cyclone Track Forecast Error Distributions with Measurements of Forecast Uncertainty

Science.gov (United States)

2016-03-01

CYCLONE TRACK FORECAST ERROR DISTRIBUTIONS WITH MEASUREMENTS OF FORECAST UNCERTAINTY by Nicholas M. Chisler March 2016 Thesis Advisor...March 2016 3. REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE RELATING TROPICAL CYCLONE TRACK FORECAST ERROR DISTRIBUTIONS...WITH MEASUREMENTS OF FORECAST UNCERTAINTY 5. FUNDING NUMBERS 6. AUTHOR(S) Nicholas M. Chisler 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES

Improving long-range dispersion predictions with ETEX real-time and a-posteriori model evaluations

International Nuclear Information System (INIS)

Desiato, F.

1997-01-01

The Italian environmental Protection Agency (ANPA), which is responsible for the evaluation of the consequences of accidental releases into the atmosphere, has participated to both the real-time (phase-1) and a-posteriori (phase-2) ETEX model evaluations. The double benchmark actually constituted an invaluable experience for better understanding the skill and limits of the present long-range dispersion modelling capabilities. In particular, the strong difference between phase-1 and phase-2 model performance emphasised the opportunity to modify, improve or tune a number of specific aspects of the overall simulation. ETEX model runs were carried out with the Lagrangian particle model APOLLO. The meteorological input was constituted by ECMWF fields. Three-hourly average concentrations paired in space and time and time-integrated concentrations were used in the evaluation of the results, based on a set of statistical indexes and concentration contour lines and scatter diagrams

Applications of the gambling score in evaluating earthquake predictions and forecasts

Science.gov (United States)

Zhuang, Jiancang; Zechar, Jeremy D.; Jiang, Changsheng; Console, Rodolfo; Murru, Maura; Falcone, Giuseppe

2010-05-01

This study presents a new method, namely the gambling score, for scoring the performance earthquake forecasts or predictions. Unlike most other scoring procedures that require a regular scheme of forecast and treat each earthquake equally, regardless their magnitude, this new scoring method compensates the risk that the forecaster has taken. Starting with a certain number of reputation points, once a forecaster makes a prediction or forecast, he is assumed to have betted some points of his reputation. The reference model, which plays the role of the house, determines how many reputation points the forecaster can gain if he succeeds, according to a fair rule, and also takes away the reputation points bet by the forecaster if he loses. This method is also extended to the continuous case of point process models, where the reputation points betted by the forecaster become a continuous mass on the space-time-magnitude range of interest. For discrete predictions, we apply this method to evaluate performance of Shebalin's predictions made by using the Reverse Tracing of Precursors (RTP) algorithm and of the outputs of the predictions from the Annual Consultation Meeting on Earthquake Tendency held by China Earthquake Administration. For the continuous case, we use it to compare the probability forecasts of seismicity in the Abruzzo region before and after the L'aquila earthquake based on the ETAS model and the PPE model.

Dispersal kernel estimation: A comparison of empirical and modelled particle dispersion in a coastal marine system

Science.gov (United States)

Hrycik, Janelle M.; Chassé, Joël; Ruddick, Barry R.; Taggart, Christopher T.

2013-11-01

Early life-stage dispersal influences recruitment and is of significance in explaining the distribution and connectivity of marine species. Motivations for quantifying dispersal range from biodiversity conservation to the design of marine reserves and the mitigation of species invasions. Here we compare estimates of real particle dispersion in a coastal marine environment with similar estimates provided by hydrodynamic modelling. We do so by using a system of magnetically attractive particles (MAPs) and a magnetic-collector array that provides measures of Lagrangian dispersion based on the time-integration of MAPs dispersing through the array. MAPs released as a point source in a coastal marine location dispersed through the collector array over a 5-7 d period. A virtual release and observed (real-time) environmental conditions were used in a high-resolution three-dimensional hydrodynamic model to estimate the dispersal of virtual particles (VPs). The number of MAPs captured throughout the collector array and the number of VPs that passed through each corresponding model location were enumerated and compared. Although VP dispersal reflected several aspects of the observed MAP dispersal, the comparisons demonstrated model sensitivity to the small-scale (random-walk) particle diffusivity parameter (Kp). The one-dimensional dispersal kernel for the MAPs had an e-folding scale estimate in the range of 5.19-11.44 km, while those from the model simulations were comparable at 1.89-6.52 km, and also demonstrated sensitivity to Kp. Variations among comparisons are related to the value of Kp used in modelling and are postulated to be related to MAP losses from the water column and (or) shear dispersion acting on the MAPs; a process that is constrained in the model. Our demonstration indicates a promising new way of 1) quantitatively and empirically estimating the dispersal kernel in aquatic systems, and 2) quantitatively assessing and (or) improving regional hydrodynamic

