A forecast of bridge engineering, 1980-2000.

DOT National Transportation Integrated Search

1979-01-01

A three-pronged study was undertaken to forecast the nature of bridge engineering and construction for the years 1980 to 2000. First, the history of bridge engineering was explored to extrapolate likely future developments. Second, a detailed questio...

A simulation model for forecasting downhill ski participation

Treesearch

Daniel J. Stynes; Daniel M. Spotts

1980-01-01

The purpose of this paper is to describe progress in the development of a general computer simulation model to forecast future levels of outdoor recreation participation. The model is applied and tested for downhill skiing in Michigan.

Evolving forecasting classifications and applications in health forecasting

PubMed Central

Soyiri, Ireneous N; Reidpath, Daniel D

2012-01-01

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

Improving the effectiveness of real-time flood forecasting through Predictive Uncertainty estimation: the multi-temporal approach

NASA Astrophysics Data System (ADS)

Barbetta, Silvia; Coccia, Gabriele; Moramarco, Tommaso; Todini, Ezio

2015-04-01

The negative effects of severe flood events are usually contrasted through structural measures that, however, do not fully eliminate flood risk. Non-structural measures, such as real-time flood forecasting and warning, are also required. Accurate stage/discharge future predictions with appropriate forecast lead-time are sought by decision-makers for implementing strategies to mitigate the adverse effects of floods. Traditionally, flood forecasting has been approached by using rainfall-runoff and/or flood routing modelling. Indeed, both types of forecasts, cannot be considered perfectly representing future outcomes because of lacking of a complete knowledge of involved processes (Todini, 2004). Nonetheless, although aware that model forecasts are not perfectly representing future outcomes, decision makers are de facto implicitly assuming the forecast of water level/discharge/volume, etc. as "deterministic" and coinciding with what is going to occur. Recently the concept of Predictive Uncertainty (PU) was introduced in hydrology (Krzysztofowicz, 1999), and several uncertainty processors were developed (Todini, 2008). PU is defined as the probability of occurrence of the future realization of a predictand (water level/discharge/volume) conditional on: i) prior observations and knowledge, ii) the available information obtained on the future value, typically provided by one or more forecast models. Unfortunately, PU has been frequently interpreted as a measure of lack of accuracy rather than the appropriate tool allowing to take the most appropriate decisions, given a model or several models' forecasts. With the aim to shed light on the benefits for appropriately using PU, a multi-temporal approach based on the MCP approach (Todini, 2008; Coccia and Todini, 2011) is here applied to stage forecasts at sites along the Upper Tiber River. Specifically, the STAge Forecasting-Rating Curve Model Muskingum-based (STAFOM-RCM) (Barbetta et al., 2014) along with the Rating-Curve Model in Real Time (RCM-RT) (Barbetta and Moramarco, 2014) are used to this end. Both models without considering rainfall information explicitly considers, at each time of forecast, the estimate of lateral contribution along the river reach for which the stage forecast is performed at downstream end. The analysis is performed for several reaches using different lead times according to the channel length. Barbetta, S., Moramarco, T., Brocca, L., Franchini, M. and Melone, F. 2014. Confidence interval of real-time forecast stages provided by the STAFOM-RCM model: the case study of the Tiber River (Italy). Hydrological Processes, 28(3),729-743. Barbetta, S. and Moramarco, T. 2014. Real-time flood forecasting by relating local stage and remote discharge. Hydrological Sciences Journal, 59(9 ), 1656-1674. Coccia, G. and Todini, E. 2011. Recent developments in predictive uncertainty assessment based on the Model Conditional Processor approach. Hydrology and Earth System Sciences, 15, 3253-3274. doi:10.5194/hess-15-3253-2011. Krzysztofowicz, R. 1999. Bayesian theory of probabilistic forecasting via deterministic hydrologic model, Water Resour. Res., 35, 2739-2750. Todini, E. 2004. Role and treatment of uncertainty in real-time flood forecasting. Hydrological Processes 18(14), 2743_2746. Todini, E. 2008. A model conditional processor to assess predictive uncertainty in flood forecasting. Intl. J. River Basin Management, 6(2): 123-137.

A simple approach to measure transmissibility and forecast incidence.

PubMed

Nouvellet, Pierre; Cori, Anne; Garske, Tini; Blake, Isobel M; Dorigatti, Ilaria; Hinsley, Wes; Jombart, Thibaut; Mills, Harriet L; Nedjati-Gilani, Gemma; Van Kerkhove, Maria D; Fraser, Christophe; Donnelly, Christl A; Ferguson, Neil M; Riley, Steven

2018-03-01

Outbreaks of novel pathogens such as SARS, pandemic influenza and Ebola require substantial investments in reactive interventions, with consequent implementation plans sometimes revised on a weekly basis. Therefore, short-term forecasts of incidence are often of high priority. In light of the recent Ebola epidemic in West Africa, a forecasting exercise was convened by a network of infectious disease modellers. The challenge was to forecast unseen "future" simulated data for four different scenarios at five different time points. In a similar method to that used during the recent Ebola epidemic, we estimated current levels of transmissibility, over variable time-windows chosen in an ad hoc way. Current estimated transmissibility was then used to forecast near-future incidence. We performed well within the challenge and often produced accurate forecasts. A retrospective analysis showed that our subjective method for deciding on the window of time with which to estimate transmissibility often resulted in the optimal choice. However, when near-future trends deviated substantially from exponential patterns, the accuracy of our forecasts was reduced. This exercise highlights the urgent need for infectious disease modellers to develop more robust descriptions of processes - other than the widespread depletion of susceptible individuals - that produce non-exponential patterns of incidence. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Assessing development pressure in the Chesapeake Bay watershed: An evaluation of two land-use change models

USGS Publications Warehouse

Claggett, Peter; Jantz, Claire A.; Goetz, S.J.; Bisland, C.

2004-01-01

Natural resource lands in the Chesapeake Bay watershed are increasingly susceptible to conversion into developed land uses, particularly as the demand for residential development grows. We assessed development pressure in the Baltimore-Washington, DC region, one of the major urban and suburban centers in the watershed. We explored the utility of two modeling approaches for forecasting future development trends and patterns by comparing results from a cellular automata model, SLEUTH (slope, land use, excluded land, urban extent, transportation), and a supply/demand/allocation model, the Western Futures Model. SLEUTH can be classified as a land-cover change model and produces projections on the basis of historic trends of changes in the extent and patterns of developed land and future land protection scenarios. The Western Futures Model derives forecasts from historic trends in housing units, a U.S. Census variable, and exogenously supplied future population projections. Each approach has strengths and weaknesses, and combining the two has advantages and limitations. ?? 2004 Kluwer Academic Publishers.

Assessing development pressure in the Chesapeake Bay watershed: an evaluation of two land-use change models.

PubMed

Claggett, Peter R; Jantz, Claire A; Goetz, Scott J; Bisland, Carin

2004-06-01

Natural resource lands in the Chesapeake Bay watershed are increasingly susceptible to conversion into developed land uses, particularly as the demand for residential development grows. We assessed development pressure in the Baltimore-Washington, DC region, one of the major urban and suburban centers in the watershed. We explored the utility of two modeling approaches for forecasting future development trends and patterns by comparing results from a cellular automata model, SLEUTH (slope, land use, excluded land, urban extent, transportation), and a supply/demand/allocation model, the Western Futures Model. SLEUTH can be classified as a land-cover change model and produces projections on the basis of historic trends of changes in the extent and patterns of developed land and future land protection scenarios. The Western Futures Model derives forecasts from historic trends in housing units, a U.S. Census variable, and exogenously supplied future population projections. Each approach has strengths and weaknesses, and combining the two has advantages and limitations.

Forecasting Responses of a Northern Peatland Carbon Cycle to Elevated CO2 and a Gradient of Experimental Warming

NASA Astrophysics Data System (ADS)

Jiang, Jiang; Huang, Yuanyuan; Ma, Shuang; Stacy, Mark; Shi, Zheng; Ricciuto, Daniel M.; Hanson, Paul J.; Luo, Yiqi

2018-03-01

The ability to forecast ecological carbon cycling is imperative to land management in a world where past carbon fluxes are no longer a clear guide in the Anthropocene. However, carbon-flux forecasting has not been practiced routinely like numerical weather prediction. This study explored (1) the relative contributions of model forcing data and parameters to uncertainty in forecasting flux- versus pool-based carbon cycle variables and (2) the time points when temperature and CO2 treatments may cause statistically detectable differences in those variables. We developed an online forecasting workflow (Ecological Platform for Assimilation of Data (EcoPAD)), which facilitates iterative data-model integration. EcoPAD automates data transfer from sensor networks, data assimilation, and ecological forecasting. We used the Spruce and Peatland Responses Under Changing Experiments data collected from 2011 to 2014 to constrain the parameters in the Terrestrial Ecosystem Model, forecast carbon cycle responses to elevated CO2 and a gradient of warming from 2015 to 2024, and specify uncertainties in the model output. Our results showed that data assimilation substantially reduces forecasting uncertainties. Interestingly, we found that the stochasticity of future external forcing contributed more to the uncertainty of forecasting future dynamics of C flux-related variables than model parameters. However, the parameter uncertainty primarily contributes to the uncertainty in forecasting C pool-related response variables. Given the uncertainties in forecasting carbon fluxes and pools, our analysis showed that statistically different responses of fast-turnover pools to various CO2 and warming treatments were observed sooner than slow-turnover pools. Our study has identified the sources of uncertainties in model prediction and thus leads to improve ecological carbon cycling forecasts in the future.

Forecasting Individual Headache Attacks Using Perceived Stress: Development of a Multivariable Prediction Model for Persons With Episodic Migraine.

PubMed

Houle, Timothy T; Turner, Dana P; Golding, Adrienne N; Porter, John A H; Martin, Vincent T; Penzien, Donald B; Tegeler, Charles H

2017-07-01

To develop and validate a prediction model that forecasts future migraine attacks for an individual headache sufferer. Many headache patients and physicians believe that precipitants of headache can be identified and avoided or managed to reduce the frequency of headache attacks. Of the numerous candidate triggers, perceived stress has received considerable attention for its association with the onset of headache in episodic and chronic headache sufferers. However, no evidence is available to support forecasting headache attacks within individuals using any of the candidate headache triggers. This longitudinal cohort with forecasting model development study enrolled 100 participants with episodic migraine with or without aura, and N = 95 contributed 4626 days of electronic diary data and were included in the analysis. Individual headache forecasts were derived from current headache state and current levels of stress using several aspects of the Daily Stress Inventory, a measure of daily hassles that is completed at the end of each day. The primary outcome measure was the presence/absence of any headache attack (head pain > 0 on a numerical rating scale of 0-10) over the next 24 h period. After removing missing data (n = 431 days), participants in the study experienced a headache attack on 1613/4195 (38.5%) days. A generalized linear mixed-effects forecast model using either the frequency of stressful events or the perceived intensity of these events fit the data well. This simple forecasting model possessed promising predictive utility with an AUC of 0.73 (95% CI 0.71-0.75) in the training sample and an AUC of 0.65 (95% CI 0.6-0.67) in a leave-one-out validation sample. This forecasting model had a Brier score of 0.202 and possessed good calibration between forecasted probabilities and observed frequencies but had only low levels of resolution (ie, sharpness). This study demonstrates that future headache attacks can be forecasted for a diverse group of individuals over time. Future work will enhance prediction through improvements in the assessment of stress as well as the development of other candidate domains to use in the models. © 2017 American Headache Society.

Short-term load forecasting using neural network for future smart grid application

NASA Astrophysics Data System (ADS)

Zennamo, Joseph Anthony, III

Short-term load forecasting of power system has been a classic problem for a long time. Not merely it has been researched extensively and intensively, but also a variety of forecasting methods has been raised. This thesis outlines some aspects and functions of smart meter. It also presents different policies and current statuses as well as future projects and objectives of SG development in several countries. Then the thesis compares main aspects about latest products of smart meter from different companies. Lastly, three types of prediction models are established in MATLAB to emulate the functions of smart grid in the short-term load forecasting, and then their results are compared and analyzed in terms of accuracy. For this thesis, more variables such as dew point temperature are used in the Neural Network model to achieve more accuracy for better short-term load forecasting results.

Short-Term State Forecasting-Based Optimal Voltage Regulation in Distribution Systems: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Rui; Jiang, Huaiguang; Zhang, Yingchen

2017-05-17

A novel short-term state forecasting-based optimal power flow (OPF) approach for distribution system voltage regulation is proposed in this paper. An extreme learning machine (ELM) based state forecaster is developed to accurately predict system states (voltage magnitudes and angles) in the near future. Based on the forecast system states, a dynamically weighted three-phase AC OPF problem is formulated to minimize the voltage violations with higher penalization on buses which are forecast to have higher voltage violations in the near future. By solving the proposed OPF problem, the controllable resources in the system are optimally coordinated to alleviate the potential severemore » voltage violations and improve the overall voltage profile. The proposed approach has been tested in a 12-bus distribution system and simulation results are presented to demonstrate the performance of the proposed approach.« less

Application of global weather and climate model output to the design and operation of wind-energy systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Curry, Judith

This project addressed the challenge of providing weather and climate information to support the operation, management and planning for wind-energy systems. The need for forecast information is extending to longer projection windows with increasing penetration of wind power into the grid and also with diminishing reserve margins to meet peak loads during significant weather events. Maintenance planning and natural gas trading is being influenced increasingly by anticipation of wind generation on timescales of weeks to months. Future scenarios on decadal time scales are needed to support assessment of wind farm siting, government planning, long-term wind purchase agreements and the regulatorymore » environment. The challenge of making wind forecasts on these longer time scales is associated with a wide range of uncertainties in general circulation and regional climate models that make them unsuitable for direct use in the design and planning of wind-energy systems. To address this challenge, CFAN has developed a hybrid statistical/dynamical forecasting scheme for delivering probabilistic forecasts on time scales from one day to seven months using what is arguably the best forecasting system in the world (European Centre for Medium Range Weather Forecasting, ECMWF). The project also provided a framework to assess future wind power through developing scenarios of interannual to decadal climate variability and change. The Phase II research has successfully developed an operational wind power forecasting system for the U.S., which is being extended to Europe and possibly Asia.« less

Albuquerque Public Schools Student Enrollment: History & Forecast 1954-1994. 1975 APS Planning Document 1.

ERIC Educational Resources Information Center

Albuquerque Public Schools, NM.

The material in this report was developed primarily as a basis for better planning and allocation of limited resources in a district experiencing a declining enrollment for the foreseeable future. The charts were developed on the basis of historical enrollment information by grade level, and forecasts were based on a standard regression formula…

An Improved Approach for Forecasting Ecological Impacts from Future Drilling in Unconventional Shale Oil and Gas Plays.

PubMed

Wolaver, Brad D; Pierre, Jon Paul; Ikonnikova, Svetlana A; Andrews, John R; McDaid, Guinevere; Ryberg, Wade A; Hibbitts, Toby J; Duran, Charles M; Labay, Benjamin J; LaDuc, Travis J

2018-04-13

Directional well drilling and hydraulic fracturing has enabled energy production from previously inaccessible resources, but caused vegetation conversion and landscape fragmentation, often in relatively undisturbed habitats. We improve forecasts of future ecological impacts from unconventional oil and gas play developments using a new, more spatially-explicit approach. We applied an energy production outlook model, which used geologic and economic data from thousands of wells and three oil price scenarios, to map future drilling patterns and evaluate the spatial distribution of vegetation conversion and habitat impacts. We forecast where future well pad construction may be most intense, illustrating with an example from the Eagle Ford Shale Play of Texas. We also illustrate the ecological utility of this approach using the Spot-tailed Earless Lizard (Holbrookia lacerata) as the focal species, which historically occupied much of the Eagle Ford and awaits a federal decision for possible Endangered Species Act protection. We found that ~17,000-45,500 wells would be drilled 2017‒2045 resulting in vegetation conversion of ~26,485-70,623 ha (0.73-1.96% of pre-development vegetation), depending on price scenario ($40-$80/barrel). Grasslands and row crop habitats were most affected (2.30 and 2.82% areal vegetation reduction). Our approach improves forecasts of where and to what extent future energy development in unconventional plays may change land-use and ecosystem services, enabling natural resource managers to anticipate and direct on-the-ground conservation actions to places where they will most effectively mitigate ecological impacts of well pads and associated infrastructure.

A Method for Forecasting the Commercial Air Traffic Schedule in the Future

NASA Technical Reports Server (NTRS)

Long, Dou; Lee, David; Gaier, Eric; Johnson, Jesse; Kostiuk, Peter

1999-01-01

This report presents an integrated set of models that forecasts air carriers' future operations when delays due to limited terminal-area capacity are considered. This report models the industry as a whole, avoiding unnecessary details of competition among the carriers. To develop the schedule outputs, we first present a model to forecast the unconstrained flight schedules in the future, based on the assumption of rational behavior of the carriers. Then we develop a method to modify the unconstrained schedules, accounting for effects of congestion due to limited NAS capacities. Our underlying assumption is that carriers will modify their operations to keep mean delays within certain limits. We estimate values for those limits from changes in planned block times reflected in the OAG. Our method for modifying schedules takes many means of reducing the delays into considerations, albeit some of them indirectly. The direct actions include depeaking, operating in off-hours, and reducing hub airports'operations. Indirect actions include using secondary airports, using larger aircraft, and selecting new hub airports, which, we assume, have already been modeled in the FAA's TAF. Users of our suite of models can substitute an alternative forecast for the TAF.

Forecasting and Maximizing Post-Secondary Futures: Dilemmas Over Negative Futures and Their Hidden Costs.

ERIC Educational Resources Information Center

Hoffman, Benjamin B.

Forecasting models for maximizing postsecondary futures and applications of the model are considered. The forecasting of broad human futures has many parallels to human futures in the field of medical prognosis. The concept of "exasperated negative" is used to refer to the suppression of critical information about a negative future with…

Potential barge transportation for inbound corn and grain

DOT National Transportation Integrated Search

1997-12-31

This research develops a model for estimating future barge and rail rates for decision making. The Box-Jenkins and the Regression Analysis with ARIMA errors forecasting methods were used to develop appropriate models for determining future rates. A s...

Gloom and doom? The future of marine capture fisheries

PubMed Central

Garcia, Serge M.; Grainger, Richard J. R.