Grey Forecast Rainfall with Flow Updating Algorithm for Real-Time Flood Forecasting

Directory of Open Access Journals (Sweden)

Jui-Yi Ho

2015-04-01

Full Text Available The dynamic relationship between watershed characteristics and rainfall-runoff has been widely studied in recent decades. Since watershed rainfall-runoff is a non-stationary process, most deterministic flood forecasting approaches are ineffective without the assistance of adaptive algorithms. The purpose of this paper is to propose an effective flow forecasting system that integrates a rainfall forecasting model, watershed runoff model, and real-time updating algorithm. This study adopted a grey rainfall forecasting technique, based on existing hourly rainfall data. A geomorphology-based runoff model can be used for simulating impacts of the changing geo-climatic conditions on the hydrologic response of unsteady and non-linear watershed system, and flow updating algorithm were combined to estimate watershed runoff according to measured flow data. The proposed flood forecasting system was applied to three watersheds; one in the United States and two in Northern Taiwan. Four sets of rainfall-runoff simulations were performed to test the accuracy of the proposed flow forecasting technique. The results indicated that the forecast and observed hydrographs are in good agreement for all three watersheds. The proposed flow forecasting system could assist authorities in minimizing loss of life and property during flood events.

Population Structure and Dispersal Patterns within and between Atlantic and Mediterranean Populations of a Large-Range Pelagic Seabird

Science.gov (United States)

Genovart, Meritxell; Thibault, Jean-Claude; Igual, José Manuel; Bauzà-Ribot, Maria del Mar; Rabouam, Corinne; Bretagnolle, Vincent

2013-01-01

Dispersal is critically linked to the demographic and evolutionary trajectories of populations, but in most seabird species it may be difficult to estimate. Using molecular tools, we explored population structure and the spatial dispersal pattern of a highly pelagic but philopatric seabird, the Cory's shearwater Calonectris diomedea. Microsatellite fragments were analysed from samples collected across almost the entire breeding range of the species. To help disentangle the taxonomic status of the two subspecies described, the Atlantic form C. d. borealis and the Mediterranean form C. d. diomedea, we analysed genetic divergence between subspecies and quantified both historical and recent migration rates between the Mediterranean and Atlantic basins. We also searched for evidence of isolation by distance (IBD) and addressed spatial patterns of gene flow. We found a low genetic structure in the Mediterranean basin. Conversely, strong genetic differentiation appeared in the Atlantic basin. Even if the species was mostly philopatric (97%), results suggest recent dispersal between basins, especially from the Atlantic to the Mediterranean (aprox. 10% of migrants/generation across the last two generations). Long-term gene flow analyses also suggested an historical exchange between basins (about 70 breeders/generation). Spatial analysis of genetic variation indicates that distance is not the main factor in shaping genetic structure in this species. Given our results we recommend gathering more data before concluded whether these taxa should be treated as two species or subspecies. PMID:23950986

Short-term residential load forecasting: Impact of calendar effects and forecast granularity

DEFF Research Database (Denmark)

Lusis, Peter; Khalilpour, Kaveh Rajab; Andrew, Lachlan

2017-01-01

forecasting for a single-customer or even down at an appliance level. Access to high resolution data from smart meters has enabled the research community to assess conventional load forecasting techniques and develop new forecasting strategies suitable for demand-side disaggregated loads. This paper studies...... how calendar effects, forecasting granularity and the length of the training set affect the accuracy of a day-ahead load forecast for residential customers. Root mean square error (RMSE) and normalized RMSE were used as forecast error metrics. Regression trees, neural networks, and support vector...... regression yielded similar average RMSE results, but statistical analysis showed that regression trees technique is significantly better. The use of historical load profiles with daily and weekly seasonality, combined with weather data, leaves the explicit calendar effects a very low predictive power...