2005-01-01

Predicting global fisheries is a high-order challenge but predictions have been made and updates are needed. Past forecasts, present trends and perspectives of key parameters of the fisheries—including potential harvest, state of stocks, supply and demand, trade, fishing technology and governance—are reviewed in detail, as the basis for new forecasts and forecasting performance assessment. The future of marine capture fisheries will be conditioned by the political, social and economic evolution of the world within which they operate. Consequently, recent global scenarios for the future world are reviewed, with the emphasis on fisheries. The main driving forces (e.g. global economic development, demography, environment, public awareness, information technology, energy, ethics) including aquaculture are described. Outlooks are provided for each aspect of the fishery sector. The conclusion puts these elements in perspective and offers the authors’ personal interpretation of the possible future pathway of fisheries, the uncertainty about it and the still unanswered questions of direct relevance in shaping that future. PMID:15713587

Forecasting Emergency Department Crowding: An External, Multi-Center Evaluation

PubMed Central

Hoot, Nathan R.; Epstein, Stephen K.; Allen, Todd L.; Jones, Spencer S.; Baumlin, Kevin M.; Chawla, Neal; Lee, Anna T.; Pines, Jesse M.; Klair, Amandeep K.; Gordon, Bradley D.; Flottemesch, Thomas J.; LeBlanc, Larry J.; Jones, Ian; Levin, Scott R.; Zhou, Chuan; Gadd, Cynthia S.; Aronsky, Dominik

2009-01-01

Objective To apply a previously described tool to forecast ED crowding at multiple institutions, and to assess its generalizability for predicting the near-future waiting count, occupancy level, and boarding count. Methods The ForecastED tool was validated using historical data from five institutions external to the development site. A sliding-window design separated the data for parameter estimation and forecast validation. Observations were sampled at consecutive 10-minute intervals during 12 months (n = 52,560) at four sites and 10 months (n = 44,064) at the fifth. Three outcome measures – the waiting count, occupancy level, and boarding count – were forecast 2, 4, 6, and 8 hours beyond each observation, and forecasts were compared to observed data at corresponding times. The reliability and calibration were measured following previously described methods. After linear calibration, the forecasting accuracy was measured using the median absolute error (MAE). Results The tool was successfully used for five different sites. Its forecasts were more reliable, better calibrated, and more accurate at 2 hours than at 8 hours. The reliability and calibration of the tool were similar between the original development site and external sites; the boarding count was an exception, which was less reliable at four out of five sites. Some variability in accuracy existed among institutions; when forecasting 4 hours into the future, the MAE of the waiting count ranged between 0.6 and 3.1 patients, the MAE of the occupancy level ranged between 9.0 and 14.5% of beds, and the MAE of the boarding count ranged between 0.9 and 2.7 patients. Conclusion The ForecastED tool generated potentially useful forecasts of input and throughput measures of ED crowding at five external sites, without modifying the underlying assumptions. Noting the limitation that this was not a real-time validation, ongoing research will focus on integrating the tool with ED information systems. PMID:19716629

A Macroecological Approach to Forecasting Fisheries Services: How much Energy Does it Take to Catch a Fish?

EPA Science Inventory

The U.S. Environmental Protection Agency is currently developing methods to quantify freshwater fisheries services (e.g., standing-stock abundance and/or biomass) at multiple spatial scales, and to forecast their future distributions. One approach uses linked, ecosystem process ...

Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operatormore » can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.« less

Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution systemmore » operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.« less

Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operatormore » can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.« less

Merging Two Futures Concepts: Issues Management and Policy Impact Analysis.

ERIC Educational Resources Information Center

Renfro, William L.; Morrison, James L.

1982-01-01

Describes a workshop held during the 1982 World Future Society's Fourth General Assembly on the combined application of issues management and policy impact analysis. The workshop participants applied futures research, forecasting, goal-setting, and policy development techniques to future problems in educational policy. (AM)

Evaluation and economic value of winter weather forecasts

NASA Astrophysics Data System (ADS)

Snyder, Derrick W.

State and local highway agencies spend millions of dollars each year to deploy winter operation teams to plow snow and de-ice roadways. Accurate and timely weather forecast information is critical for effective decision making. Students from Purdue University partnered with the Indiana Department of Transportation to create an experimental winter weather forecast service for the 2012-2013 winter season in Indiana to assist in achieving these goals. One forecast product, an hourly timeline of winter weather hazards produced daily, was evaluated for quality and economic value. Verification of the forecasts was performed with data from the Rapid Refresh numerical weather model. Two objective verification criteria were developed to evaluate the performance of the timeline forecasts. Using both criteria, the timeline forecasts had issues with reliability and discrimination, systematically over-forecasting the amount of winter weather that was observed while also missing significant winter weather events. Despite these quality issues, the forecasts still showed significant, but varied, economic value compared to climatology. Economic value of the forecasts was estimated to be 29.5 million or 4.1 million, depending on the verification criteria used. Limitations of this valuation system are discussed and a framework is developed for more thorough studies in the future.

Future-Orientated Approaches to Curriculum Development: Fictive Scripting

ERIC Educational Resources Information Center

Garraway, James

2017-01-01

Though the future cannot be accurately predicted, it is possible to envisage a number of probable developments which can promote thinking about the future and so promote a more informed stance about what should or should not be done. Studies in technology and society have claimed that the use of a type of forecasting using plausible but imaginary…

Aviation Forecasting in ICAO

NASA Technical Reports Server (NTRS)

Mcmahon, J.

1972-01-01

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

Probabilistic accounting of uncertainty in forecasts of species distributions under climate change

Treesearch

Seth J. Wenger; Nicholas A. Som; Daniel C. Dauwalter; Daniel J. Isaak; Helen M. Neville; Charles H. Luce; Jason B. Dunham; Michael K. Young; Kurt D. Fausch; Bruce E. Rieman

2013-01-01

Forecasts of species distributions under future climates are inherently uncertain, but there have been few attempts to describe this uncertainty comprehensively in a probabilistic manner. We developed a Monte Carlo approach that accounts for uncertainty within generalized linear regression models (parameter uncertainty and residual error), uncertainty among competing...

From Tornadoes to Earthquakes: Forecast Verification for Binary Events Applied to the 1999 Chi-Chi, Taiwan, Earthquake

NASA Astrophysics Data System (ADS)

Chen, C.; Rundle, J. B.; Holliday, J. R.; Nanjo, K.; Turcotte, D. L.; Li, S.; Tiampo, K. F.

2005-12-01

Forecast verification procedures for statistical events with binary outcomes typically rely on the use of contingency tables and Relative Operating Characteristic (ROC) diagrams. Originally developed for the statistical evaluation of tornado forecasts on a county-by-county basis, these methods can be adapted to the evaluation of competing earthquake forecasts. Here we apply these methods retrospectively to two forecasts for the m = 7.3 1999 Chi-Chi, Taiwan, earthquake. These forecasts are based on a method, Pattern Informatics (PI), that locates likely sites for future large earthquakes based on large change in activity of the smallest earthquakes. A competing null hypothesis, Relative Intensity (RI), is based on the idea that future large earthquake locations are correlated with sites having the greatest frequency of small earthquakes. We show that for Taiwan, the PI forecast method is superior to the RI forecast null hypothesis. Inspection of the two maps indicates that their forecast locations are indeed quite different. Our results confirm an earlier result suggesting that the earthquake preparation process for events such as the Chi-Chi earthquake involves anomalous changes in activation or quiescence, and that signatures of these processes can be detected in precursory seismicity data. Furthermore, we find that our methods can accurately forecast the locations of aftershocks from precursory seismicity changes alone, implying that the main shock together with its aftershocks represent a single manifestation of the formation of a high-stress region nucleating prior to the main shock.

Long range forecasts of the Northern Hemisphere anomalies with antecedent sea surface temperature patterns

NASA Technical Reports Server (NTRS)

Kung, Ernest C.

1994-01-01

The contract research has been conducted in the following three major areas: analysis of numerical simulations and parallel observations of atmospheric blocking, diagnosis of the lower boundary heating and the response of the atmospheric circulation, and comprehensive assessment of long-range forecasting with numerical and regression methods. The essential scientific and developmental purpose of this contract research is to extend our capability of numerical weather forecasting by the comprehensive general circulation model. The systematic work as listed above is thus geared to developing a technological basis for future NASA long-range forecasting.

Forecasting relative impacts of land use on anadromous fish habitat to guide conservation planning.

PubMed

Lohse, Kathleen A; Newburn, David A; Opperman, Jeff J; Merenlender, Adina M

2008-03-01

Land use change can adversely affect water quality and freshwater ecosystems, yet our ability to predict how systems will respond to different land uses, particularly rural-residential development, is limited by data availability and our understanding of biophysical thresholds. In this study, we use spatially explicit parcel-level data to examine the influence of land use (including urban, rural-residential, and vineyard) on salmon spawning substrate quality in tributaries of the Russian River in California. We develop a land use change model to forecast the probability of losses in high-quality spawning habitat and recommend priority areas for incentive-based land conservation efforts. Ordinal logistic regression results indicate that all three land use types were negatively associated with spawning substrate quality, with urban development having the largest marginal impact. For two reasons, however, forecasted rural-residential and vineyard development have much larger influences on decreasing spawning substrate quality relative to urban development. First, the land use change model estimates 10 times greater land use conversion to both rural-residential and vineyard compared to urban. Second, forecasted urban development is concentrated in the most developed watersheds, which already have poor spawning substrate quality, such that the marginal response to future urban development is less significant. To meet the goals of protecting salmonid spawning habitat and optimizing investments in salmon recovery, we suggest investing in watersheds where future rural-residential development and vineyards threaten high-quality fish habitat, rather than the most developed watersheds, where land values are higher.

Market-based demand forecasting promotes informed strategic financial planning.

PubMed

Beech, A J

2001-11-01

Market-based demand forecasting is a method of estimating future demand for a healthcare organization's services by using a broad range of data that describe the nature of demand within the organization's service area. Such data include the primary and secondary service areas, the service-area populations by various demographic groupings, discharge utilization rates, market size, and market share by service line and organizationwide. Based on observable market dynamics, strategic planners can make a variety of explicit assumptions about future trends regarding these data to develop scenarios describing potential future demand. Financial planners then can evaluate each scenario to determine its potential effect on selected financial and operational measures, such as operating margin, days cash on hand, and debt-service coverage, and develop a strategic financial plan that covers a range of contingencies.

Future Studies in the K-12 Curriculum.

ERIC Educational Resources Information Center

Haas, John D.

This guide is designed to help elementary and secondary school teachers and curriculum developers plan units on the future. It is presented in five sections. Section I discusses the origins of the modern futures movement and the concepts of future studies, time dimensions, global approach, self-fulfilling and self-defeating forecasts, and types of…

Probabilistic Solar Wind and Geomagnetic Forecasting Using an Analogue Ensemble or "Similar Day" Approach

NASA Astrophysics Data System (ADS)

Owens, M. J.; Riley, P.; Horbury, T. S.

2017-05-01

Effective space-weather prediction and mitigation requires accurate forecasting of near-Earth solar-wind conditions. Numerical magnetohydrodynamic models of the solar wind, driven by remote solar observations, are gaining skill at forecasting the large-scale solar-wind features that give rise to near-Earth variations over days and weeks. There remains a need for accurate short-term (hours to days) solar-wind forecasts, however. In this study we investigate the analogue ensemble (AnEn), or "similar day", approach that was developed for atmospheric weather forecasting. The central premise of the AnEn is that past variations that are analogous or similar to current conditions can be used to provide a good estimate of future variations. By considering an ensemble of past analogues, the AnEn forecast is inherently probabilistic and provides a measure of the forecast uncertainty. We show that forecasts of solar-wind speed can be improved by considering both speed and density when determining past analogues, whereas forecasts of the out-of-ecliptic magnetic field [BN] are improved by also considering the in-ecliptic magnetic-field components. In general, the best forecasts are found by considering only the previous 6 - 12 hours of observations. Using these parameters, the AnEn provides a valuable probabilistic forecast for solar-wind speed, density, and in-ecliptic magnetic field over lead times from a few hours to around four days. For BN, which is central to space-weather disturbance, the AnEn only provides a valuable forecast out to around six to seven hours. As the inherent predictability of this parameter is low, this is still likely a marked improvement over other forecast methods. We also investigate the use of the AnEn in forecasting geomagnetic indices Dst and Kp. The AnEn provides a valuable probabilistic forecast of both indices out to around four days. We outline a number of future improvements to AnEn forecasts of near-Earth solar-wind and geomagnetic conditions.

Assimilating the Future for Better Forecasts and Earlier Warnings

NASA Astrophysics Data System (ADS)

Du, H.; Wheatcroft, E.; Smith, L. A.

2016-12-01

Multi-model ensembles have become popular tools to account for some of the uncertainty due to model inadequacy in weather and climate simulation-based predictions. The current multi-model forecasts focus on combining single model ensemble forecasts by means of statistical post-processing. Assuming each model is developed independently or with different primary target variables, each is likely to contain different dynamical strengths and weaknesses. Using statistical post-processing, such information is only carried by the simulations under a single model ensemble: no advantage is taken to influence simulations under the other models. A novel methodology, named Multi-model Cross Pollination in Time, is proposed for multi-model ensemble scheme with the aim of integrating the dynamical information regarding the future from each individual model operationally. The proposed approach generates model states in time via applying data assimilation scheme(s) to yield truly "multi-model trajectories". It is demonstrated to outperform traditional statistical post-processing in the 40-dimensional Lorenz96 flow. Data assimilation approaches are originally designed to improve state estimation from the past to the current time. The aim of this talk is to introduce a framework that uses data assimilation to improve model forecasts at future time (not to argue for any one particular data assimilation scheme). Illustration of applying data assimilation "in the future" to provide early warning of future high-impact events is also presented.

Delphi in Criminal Justice Policy: A Case Study on Judgmental Forecasting

ERIC Educational Resources Information Center

Loyens, Kim; Maesschalck, Jeroen; Bouckaert, Geert

2011-01-01

This article provides an in-depth case study analysis of a pilot project organized by the section "Strategic Analysis" of the Belgian Federal Police. Using the Delphi method, which is a judgmental forecasting technique, a panel of experts was questioned about future developments of crime, based on their expertise in criminal or social…

Projections of global health outcomes from 2005 to 2060 using the International Futures integrated forecasting model

PubMed Central

Hughes, Barry B; Peterson, Cecilia M; Rothman, Dale S; Solórzano, José R; Mathers, Colin D; Dickson, Janet R

2011-01-01

Abstract Objective To develop an integrated health forecasting model as part of the International Futures (IFs) modelling system. Methods The IFs model begins with the historical relationships between economic and social development and cause-specific mortality used by the Global Burden of Disease project but builds forecasts from endogenous projections of these drivers by incorporating forward linkages from health outcomes back to inputs like population and economic growth. The hybrid IFs system adds alternative structural formulations for causes not well served by regression models and accounts for changes in proximate health risk factors. Forecasts are made to 2100 but findings are reported to 2060. Findings The base model projects that deaths from communicable diseases (CDs) will decline by 50%, whereas deaths from both non-communicable diseases (NCDs) and injuries will more than double. Considerable cross-national convergence in life expectancy will occur. Climate-induced fluctuations in agricultural yield will cause little excess childhood mortality from CDs, although other climate−health pathways were not explored. An optimistic scenario will produce 39 million fewer deaths in 2060 than a pessimistic one. Our forward linkage model suggests that an optimistic scenario would result in a 20% per cent increase in gross domestic product (GDP) per capita, despite one billion additional people. Southern Asia would experience the greatest relative mortality reduction and the largest resulting benefit in per capita GDP. Conclusion Long-term, integrated health forecasting helps us understand the links between health and other markers of human progress and offers powerful insight into key points of leverage for future improvements. PMID:21734761

Projections of global health outcomes from 2005 to 2060 using the International Futures integrated forecasting model.

PubMed

Hughes, Barry B; Kuhn, Randall; Peterson, Cecilia M; Rothman, Dale S; Solórzano, José R; Mathers, Colin D; Dickson, Janet R

2011-07-01

To develop an integrated health forecasting model as part of the International Futures (IFs) modelling system. The IFs model begins with the historical relationships between economic and social development and cause-specific mortality used by the Global Burden of Disease project but builds forecasts from endogenous projections of these drivers by incorporating forward linkages from health outcomes back to inputs like population and economic growth. The hybrid IFs system adds alternative structural formulations for causes not well served by regression models and accounts for changes in proximate health risk factors. Forecasts are made to 2100 but findings are reported to 2060. The base model projects that deaths from communicable diseases (CDs) will decline by 50%, whereas deaths from both non-communicable diseases (NCDs) and injuries will more than double. Considerable cross-national convergence in life expectancy will occur. Climate-induced fluctuations in agricultural yield will cause little excess childhood mortality from CDs, although other climate-health pathways were not explored. An optimistic scenario will produce 39 million fewer deaths in 2060 than a pessimistic one. Our forward linkage model suggests that an optimistic scenario would result in a 20% per cent increase in gross domestic product (GDP) per capita, despite one billion additional people. Southern Asia would experience the greatest relative mortality reduction and the largest resulting benefit in per capita GDP. Long-term, integrated health forecasting helps us understand the links between health and other markers of human progress and offers powerful insight into key points of leverage for future improvements.

Back to the Future: Anticipating and Preparing for Change.

ERIC Educational Resources Information Center

Lapin, Joel D.

1992-01-01

Explains how colleges can take control of their futures by anticipating needs and demands. Describes environmental scanning, a way of identifying future concerns based on current trends and emerging issues. Provides examples of colleges that used forecasting and scanning to develop new courses and refine mission statements. (DMM)

The Future of African-Americans to the Year 2000. Summary Report.

ERIC Educational Resources Information Center

Congressional Task Force on the Future of African-Americans, Washington, DC.

This summary report highlights the major features of a comprehensive analysis and forecast of the future of African-Americans. Section 1 discusses the future of the United States. Section 2, "The Past and Present," covers the following topics: (1) "Employment and Economic Development"; (2) "Health"; (3)…

Forecasting the response of Earth's surface to future climatic and land use changes: A review of methods and research needs

DOE PAGES

Pelletier, Jon D.; Murray, A. Brad; Pierce, Jennifer L.; ...

2015-07-14

In the future, Earth will be warmer, precipitation events will be more extreme, global mean sea level will rise, and many arid and semiarid regions will be drier. Human modifications of landscapes will also occur at an accelerated rate as developed areas increase in size and population density. We now have gridded global forecasts, being continually improved, of the climatic and land use changes (C&LUC) that are likely to occur in the coming decades. However, besides a few exceptions, consensus forecasts do not exist for how these C&LUC will likely impact Earth-surface processes and hazards. In some cases, we havemore » the tools to forecast the geomorphic responses to likely future C&LUC. Fully exploiting these models and utilizing these tools will require close collaboration among Earth-surface scientists and Earth-system modelers. This paper assesses the state-of-the-art tools and data that are being used or could be used to forecast changes in the state of Earth's surface as a result of likely future C&LUC. We also propose strategies for filling key knowledge gaps, emphasizing where additional basic research and/or collaboration across disciplines are necessary. The main body of the paper addresses cross-cutting issues, including the importance of nonlinear/threshold-dominated interactions among topography, vegetation, and sediment transport, as well as the importance of alternate stable states and extreme, rare events for understanding and forecasting Earth-surface response to C&LUC. Five supplements delve into different scales or process zones (global-scale assessments and fluvial, aeolian, glacial/periglacial, and coastal process zones) in detail.« less

Norway and Cuba Continue Collaborating to Build Capacity to Improve Weather Forecasting

NASA Astrophysics Data System (ADS)

Antuña, Juan Carlos; Kalnay, Eugenia; Mesquita, Michel D. S.

2014-06-01

The Future of Climate Extremes in the Caribbean Extreme Cuban Climate (XCUBE) project, which is funded by the Norwegian Directorate for Civil Protection as part of an assignment for the Norwegian Ministry of Foreign Affairs to support scientific cooperation between Norway and Cuba, carried out a training workshop on seasonal forecasting, reanalysis data, and weather research and forecasting (WRF). The workshop was a follow-up to the XCUBE workshop conducted in Havana in 2013 and provided Cuban scientists with access to expertise on seasonal forecasting, the WRF model developed by the National Center for Atmospheric Research (NCAR) and the community, data assimilation, and reanalysis.

From Research to Operations: Transitioning Noaa's Lake Erie Harmful Algal Bloom Forecast System

NASA Astrophysics Data System (ADS)

Kavanaugh, K. E.; Stumpf, R. P.

2016-02-01

A key priority of NOAA's Harmful Algal Bloom Operational Forecast System (HAB-OFS) is to leverage the Ecological Forecasting Roadmap to systematically transition to operations scientifically mature HAB forecasts in regions of the country where there is a strong user need identified and an operational framework can be supported. While in the demonstration phase, the Lake Erie HAB forecast has proven its utility. Over the next two years, NOAA will be transitioning the Lake Erie HAB forecast to operations with an initial operating capability established in the HAB OFS' operational infrastructure by the 2016 bloom season. Blooms of cyanobacteria are a recurring problem in Lake Erie, and the dominant bloom forming species, Microcystis aeruginosa, produces a toxin called microcystin that is poisonous to humans, livestock and pets. Once the toxins have contaminated the source water used for drinking water, it is costly for public water suppliers to remove them. As part of the Lake Erie HAB forecast demonstration, NOAA has provided information regarding the cyanobacterial blooms in a biweekly Experimental HAB Bulletin, which includes information about the current and forecasted distribution, toxicity, potential for vertical mixing or scum formation, mixing of the water column, and predictions of bloom decline. Coastal resource managers, public water suppliers and public health officials use the Experimental HAB Bulletins to respond to and mitigate the impacts of cyanobacterial blooms. The transition to operations will benefit stakeholders through ensuring that future Lake Erie HAB forecast products are sustained, systematic, reliable, and robust. Once operational, the forecasts will continue to be assessed and improvements will be made based on the results of emerging scientific research. In addition, the lessons learned from the Lake Erie transition will be used to streamline the process for future HAB forecasts presently in development.

An overview of health forecasting.

PubMed

Soyiri, Ireneous N; Reidpath, Daniel D

2013-01-01

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

High Spatial Resolution Forecasting of Long-Term Monthly Precipitation and Mean Temperature Trends in Data Scarce Regions

NASA Astrophysics Data System (ADS)

Mosier, T. M.; Hill, D. F.; Sharp, K. V.

2013-12-01

High spatial resolution time-series data are critical for many hydrological and earth science studies. Multiple groups have developed historical and forecast datasets of high-resolution monthly time-series for regions of the world such as the United States (e.g. PRISM for hindcast data and MACA for long-term forecasts); however, analogous datasets have not been available for most data scarce regions. The current work fills this data need by producing and freely distributing hindcast and forecast time-series datasets of monthly precipitation and mean temperature for all global land surfaces, gridded at a 30 arc-second resolution. The hindcast data are constructed through a Delta downscaling method, using as inputs 0.5 degree monthly time-series and 30 arc-second climatology global weather datasets developed by Willmott & Matsuura and WorldClim, respectively. The forecast data are formulated using a similar downscaling method, but with an additional step to remove bias from the climate variable's probability distribution over each region of interest. The downscaling package is designed to be compatible with a number of general circulation models (GCM) (e.g. with GCMs developed for the IPCC AR4 report and CMIP5), and is presently implemented using time-series data from the NCAR CESM1 model in conjunction with 30 arc-second future decadal climatologies distributed by the Consultative Group on International Agricultural Research. The resulting downscaled datasets are 30 arc-second time-series forecasts of monthly precipitation and mean temperature available for all global land areas. As an example of these data, historical and forecast 30 arc-second monthly time-series from 1950 through 2070 are created and analyzed for the region encompassing Pakistan. For this case study, forecast datasets corresponding to the future representative concentration pathways 45 and 85 scenarios developed by the IPCC are presented and compared. This exercise highlights a range of potential meteorological trends for the Pakistan region and more broadly serves to demonstrate the utility of the presented 30 arc-second monthly precipitation and mean temperature datasets for use in data scarce regions.

An approach to forecasting health expenditures, with application to the U.S. Medicare system.

PubMed

Lee, Ronald; Miller, Timoth

2002-10-01

To quantify uncertainty in forecasts of health expenditures. Stochastic time series models are estimated for historical variations in fertility, mortality, and health spending per capita in the United States, and used to generate stochastic simulations of the growth of Medicare expenditures. Individual health spending is modeled to depend on the number of years until death. A simple accounting model is developed for forecasting health expenditures, using the U.S. Medicare system as an example. Medicare expenditures are projected to rise from 2.2 percent of GDP (gross domestic product) to about 8 percent of GDP by 2075. This increase is due in equal measure to increasing health spending per beneficiary and to population aging. The traditional projection method constructs high, medium, and low scenarios to assess uncertainty, an approach that has many problems. Using stochastic forecasting, we find a 95 percent probability that Medicare spending in 2075 will fall between 4 percent and 18 percent of GDP, indicating a wide band of uncertainty. Although there is substantial uncertainty about future mortality decline, it contributed little to uncertainty about future Medicare spending, since lower mortality both raises the number of elderly, tending to raise spending, and is associated with improved health of the elderly, tending to reduce spending. Uncertainty about fertility, by contrast, leads to great uncertainty about the future size of the labor force, and therefore adds importantly to uncertainty about the health-share of GDP. In the shorter term, the major source of uncertainty is health spending per capita. History is a valuable guide for quantifying our uncertainty about future health expenditures. The probabilistic model we present has several advantages over the high-low scenario approach to forecasting. It indicates great uncertainty about future Medicare expenditures relative to GDP.

Forecasting, Forecasting

Treesearch

Michael A. Fosberg

1987-01-01

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

The ecological forecast horizon, and examples of its uses and determinants

PubMed Central

Petchey, Owen L; Pontarp, Mikael; Massie, Thomas M; Kéfi, Sonia; Ozgul, Arpat; Weilenmann, Maja; Palamara, Gian Marco; Altermatt, Florian; Matthews, Blake; Levine, Jonathan M; Childs, Dylan Z; McGill, Brian J; Schaepman, Michael E; Schmid, Bernhard; Spaak, Piet; Beckerman, Andrew P; Pennekamp, Frank; Pearse, Ian S; Vasseur, David

2015-01-01

Forecasts of ecological dynamics in changing environments are increasingly important, and are available for a plethora of variables, such as species abundance and distribution, community structure and ecosystem processes. There is, however, a general absence of knowledge about how far into the future, or other dimensions (space, temperature, phylogenetic distance), useful ecological forecasts can be made, and about how features of ecological systems relate to these distances. The ecological forecast horizon is the dimensional distance for which useful forecasts can be made. Five case studies illustrate the influence of various sources of uncertainty (e.g. parameter uncertainty, environmental variation, demographic stochasticity and evolution), level of ecological organisation (e.g. population or community), and organismal properties (e.g. body size or number of trophic links) on temporal, spatial and phylogenetic forecast horizons. Insights from these case studies demonstrate that the ecological forecast horizon is a flexible and powerful tool for researching and communicating ecological predictability. It also has potential for motivating and guiding agenda setting for ecological forecasting research and development. PMID:25960188

New product forecasting with limited or no data

NASA Astrophysics Data System (ADS)

Ismai, Zuhaimy; Abu, Noratikah; Sufahani, Suliadi

2016-10-01

In the real world, forecasts would always be based on historical data with the assumption that the behaviour be the same for the future. But how do we forecast when there is no such data available? New product or new technologies normally has limited amount of data available. Knowing that forecasting is valuable for decision making, this paper presents forecasting of new product or new technologies using aggregate diffusion models and modified Bass Model. A newly launched Proton car and its penetration was chosen to demonstrate the possibility of forecasting sales demand where there is limited or no data available. The model was developed to forecast diffusion of new vehicle or an innovation in the Malaysian society. It is to represent the level of spread on the new vehicle among a given set of the society in terms of a simple mathematical function that elapsed since the introduction of the new product. This model will forecast the car sales volume. A procedure of the proposed diffusion model was designed and the parameters were estimated. Results obtained by applying the proposed diffusion model and numerical calculation shows that the model is robust and effective for forecasting demand of the new vehicle. The results reveal that newly developed modified Bass diffusion of demand function has significantly contributed for forecasting the diffusion of new Proton car or new product.

Forecasts of county-level land uses under three future scenarios: a technical document supporting the Forest Service 2010 RPA Assessment

Treesearch

David N. Wear

2011-01-01

Accurately forecasting future forest conditions and the implications for ecosystem services depends on understanding land use dynamics. In support of the 2010 Renewable Resources Planning Act (RPA) Assessment, we forecast changes in land uses for the coterminous United States in response to three scenarios. Our land use models forecast urbanization in response to the...

Optimal Day-Ahead Scheduling of a Hybrid Electric Grid Using Weather Forecasts

DTIC Science & Technology

2013-12-01

ahead scheduling, Weather forecast , Wind power , Photovoltaic Power 15. NUMBER OF PAGES 107 16. PRICE CODE 17. SECURITY CLASSIFICATION OF...cost can be reached by accurately anticipating the future renewable power productions. This thesis suggests the use of weather forecasts to establish...reached by accurately anticipating the future renewable power productions. This thesis suggests the use of weather forecasts to establish day-ahead

Monitoring and Modeling: The Future of Volcanic Eruption Forecasting

NASA Astrophysics Data System (ADS)

Poland, M. P.; Pritchard, M. E.; Anderson, K. R.; Furtney, M.; Carn, S. A.

2016-12-01

Eruption forecasting typically uses monitoring data from geology, gas geochemistry, geodesy, and seismology, to assess the likelihood of future eruptive activity. Occasionally, months to years of warning are possible from specific indicators (e.g., deep LP earthquakes, elevated CO2 emissions, and aseismic deformation) or a buildup in one or more monitoring parameters. More often, observable changes in unrest occur immediately before eruption, as magma is rising toward the surface. In some cases, little or no detectable unrest precedes eruptive activity. Eruption forecasts are usually based on the experience of volcanologists studying the activity, but two developing fields offer a potential leap beyond this practice. First, remote sensing data, which can track thermal, gas, and ash emissions, as well as surface deformation, are increasingly available, allowing statistically significant research into the characteristics of unrest. For example, analysis of hundreds of volcanoes indicates that deformation is a more common pre-eruptive phenomenon than thermal anomalies, and that most episodes of satellite-detected unrest are not immediately followed by eruption. Such robust datasets inform the second development—probabilistic models of eruption potential, especially those that are based on physical-chemical models of the dynamics of magma accumulation and ascent. Both developments are essential for refining forecasts and reducing false positives. For example, many caldera systems have not erupted but are characterized by unrest that, in another context, would elicit strong concern from volcanologists. More observations of this behavior and better understanding of the underlying physics of unrest will improve forecasts of such activity. While still many years from implementation as a forecasting tool, probabilistic physio-chemical models incorporating satellite data offer a complement to expert assessments that, together, can form a powerful forecasting approach.

Operational wind shear detection and warning - The 'CLAWS' experience at Denver and future objectives

NASA Technical Reports Server (NTRS)

Mccarthy, John; Wilson, James W.; Hjelmfelt, Mark R.

1986-01-01

An operational wind shear detection and warning experiment was conducted at Denver's Stapleton International Airport in summer 1984. Based on meteorological interpretation of scope displays from a Doppler weather radar, warnings were transmitted to the air traffic control tower via voice radio. Analyses of results indicated real skill in daily microburst forecasts and very short-term (less than 5-min) warnings. Wind shift advisories with 15-30 min forecasts, permitted more efficient runway reconfigurations. Potential fuel savings were estimated at $875,000/yr at Stapleton. The philosophy of future development toward an automated, operational system is discussed.

DEFENDER: Detecting and Forecasting Epidemics Using Novel Data-Analytics for Enhanced Response.

PubMed

Thapen, Nicholas; Simmie, Donal; Hankin, Chris; Gillard, Joseph

2016-01-01

In recent years social and news media have increasingly been used to explain patterns in disease activity and progression. Social media data, principally from the Twitter network, has been shown to correlate well with official disease case counts. This fact has been exploited to provide advance warning of outbreak detection, forecasting of disease levels and the ability to predict the likelihood of individuals developing symptoms. In this paper we introduce DEFENDER, a software system that integrates data from social and news media and incorporates algorithms for outbreak detection, situational awareness and forecasting. As part of this system we have developed a technique for creating a location network for any country or region based purely on Twitter data. We also present a disease nowcasting (forecasting the current but still unknown level) approach which leverages counts from multiple symptoms, which was found to improve the nowcasting accuracy by 37 percent over a model that used only previous case data. Finally we attempt to forecast future levels of symptom activity based on observed user movement on Twitter, finding a moderate gain of 5 percent over a time series forecasting model.

Real-Time CME Forecasting Using HMI Active-Region Magnetograms and Flare History

NASA Technical Reports Server (NTRS)

Falconer, David; Moore, Ron; Barghouty, Abdulnasser F.; Khazanov, Igor

2011-01-01

We have recently developed a method of predicting an active region s probability of producing a CME, an X-class Flare, an M-class Flare, or a Solar Energetic Particle Event from a free-energy proxy measured from SOHO/MDI line-of-sight magnetograms. This year we have added three major improvements to our forecast tool: 1) Transition from MDI magnetogram to SDO/HMI magnetogram allowing us near-real-time forecasts, 2) Automation of acquisition and measurement of HMI magnetograms giving us near-real-time forecasts (no older than 2 hours), and 3) Determination of how to improve forecast by using the active region s previous flare history in combination with its free-energy proxy. HMI was turned on in May 2010 and MDI was turned off in April 2011. Using the overlap period, we have calibrated HMI to yield what MDI would measure. This is important since the value of the free-energy proxy used for our forecast is resolution dependent, and the forecasts are made from results of a 1996-2004 database of MDI observations. With near-real-time magnetograms from HMI, near-real-time forecasts are now possible. We have augmented the code so that it continually acquires and measures new magnetograms as they become available online, and updates the whole-sun forecast from the coming day. The next planned improvement is to use an active region s previous flare history, in conjunction with its free-energy proxy, to forecast the active region s event rate. It has long been known that active regions that have produced flares in the past are likely to produce flares in the future, and that active regions that are nonpotential (have large free-energy) are more likely to produce flares in the future. This year we have determined that persistence of flaring is not just a reflection of an active region s free energy. In other words, after controlling for free energy, we have found that active regions that have flared recently are more likely to flare in the future.

Current and future data assimilation development in the Copernicus Atmosphere Monitoring Service

NASA Astrophysics Data System (ADS)

Engelen, R. J.; Ades, M.; Agusti-panareda, A.; Flemming, J.; Inness, A.; Kipling, Z.; Parrington, M.; Peuch, V. H.

2017-12-01

The European Copernicus Atmosphere Monitoring Service (CAMS) operationally provides daily forecasts of global atmospheric composition and regional air quality. The global forecasting system is using ECMWF's Integrated Forecasting System (IFS), which is used for numerical weather prediction and which has been extended with modules for atmospheric chemistry, aerosols and greenhouse gases. The system assimilates observations from more than 60 satellite sensors to constrain both the meteorology and the atmospheric composition species. While an operational forecasting system needs to be robust and reliable, it also needs to stay state-of-the-art to provide the best possible forecasts. Continuous development is therefore an important component of the CAMS systems. We will present on-going efforts on improving the 4D-Var data assimilation system, such as using ensemble data assimilation to improve the background error covariances and more accurate use of satellite observations. We will also outline plans for including emissions in the daily CAMS analyses, which is an area where research activities have a large potential to feed into operational applications.

The development rainfall forecasting using kalman filter

NASA Astrophysics Data System (ADS)

Zulfi, Mohammad; Hasan, Moh.; Dwidja Purnomo, Kosala

2018-04-01

Rainfall forecasting is very interesting for agricultural planing. Rainfall information is useful to make decisions about the plan planting certain commodities. In this studies, the rainfall forecasting by ARIMA and Kalman Filter method. Kalman Filter method is used to declare a time series model of which is shown in the form of linear state space to determine the future forecast. This method used a recursive solution to minimize error. The rainfall data in this research clustered by K-means clustering. Implementation of Kalman Filter method is for modelling and forecasting rainfall in each cluster. We used ARIMA (p,d,q) to construct a state space for KalmanFilter model. So, we have four group of the data and one model in each group. In conclusions, Kalman Filter method is better than ARIMA model for rainfall forecasting in each group. It can be showed from error of Kalman Filter method that smaller than error of ARIMA model.

Combining a Spatial Model and Demand Forecasts to Map Future Surface Coal Mining in Appalachia

PubMed Central

Strager, Michael P.; Strager, Jacquelyn M.; Evans, Jeffrey S.; Dunscomb, Judy K.; Kreps, Brad J.; Maxwell, Aaron E.

2015-01-01

Predicting the locations of future surface coal mining in Appalachia is challenging for a number of reasons. Economic and regulatory factors impact the coal mining industry and forecasts of future coal production do not specifically predict changes in location of future coal production. With the potential environmental impacts from surface coal mining, prediction of the location of future activity would be valuable to decision makers. The goal of this study was to provide a method for predicting future surface coal mining extents under changing economic and regulatory forecasts through the year 2035. This was accomplished by integrating a spatial model with production demand forecasts to predict (1 km2) gridded cell size land cover change. Combining these two inputs was possible with a ratio which linked coal extraction quantities to a unit area extent. The result was a spatial distribution of probabilities allocated over forecasted demand for the Appalachian region including northern, central, southern, and eastern Illinois coal regions. The results can be used to better plan for land use alterations and potential cumulative impacts. PMID:26090883

Critical Materials Institute

DOE Office of Scientific and Technical Information (OSTI.GOV)

King, Alex

2013-01-09

Ames Laboratory Director Alex King talks about the goals of the Critical Materials Institute in diversifying the supply of critical materials, developing substitute materials, developing tools and techniques for recycling critical materials, and forecasting materials needs to avoid future shortages.

Critical Materials Institute

ScienceCinema

King, Alex

2017-12-22

Ames Laboratory Director Alex King talks about the goals of the Critical Materials Institute in diversifying the supply of critical materials, developing substitute materials, developing tools and techniques for recycling critical materials, and forecasting materials needs to avoid future shortages.

Climate Forecasts and Water Resource Management: Applications for a Developing Country

NASA Astrophysics Data System (ADS)

Brown, C.; Rogers, P.

2002-05-01

While the quantity of water on the planet earth is relatively constant, the demand for water is continuously increasing. Population growth leads to linear increases in water demand, and economic growth leads to further demand growth. Strzepek et al. calculate that with a United Nations mean population estimate of 8.5 billion people by 2025 and globally balanced economic growth, water use could increase by 70% over that time (Strzepek et al., 1995). For developing nations especially, supplying water for this growing demand requires the construction of new water supply infrastructure. The prospect of designing and constructing long life-span infrastructure is clouded by the uncertainty of future climate. The availability of future water resources is highly dependent on future climate. With realization of the nonstationarity of climate, responsible design emphasizes resiliency and robustness of water resource systems (IPCC, 1995; Gleick et al., 1999). Resilient systems feature multiple sources and complex transport and distribution systems, and so come at a high economic and environmental price. A less capital-intense alternative to creating resilient and robust water resource systems is the use of seasonal climate forecasts. Such forecasts provide adequate lead time and accuracy to allow water managers and water-based sectors such as agriculture or hydropower to optimize decisions for the expected water supply. This study will assess the use of seasonal climate forecasts from regional climate models as a method to improve water resource management in systems with limited water supply infrastructure

Robustness of disaggregate oil and gas discovery forecasting models

USGS Publications Warehouse

Attanasi, E.D.; Schuenemeyer, J.H.