Extended Range Prediction of Indian Summer Monsoon: Current status

Science.gov (United States)

Sahai, A. K.; Abhilash, S.; Borah, N.; Joseph, S.; Chattopadhyay, R.; S, S.; Rajeevan, M.; Mandal, R.; Dey, A.

2014-12-01

The main focus of this study is to develop forecast consensus in the extended range prediction (ERP) of monsoon Intraseasonal oscillations using a suit of different variants of Climate Forecast system (CFS) model. In this CFS based Grand MME prediction system (CGMME), the ensemble members are generated by perturbing the initial condition and using different configurations of CFSv2. This is to address the role of different physical mechanisms known to have control on the error growth in the ERP in the 15-20 day time scale. The final formulation of CGMME is based on 21 ensembles of the standalone Global Forecast System (GFS) forced with bias corrected forecasted SST from CFS, 11 low resolution CFST126 and 11 high resolution CFST382. Thus, we develop the multi-model consensus forecast for the ERP of Indian summer monsoon (ISM) using a suite of different variants of CFS model. This coordinated international effort lead towards the development of specific tailor made regional forecast products over Indian region. Skill of deterministic and probabilistic categorical rainfall forecast as well the verification of large-scale low frequency monsoon intraseasonal oscillations has been carried out using hindcast from 2001-2012 during the monsoon season in which all models are initialized at every five days starting from 16May to 28 September. The skill of deterministic forecast from CGMME is better than the best participating single model ensemble configuration (SME). The CGMME approach is believed to quantify the uncertainty in both initial conditions and model formulation. Main improvement is attained in probabilistic forecast which is because of an increase in the ensemble spread, thereby reducing the error due to over-confident ensembles in a single model configuration. For probabilistic forecast, three tercile ranges are determined by ranking method based on the percentage of ensemble members from all the participating models falls in those three categories. CGMME further

A national econometric forecasting model of the dental sector.

Science.gov (United States)

Feldstein, P J; Roehrig, C S

1980-01-01

The Econometric Model of the the Dental Sector forecasts a broad range of dental sector variables, including dental care prices; the amount of care produced and consumed; employment of hygienists, dental assistants, and clericals; hours worked by dentists; dental incomes; and number of dentists. These forecasts are based upon values specified by the user for the various factors which help determine the supply an demand for dental care, such as the size of the population, per capita income, the proportion of the population covered by private dental insurance, the cost of hiring clericals and dental assistants, and relevant government policies. In a test of its reliability, the model forecast dental sector behavior quite accurately for the period 1971 through 1977. PMID:7461974

The management of subsurface uncertainty using probabilistic modeling of life cycle production forecasts and cash flows

International Nuclear Information System (INIS)

Olatunbosun, O. O.

1998-01-01

The subject pertains to the implementation of the full range of subsurface uncertainties in life cycle probabilistic forecasting and its extension to project cash flows using the methodology of probabilities. A new tool has been developed in the probabilistic application of Crystal-Ball which can model reservoir volumetrics, life cycle production forecasts and project cash flows in a single environment. The tool is modular such that the volumetrics and cash flow modules are optional. Production forecasts are often generated by applying a decline equation to single best estimate values of input parameters such as initial potential, decline rate, abandonment rate etc -or sometimes by results of reservoir simulation. This new tool provides a means of implementing the full range of uncertainties and interdependencies of the input parameters into the production forecasts by defining the input parameters as probability density functions, PDFs and performing several iterations to generate an expectation curve forecast. Abandonment rate is implemented in each iteration via a link to an OPEX model. The expectation curve forecast is input into a cash flow model to generate a probabilistic NPV. Base case and sensitivity runs from reservoir simulation can likewise form the basis for a probabilistic production forecast from which a probabilistic cash flow can be generated. A good illustration of the application of this tool is in the modelling of the production forecast for a well that encounters its target reservoirs in OUT/ODT situation and thus has significant uncertainties. The uncertainty in presence and size (if present) of gas cap and dependency between ultimate recovery and initial potential amongst other uncertainties can be easily implemented in the production forecast with this tool. From the expectation curve forecast, a probabilistic NPV can be easily generated. Possible applications of this tool include: i. estimation of range of actual recoverable volumes based