1989-01-01

The trend in forecasting oil and gas discoveries has been to develop and use models that allow forecasts of the size distribution of future discoveries. From such forecasts, exploration and development costs can more readily be computed. Two classes of these forecasting models are the Arps-Roberts type models and the 'creaming method' models. This paper examines the robustness of the forecasts made by these models when the historical data on which the models are based have been subject to economic upheavals or when historical discovery data are aggregated from areas having widely differing economic structures. Model performance is examined in the context of forecasting discoveries for offshore Texas State and Federal areas. The analysis shows how the model forecasts are limited by information contained in the historical discovery data. Because the Arps-Roberts type models require more regularity in discovery sequence than the creaming models, prior information had to be introduced into the Arps-Roberts models to accommodate the influence of economic changes. The creaming methods captured the overall decline in discovery size but did not easily allow introduction of exogenous information to compensate for incomplete historical data. Moreover, the predictive log normal distribution associated with the creaming model methods appears to understate the importance of the potential contribution of small fields. ?? 1989.

A hybrid spatiotemporal drought forecasting model for operational use

NASA Astrophysics Data System (ADS)

Vasiliades, L.; Loukas, A.

2010-09-01

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

The Quality-of-Life (QOL) Research Movement: Past, Present, and Future

ERIC Educational Resources Information Center

Sirgy, M. Joseph; Michalos, Alex C.; Ferriss, Abbott L.; Easterlin, Richard A.; Pavot, William; Patrick, Donald

2006-01-01

The purpose of this paper is to trace the history of the social indicators or quality-of-life (QOL) research movement up to today, forecast future developments, and pave the way for future growth. Broadly speaking, we tried to review historical antecedents from the point of view of different disciplines, with specialists in each discipline…

Stakeholders' perceptions of social-ecological systems and the information they use in the management of freshwater resources in Guanacaste, Costa Rica

NASA Astrophysics Data System (ADS)

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

2014-12-01

Climate change is expected to increase the chances of drought, and shift precipitation patterns in seasonally dry places. In some places, the heuristics or "rules of thumb" that stakeholders use may no longer be reliable for the effective management of water resources. This can have dire consequences for social and ecological systems, especially in developing countries. Scientists and policymakers view climate forecasts as one way for improving informed decision-making about freshwater resources. However, successful communication requires that stakeholders understand and are able to use such information. To develop effective communications, it is critical to characterize stakeholders' understanding of social-ecological systems as related to water, the type of information used to inform management decisions, and the perceived value of forecast information. To achieve our objective, we conducted 40 semi-structured interviews with farmers, water managers, hydroelectric utilities, local climate experts, tourism industry representatives, and members of the general public in the semi-arid region of Guanacaste, Costa Rica. People believe that they have enough water at this time however they believe that the region will become much drier in the future, which they attribute to climate change, El Nino/La Nina, and deforestation. With respect to the value of forecast information, we found that the scale of decision-making (e.g., irrigation district versus small farmer) was associated with a stakeholders' level of "technical sophistication" and trust in government. In future work, we will evaluate the prevalence of these beliefs and practices in the larger population in order to identify effective ways to tailor the presentation of forecast information for different audiences. This work provides insight into the development of forecast communications to improve the management of resources in development countries in the face of a changing climate.

Method for Assessing Impacts of Global Sea Level Rise on Navigation Gate Operations

NASA Astrophysics Data System (ADS)

Obrien, P. S.; White, K. D.; Friedman, D.

2015-12-01

Coastal navigation infrastructure may be highly vulnerable to changing climate, including increasing sea levels and altered frequency and intensity of coastal storms. Future gate operations impacted by global sea level rise will pose unique challenges, especially for structures 50 years and older. Our approach is to estimate future changes in gate operational frequency based on a bootstrapping method to forecast future water levels. A case study will be presented to determine future changes in frequency of operations over the next 100 years. A statistical model in the R programming language was developed to apply future sea level rise projections using the three sea level rise scenarios prescribed by USACE Engineer Regulation ER 1100-2-8162. Information derived from the case study will help forecast changes in operational costs caused by increased gate operations and inform timing of decisions on adaptation measures.

Using Analog Ensemble to generate spatially downscaled probabilistic wind power forecasts

NASA Astrophysics Data System (ADS)

Delle Monache, L.; Shahriari, M.; Cervone, G.

2017-12-01

We use the Analog Ensemble (AnEn) method to generate probabilistic 80-m wind power forecasts. We use data from the NCEP GFS ( 28 km resolution) and NCEP NAM (12 km resolution). We use forecasts data from NAM and GFS, and analysis data from NAM which enables us to: 1) use a lower-resolution model to create higher-resolution forecasts, and 2) use a higher-resolution model to create higher-resolution forecasts. The former essentially increases computing speed and the latter increases forecast accuracy. An aggregated model of the former can be compared against the latter to measure the accuracy of the AnEn spatial downscaling. The AnEn works by taking a deterministic future forecast and comparing it with past forecasts. The model searches for the best matching estimates within the past forecasts and selects the predictand value corresponding to these past forecasts as the ensemble prediction for the future forecast. Our study is based on predicting wind speed and air density at more than 13,000 grid points in the continental US. We run the AnEn model twice: 1) estimating 80-m wind speed by using predictor variables such as temperature, pressure, geopotential height, U-component and V-component of wind, 2) estimating air density by using predictors such as temperature, pressure, and relative humidity. We use the air density values to correct the standard wind power curves for different values of air density. The standard deviation of the ensemble members (i.e. ensemble spread) will be used as the degree of difficulty to predict wind power at different locations. The value of the correlation coefficient between the ensemble spread and the forecast error determines the appropriateness of this measure. This measure is prominent for wind farm developers as building wind farms in regions with higher predictability will reduce the real-time risks of operating in the electricity markets.

Evaluating hydrological response to forecasted land-use change—scenario testing with the automated geospatial watershed assessment (AGWA) tool

USGS Publications Warehouse

Kepner, William G.; Semmens, Darius J.; Hernandez, Mariano; Goodrich, David C.

2009-01-01

Envisioning and evaluating future scenarios has emerged as a critical component of both science and social decision-making. The ability to assess, report, map, and forecast the life support functions of ecosystems is absolutely critical to our capacity to make informed decisions to maintain the sustainable nature of our ecosystem services now and into the future. During the past two decades, important advances in the integration of remote imagery, computer processing, and spatial-analysis technologies have been used to develop landscape information that can be integrated with hydrologic models to determine long-term change and make predictive inferences about the future. Two diverse case studies in northwest Oregon (Willamette River basin) and southeastern Arizona (San Pedro River) were examined in regard to future land use scenarios relative to their impact on surface water conditions (e.g., sediment yield and surface runoff) using hydrologic models associated with the Automated Geospatial Watershed Assessment (AGWA) tool. The base reference grid for land cover was modified in both study locations to reflect stakeholder preferences 20 to 60 yrs into the future, and the consequences of landscape change were evaluated relative to the selected future scenarios. The two studies provide examples of integrating hydrologic modeling with a scenario analysis framework to evaluate plausible future forecasts and to understand the potential impact of landscape change on ecosystem services.

A global flash flood forecasting system

NASA Astrophysics Data System (ADS)

Baugh, Calum; Pappenberger, Florian; Wetterhall, Fredrik; Hewson, Tim; Zsoter, Ervin

2016-04-01

The sudden and devastating nature of flash flood events means it is imperative to provide early warnings such as those derived from Numerical Weather Prediction (NWP) forecasts. Currently such systems exist on basin, national and continental scales in Europe, North America and Australia but rely on high resolution NWP forecasts or rainfall-radar nowcasting, neither of which have global coverage. To produce global flash flood forecasts this work investigates the possibility of using forecasts from a global NWP system. In particular we: (i) discuss how global NWP can be used for flash flood forecasting and discuss strengths and weaknesses; (ii) demonstrate how a robust evaluation can be performed given the rarity of the event; (iii) highlight the challenges and opportunities in communicating flash flood uncertainty to decision makers; and (iv) explore future developments which would significantly improve global flash flood forecasting. The proposed forecast system uses ensemble surface runoff forecasts from the ECMWF H-TESSEL land surface scheme. A flash flood index is generated using the ERIC (Enhanced Runoff Index based on Climatology) methodology [Raynaud et al., 2014]. This global methodology is applied to a series of flash floods across southern Europe. Results from the system are compared against warnings produced using the higher resolution COSMO-LEPS limited area model. The global system is evaluated by comparing forecasted warning locations against a flash flood database of media reports created in partnership with floodlist.com. To deal with the lack of objectivity in media reports we carefully assess the suitability of different skill scores and apply spatial uncertainty thresholds to the observations. To communicate the uncertainties of the flash flood system output we experiment with a dynamic region-growing algorithm. This automatically clusters regions of similar return period exceedence probabilities, thus presenting the at-risk areas at a spatial resolution appropriate to the NWP system. We then demonstrate how these warning areas could eventually complement existing global systems such as the Global Flood Awareness System (GloFAS), to give warnings of flash floods. This work demonstrates the possibility of creating a global flash flood forecasting system based on forecasts from existing global NWP systems. Future developments, in post-processing for example, will need to address an under-prediction bias, for extreme point rainfall, that is innate to current-generation global models.

Forecasting consequences of changing sea ice availability for Pacific walruses

USGS Publications Warehouse

Udevitz, Mark S.; Jay, Chadwick V.; Taylor, Rebecca; Fischbach, Anthony S.; Beatty, William S.; Noren, Shawn R.

2017-01-01

The accelerating rate of anthropogenic alteration and disturbance of environments has increased the need for forecasting effects of environmental change on fish and wildlife populations. Models linking projections of environmental change with behavioral responses and bioenergetic effects can provide a basis for these forecasts. There is particular interest in forecasting effects of projected reductions in sea ice availability on Pacific walruses (Odobenus rosmarus divergens). Declining extent of summer sea ice in the Chukchi Sea has caused Pacific walruses to increase use of coastal haulouts and decrease use of more productive offshore feeding areas. Such climate-induced changes in distribution and behavior could ultimately affect the status of the population. We developed behavioral models to relate changes in sea ice availability to adult female walrus movements and activity levels, and adapted previously developed bioenergetics models to relate those activity levels to energy requirements and the ability to meet those requirements. We then linked these models to general circulation model projections of future ice availability to forecast autumn body condition for female walruses during mid- and late-century time periods. Our results suggest that as sea ice becomes less available in the Chukchi Sea, female walruses will spend more time in the southwestern region of that sea, less time resting, and less time foraging. Median forecasted autumn body masses were 7–12% lower in future scenarios than during recent times, but posterior distributions broadly overlapped and median forecasted seasonal mass losses (15–34%) were comparable to seasonal mass losses routinely experienced by other pinnipeds. These seasonal reductions in body condition would be unlikely to result in demographic effects, but if walruses were unable to rebuild endogenous reserves while wintering in the Bering Sea, cumulative effects could have implications for reproduction and survival, ultimately affecting the status of the Pacific walrus population. Our approach provides a general framework for forecasting consequences of the broad range of environmental changes and anthropogenic disturbances that may affect bioenergetics through behavioral responses or changes in prey availability.

Extended Kalman Filter framework for forecasting shoreline evolution

USGS Publications Warehouse

Long, Joseph; Plant, Nathaniel G.

2012-01-01

A shoreline change model incorporating both long- and short-term evolution is integrated into a data assimilation framework that uses sparse observations to generate an updated forecast of shoreline position and to estimate unobserved geophysical variables and model parameters. Application of the assimilation algorithm provides quantitative statistical estimates of combined model-data forecast uncertainty which is crucial for developing hazard vulnerability assessments, evaluation of prediction skill, and identifying future data collection needs. Significant attention is given to the estimation of four non-observable parameter values and separating two scales of shoreline evolution using only one observable morphological quantity (i.e. shoreline position).

Development of a mobile app for flash flood alerting and data cataloging

NASA Astrophysics Data System (ADS)

Gourley, J. J.; Flamig, Z.; Nguyen, M.

2016-12-01

No matter how accurate and specific a forecast of flash flooding is made, there are local nuances with the communities related to the built environment that often dictate the locations and magnitudes of impacts. These are difficult, if not impossible, to identify, classify, and measure using remote sensing methods. This presentation presents a Thriving Earth Exchange project that is developing a mobile app that serves two purposes. First, it will provide detailed forecasts of flash flooding down to the 1-km pixel scale with 10-min updates using the state-of-the-science hydrologic forecasting system called FLASH. The display of model outputs on an app will greatly facilitate their use and can potentially increase first responders' reactions to the specific locations of impending disasters. Then, the first responders will have the capability of reporting the geotagged impacts they are witnessing, including those local "trouble spots". Over time, we will catalog the trouble spots for the community so that they can be flagged in future events. If proven effective, the app will then be advertised in other flood-prone communities and the database will be expanded accordingly. In summary, we are engaging local communities to provide information that can inform and improve future forecasts of flash flood, ultimately reducing their impacts and saving lives.

Futures of global urban expansion: uncertainties and implications for biodiversity conservation

NASA Astrophysics Data System (ADS)

Güneralp, B.; Seto, K. C.

2013-03-01

Urbanization will place significant pressures on biodiversity across the world. However, there are large uncertainties in the amount and location of future urbanization, particularly urban land expansion. Here, we present a global analysis of urban extent circa 2000 and probabilistic forecasts of urban expansion for 2030 near protected areas and in biodiversity hotspots. We estimate that the amount of urban land within 50 km of all protected area boundaries will increase from 450 000 km2 circa 2000 to 1440 000 ± 65 000 km2 in 2030. Our analysis shows that protected areas around the world will experience significant increases in urban land within 50 km of their boundaries. China will experience the largest increase in urban land near protected areas with 304 000 ± 33 000 km2 of new urban land to be developed within 50 km of protected area boundaries. The largest urban expansion in biodiversity hotspots, over 100 000 ± 25 000 km2, is forecasted to occur in South America. Uncertainties in the forecasts of the amount and location of urban land expansion reflect uncertainties in their underlying drivers including urban population and economic growth. The forecasts point to the need to reconcile urban development and biodiversity conservation strategies.

Land use change modeling through scenario-based cellular automata Markov: improving spatial forecasting.

PubMed

Jahanishakib, Fatemeh; Mirkarimi, Seyed Hamed; Salmanmahiny, Abdolrassoul; Poodat, Fatemeh

2018-05-08

Efficient land use management requires awareness of past changes, present actions, and plans for future developments. Part of these requirements is achieved using scenarios that describe a future situation and the course of changes. This research aims to link scenario results with spatially explicit and quantitative forecasting of land use development. To develop land use scenarios, SMIC PROB-EXPERT and MORPHOL methods were used. It revealed eight scenarios as the most probable. To apply the scenarios, we considered population growth rate and used a cellular automata-Markov chain (CA-MC) model to implement the quantified changes described by each scenario. For each scenario, a set of landscape metrics was used to assess the ecological integrity of land use classes in terms of fragmentation and structural connectivity. The approach enabled us to develop spatial scenarios of land use change and detect their differences for choosing the most integrated landscape pattern in terms of landscape metrics. Finally, the comparison between paired forecasted scenarios based on landscape metrics indicates that scenarios 1-1, 2-2, 3-2, and 4-1 have a more suitable integrity. The proposed methodology for developing spatial scenarios helps executive managers to create scenarios with many repetitions and customize spatial patterns in real world applications and policies.

Ensemble Streamflow Prediction in Korea: Past and Future 5 Years

NASA Astrophysics Data System (ADS)

Jeong, D.; Kim, Y.; Lee, J.

2005-05-01

The Ensemble Streamflow Prediction (ESP) approach was first introduced in 2000 by the Hydrology Research Group (HRG) at Seoul National University as an alternative probabilistic forecasting technique for improving the 'Water Supply Outlook' That is issued every month by the Ministry of Construction and Transportation in Korea. That study motivated the Korea Water Resources Corporation (KOWACO) to establish their seasonal probabilistic forecasting system for the 5 major river basins using the ESP approach. In cooperation with the HRG, the KOWACO developed monthly optimal multi-reservoir operating systems for the Geum river basin in 2004, which coupled the ESP forecasts with an optimization model using sampling stochastic dynamic programming. The user interfaces for both ESP and SSDP have also been designed for the developed computer systems to become more practical. More projects for developing ESP systems to the other 3 major river basins (i.e. the Nakdong, Han and Seomjin river basins) was also completed by the HRG and KOWACO at the end of December 2004. Therefore, the ESP system has become the most important mid- and long-term streamflow forecast technique in Korea. In addition to the practical aspects, resent research experience on ESP has raised some concerns into ways of improving the accuracy of ESP in Korea. Jeong and Kim (2002) performed an error analysis on its resulting probabilistic forecasts and found that the modeling error is dominant in the dry season, while the meteorological error is dominant in the flood season. To address the first issue, Kim et al. (2004) tested various combinations and/or combining techniques and showed that the ESP probabilistic accuracy could be improved considerably during the dry season when the hydrologic models were combined and/or corrected. In addition, an attempt was also made to improve the ESP accuracy for the flood season using climate forecast information. This ongoing project handles three types of climate forecast information: (1) the Monthly Industrial Meteorology Information Magazine (MIMIM) of the Korea Meteorological Administration (2) the Global Data Assimilation Prediction System (GDAPS), and (3) the US National Centers for Environmental Prediction (NCEP). Each of these forecasts is issued in a unique format: (1) MIMIM is a most-probable-event forecast, (2) GDAPS is a single series of deterministic forecasts, and (3) NCEP is an ensemble of deterministic forecasts. Other minor issues include how long the initial conditions influences the ESP accuracy, and how many ESP scenarios are needed to obtain the best accuracy. This presentation also addresses some future research that is needed for ESP in Korea.

A novel grey-fuzzy-Markov and pattern recognition model for industrial accident forecasting

NASA Astrophysics Data System (ADS)

Edem, Inyeneobong Ekoi; Oke, Sunday Ayoola; Adebiyi, Kazeem Adekunle

2017-10-01

Industrial forecasting is a top-echelon research domain, which has over the past several years experienced highly provocative research discussions. The scope of this research domain continues to expand due to the continuous knowledge ignition motivated by scholars in the area. So, more intelligent and intellectual contributions on current research issues in the accident domain will potentially spark more lively academic, value-added discussions that will be of practical significance to members of the safety community. In this communication, a new grey-fuzzy-Markov time series model, developed from nondifferential grey interval analytical framework has been presented for the first time. This instrument forecasts future accident occurrences under time-invariance assumption. The actual contribution made in the article is to recognise accident occurrence patterns and decompose them into grey state principal pattern components. The architectural framework of the developed grey-fuzzy-Markov pattern recognition (GFMAPR) model has four stages: fuzzification, smoothening, defuzzification and whitenisation. The results of application of the developed novel model signify that forecasting could be effectively carried out under uncertain conditions and hence, positions the model as a distinctly superior tool for accident forecasting investigations. The novelty of the work lies in the capability of the model in making highly accurate predictions and forecasts based on the availability of small or incomplete accident data.