Spatial electric load forecasting

CERN Document Server

Willis, H Lee

2002-01-01

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

Hydro-meteorological evaluation of downscaled global ensemble rainfall forecasts

Science.gov (United States)

Gaborit, Étienne; Anctil, François; Fortin, Vincent; Pelletier, Geneviève

2013-04-01

Ensemble rainfall forecasts are of high interest for decision making, as they provide an explicit and dynamic assessment of the uncertainty in the forecast (Ruiz et al. 2009). However, for hydrological forecasting, their low resolution currently limits their use to large watersheds (Maraun et al. 2010). In order to bridge this gap, various implementations of the statistic-stochastic multi-fractal downscaling technique presented by Perica and Foufoula-Georgiou (1996) were compared, bringing Environment Canada's global ensemble rainfall forecasts from a 100 by 70-km resolution down to 6 by 4-km, while increasing each pixel's rainfall variance and preserving its original mean. For comparison purposes, simpler methods were also implemented such as the bi-linear interpolation, which disaggregates global forecasts without modifying their variance. The downscaled meteorological products were evaluated using different scores and diagrams, from both a meteorological and a hydrological view points. The meteorological evaluation was conducted comparing the forecasted rainfall depths against nine days of observed values taken from Québec City rain gauge database. These 9 days present strong precipitation events occurring during the summer of 2009. For the hydrologic evaluation, the hydrological models SWMM5 and (a modified version of) GR4J were implemented on a small 6 km2 urban catchment located in the Québec City region. Ensemble hydrologic forecasts with a time step of 3 hours were then performed over a 3-months period of the summer of 2010 using the original and downscaled ensemble rainfall forecasts. The most important conclusions of this work are that the overall quality of the forecasts was preserved during the disaggregation procedure and that the disaggregated products using this variance-enhancing method were of similar quality than bi-linear interpolation products. However, variance and dispersion of the different members were, of course, much improved for the

Precipitable water vapour forecasting: a tool for optimizing IR observations at Roque de los Muchachos Observatory.

Science.gov (United States)

Pérez-Jordán, G.; Castro-Almazán, J. A.; Muñoz-Tuñón, C.

2018-04-01

We validate the Weather Research and Forecasting (WRF) model for precipitable water vapour (PWV) forecasting as a fully operational tool for optimizing astronomical infrared (IR) observations at Roque de los Muchachos Observatory (ORM). For the model validation we used GNSS-based (Global Navigation Satellite System) data from the PWV monitor located at the ORM. We have run WRF every 24 h for near two months, with a horizon of 48 hours (hourly forecasts), from 2016 January 11 to 2016 March 4. These runs represent 1296 hourly forecast points. The validation is carried out using different approaches: performance as a function of the forecast range, time horizon accuracy, performance as a function of the PWV value, and performance of the operational WRF time series with 24- and 48-hour horizons. Excellent agreement was found between the model forecasts and observations, with R =0.951 and R =0.904 for the 24- and 48-h forecast time series respectively. The 48-h forecast was further improved by correcting a time lag of 2 h found in the predictions. The final errors, taking into account all the uncertainties involved, are 1.75 mm for the 24-h forecasts and 1.99 mm for 48 h. We found linear trends in both the correlation and RMSE of the residuals (measurements - forecasts) as a function of the forecast range within the horizons analysed (up to 48 h). In summary, the WRF performance is excellent and accurate, thus allowing it to be implemented as an operational tool at the ORM.

Electricity demand forecasting techniques

International Nuclear Information System (INIS)

Gnanalingam, K.

1994-01-01

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

Air Pollution Forecasts: An Overview.

Science.gov (United States)

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

2018-04-17

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

Air Pollution Forecasts: An Overview

Directory of Open Access Journals (Sweden)

Lu Bai

2018-04-01

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

Air Pollution Forecasts: An Overview

Science.gov (United States)

Bai, Lu; Wang, Jianzhou; Lu, Haiyan

2018-01-01

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

Extending to seasonal scales the current usage of short range weather forecasts and climate projections for water management in Spain

Science.gov (United States)

Rodriguez-Camino, Ernesto; Voces, José; Sánchez, Eroteida; Navascues, Beatriz; Pouget, Laurent; Roldan, Tamara; Gómez, Manuel; Cabello, Angels; Comas, Pau; Pastor, Fernando; Concepción García-Gómez, M.°; José Gil, Juan; Gil, Delfina; Galván, Rogelio; Solera, Abel

2016-04-01

This presentation, first, briefly describes the current use of weather forecasts and climate projections delivered by AEMET for water management in Spain. The potential use of seasonal climate predictions for water -in particular dams- management is then discussed more in-depth, using a pilot experience carried out by a multidisciplinary group coordinated by AEMET and DG for Water of Spain. This initiative is being developed in the framework of the national implementation of the GFCS and the European project, EUPORIAS. Among the main components of this experience there are meteorological and hydrological observations, and an empirical seasonal forecasting technique that provides an ensemble of water reservoir inflows. These forecasted inflows feed a prediction model for the dam state that has been adapted for this purpose. The full system is being tested retrospectively, over several decades, for selected water reservoirs located in different Spanish river basins. The assessment includes an objective verification of the probabilistic seasonal forecasts using standard metrics, and the evaluation of the potential social and economic benefits, with special attention to drought and flooding conditions. The methodology of implementation of these seasonal predictions in the decision making process is being developed in close collaboration with final users participating in this pilot experience.

Forecast Accuracy Uncertainty and Momentum

OpenAIRE

Bing Han; Dong Hong; Mitch Warachka

2009-01-01

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

An experimental system for flood risk forecasting at global scale

Science.gov (United States)

Alfieri, L.; Dottori, F.; Kalas, M.; Lorini, V.; Bianchi, A.; Hirpa, F. A.; Feyen, L.; Salamon, P.

2016-12-01

Global flood forecasting and monitoring systems are nowadays a reality and are being applied by an increasing range of users and practitioners in disaster risk management. Furthermore, there is an increasing demand from users to integrate flood early warning systems with risk based forecasts, combining streamflow estimations with expected inundated areas and flood impacts. To this end, we have developed an experimental procedure for near-real time flood mapping and impact assessment based on the daily forecasts issued by the Global Flood Awareness System (GloFAS). The methodology translates GloFAS streamflow forecasts into event-based flood hazard maps based on the predicted flow magnitude and the forecast lead time and a database of flood hazard maps with global coverage. Flood hazard maps are then combined with exposure and vulnerability information to derive flood risk. Impacts of the forecasted flood events are evaluated in terms of flood prone areas, potential economic damage, and affected population, infrastructures and cities. To further increase the reliability of the proposed methodology we integrated model-based estimations with an innovative methodology for social media monitoring, which allows for real-time verification of impact forecasts. The preliminary tests provided good results and showed the potential of the developed real-time operational procedure in helping emergency response and management. In particular, the link with social media is crucial for improving the accuracy of impact predictions.

An intercomparison of approaches for improving operational seasonal streamflow forecasts

Science.gov (United States)

Mendoza, Pablo A.; Wood, Andrew W.; Clark, Elizabeth; Rothwell, Eric; Clark, Martyn P.; Nijssen, Bart; Brekke, Levi D.; Arnold, Jeffrey R.

2017-07-01

For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs) and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHCs and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the US Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches - statistical regression against IHCs and model-based ensemble streamflow prediction (ESP) - and then systematically intercompare WSFs across a range of lead times. Additional methods include (i) statistical techniques using climate information either from standard indices or from climate reanalysis variables and (ii) several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (hierarchical ensemble streamflow prediction - HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are (1) objective approaches supporting methodologically

Evolutionary Forecast Engines for Solar Meteorology

Science.gov (United States)

Coimbra, C. F.