Analysis of Science and Technology Trend Based on Word Usage in Digitized Books

NASA Astrophysics Data System (ADS)

Yun, Jinhyuk; Kim, Pan-Jun; Jeong, Hawoong

2013-03-01

Throughout mankind's history, forecasting and predicting future has been a long-lasting interest to our society. Many fortune-tellers have tried to forecast the future by ``divine'' items. Sci-fi writers have also imagined what the future would look like. However most of them have been illogical and unscientific. Meanwhile, scientists have also attempted to discover future trend of science. Many researchers have used quantitative models to study how new ideas are used and spread. Besides the modeling works, in the early 21st century, the rise of data science has provided another prospect of forecasting future. However many studies have focused on very limited set of period or age, due to the limitations of dataset. Hence, many questions still remained unanswered. Fortunately, Google released a new dataset named ``Google N-Gram Dataset.'' This dataset provides us with 5 million words worth of literature dating from 1520 to 2008, and this is nearly 4% of publications ever printed. With this new time-varying dataset, we studied the spread and development of technologies by searching ``Science and Technology'' related words from 1800 to 2000. By statistical analysis, some general scaling laws were discovered. And finally, we determined factors that strongly affect the lifecycle of a word.

Flash flood forecasting using simplified hydrological models, radar rainfall forecasts and data assimilation

NASA Astrophysics Data System (ADS)

Smith, P. J.; Beven, K.; Panziera, L.

2012-04-01

The issuing of timely flood alerts may be dependant upon the ability to predict future values of water level or discharge at locations where observations are available. Catchments at risk of flash flooding often have a rapid natural response time, typically less then the forecast lead time desired for issuing alerts. This work focuses on the provision of short-range (up to 6 hours lead time) predictions of discharge in small catchments based on utilising radar forecasts to drive a hydrological model. An example analysis based upon the Verzasca catchment (Ticino, Switzerland) is presented. Parsimonious time series models with a mechanistic interpretation (so called Data-Based Mechanistic model) have been shown to provide reliable accurate forecasts in many hydrological situations. In this study such a model is developed to predict the discharge at an observed location from observed precipitation data. The model is shown to capture the snow melt response at this site. Observed discharge data is assimilated to improve the forecasts, of up to two hours lead time, that can be generated from observed precipitation. To generate forecasts with greater lead time ensemble precipitation forecasts are utilised. In this study the Nowcasting ORographic precipitation in the Alps (NORA) product outlined in more detail elsewhere (Panziera et al. Q. J. R. Meteorol. Soc. 2011; DOI:10.1002/qj.878) is utilised. NORA precipitation forecasts are derived from historical analogues based on the radar field and upper atmospheric conditions. As such, they avoid the need to explicitly model the evolution of the rainfall field through for example Lagrangian diffusion. The uncertainty in the forecasts is represented by characterisation of the joint distribution of the observed discharge, the discharge forecast using the (in operational conditions unknown) future observed precipitation and that forecast utilising the NORA ensembles. Constructing the joint distribution in this way allows the full historic record of data at the site to inform the predictive distribution. It is shown that, in part due to the limited availability of forecasts, the uncertainty in the relationship between the NORA based forecasts and other variates dominated the resulting predictive uncertainty.

Unmanned Aircraft System (UAS) service demand 2015 - 2035 : literature review & projections of future usage, technical report, version 1.0 - February 2014

DOT National Transportation Integrated Search

2014-02-01

This report assesses opportunities, risks, and challenges attendant to future development and deployment of UAS within the National Airspace System (NAS) affecting UAS forecast growth from 2015 to 2035. Analysis of four key areas is performed: techno...

California's transition from conventional snowpack measurements to a developing remote sensing capability for water supply forecasting

NASA Technical Reports Server (NTRS)

Brown, A. J.; Peterson, N.

1980-01-01

California's Snow Survey Program and water supply forecasting procedures are described. A review is made of current activities and program direction on such matters as: the growing statewide network of automatic snow sensors; restrictions on the gathering hydrometeorological data in areas designated as wilderness; the use of satellite communications, which both provides a flexible network without mountaintop repeaters and satisfies the need for unobtrusiveness in wilderness areas; and the increasing operational use of snow covered area (SCA) obtained from satellite imagery, which, combined with water equivalent from snow sensors, provides a high correlation to the volumes and rates of snowmelt runoff. Also examined are the advantages of remote sensing; the anticipated effects of a new input of basin wide index of water equivalent, such as the obtained through microwave techniques, on future forecasting opportunities; and the future direction and goals of the California Snow Surveys Program.

Projecting technology change to improve space technology planning and systems management

NASA Astrophysics Data System (ADS)

Walk, Steven Robert

2011-04-01

Projecting technology performance evolution has been improving over the years. Reliable quantitative forecasting methods have been developed that project the growth, diffusion, and performance of technology in time, including projecting technology substitutions, saturation levels, and performance improvements. These forecasts can be applied at the early stages of space technology planning to better predict available future technology performance, assure the successful selection of technology, and improve technology systems management strategy. Often what is published as a technology forecast is simply scenario planning, usually made by extrapolating current trends into the future, with perhaps some subjective insight added. Typically, the accuracy of such predictions falls rapidly with distance in time. Quantitative technology forecasting (QTF), on the other hand, includes the study of historic data to identify one of or a combination of several recognized universal technology diffusion or substitution patterns. In the same manner that quantitative models of physical phenomena provide excellent predictions of system behavior, so do QTF models provide reliable technological performance trajectories. In practice, a quantitative technology forecast is completed to ascertain with confidence when the projected performance of a technology or system of technologies will occur. Such projections provide reliable time-referenced information when considering cost and performance trade-offs in maintaining, replacing, or migrating a technology, component, or system. This paper introduces various quantitative technology forecasting techniques and illustrates their practical application in space technology and technology systems management.

The European Drought Observatory (EDO): Current State and Future Directions

NASA Astrophysics Data System (ADS)

Vogt, J.; Singleton, A.; Sepulcre, G.; Micale, F.; Barbosa, P.

2012-12-01

Europe has repeatedly been affected by droughts, resulting in considerable ecological and economic damage and climate change studies indicate a trend towards increasing climate variability most likely resulting in more frequent drought occurrences also in Europe. Against this background, the European Commission's Joint Research Centre (JRC) is developing methods and tools for assessing, monitoring and forecasting droughts in Europe and develops a European Drought Observatory (EDO) to complement and integrate national activities with a European view. At the core of the European Drought Observatory (EDO) is a portal, including a map server, a metadata catalogue, a media-monitor and analysis tools. The map server presents Europe-wide up-to-date information on the occurrence and severity of droughts, which is complemented by more detailed information provided by regional, national and local observatories through OGC compliant web mapping and web coverage services. In addition, time series of historical maps as well as graphs of the temporal evolution of drought indices for individual grid cells and administrative regions in Europe can be retrieved and analysed. Current work is focusing on validating the available products, improving the functionalities, extending the linkage to additional national and regional drought information systems and improving medium to long-range probabilistic drought forecasting products. Probabilistic forecasts are attractive in that they provide an estimate of the range of uncertainty in a particular forecast. Longer-term goals include the development of long-range drought forecasting products, the analysis of drought hazard and risk, the monitoring of drought impact and the integration of EDO in a global drought information system. The talk will provide an overview on the development and state of EDO, the different products, and the ways to include a wide range of stakeholders (i.e. European, national river basin, and local authorities) in the development of the system as well as an outlook on the future developments.

Development of demand forecasting tool for natural resources recouping from municipal solid waste.

PubMed

Zaman, Atiq Uz; Lehmann, Steffen

2013-10-01

Sustainable waste management requires an integrated planning and design strategy for reliable forecasting of waste generation, collection, recycling, treatment and disposal for the successful development of future residential precincts. The success of the future development and management of waste relies to a high extent on the accuracy of the prediction and on a comprehensive understanding of the overall waste management systems. This study defies the traditional concepts of waste, in which waste was considered as the last phase of production and services, by putting forward the new concept of waste as an intermediate phase of production and services. The study aims to develop a demand forecasting tool called 'zero waste index' (ZWI) for measuring the natural resources recouped from municipal solid waste. The ZWI (ZWI demand forecasting tool) quantifies the amount of virgin materials recovered from solid waste and subsequently reduces extraction of natural resources. In addition, the tool estimates the potential amount of energy, water and emissions avoided or saved by the improved waste management system. The ZWI is tested in a case study of waste management systems in two developed cities: Adelaide (Australia) and Stockholm (Sweden). The ZWI of waste management systems in Adelaide and Stockholm is 0.33 and 0.17 respectively. The study also enumerates per capita energy savings of 2.9 GJ and 2.83 GJ, greenhouse gas emissions reductions of 0.39 tonnes (CO2e) and 0.33 tonnes (CO2e), as well as water savings of 2.8 kL and 0.92 kL in Adelaide and Stockholm respectively.

Planning in the 90's for Community Colleges: Coping with Rapid Change by Linking Futures Research with Professional Development.

ERIC Educational Resources Information Center

Kelly, Diana K.

The rate of the change now occurring outside of community colleges has made long-range planning an especially difficult task. Futures research, which attempts to forecast future scenarios by studying societal, economic, and demographic trends, can be used effectively to facilitate the institutional planning process by anticipating both internal…

Does the OVX matter for volatility forecasting? Evidence from the crude oil market

NASA Astrophysics Data System (ADS)

Lv, Wendai

2018-02-01

In this paper, I investigate that whether the OVX and its truncated parts with a certain threshold can significantly help in forecasting the oil futures price volatility basing on the Heterogeneous Autoregressive model of Realized Volatility (HAR-RV). In-sample estimation results show that the OVX has a significantly positive impact on futures volatility. The impact of large OVX on future volatility has slightly powerful compared to the small ones. Moreover, the HARQ-RV model outperforms the HAR-RV in predicting the oil futures volatility. More importantly, the decomposed OVX have more powerful in forecasting the oil futures price volatility compared to the OVX itself.

Algorithm aversion: people erroneously avoid algorithms after seeing them err.

PubMed

Dietvorst, Berkeley J; Simmons, Joseph P; Massey, Cade

2015-02-01

Research shows that evidence-based algorithms more accurately predict the future than do human forecasters. Yet when forecasters are deciding whether to use a human forecaster or a statistical algorithm, they often choose the human forecaster. This phenomenon, which we call algorithm aversion, is costly, and it is important to understand its causes. We show that people are especially averse to algorithmic forecasters after seeing them perform, even when they see them outperform a human forecaster. This is because people more quickly lose confidence in algorithmic than human forecasters after seeing them make the same mistake. In 5 studies, participants either saw an algorithm make forecasts, a human make forecasts, both, or neither. They then decided whether to tie their incentives to the future predictions of the algorithm or the human. Participants who saw the algorithm perform were less confident in it, and less likely to choose it over an inferior human forecaster. This was true even among those who saw the algorithm outperform the human.

Techniques for water demand analysis and forecasting: Puerto Rico, a case study

USGS Publications Warehouse

Attanasi, E.D.; Close, E.R.; Lopez, M.A.

1975-01-01

The rapid economic growth of the Commonwealth-of Puerto Rico since 1947 has brought public pressure on Government agencies for rapid development of public water supply and waste treatment facilities. Since 1945 the Puerto Rico Aqueduct and Sewer Authority has had the responsibility for planning, developing and operating water supply and waste treatment facilities on a municipal basis. The purpose of this study was to develop operational techniques whereby a planning agency, such as the Puerto Rico Aqueduct and Sewer Authority, could project the temporal and spatial distribution of .future water demands. This report is part of a 2-year cooperative study between the U.S. Geological Survey and the Environmental Quality Board of the Commonwealth of Puerto Rico, for the development of systems analysis techniques for use in water resources planning. While the Commonwealth was assisted in the development of techniques to facilitate ongoing planning, the U.S. Geological Survey attempted to gain insights in order to better interface its data collection efforts with the planning process. The report reviews the institutional structure associated with water resources planning for the Commonwealth. A brief description of alternative water demand forecasting procedures is presented and specific techniques and analyses of Puerto Rico demand data are discussed. Water demand models for a specific area of Puerto Rico are then developed. These models provide a framework for making several sets of water demand forecasts based on alternative economic and demographic assumptions. In the second part of this report, the historical impact of water resources investment on regional economic development is analyzed and related to water demand .forecasting. Conclusions and future data needs are in the last section.

EVENT PREDICTION AND AFFECTIVE FORECASTING IN DEPRESSIVE COGNITION: USING EMOTION AS INFORMATION ABOUT THE FUTURE

PubMed Central

MARROQUÍN, BRETT; NOLEN-HOEKSEMA, SUSAN

2015-01-01

Depression is characterized by a bleak view of the future, but the mechanisms through which depressed mood is integrated into basic processes of future-oriented cognition are unclear. We hypothesized that dysphoric individuals’ predictions of what will happen in the future (likelihood estimation) and how the future will feel (affective forecasting) are attributable to individual differences in incorporating present emotion as judgment-relevant information. Dysphoric individuals (n = 77) made pessimistic likelihood estimates and blunted positive affective forecasts relative to controls (n = 84). These differences were mediated by dysphoric individuals’ tendencies to rely on negative emotion as information more than controls—and on positive emotion less—independent of anhedonia. These findings suggest that (1) blunted positive affective forecasting is a distinctive component of depressive future-oriented cognition, and (2) future-oriented cognitive processes are linked not just to current emotional state, but also to individual variation in using that emotion as information. This role of individual differences elucidates basic mechanisms in future-oriented cognition, and suggests routes for intervention on interrelated cognitive and affective processes in depression. PMID:26146452

EVENT PREDICTION AND AFFECTIVE FORECASTING IN DEPRESSIVE COGNITION: USING EMOTION AS INFORMATION ABOUT THE FUTURE.

PubMed

Marroquín, Brett; Nolen-Hoeksema, Susan

2015-02-01

Depression is characterized by a bleak view of the future, but the mechanisms through which depressed mood is integrated into basic processes of future-oriented cognition are unclear. We hypothesized that dysphoric individuals' predictions of what will happen in the future ( likelihood estimation ) and how the future will feel ( affective forecasting ) are attributable to individual differences in incorporating present emotion as judgment-relevant information. Dysphoric individuals ( n = 77) made pessimistic likelihood estimates and blunted positive affective forecasts relative to controls ( n = 84). These differences were mediated by dysphoric individuals' tendencies to rely on negative emotion as information more than controls-and on positive emotion less-independent of anhedonia. These findings suggest that (1) blunted positive affective forecasting is a distinctive component of depressive future-oriented cognition, and (2) future-oriented cognitive processes are linked not just to current emotional state, but also to individual variation in using that emotion as information. This role of individual differences elucidates basic mechanisms in future-oriented cognition, and suggests routes for intervention on interrelated cognitive and affective processes in depression.

Forecasting Cause-Specific Mortality in Korea up to Year 2032.

PubMed

Yun, Jae-Won; Son, Mia

2016-08-01

Forecasting cause-specific mortality can help estimate the future burden of diseases and provide a clue for preventing diseases. Our objective was to forecast the mortality for causes of death in the future (2013-2032) based on the past trends (1983-2012) in Korea. The death data consisted of 12 major causes of death from 1983 to 2012 and the population data consisted of the observed and estimated populations (1983-2032) in Korea. The modified age-period-cohort model with an R-based program, nordpred software, was used to forecast future mortality. Although the age-standardized rates for the world standard population for both sexes are expected to decrease from 2008-2012 to 2028-2032 (males: -31.4%, females: -32.3%), the crude rates are expected to increase (males: 46.3%, females: 33.4%). The total number of deaths is also estimated to increase (males: 52.7%, females: 41.9%). Additionally, the largest contribution to the overall change in deaths was the change in the age structures. Several causes of death are projected to increase in both sexes (cancer, suicide, heart diseases, pneumonia and Alzheimer's disease), while others are projected to decrease (cerebrovascular diseases, liver diseases, diabetes mellitus, traffic accidents, chronic lower respiratory diseases, and pulmonary tuberculosis). Cancer is expected to be the highest cause of death for both the 2008-2012 and 2028-2032 time periods in Korea. To reduce the disease burden, projections of the future cause-specific mortality should be used as fundamental data for developing public health policies.

Draft Forecasts from Real-Time Runs of Physics-Based Models - A Road to the Future

NASA Technical Reports Server (NTRS)

Hesse, Michael; Rastatter, Lutz; MacNeice, Peter; Kuznetsova, Masha

2008-01-01

The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second focus of CCMC activities is on validation and verification of space weather models, and on the transition of appropriate models to space weather forecast centers. As part of the latter activity, the CCMC develops real-time simulation systems that stress models through routine execution. A by-product of these real-time calculations is the ability to derive model products, which may be useful for space weather operators. After consultations with NOAA/SEC and with AFWA, CCMC has developed a set of tools as a first step to make real-time model output useful to forecast centers. In this presentation, we will discuss the motivation for this activity, the actions taken so far, and options for future tools from model output.

DEFENDER: Detecting and Forecasting Epidemics Using Novel Data-Analytics for Enhanced Response

PubMed Central

Simmie, Donal; Hankin, Chris; Gillard, Joseph

2016-01-01

In recent years social and news media have increasingly been used to explain patterns in disease activity and progression. Social media data, principally from the Twitter network, has been shown to correlate well with official disease case counts. This fact has been exploited to provide advance warning of outbreak detection, forecasting of disease levels and the ability to predict the likelihood of individuals developing symptoms. In this paper we introduce DEFENDER, a software system that integrates data from social and news media and incorporates algorithms for outbreak detection, situational awareness and forecasting. As part of this system we have developed a technique for creating a location network for any country or region based purely on Twitter data. We also present a disease nowcasting (forecasting the current but still unknown level) approach which leverages counts from multiple symptoms, which was found to improve the nowcasting accuracy by 37 percent over a model that used only previous case data. Finally we attempt to forecast future levels of symptom activity based on observed user movement on Twitter, finding a moderate gain of 5 percent over a time series forecasting model. PMID:27192059

Integrating Remote Sensing and Disease Surveillance to Forecast Malaria Epidemics

NASA Astrophysics Data System (ADS)

Wimberly, M. C.; Beyane, B.; DeVos, M.; Liu, Y.; Merkord, C. L.; Mihretie, A.

2015-12-01

Advance information about the timing and locations of malaria epidemics can facilitate the targeting of resources for prevention and emergency response. Early detection methods can detect incipient outbreaks by identifying deviations from expected seasonal patterns, whereas early warning approaches typically forecast future malaria risk based on lagged responses to meteorological factors. A critical limiting factor for implementing either of these approaches is the need for timely and consistent acquisition, processing and analysis of both environmental and epidemiological data. To address this need, we have developed EPIDEMIA - an integrated system for surveillance and forecasting of malaria epidemics. The EPIDEMIA system includes a public health interface for uploading and querying weekly surveillance reports as well as algorithms for automatically validating incoming data and updating the epidemiological surveillance database. The newly released EASTWeb 2.0 software application automatically downloads, processes, and summaries remotely-sensed environmental data from multiple earth science data archives. EASTWeb was implemented as a component of the EPIDEMIA system, which combines the environmental monitoring data and epidemiological surveillance data into a unified database that supports both early detection and early warning models. Dynamic linear models implemented with Kalman filtering were used to carry out forecasting and model updating. Preliminary forecasts have been disseminated to public health partners in the Amhara Region of Ethiopia and will be validated and refined as the EPIDEMIA system ingests new data. In addition to continued model development and testing, future work will involve updating the public health interface to provide a broader suite of outbreak alerts and data visualization tools that are useful to our public health partners. The EPIDEMIA system demonstrates a feasible approach to synthesizing the information from epidemiological surveillance systems and remotely-sensed environmental monitoring systems to improve malaria epidemic detection and forecasting.

Improved Modeling Tools Development for High Penetration Solar

DOE Office of Scientific and Technical Information (OSTI.GOV)

Washom, Byron; Meagher, Kevin

2014-12-11

One of the significant objectives of the High Penetration solar research is to help the DOE understand, anticipate, and minimize grid operation impacts as more solar resources are added to the electric power system. For Task 2.2, an effective, reliable approach to predicting solar energy availability for energy generation forecasts using the University of California, San Diego (UCSD) Sky Imager technology has been demonstrated. Granular cloud and ramp forecasts for the next 5 to 20 minutes over an area of 10 square miles were developed. Sky images taken every 30 seconds are processed to determine cloud locations and cloud motionmore » vectors yielding future cloud shadow locations respective to distributed generation or utility solar power plants in the area. The performance of the method depends on cloud characteristics. On days with more advective cloud conditions, the developed method outperforms persistence forecasts by up to 30% (based on mean absolute error). On days with dynamic conditions, the method performs worse than persistence. Sky Imagers hold promise for ramp forecasting and ramp mitigation in conjunction with inverter controls and energy storage. The pre-commercial Sky Imager solar forecasting algorithm was documented with licensing information and was a Sunshot website highlight.« less

First Assessment of Itaipu Dam Ensemble Inflow Forecasting System

NASA Astrophysics Data System (ADS)

Mainardi Fan, Fernando; Machado Vieira Lisboa, Auder; Gomes Villa Trinidad, Giovanni; Rógenes Monteiro Pontes, Paulo; Collischonn, Walter; Tucci, Carlos; Costa Buarque, Diogo

2017-04-01

Inflow forecasting for Hydropower Plants (HPP) Dams is one of the prominent uses for hydrological forecasts. A very important HPP in terms of energy generation for South America is the Itaipu Dam, located in the Paraná River, between Brazil and Paraguay countries, with a drainage area of 820.000km2. In this work, we present the development of an ensemble forecasting system for Itaipu, operational since November 2015. The system is based in the MGB-IPH hydrological model, includes hydrodynamics simulations of the main river, and is run every day morning forced by seven different rainfall forecasts: (i) CPTEC-ETA 15km; (ii) CPTEC-BRAMS 5km; (iii) SIMEPAR WRF Ferrier; (iv) SIMEPAR WRF Lin; (v) SIMEPAR WRF Morrison; (vi) SIMEPAR WRF WDM6; (vii) SIMEPAR MEDIAN. The last one (vii) corresponds to the median value of SIMEPAR WRF model versions (iii to vi) rainfall forecasts. Besides the developed system, the "traditional" method for inflow forecasting generation for the Itaipu Dam is also run every day. This traditional method consists in the approximation of the future inflow based on the discharge tendency of upstream telemetric gauges. Nowadays, after all the forecasts are run, the hydrology team of Itaipu develop a consensus forecast, based on all obtained results, which is the one used for the Itaipu HPP Dam operation. After one year of operation a first evaluation of the Ensemble Forecasting System was conducted. Results show that the system performs satisfactory for rising flows up to five days lead time. However, some false alarms were also issued by most ensemble members in some cases. And not in all cases the system performed better than the traditional method, especially during hydrograph recessions. In terms of meteorological forecasts, some members usage are being discontinued. In terms of the hydrodynamics representation, it seems that a better information of rivers cross section could improve hydrographs recession curves forecasts. Those opportunities for improvements are currently being addressed in the system next update.

An Approach to Forecasting Health Expenditures, with Application to the U.S. Medicare System

PubMed Central

Lee, Ronald; Miller, Timothy

2002-01-01

Objective To quantify uncertainty in forecasts of health expenditures. Study Design Stochastic time series models are estimated for historical variations in fertility, mortality, and health spending per capita in the United States, and used to generate stochastic simulations of the growth of Medicare expenditures. Individual health spending is modeled to depend on the number of years until death. Data Sources/Study Setting A simple accounting model is developed for forecasting health expenditures, using the U.S. Medicare system as an example. Principal Findings Medicare expenditures are projected to rise from 2.2 percent of GDP (gross domestic product) to about 8 percent of GDP by 2075. This increase is due in equal measure to increasing health spending per beneficiary and to population aging. The traditional projection method constructs high, medium, and low scenarios to assess uncertainty, an approach that has many problems. Using stochastic forecasting, we find a 95 percent probability that Medicare spending in 2075 will fall between 4 percent and 18 percent of GDP, indicating a wide band of uncertainty. Although there is substantial uncertainty about future mortality decline, it contributed little to uncertainty about future Medicare spending, since lower mortality both raises the number of elderly, tending to raise spending, and is associated with improved health of the elderly, tending to reduce spending. Uncertainty about fertility, by contrast, leads to great uncertainty about the future size of the labor force, and therefore adds importantly to uncertainty about the health-share of GDP. In the shorter term, the major source of uncertainty is health spending per capita. Conclusions History is a valuable guide for quantifying our uncertainty about future health expenditures. The probabilistic model we present has several advantages over the high–low scenario approach to forecasting. It indicates great uncertainty about future Medicare expenditures relative to GDP. PMID:12479501

Modelling both dominance and species distribution provides a more complete picture of changes to mangrove ecosystems under climate change.

PubMed

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

2015-08-01

Dominant species influence the composition and abundance of other species present in ecosystems. However, forecasts of distributional change under future climates have predominantly focused on changes in species distribution and ignored possible changes in spatial and temporal patterns of dominance. We develop forecasts of spatial changes for the distribution of species dominance, defined in terms of basal area, and for species occurrence, in response to sea level rise for three tree taxa within an extensive mangrove ecosystem in northern Australia. Three new metrics are provided, indicating the area expected to be suitable under future conditions (Eoccupied ), the instability of suitable area (Einstability ) and the overlap between the current and future spatial distribution (Eoverlap ). The current dominance and occurrence were modelled in relation to a set of environmental variables using boosted regression tree (BRT) models, under two scenarios of seedling establishment: unrestricted and highly restricted. While forecasts of spatial change were qualitatively similar for species occurrence and dominance, the models of species dominance exhibited higher metrics of model fit and predictive performance, and the spatial pattern of future dominance was less similar to the current pattern than was the case for the distributions of species occurrence. This highlights the possibility of greater changes in the spatial patterning of mangrove tree species dominance under future sea level rise. Under the restricted seedling establishment scenario, the area occupied by or dominated by a species declined between 42.1% and 93.8%, while for unrestricted seedling establishment, the area suitable for dominance or occurrence of each species varied from a decline of 68.4% to an expansion of 99.5%. As changes in the spatial patterning of dominance are likely to cause a cascade of effects throughout the ecosystem, forecasting spatial changes in dominance provides new and complementary information in addition to that provided by forecasts of species occurrence. © 2015 John Wiley & Sons Ltd.

The Emergence of Simulation and Gaming.

ERIC Educational Resources Information Center

Becker, Henk A.

1980-01-01

Describes the historical and international development of simulation and gaming in terms of simulation as analytical models, and games as communicative models; and forecasts possible futures of simulation and gaming. (CMV)

Validation of Community Models: 2. Development of a Baseline, Using the Wang-Sheeley-Arge Model

NASA Technical Reports Server (NTRS)

MacNeice, Peter

2009-01-01

This paper is the second in a series providing independent validation of community models of the outer corona and inner heliosphere. Here I present a comprehensive validation of the Wang-Sheeley-Arge (WSA) model. These results will serve as a baseline against which to compare the next generation of comparable forecasting models. The WSA model is used by a number of agencies to predict Solar wind conditions at Earth up to 4 days into the future. Given its importance to both the research and forecasting communities, it is essential that its performance be measured systematically and independently. I offer just such an independent and systematic validation. I report skill scores for the model's predictions of wind speed and interplanetary magnetic field (IMF) polarity for a large set of Carrington rotations. The model was run in all its routinely used configurations. It ingests synoptic line of sight magnetograms. For this study I generated model results for monthly magnetograms from multiple observatories, spanning the Carrington rotation range from 1650 to 2074. I compare the influence of the different magnetogram sources and performance at quiet and active times. I also consider the ability of the WSA model to forecast both sharp transitions in wind speed from slow to fast wind and reversals in the polarity of the radial component of the IMF. These results will serve as a baseline against which to compare future versions of the model as well as the current and future generation of magnetohydrodynamic models under development for forecasting use.

An investigation into incident duration forecasting for FleetForward

DOT National Transportation Integrated Search

2000-08-01

Traffic condition forecasting is the process of estimating future traffic conditions based on current and archived data. Real-time forecasting is becoming an important tool in Intelligent Transportation Systems (ITS). This type of forecasting allows ...

Toward a new Federal policy for technology: The outline emerges

NASA Technical Reports Server (NTRS)

Logsdon, J. M.

1972-01-01

The development is traced of a new federal policy for the support and use of technology. The premises underlying such a policy and forecasts likely future developments are analyzed. The first Presidential message on science and technology is included.

Spatio-Temporal Change Modeling of Lulc: a Semantic Kriging Approach

NASA Astrophysics Data System (ADS)

Bhattacharjee, S.; Ghosh, S. K.

2015-07-01

Spatio-temporal land-use/ land-cover (LULC) change modeling is important to forecast the future LULC distribution, which may facilitate natural resource management, urban planning, etc. The spatio-temporal change in LULC trend often exhibits non-linear behavior, due to various dynamic factors, such as, human intervention (e.g., urbanization), environmental factors, etc. Hence, proper forecasting of LULC distribution should involve the study and trend modeling of historical data. Existing literatures have reported that the meteorological attributes (e.g., NDVI, LST, MSI), are semantically related to the terrain. Being influenced by the terrestrial dynamics, the temporal changes of these attributes depend on the LULC properties. Hence, incorporating meteorological knowledge into the temporal prediction process may help in developing an accurate forecasting model. This work attempts to study the change in inter-annual LULC pattern and the distribution of different meteorological attributes of a region in Kolkata (a metropolitan city in India) during the years 2000-2010 and forecast the future spread of LULC using semantic kriging (SemK) approach. A new variant of time-series SemK is proposed, namely Rev-SemKts to capture the multivariate semantic associations between different attributes. From empirical analysis, it may be observed that the augmentation of semantic knowledge in spatio-temporal modeling of meteorological attributes facilitate more precise forecasting of LULC pattern.

Analysis and Forecasting of Shoreline Position

NASA Astrophysics Data System (ADS)

Barton, C. C.; Tebbens, S. F.

2007-12-01

Analysis of historical shoreline positions on sandy coasts, in the geologic record, and study of sea-level rise curves reveals that the dynamics of the underlying processes produce temporal/spatial signals that exhibit power scaling and are therefore self-affine fractals. Self-affine time series signals can be quantified over many orders of magnitude in time and space in terms of persistence, a measure of the degree of correlation between adjacent values in the stochastic portion of a time series. Fractal statistics developed for self-affine time series are used to forecast a probability envelope bounding future shoreline positions. The envelope provides the standard deviation as a function of three variables: persistence, a constant equal to the value of the power spectral density when 1/period equals 1, and the number of time increments. The persistence of a twenty-year time series of the mean-high-water (MHW) shoreline positions was measured for four profiles surveyed at Duck, NC at the Field Research Facility (FRF) by the U.S. Army Corps of Engineers. The four MHW shoreline time series signals are self-affine with persistence ranging between 0.8 and 0.9, which indicates that the shoreline position time series is weakly persistent (where zero is uncorrelated), and has highly varying trends for all time intervals sampled. Forecasts of a probability envelope for future MHW positions are made for the 20 years of record and beyond to 50 years from the start of the data records. The forecasts describe the twenty-year data sets well and indicate that within a 96% confidence envelope, future decadal MHW shoreline excursions should be within 14.6 m of the position at the start of data collection. This is a stable-oscillatory shoreline. The forecasting method introduced here includes the stochastic portion of the time series while the traditional method of predicting shoreline change reduces the time series to a linear trend line fit to historic shoreline positions and extrapolated linearly to forecast future positions with a linearly increasing mean that breaks the confidence envelope eight years into the future and continues to increase. The traditional method is a poor representation of the observed shoreline position time series and is a poor basis for extrapolating future shoreline positions.

Ecological Forecasting in the Applied Sciences Program and Input to the Decadal Survey

NASA Technical Reports Server (NTRS)

Skiles, Joseph

2015-01-01

Ecological forecasting uses knowledge of physics, ecology and physiology to predict how ecosystems will change in the future in response to environmental factors. Further, Ecological Forecasting employs observations and models to predict the effects of environmental change on ecosystems. In doing so, it applies information from the physical, biological, and social sciences and promotes a scientific synthesis across the domains of physics, geology, chemistry, biology, and psychology. The goal is reliable forecasts that allow decision makers access to science-based tools in order to project changes in living systems. The next decadal survey will direct the development Earth Observation sensors and satellites for the next ten years. It is important that these new sensors and satellites address the requirements for ecosystem models, imagery, and other data for resource management. This presentation will give examples of these model inputs and some resources needed for NASA to continue effective Ecological Forecasting.

Deciding the Future: A Forecast of Responsibilities of Secondary Teachers of English, 1970-2000 AD.

ERIC Educational Resources Information Center

Farrell, Edmund J.

This document is a slightly revised version of author's Ph.D. Dissertation, "A Forecast of Responsibilities of Secondary Teachers of English 1970-2000 A.D., with Implications for Teacher Education" (ED 049 253). A study in two parts, Part I presents the need for future planning in education; discusses briefly methodologies for forecasting the…

Quantifying Uncertainty in Flood Inundation Mapping Using Streamflow Ensembles and Multiple Hydraulic Modeling Techniques

NASA Astrophysics Data System (ADS)

Hosseiny, S. M. H.; Zarzar, C.; Gomez, M.; Siddique, R.; Smith, V.; Mejia, A.; Demir, I.

2016-12-01

The National Water Model (NWM) provides a platform for operationalize nationwide flood inundation forecasting and mapping. The ability to model flood inundation on a national scale will provide invaluable information to decision makers and local emergency officials. Often, forecast products use deterministic model output to provide a visual representation of a single inundation scenario, which is subject to uncertainty from various sources. While this provides a straightforward representation of the potential inundation, the inherent uncertainty associated with the model output should be considered to optimize this tool for decision making support. The goal of this study is to produce ensembles of future flood inundation conditions (i.e. extent, depth, and velocity) to spatially quantify and visually assess uncertainties associated with the predicted flood inundation maps. The setting for this study is located in a highly urbanized watershed along the Darby Creek in Pennsylvania. A forecasting framework coupling the NWM with multiple hydraulic models was developed to produce a suite ensembles of future flood inundation predictions. Time lagged ensembles from the NWM short range forecasts were used to account for uncertainty associated with the hydrologic forecasts. The forecasts from the NWM were input to iRIC and HEC-RAS two-dimensional software packages, from which water extent, depth, and flow velocity were output. Quantifying the agreement between output ensembles for each forecast grid provided the uncertainty metrics for predicted flood water inundation extent, depth, and flow velocity. For visualization, a series of flood maps that display flood extent, water depth, and flow velocity along with the underlying uncertainty associated with each of the forecasted variables were produced. The results from this study demonstrate the potential to incorporate and visualize model uncertainties in flood inundation maps in order to identify the high flood risk zones.

How will climate novelty influence ecological forecasts? Using the Quaternary to assess future reliability.

PubMed

Fitzpatrick, Matthew C; Blois, Jessica L; Williams, John W; Nieto-Lugilde, Diego; Maguire, Kaitlin C; Lorenz, David J

2018-03-23

Future climates are projected to be highly novel relative to recent climates. Climate novelty challenges models that correlate ecological patterns to climate variables and then use these relationships to forecast ecological responses to future climate change. Here, we quantify the magnitude and ecological significance of future climate novelty by comparing it to novel climates over the past 21,000 years in North America. We then use relationships between model performance and climate novelty derived from the fossil pollen record from eastern North America to estimate the expected decrease in predictive skill of ecological forecasting models as future climate novelty increases. We show that, in the high emissions scenario (RCP 8.5) and by late 21st century, future climate novelty is similar to or higher than peak levels of climate novelty over the last 21,000 years. The accuracy of ecological forecasting models is projected to decline steadily over the coming decades in response to increasing climate novelty, although models that incorporate co-occurrences among species may retain somewhat higher predictive skill. In addition to quantifying future climate novelty in the context of late Quaternary climate change, this work underscores the challenges of making reliable forecasts to an increasingly novel future, while highlighting the need to assess potential avenues for improvement, such as increased reliance on geological analogs for future novel climates and improving existing models by pooling data through time and incorporating assemblage-level information. © 2018 John Wiley & Sons Ltd.

Influenza forecasting with Google Flu Trends.

PubMed

Dugas, Andrea Freyer; Jalalpour, Mehdi; Gel, Yulia; Levin, Scott; Torcaso, Fred; Igusa, Takeru; Rothman, Richard E

2013-01-01

We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy. Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004-2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets. Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.

A Crisis in Space--A Futuristic Simulation Using Creative Problem Solving.

ERIC Educational Resources Information Center

Clode, Linda

1992-01-01

An enrichment program developed for sixth-grade gifted students combined creative problem solving with future studies in a way that would simulate real life crisis problem solving. The program involved forecasting problems of the future requiring evacuation of Earth, assuming roles on a spaceship, and simulating crises as the spaceship traveled to…

Early Transition and Use of VIIRS and GOES-R Products by NWS Forecast Offices

NASA Technical Reports Server (NTRS)

Fuell, Kevin K.; Smith, Mathew; Jedlovec, Gary

2012-01-01

The Visible Infrared Imaging Radiometer Suite (VIIRS) on the NPOESS Preparatory Project (NPP) satellite, part of the Joint Polar Satellite System (JPSS), and the ABI and GLM sensors scheduled for the GOES-R geostationary satellite will bring advanced observing capabilities to the operational weather community. The NASA Short-term Prediction Research and Transition (SPoRT) project at Marshall Space Flight Center has been facilitating the use of real-time experimental and research satellite data by NWS Weather Forecast Offices (WFOs) for a number of years to demonstrate the planned capabilities of future sensors to address particular forecast challenges through improve situational awareness and short-term weather forecasts. For the NOAA GOES-R Proving Ground (PG) activity, SPoRT is developing and disseminating selected GOES-R proxy products to collaborating WFOs and National Centers. SPoRT developed the a pseudo-Geostationary Lightning Mapper product and helped in the transition of the Algorithm Working Group (AWG) Convective Initiation (CI) proxy product for the Hazardous Weather Testbed (HWT) Spring Experiment,. Along with its partner WFOs, SPoRT is evaluating MODIS/GOES Hybrid products, which brings ABI-like data sets from existing NASA instrumentation in front of the forecaster for everyday use. The Hybrid uses near real-time MODIS imagery to demonstrate future ABI capabilities, while utilizing standard GOES imagery to provide the temporal frequency of geostationary imagery expected by operational forecasters. In addition, SPoRT is collaborating with the GOES-R hydrology AWG to transition a baseline proxy product for rainfall rate / quantitative precipitation estimate (QPE) to the OCONUS regions. For VIIRS, SPoRT is demonstrating multispectral observing capabilities and the utility of low-light channels not previously available on operational weather satellites to address a variety of weather forecast challenges. This presentation will discuss the results of transitioning these products to collaborating WFOs throughout the country.