2012-12-01

variability period from January 1, 2011 to April 30, 2011 identified by "P1"; - a medium variability period from May 1, 2011 to June 30, 2011 identified by "P2"; - a low variability period from July 1, 2011 to August 15, 2011 identified by "P3". Conclusions The application of evolutionary forecasting methods enhances the forecasting skill, with or without exogenous variables. Stochastic learning offers many advantages over purely deterministic models. Stochastic learning methods on their own are competitive in several time horizons of interest, and can be hybridized with exogenous data to provide continuous real-time improvement over physical models at all time scales. The GA/ANN method yields optimal forecasts for different quality levels of telemetry, and is robust over a wide range of time horizons.Forecast Horizon: 1 hour (all values in kW, except for R2) for Non-Exogenous InputsStatistical error metrics for the 1-hour ahead forecasts for several stochastic methodologies, and for different solar variability seasons (P1-P3).

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

Directory of Open Access Journals (Sweden)

S. W. D. Turner

2017-09-01

Full Text Available Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

Operational Hydrologic Forecasts in the Columbia River Basin

Science.gov (United States)

Shrestha, K. Y.; Curry, J. A.; Webster, P. J.; Toma, V. E.; Jelinek, M.

2013-12-01

The Columbia River Basin (CRB) covers an area of ~670,000 km2 and stretches across parts of seven U.S. states and one Canadian province. The basin is subject to a variable climate, and moisture stored in snowpack during the winter is typically released in spring and early summer. These releases contribute to rapid increases in flow. A number of impoundments have been constructed on the Columbia River main stem and its tributaries for the purposes of flood control, navigation, irrigation, recreation, and hydropower. Storage reservoirs allow water managers to adjust natural flow patterns to benefit water and energy demands. In the past decade, the complexity of water resource management issues in the basin has amplified the importance of streamflow forecasting. Medium-range (1-10 day) numerical weather forecasts of precipitation and temperature can be used to drive hydrological models. In this work, probabilistic meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF) are used to force the Variable Infiltration Capacity (VIC) model. Soil textures were obtained from FAO data; vegetation types / land cover information from UMD land cover data; stream networks from USGS HYDRO1k; and elevations from CGIAR version 4 SRTM data. The surface energy balance in 0.25° (~25 km) cells is closed through an iterative process operating at a 6 hour timestep. Output fluxes from a number of cells in the basin are combined through one-dimensional flow routing predicated on assumptions of linearity and time invariance. These combinations lead to daily mean streamflow estimates at key locations throughout the basin. This framework is suitable for ingesting daily numerical weather prediction data, and was calibrated using USGS mean daily streamflow data at the Dalles Dam (TDA). Operational streamflow forecasts in the CRB have been active since October 2012. These are 'naturalized' or unregulated forecasts. In 2013, increases of ~2600 m3/s (~48% of

Comparison of Statistical Post-Processing Methods for Probabilistic Wind Speed Forecasting

Science.gov (United States)

Han, Keunhee; Choi, JunTae; Kim, Chansoo

2018-02-01

In this study, the statistical post-processing methods that include bias-corrected and probabilistic forecasts of wind speed measured in PyeongChang, which is scheduled to host the 2018 Winter Olympics, are compared and analyzed to provide more accurate weather information. The six post-processing methods used in this study are as follows: mean bias-corrected forecast, mean and variance bias-corrected forecast, decaying averaging forecast, mean absolute bias-corrected forecast, and the alternative implementations of ensemble model output statistics (EMOS) and Bayesian model averaging (BMA) models, which are EMOS and BMA exchangeable models by assuming exchangeable ensemble members and simplified version of EMOS and BMA models. Observations for wind speed were obtained from the 26 stations in PyeongChang and 51 ensemble member forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF Directorate, 2012) that were obtained between 1 May 2013 and 18 March 2016. Prior to applying the post-processing methods, reliability analysis was conducted by using rank histograms to identify the statistical consistency of ensemble forecast and corresponding observations. Based on the results of our study, we found that the prediction skills of probabilistic forecasts of EMOS and BMA models were superior to the biascorrected forecasts in terms of deterministic prediction, whereas in probabilistic prediction, BMA models showed better prediction skill than EMOS. Even though the simplified version of BMA model exhibited best prediction skill among the mentioned six methods, the results showed that the differences of prediction skills between the versions of EMOS and BMA were negligible.

Forecasting short-term wind farm production in complex terrain. Volume 1

International Nuclear Information System (INIS)

LeBlanc, M.