Research and Development for Technology Evolution Potential Forecasting System

NASA Astrophysics Data System (ADS)

Gao, Changqing; Cao, Shukun; Wang, Yuzeng; Ai, Changsheng; Ze, Xiangbo

Technology forecasting is a powerful weapon for many enterprises to gain an animate future. Evolutionary potential radar plot is a necessary step of some valuable methods to help the technology managers with right technical strategy. A software system for Technology Evolution Potential Forecasting (TEPF) with automatic radar plot drawing is introduced in this paper. The framework of the system and the date structure describing the concrete evolution pattern are illustrated in details. And the algorithm for radar plot drawing is researched. It is proved that the TEPF system is an effective tool during the technology strategy analyzing process with a referenced case study.

Towards real-time eruption forecasting in the Auckland Volcanic Field: application of BET_EF during the New Zealand National Disaster Exercise `Ruaumoko'

NASA Astrophysics Data System (ADS)

Lindsay, Jan; Marzocchi, Warner; Jolly, Gill; Constantinescu, Robert; Selva, Jacopo; Sandri, Laura

2010-03-01

The Auckland Volcanic Field (AVF) is a young basaltic field that lies beneath the urban area of Auckland, New Zealand’s largest city. Over the past 250,000 years the AVF has produced at least 49 basaltic centers; the last eruption was only 600 years ago. In recognition of the high risk associated with a possible future eruption in Auckland, the New Zealand government ran Exercise Ruaumoko in March 2008, a test of New Zealand’s nation-wide preparedness for responding to a major disaster resulting from a volcanic eruption in Auckland City. The exercise scenario was developed in secret, and covered the period of precursory activity up until the eruption. During Exercise Ruaumoko we adapted a recently developed statistical code for eruption forecasting, namely BET_EF (Bayesian Event Tree for Eruption Forecasting), to independently track the unrest evolution and to forecast the most likely onset time, location and style of the initial phase of the simulated eruption. The code was set up before the start of the exercise by entering reliable information on the past history of the AVF as well as the monitoring signals expected in the event of magmatic unrest and an impending eruption. The average probabilities calculated by BET_EF during Exercise Ruaumoko corresponded well to the probabilities subjectively (and independently) estimated by the advising scientists (differences of few percentage units), and provided a sound forecast of the timing (before the event, the eruption probability reached 90%) and location of the eruption. This application of BET_EF to a volcanic field that has experienced no historical activity and for which otherwise limited prior information is available shows its versatility and potential usefulness as a tool to aid decision-making for a wide range of volcano types. Our near real-time application of BET_EF during Exercise Ruaumoko highlighted its potential to clarify and possibly optimize decision-making procedures in a future AVF eruption crisis, and as a rational starting point for discussions in a scientific advisory group. It also stimulated valuable scientific discussion around how a future AVF eruption might progress, and highlighted areas of future volcanological research that would reduce epistemic uncertainties through the development of better input models.

Spaceborne sensors (1983-2000 AD): A forecast of technology

NASA Technical Reports Server (NTRS)

Kostiuk, T.; Clark, B. P.

1984-01-01

A technical review and forecast of space technology as it applies to spaceborne sensors for future NASA missions is presented. A format for categorization of sensor systems covering the entire electromagnetic spectrum, including particles and fields is developed. Major generic sensor systems are related to their subsystems, components, and to basic research and development. General supporting technologies such as cryogenics, optical design, and data processing electronics are addressed where appropriate. The dependence of many classes of instruments on common components, basic R&D and support technologies is also illustrated. A forecast of important system designs and instrument and component performance parameters is provided for the 1983-2000 AD time frame. Some insight into the scientific and applications capabilities and goals of the sensor systems is also given.

Space Weather Products at the Community Coordinated Modeling Center

NASA Technical Reports Server (NTRS)

Hesse, Michael; Kuznetsova, M.; Pulkkinen, A.; Maddox, M.; Rastaetter, L.; Berrios, D.; MacNeice, P.

2010-01-01

The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second CCMC activity is to support Space Weather forecasting at national Space Weather Forecasting Centers. This second activity involves model evaluations, model transitions to operations, and the development of space weather forecasting tools. Owing to the pace of development in the science community, new model capabilities emerge frequently. Consequently, space weather products and tools involve not only increased validity, but often entirely new capabilities. This presentation will review the present state of space weather tools as well as point out emerging future capabilities.

Demographic Analysis and Planning for the Future. No. 13.

ERIC Educational Resources Information Center

Efird, Cathy M.

The basic sources and types of demographic data available for future planning for the developmentally disabled are reviewed and a frame work for data organization is suggested. It is explained that future forecasts may be undertaken by the following principles: trend forecasting or extrapolation; scenario construction; models, games, and…

Natural Gas Prices Forecast Comparison--AEO vs. Natural Gas Markets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wong-Parodi, Gabrielle; Lekov, Alex; Dale, Larry

This paper evaluates the accuracy of two methods to forecast natural gas prices: using the Energy Information Administration's ''Annual Energy Outlook'' forecasted price (AEO) and the ''Henry Hub'' compared to U.S. Wellhead futures price. A statistical analysis is performed to determine the relative accuracy of the two measures in the recent past. A statistical analysis suggests that the Henry Hub futures price provides a more accurate average forecast of natural gas prices than the AEO. For example, the Henry Hub futures price underestimated the natural gas price by 35 cents per thousand cubic feet (11.5 percent) between 1996 and 2003more » and the AEO underestimated by 71 cents per thousand cubic feet (23.4 percent). Upon closer inspection, a liner regression analysis reveals that two distinct time periods exist, the period between 1996 to 1999 and the period between 2000 to 2003. For the time period between 1996 to 1999, AEO showed a weak negative correlation (R-square = 0.19) between forecast price by actual U.S. Wellhead natural gas price versus the Henry Hub with a weak positive correlation (R-square = 0.20) between forecasted price and U.S. Wellhead natural gas price. During the time period between 2000 to 2003, AEO shows a moderate positive correlation (R-square = 0.37) between forecasted natural gas price and U.S. Wellhead natural gas price versus the Henry Hub that show a moderate positive correlation (R-square = 0.36) between forecast price and U.S. Wellhead natural gas price. These results suggest that agencies forecasting natural gas prices should consider incorporating the Henry Hub natural gas futures price into their forecasting models along with the AEO forecast. Our analysis is very preliminary and is based on a very small data set. Naturally the results of the analysis may change, as more data is made available.« less

Comparison of the economic impact of different wind power forecast systems for producers

NASA Astrophysics Data System (ADS)

Alessandrini, S.; Davò, F.; Sperati, S.; Benini, M.; Delle Monache, L.

2014-05-01

Deterministic forecasts of wind production for the next 72 h at a single wind farm or at the regional level are among the main end-users requirement. However, for an optimal management of wind power production and distribution it is important to provide, together with a deterministic prediction, a probabilistic one. A deterministic forecast consists of a single value for each time in the future for the variable to be predicted, while probabilistic forecasting informs on probabilities for potential future events. This means providing information about uncertainty (i.e. a forecast of the PDF of power) in addition to the commonly provided single-valued power prediction. A significant probabilistic application is related to the trading of energy in day-ahead electricity markets. It has been shown that, when trading future wind energy production, using probabilistic wind power predictions can lead to higher benefits than those obtained by using deterministic forecasts alone. In fact, by using probabilistic forecasting it is possible to solve economic model equations trying to optimize the revenue for the producer depending, for example, on the specific penalties for forecast errors valid in that market. In this work we have applied a probabilistic wind power forecast systems based on the "analog ensemble" method for bidding wind energy during the day-ahead market in the case of a wind farm located in Italy. The actual hourly income for the plant is computed considering the actual selling energy prices and penalties proportional to the unbalancing, defined as the difference between the day-ahead offered energy and the actual production. The economic benefit of using a probabilistic approach for the day-ahead energy bidding are evaluated, resulting in an increase of 23% of the annual income for a wind farm owner in the case of knowing "a priori" the future energy prices. The uncertainty on price forecasting partly reduces the economic benefit gained by using a probabilistic energy forecast system.

Integrated urban systems model with multiple transportation supply agents.

DOT National Transportation Integrated Search

2012-10-01

This project demonstrates the feasibility of developing quantitative models that can forecast future networks under : current and alternative transportation planning processes. The current transportation planning process is modeled : based on empiric...

Research on time series data prediction based on clustering algorithm - A case study of Yuebao

NASA Astrophysics Data System (ADS)

Lu, Xu; Zhao, Tianzhong

2017-08-01

Forecasting is the prerequisite for making scientific decisions, it is based on the past information of the research on the phenomenon, and combined with some of the factors affecting this phenomenon, then using scientific methods to forecast the development trend of the future, it is an important way for people to know the world. This is particularly important in the prediction of financial data, because proper financial data forecasts can provide a great deal of help to financial institutions in their strategic implementation, strategic alignment and risk control. However, the current forecasts of financial data generally use the method of forecast of overall data, which lack of consideration of customer behavior and other factors in the financial data forecasting process, and they are important factors influencing the change of financial data. Based on this situation, this paper analyzed the data of Yuebao, and according to the user's attributes and the operating characteristics, this paper classified 567 users of Yuebao, and made further predicted the data of Yuebao for every class of users, the results showed that the forecasting model in this paper can meet the demand of forecasting.

A Model For Change: An Approach for Forecasting Well-Being ...

EPA Pesticide Factsheets

Every community decision incorporates a "forecasting" strategy (whether formal or implicit) to help visualize expected results and evaluate the potential “feelings” that people living in that community may have about those results. With more communities seeking to make decisions based on sustainable alternatives, forecasting efforts that examine potential impacts of decisions on overall community well-being may prove to be valuable for not only gaging future benefits and trade-offs, but also for recognizing a community’s affective response to the outcomes of those decisions. This paper describes a forecasting approach based on concepts introduced in the development of the U.S. Environmental Protection Agency’s (US EPA) Human Well-Being Index (HWBI) (Smith, et. al. 2014; Summers et al. 2014). The approach examines the relationships among selected economic, environmental and social services that can be directly impacted by community decisions and eight domains of human well-being. Using models developed from constructed- or fixed-effect step-wise and multiple regressions and eleven years of data (2000-2010), these relationship functions may be used to characterize likely direct impacts of decisions on future well-being as well as the possible intended and unintended secondary and tertiary effects relative to any main decision effects. This paper describes an approach to using HWBI in decision making models to characterize likely impacts of decisions on fut

Uses and Applications of Climate Forecasts for Power Utilities.

NASA Astrophysics Data System (ADS)

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

1995-05-01

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

Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence.

PubMed

Anwar, Mohammad Y; Lewnard, Joseph A; Parikh, Sunil; Pitzer, Virginia E

2016-11-22

Malaria remains endemic in Afghanistan. National control and prevention strategies would be greatly enhanced through a better ability to forecast future trends in disease incidence. It is, therefore, of interest to develop a predictive tool for malaria patterns based on the current passive and affordable surveillance system in this resource-limited region. This study employs data from Ministry of Public Health monthly reports from January 2005 to September 2015. Malaria incidence in Afghanistan was forecasted using autoregressive integrated moving average (ARIMA) models in order to build a predictive tool for malaria surveillance. Environmental and climate data were incorporated to assess whether they improve predictive power of models. Two models were identified, each appropriate for different time horizons. For near-term forecasts, malaria incidence can be predicted based on the number of cases in the four previous months and 12 months prior (Model 1); for longer-term prediction, malaria incidence can be predicted using the rates 1 and 12 months prior (Model 2). Next, climate and environmental variables were incorporated to assess whether the predictive power of proposed models could be improved. Enhanced vegetation index was found to have increased the predictive accuracy of longer-term forecasts. Results indicate ARIMA models can be applied to forecast malaria patterns in Afghanistan, complementing current surveillance systems. The models provide a means to better understand malaria dynamics in a resource-limited context with minimal data input, yielding forecasts that can be used for public health planning at the national level.

A cross impact methodology for the assessment of US telecommunications system with application to fiber optics development: Executive summary

NASA Technical Reports Server (NTRS)

Martino, J. P.; Lenz, R. C., Jr.; Chen, K. L.

1979-01-01

A cross impact model of the U.S. telecommunications system was developed. For this model, it was necessary to prepare forecasts of the major segments of the telecommunications system, such as satellites, telephone, TV, CATV, radio broadcasting, etc. In addition, forecasts were prepared of the traffic generated by a variety of new or expanded services, such as electronic check clearing and point of sale electronic funds transfer. Finally, the interactions among the forecasts were estimated (the cross impacts). Both the forecasts and the cross impacts were used as inputs to the cross impact model, which could then be used to stimulate the future growth of the entire U.S. telecommunications system. By varying the inputs, technology changes or policy decisions with regard to any segment of the system could be evaluated in the context of the remainder of the system. To illustrate the operation of the model, a specific study was made of the deployment of fiber optics, throughout the telecommunications system.

A cross impact methodology for the assessment of US telecommunications system with application to fiber optics development, volume 1

NASA Technical Reports Server (NTRS)

Martino, J. P.; Lenz, R. C., Jr.; Chen, K. L.; Kahut, P.; Sekely, R.; Weiler, J.

1979-01-01

A cross impact model of the U.S. telecommunications system was developed. It was necessary to prepare forecasts of the major segments of the telecommunications system, such as satellites, telephone, TV, CATV, radio broadcasting, etc. In addition, forecasts were prepared of the traffic generated by a variety of new or expanded services, such as electronic check clearing and point of sale electronic funds transfer. Finally, the interactions among the forecasts were estimated (the cross impact). Both the forecasts and the cross impacts were used as inputs to the cross impact model, which could then be used to stimulate the future growth of the entire U.S. telecommunications system. By varying the inputs, technology changes or policy decisions with regard to any segment of the system could be evaluated in the context of the remainder of the system. To illustrate the operation of the model, a specific study was made of the deployment of fiber optics throughout the telecommunications system.

Type- and Subtype-Specific Influenza Forecast.

PubMed

Kandula, Sasikiran; Yang, Wan; Shaman, Jeffrey

2017-03-01

Prediction of the growth and decline of infectious disease incidence has advanced considerably in recent years. As these forecasts improve, their public health utility should increase, particularly as interventions are developed that make explicit use of forecast information. It is the task of the research community to increase the content and improve the accuracy of these infectious disease predictions. Presently, operational real-time forecasts of total influenza incidence are produced at the municipal and state level in the United States. These forecasts are generated using ensemble simulations depicting local influenza transmission dynamics, which have been optimized prior to forecast with observations of influenza incidence and data assimilation methods. Here, we explore whether forecasts targeted to predict influenza by type and subtype during 2003-2015 in the United States were more or less accurate than forecasts targeted to predict total influenza incidence. We found that forecasts separated by type/subtype generally produced more accurate predictions and, when summed, produced more accurate predictions of total influenza incidence. These findings indicate that monitoring influenza by type and subtype not only provides more detailed observational content but supports more accurate forecasting. More accurate forecasting can help officials better respond to and plan for current and future influenza activity. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Results of the Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California.

PubMed

Lee, Ya-Ting; Turcotte, Donald L; Holliday, James R; Sachs, Michael K; Rundle, John B; Chen, Chien-Chih; Tiampo, Kristy F

2011-10-04

The Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California was the first competitive evaluation of forecasts of future earthquake occurrence. Participants submitted expected probabilities of occurrence of M ≥ 4.95 earthquakes in 0.1° × 0.1° cells for the period 1 January 1, 2006, to December 31, 2010. Probabilities were submitted for 7,682 cells in California and adjacent regions. During this period, 31 M ≥ 4.95 earthquakes occurred in the test region. These earthquakes occurred in 22 test cells. This seismic activity was dominated by earthquakes associated with the M = 7.2, April 4, 2010, El Mayor-Cucapah earthquake in northern Mexico. This earthquake occurred in the test region, and 16 of the other 30 earthquakes in the test region could be associated with it. Nine complete forecasts were submitted by six participants. In this paper, we present the forecasts in a way that allows the reader to evaluate which forecast is the most "successful" in terms of the locations of future earthquakes. We conclude that the RELM test was a success and suggest ways in which the results can be used to improve future forecasts.

Results of the Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California

PubMed Central

Lee, Ya-Ting; Turcotte, Donald L.; Holliday, James R.; Sachs, Michael K.; Rundle, John B.; Chen, Chien-Chih; Tiampo, Kristy F.

2011-01-01

The Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California was the first competitive evaluation of forecasts of future earthquake occurrence. Participants submitted expected probabilities of occurrence of M≥4.95 earthquakes in 0.1° × 0.1° cells for the period 1 January 1, 2006, to December 31, 2010. Probabilities were submitted for 7,682 cells in California and adjacent regions. During this period, 31 M≥4.95 earthquakes occurred in the test region. These earthquakes occurred in 22 test cells. This seismic activity was dominated by earthquakes associated with the M = 7.2, April 4, 2010, El Mayor–Cucapah earthquake in northern Mexico. This earthquake occurred in the test region, and 16 of the other 30 earthquakes in the test region could be associated with it. Nine complete forecasts were submitted by six participants. In this paper, we present the forecasts in a way that allows the reader to evaluate which forecast is the most “successful” in terms of the locations of future earthquakes. We conclude that the RELM test was a success and suggest ways in which the results can be used to improve future forecasts. PMID:21949355

Short Term Weather Forecasting and Long Term Climate Predictions in Mesoamerica

NASA Astrophysics Data System (ADS)

Hardin, D. M.; Daniel, I.; Mecikalski, J.; Graves, S.

2008-05-01

The SERVIR project utilizes several predictive models to support regional monitoring and decision support in Mesoamerica. Short term forecasts ranging from a few hours to several days produce more than 30 data products that are used daily by decision makers, as well as news organizations in the region. The forecast products can be visualized in both two and three dimensional viewers such as Google Maps and Google Earth. Other viewers developed specifically for the Mesoamerican region by the University of Alabama in Huntsville and the Institute for the Application of Geospatial Technologies in Auburn New York can also be employed. In collaboration with the NASA Short Term Prediction Research and Transition (SpoRT) Center SERVIR utilizes the Weather Research and Forecast (WRF) model to produce short-term (24 hr) regional weather forecasts twice a day. Temperature, precipitation, wind, and other variables are forecast in 10km and 30km grids over the Mesoamerica region. Using the PSU/NCAR Mesoscale Model, known as MM5, SERVIR produces 48 hour- forecasts of soil temperature, two meter surface temperature, three hour accumulated precipitation, winds at different heights, and other variables. These are forecast hourly in 9km grids. Working in collaboration with the Atmospheric Science Department of the University of Alabama in Huntsville produces a suite of short-term (0-6 hour) weather prediction products are generated. These "convective initiation" products predict the onset of thunderstorm rainfall and lightning within a 1-hour timeframe. Models are also employed for long term predictions. The SERVIR project, under USAID funding, has developed comprehensive regional climate change scenarios of Mesoamerica for future years: 2010, 2015, 2025, 2050, and 2099. These scenarios were created using the Pennsylvania State University/National Center for Atmospheric Research (MM5) model and processed on the Oak Ridge National Laboratory Cheetah supercomputer. The goal of these Mesoamerican climate change scenarios is to better understand the regional climate, the major controls, and how it might be expected to change in the future. This presentation will present a summary of the model results and show the application of these data in preparation for and response to recent tropical storms.