2005-01-01

Wind energy forecasting adds financial value to wind farms and may soon become a regulatory requirement. A robust information technology system is essential for addressing industry demands. Various forecasting methodologies for short-term wind production in complex terrain were presented. Numerical weather predictions were discussed with reference to supervisory control and data acquisition (SCADA) system site measurements. Forecasting methods using wind speed, direction, temperature and pressure, as well as issues concerning statistical modelling were presented. Model output statistics and neural networks were reviewed, as well as significant components of error. Results from a Garrad Hassan forecaster with a European wind farm were presented, including wind speed evaluation, and forecast horizon for T + 1 hours, T + 12 hours, and T + 36 hours. It was suggested that buy prices often reflect the cost of under-prediction, and that forecasting has more potential where the spread is greatest. Accurate T + 19 hours to T + 31 hours could enable participation in the day-ahead market, which is less volatile and prices are usually better. Estimates of possible profits per annum through the use of GH forecaster power predictions were presented, calculated over and above spilling power to the grid. It was concluded that accurate forecasts combined with certainty evaluation enables the optimization of wind energy in the market, and is applicable to a wide range of weather regimes and terrain types. It was suggested that site feedback is essential for good forecasts at short horizons, and that the value of forecasting is dependent on the market. refs., tabs., figs

The european flood alert system EFAS – Part 2: Statistical skill assessment of probabilistic and deterministic operational forecasts

Directory of Open Access Journals (Sweden)

J. C. Bartholmes

2009-02-01

Full Text Available Since 2005 the European Flood Alert System (EFAS has been producing probabilistic hydrological forecasts in pre-operational mode at the Joint Research Centre (JRC of the European Commission. EFAS aims at increasing preparedness for floods in trans-national European river basins by providing medium-range deterministic and probabilistic flood forecasting information, from 3 to 10 days in advance, to national hydro-meteorological services.

This paper is Part 2 of a study presenting the development and skill assessment of EFAS. In Part 1, the scientific approach adopted in the development of the system has been presented, as well as its basic principles and forecast products. In the present article, two years of existing operational EFAS forecasts are statistically assessed and the skill of EFAS forecasts is analysed with several skill scores. The analysis is based on the comparison of threshold exceedances between proxy-observed and forecasted discharges. Skill is assessed both with and without taking into account the persistence of the forecasted signal during consecutive forecasts.

Skill assessment approaches are mostly adopted from meteorology and the analysis also compares probabilistic and deterministic aspects of EFAS. Furthermore, the utility of different skill scores is discussed and their strengths and shortcomings illustrated. The analysis shows the benefit of incorporating past forecasts in the probability analysis, for medium-range forecasts, which effectively increases the skill of the forecasts.

Improving GEFS Weather Forecasts for Indian Monsoon with Statistical Downscaling

Science.gov (United States)

Agrawal, Ankita; Salvi, Kaustubh; Ghosh, Subimal

2014-05-01

Weather forecast has always been a challenging research problem, yet of a paramount importance as it serves the role of 'key input' in formulating modus operandi for immediate future. Short range rainfall forecasts influence a wide range of entities, right from agricultural industry to a common man. Accurate forecasts actually help in minimizing the possible damage by implementing pre-decided plan of action and hence it is necessary to gauge the quality of forecasts which might vary with the complexity of weather state and regional parameters. Indian Summer Monsoon Rainfall (ISMR) is one such perfect arena to check the quality of weather forecast not only because of the level of intricacy in spatial and temporal patterns associated with it, but also the amount of damage it can cause (because of poor forecasts) to the Indian economy by affecting agriculture Industry. The present study is undertaken with the rationales of assessing, the ability of Global Ensemble Forecast System (GEFS) in predicting ISMR over central India and the skill of statistical downscaling technique in adding value to the predictions by taking them closer to evidentiary target dataset. GEFS is a global numerical weather prediction system providing the forecast results of different climate variables at a fine resolution (0.5 degree and 1 degree). GEFS shows good skills in predicting different climatic variables but fails miserably over rainfall predictions for Indian summer monsoon rainfall, which is evident from a very low to negative correlation values between predicted and observed rainfall. Towards the fulfilment of second rationale, the statistical relationship is established between the reasonably well predicted climate variables (GEFS) and observed rainfall. The GEFS predictors are treated with multicollinearity and dimensionality reduction techniques, such as principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO). Statistical relationship is

Training the next generation of scientists in Weather Forecasting: new approaches with real models

Science.gov (United States)

Carver, Glenn; Váňa, Filip; Siemen, Stephan; Kertesz, Sandor; Keeley, Sarah

2014-05-01

The European Centre for Medium Range Weather Forecasts operationally produce medium range forecasts using what is internationally acknowledged as the world leading global weather forecast model. Future development of this scientifically advanced model relies on a continued availability of experts in the field of meteorological science and with high-level software skills. ECMWF therefore has a vested interest in young scientists and University graduates developing the necessary skills in numerical weather prediction including both scientific and technical aspects. The OpenIFS project at ECMWF maintains a portable version of the ECMWF forecast model (known as IFS) for use in education and research at Universities, National Meteorological Services and other research and education organisations. OpenIFS models can be run on desktop or high performance computers to produce weather forecasts in a similar way to the operational forecasts at ECMWF. ECMWF also provide the Metview desktop application, a modern, graphical, and easy to use tool for analysing and visualising forecasts that is routinely used by scientists and forecasters at ECMWF and other institutions. The combination of Metview with the OpenIFS models has the potential to deliver classroom-friendly tools allowing students to apply their theoretical knowledge to real-world examples using a world-leading weather forecasting model. In this paper we will describe how the OpenIFS model has been used for teaching. We describe the use of Linux based 'virtual machines' pre-packaged on USB sticks that support a technically easy and safe way of providing 'classroom-on-a-stick' learning environments for advanced training in numerical weather prediction. We welcome discussions with interested parties.

klax 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...

kprc 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...

katl 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...

kmcn 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...

kogb 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...

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...

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...

kiwa 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...

kavp 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...

kdca 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...

kbwg 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...

kdfw 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...

kssi 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...

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...

ksrq 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...

kpvd 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...

kisp 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...

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...

pmdy 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...

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...

khib 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...

pavd 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...

kfar 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...

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...

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...

klse 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...

ksts 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...

koth 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...

kbfl 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...

ksgf 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...

klch 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...

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...

krog 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...

kbjc 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...

ksea 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...

kbwi 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...

kftw 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...

kpuw 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...

kabq 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...

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...

khio 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...

klaf 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...

kfoe 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...

ksmx 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...

kipt 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...

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...

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...

katy 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...

tjmz 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...

kdet 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...

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...

kbur 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...

krkd 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...

pawg 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...

kloz 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...

kcec 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...

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...

paor 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...

kavl 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...

kdrt 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...

kstl 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...

kbfi 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...

khsv 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...

pafa 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...

kekn 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...

tncm 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...

kith 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...

kgnv 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...

ktoi 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...

kgso 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...

nstu 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...

kpbi Terminal Aerodrome Forecast

Data.gov (United States)

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

kgdv Terminal Aerodrome Forecast

Data.gov (United States)

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

kcmx Terminal Aerodrome Forecast

Data.gov (United States)

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

kdls Terminal Aerodrome Forecast

Data.gov (United States)

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

koaj Terminal Aerodrome Forecast

Data.gov (United States)

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

krhi Terminal Aerodrome Forecast

Data.gov (United States)

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

kbpk Terminal Aerodrome Forecast

Data.gov (United States)

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

khuf Terminal Aerodrome Forecast

Data.gov (United States)

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

kont Terminal Aerodrome Forecast

Data.gov (United States)

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

kyng 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...

kcwa 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...

kflg 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...

ktup 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...

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...

kink 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...

krut 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...

kbli 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...

kaoo 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...

klit 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...

panc Terminal Aerodrome Forecast

Data.gov (United States)

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

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...

pgwt Terminal Aerodrome Forecast

Data.gov (United States)

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

kpsp Terminal Aerodrome Forecast

Data.gov (United States)

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

kbih Terminal Aerodrome Forecast

Data.gov (United States)

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

kdnl Terminal Aerodrome Forecast

Data.gov (United States)

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

kart Terminal Aerodrome Forecast

Data.gov (United States)

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

kilm Terminal Aerodrome Forecast

Data.gov (United States)

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

kpne Terminal Aerodrome Forecast