Flood Risk Assessment and Forecasting for the Ganges-Brahmaputra-Meghna River Basins

NASA Astrophysics Data System (ADS)

Hopson, T. M.; Priya, S.; Young, W.; Avasthi, A.; Clayton, T. D.; Brakenridge, G. R.; Birkett, C. M.; Riddle, E. E.; Broman, D.; Boehnert, J.; Sampson, K. M.; Kettner, A.; Singh, D.

2017-12-01

During the 2017 South Asia monsoon, torrential rains and catastrophic floods affected more than 45 million people, including 16 million children, across the Ganges-Brahmaputra-Meghna (GBM) basins. The basin is recognized as one of the world's most disaster-prone regions, with severe floods occurring almost annually causing extreme loss of life and property. In light of this vulnerability, the World Bank and collaborators have contributed toward reducing future flood impacts through recent developments to improve operational preparedness for such events, as well as efforts in more general preparedness and resilience building through planning based on detailed risk assessments. With respect to improved event-specific flood preparedness through operational warnings, we discuss a new forecasting system that provides probability-based flood forecasts developed for more than 85 GBM locations. Forecasts are available online, along with near-real-time data maps of rainfall (predicted and actual) and river levels. The new system uses multiple data sets and multiple models to enhance forecasting skill, and provides improved forecasts up to 16 days in advance of the arrival of high waters. These longer lead times provide the opportunity to save both lives and livelihoods. With sufficient advance notice, for example, farmers can harvest a threatened rice crop or move vulnerable livestock to higher ground. Importantly, the forecasts not only predict future water levels but indicate the level of confidence in each forecast. Knowing whether the probability of a danger-level flood is 10 percent or 90 percent helps people to decide what, if any, action to take. With respect to efforts in general preparedness and resilience building, we also present a recent flood risk assessment, and how it provides, for the first time, a numbers-based view of the impacts of different size floods across the Ganges basin. The findings help identify priority areas for tackling flood risks (for example, relocating levees, improving flood warning systems, or boosting overall economic resilience). The assessment includes the locations and numbers of people at risk, as well as the locations and value of buildings, roads and railways, and crops at risk. An accompanying atlas includes easy-to-use risk maps and tables for the Ganges basins.

Forecasting Cause-Specific Mortality in Korea up to Year 2032

PubMed Central

2016-01-01

Forecasting cause-specific mortality can help estimate the future burden of diseases and provide a clue for preventing diseases. Our objective was to forecast the mortality for causes of death in the future (2013-2032) based on the past trends (1983-2012) in Korea. The death data consisted of 12 major causes of death from 1983 to 2012 and the population data consisted of the observed and estimated populations (1983-2032) in Korea. The modified age-period-cohort model with an R-based program, nordpred software, was used to forecast future mortality. Although the age-standardized rates for the world standard population for both sexes are expected to decrease from 2008-2012 to 2028-2032 (males: -31.4%, females: -32.3%), the crude rates are expected to increase (males: 46.3%, females: 33.4%). The total number of deaths is also estimated to increase (males: 52.7%, females: 41.9%). Additionally, the largest contribution to the overall change in deaths was the change in the age structures. Several causes of death are projected to increase in both sexes (cancer, suicide, heart diseases, pneumonia and Alzheimer’s disease), while others are projected to decrease (cerebrovascular diseases, liver diseases, diabetes mellitus, traffic accidents, chronic lower respiratory diseases, and pulmonary tuberculosis). Cancer is expected to be the highest cause of death for both the 2008-2012 and 2028-2032 time periods in Korea. To reduce the disease burden, projections of the future cause-specific mortality should be used as fundamental data for developing public health policies. PMID:27478326

Studies in short haul air transportation in the California corridor: Effects of design runway length; community acceptance; impact of return on investment and fuel cost increases. Volume 2: Appendices

NASA Technical Reports Server (NTRS)

Shevell, R. S.; Jones, D. W., Jr.

1973-01-01

The development of a forecast model for short haul air transportation systems in the California Corridor is discussed. The factors which determine the level of air traffic demand are identified. A forecast equation for use in airport utilization analysis is developed. A mathematical model is submitted to show the relationship between population, employment, and income for indicating future air transportation utilization. Diagrams and tables of data are included to support the conclusions reached regarding air transportation economic factors.

When idols look into the future: fair treatment modulates the affective forecasting error in talent show candidates.

PubMed

Feys, Marjolein; Anseel, Frederik

2015-03-01

People's affective forecasts are often inaccurate because they tend to overestimate how they will feel after an event. As life decisions are often based on affective forecasts, it is crucial to find ways to manage forecasting errors. We examined the impact of a fair treatment on forecasting errors in candidates in a Belgian reality TV talent show. We found that perceptions of fair treatment increased the forecasting error for losers (a negative audition decision) but decreased it for winners (a positive audition decision). For winners, this effect was even more pronounced when candidates were highly invested in their self-view as a future pop idol whereas for losers, the effect was more pronounced when importance was low. The results in this study point to a potential paradox between maximizing happiness and decreasing forecasting errors. A fair treatment increased the forecasting error for losers, but actually made them happier. © 2014 The British Psychological Society.

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

NASA Astrophysics Data System (ADS)

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

2017-09-01

Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better) rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009) are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean) rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region). Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 %) in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows), whereas for the 3-month ahead lead time, GloSea5 forecasts account for ˜ 70 % of the forecast skill (mostly in areas of high rainfall to the north and west) and only 30 % of the skill arises from hydrological memory (typically groundwater-dominated areas). Given the high spatial heterogeneity in typical patterns of UK rainfall and evaporation, future development of skilful spatially distributed seasonal forecasts could lead to substantial improvements in seasonal flow forecast capability, potentially benefitting practitioners interested in predicting hydrological extremes, not only in the UK but also across Europe.

A framework for developing foresight in natural resource management

Treesearch

Kay E. Strong

2012-01-01

This paper describes a fundamental framework for anticipating and influencing the future that has been used to prepare professional futurists at the University of Houston for more than 35 years. The overview of the framework addresses how futures researchers organize information about changes in the world (e.g., by defining the domain, or scope, of the forecasting...

Future Scenarios in Communications. [Student's Guide.] Preparing for Tomorrow's World.

ERIC Educational Resources Information Center

Iozzi, Louis A.; And Others

The purpose of this module is to introduce students (grades 7-8) to the concept of change and factors influencing change. The module is composed of two major sections. Section 1 examines the development of the telephone system in the United States and introduces four futures forecasting techniques (Delphi probe, cross-impact matrix, trend…

An online tool for Operational Probabilistic Drought Forecasting System (OPDFS): a Statistical-Dynamical Framework

NASA Astrophysics Data System (ADS)

Zarekarizi, M.; Moradkhani, H.; Yan, H.

2017-12-01

The Operational Probabilistic Drought Forecasting System (OPDFS) is an online tool recently developed at Portland State University for operational agricultural drought forecasting. This is an integrated statistical-dynamical framework issuing probabilistic drought forecasts monthly for the lead times of 1, 2, and 3 months. The statistical drought forecasting method utilizes copula functions in order to condition the future soil moisture values on the antecedent states. Due to stochastic nature of land surface properties, the antecedent soil moisture states are uncertain; therefore, data assimilation system based on Particle Filtering (PF) is employed to quantify the uncertainties associated with the initial condition of the land state, i.e. soil moisture. PF assimilates the satellite soil moisture data to Variable Infiltration Capacity (VIC) land surface model and ultimately updates the simulated soil moisture. The OPDFS builds on the NOAA's seasonal drought outlook by offering drought probabilities instead of qualitative ordinal categories and provides the user with the probability maps associated with a particular drought category. A retrospective assessment of the OPDFS showed that the forecasting of the 2012 Great Plains and 2014 California droughts were possible at least one month in advance. The OPDFS offers a timely assistance to water managers, stakeholders and decision-makers to develop resilience against uncertain upcoming droughts.

Drought forecasting in Luanhe River basin involving climatic indices

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Probabilistic empirical prediction of seasonal climate: evaluation and potential applications

NASA Astrophysics Data System (ADS)

Dieppois, B.; Eden, J.; van Oldenborgh, G. J.

2017-12-01

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a new evaluation of an established empirical system used to predict seasonal climate across the globe. Forecasts for surface air temperature, precipitation and sea level pressure are produced by the KNMI Probabilistic Empirical Prediction (K-PREP) system every month and disseminated via the KNMI Climate Explorer (climexp.knmi.nl). K-PREP is based on multiple linear regression and built on physical principles to the fullest extent with predictive information taken from the global CO2-equivalent concentration, large-scale modes of variability in the climate system and regional-scale information. K-PREP seasonal forecasts for the period 1981-2016 will be compared with corresponding dynamically generated forecasts produced by operational forecast systems. While there are many regions of the world where empirical forecast skill is extremely limited, several areas are identified where K-PREP offers comparable skill to dynamical systems. We discuss two key points in the future development and application of the K-PREP system: (a) the potential for K-PREP to provide a more useful basis for reference forecasts than those based on persistence or climatology, and (b) the added value of including K-PREP forecast information in multi-model forecast products, at least for known regions of good skill. We also discuss the potential development of stakeholder-driven applications of the K-PREP system, including empirical forecasts for circumboreal fire activity.

Engaging Earth- and Environmental-Science Undergraduates Through Weather Discussions and an eLearning Weather Forecasting Contest

NASA Astrophysics Data System (ADS)

Schultz, David M.; Anderson, Stuart; Seo-Zindy, Ryo

2013-06-01

For students who major in meteorology, engaging in weather forecasting can motivate learning, develop critical-thinking skills, improve their written communication, and yield better forecasts. Whether such advances apply to students who are not meteorology majors has been less demonstrated. To test this idea, a weather discussion and an eLearning weather forecasting contest were devised for a meteorology course taken by third-year undergraduate earth- and environmental-science students. The discussion consisted of using the recent, present, and future weather to amplify the topics of the week's lectures. Then, students forecasted the next day's high temperature and the probability of precipitation for Woodford, the closest official observing site to Manchester, UK. The contest ran for 10 weeks, and the students received credit for participation. The top students at the end of the contest received bonus points on their final grade. A Web-based forecast contest application was developed to register the students, receive their forecasts, and calculate weekly standings. Students who were successful in the forecast contest were not necessarily those who achieved the highest scores on the tests, demonstrating that the contest was possibly testing different skills than traditional learning. Student evaluations indicate that the weather discussion and contest were reasonably successful in engaging students to learn about the weather outside of the classroom, synthesize their knowledge from the lectures, and improve their practical understanding of the weather. Therefore, students taking a meteorology class, but not majoring in meteorology, can derive academic benefits from weather discussions and forecast contests. Nevertheless, student evaluations also indicate that better integration of the lectures, weather discussions, and the forecasting contests is necessary.

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

NASA Astrophysics Data System (ADS)

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.

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Singh, Vijay P.; Xia, Youlong

2018-03-01

Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large-scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high-quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

Net-zero Building Cluster Simulations and On-line Energy Forecasting for Adaptive and Real-Time Control and Decisions

NASA Astrophysics Data System (ADS)

Li, Xiwang

Buildings consume about 41.1% of primary energy and 74% of the electricity in the U.S. Moreover, it is estimated by the National Energy Technology Laboratory that more than 1/4 of the 713 GW of U.S. electricity demand in 2010 could be dispatchable if only buildings could respond to that dispatch through advanced building energy control and operation strategies and smart grid infrastructure. In this study, it is envisioned that neighboring buildings will have the tendency to form a cluster, an open cyber-physical system to exploit the economic opportunities provided by a smart grid, distributed power generation, and storage devices. Through optimized demand management, these building clusters will then reduce overall primary energy consumption and peak time electricity consumption, and be more resilient to power disruptions. Therefore, this project seeks to develop a Net-zero building cluster simulation testbed and high fidelity energy forecasting models for adaptive and real-time control and decision making strategy development that can be used in a Net-zero building cluster. The following research activities are summarized in this thesis: 1) Development of a building cluster emulator for building cluster control and operation strategy assessment. 2) Development of a novel building energy forecasting methodology using active system identification and data fusion techniques. In this methodology, a systematic approach for building energy system characteristic evaluation, system excitation and model adaptation is included. The developed methodology is compared with other literature-reported building energy forecasting methods; 3) Development of the high fidelity on-line building cluster energy forecasting models, which includes energy forecasting models for buildings, PV panels, batteries and ice tank thermal storage systems 4) Small scale real building validation study to verify the performance of the developed building energy forecasting methodology. The outcomes of this thesis can be used for building cluster energy forecasting model development and model based control and operation optimization. The thesis concludes with a summary of the key outcomes of this research, as well as a list of recommendations for future work.

How bootstrap can help in forecasting time series with more than one seasonal pattern

NASA Astrophysics Data System (ADS)

Cordeiro, Clara; Neves, M. Manuela

2012-09-01

The search for the future is an appealing challenge in time series analysis. The diversity of forecasting methodologies is inevitable and is still in expansion. Exponential smoothing methods are the launch platform for modelling and forecasting in time series analysis. Recently this methodology has been combined with bootstrapping revealing a good performance. The algorithm (Boot. EXPOS) using exponential smoothing and bootstrap methodologies, has showed promising results for forecasting time series with one seasonal pattern. In case of more than one seasonal pattern, the double seasonal Holt-Winters methods and the exponential smoothing methods were developed. A new challenge was now to combine these seasonal methods with bootstrap and carry over a similar resampling scheme used in Boot. EXPOS procedure. The performance of such partnership will be illustrated for some well-know data sets existing in software.

The transport forecast - an important stage of transport management

NASA Astrophysics Data System (ADS)

Dragu, Vasile; Dinu, Oana; Oprea, Cristina; Alina Roman, Eugenia

2017-10-01

The transport system is a powerful system with varying loads in operation coming from changes in freight and passenger traffic in different time periods. The variations are due to the specific conditions of organization and development of socio-economic activities. The causes of varying loads can be included in three groups: economic, technical and organizational. The assessing of transport demand variability leads to proper forecast and development of the transport system, knowing that the market price is determined on equilibrium between supply and demand. The reduction of transport demand variability through different technical solutions, organizational, administrative, legislative leads to an increase in the efficiency and effectiveness of transport. The paper presents a new way of assessing the future needs of transport through dynamic series. Both researchers and practitioners in transport planning can benefit from the research results. This paper aims to analyze in an original approach how a good transport forecast can lead to a better management in transport, with significant effects on transport demand full meeting in quality terms. The case study shows how dynamic series of statistics can be used to identify the size of future demand addressed to the transport system.

Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media.

PubMed

Goode, Brian J; Krishnan, Siddharth; Roan, Michael; Ramakrishnan, Naren

2015-01-01

Online social media activity can often be a precursor to disruptive events such as protests, strikes, and "occupy" movements. We have observed that such civil unrest can galvanize supporters through social networks and help recruit activists to their cause. Understanding the dynamics of social network cascades and extrapolating their future growth will enable an analyst to detect or forecast major societal events. Existing work has primarily used structural and temporal properties of cascades to predict their future behavior. But factors like societal pressure, alignment of individual interests with broader causes, and perception of expected benefits also affect protest participation in social media. Here we develop an analysis framework using a differential game theoretic approach to characterize the cost of participating in a cascade, and demonstrate how we can combine such cost features with classical properties to forecast the future behavior of cascades. Using data from Twitter, we illustrate the effectiveness of our models on the "Brazilian Spring" and Venezuelan protests that occurred in June 2013 and November 2013, respectively. We demonstrate how our framework captures both qualitative and quantitative aspects of how these uprisings manifest through the lens of tweet volume on Twitter social media.

Bayesian quantitative precipitation forecasts in terms of quantiles

NASA Astrophysics Data System (ADS)

Bentzien, Sabrina; Friederichs, Petra

2014-05-01

Ensemble prediction systems (EPS) for numerical weather predictions on the mesoscale are particularly developed to obtain probabilistic guidance for high impact weather. An EPS not only issues a deterministic future state of the atmosphere but a sample of possible future states. Ensemble postprocessing then translates such a sample of forecasts into probabilistic measures. This study focus on probabilistic quantitative precipitation forecasts in terms of quantiles. Quantiles are particular suitable to describe precipitation at various locations, since no assumption is required on the distribution of precipitation. The focus is on the prediction during high-impact events and related to the Volkswagen Stiftung funded project WEX-MOP (Mesoscale Weather Extremes - Theory, Spatial Modeling and Prediction). Quantile forecasts are derived from the raw ensemble and via quantile regression. Neighborhood method and time-lagging are effective tools to inexpensively increase the ensemble spread, which results in more reliable forecasts especially for extreme precipitation events. Since an EPS provides a large amount of potentially informative predictors, a variable selection is required in order to obtain a stable statistical model. A Bayesian formulation of quantile regression allows for inference about the selection of predictive covariates by the use of appropriate prior distributions. Moreover, the implementation of an additional process layer for the regression parameters accounts for spatial variations of the parameters. Bayesian quantile regression and its spatially adaptive extension is illustrated for the German-focused mesoscale weather prediction ensemble COSMO-DE-EPS, which runs (pre)operationally since December 2010 at the German Meteorological Service (DWD). Objective out-of-sample verification uses the quantile score (QS), a weighted absolute error between quantile forecasts and observations. The QS is a proper scoring function and can be decomposed into reliability, resolutions and uncertainty parts. A quantile reliability plot gives detailed insights in the predictive performance of the quantile forecasts.

Evaluating Snow Data Assimilation Framework for Streamflow Forecasting Applications Using Hindcast Verification

NASA Astrophysics Data System (ADS)

Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.

2012-12-01

Snow water equivalent (SWE) estimation is a key factor in producing reliable streamflow simulations and forecasts in snow dominated areas. However, measuring or predicting SWE has significant uncertainty. Sequential data assimilation, which updates states using both observed and modeled data based on error estimation, has been shown to reduce streamflow simulation errors but has had limited testing for forecasting applications. In the current study, a snow data assimilation framework integrated with the National Weather System River Forecasting System (NWSRFS) is evaluated for use in ensemble streamflow prediction (ESP). Seasonal water supply ESP hindcasts are generated for the North Fork of the American River Basin (NFARB) in northern California. Parameter sets from the California Nevada River Forecast Center (CNRFC), the Differential Evolution Adaptive Metropolis (DREAM) algorithm and the Multistep Automated Calibration Scheme (MACS) are tested both with and without sequential data assimilation. The traditional ESP method considers uncertainty in future climate conditions using historical temperature and precipitation time series to generate future streamflow scenarios conditioned on the current basin state. We include data uncertainty analysis in the forecasting framework through the DREAM-based parameter set which is part of a recently developed Integrated Uncertainty and Ensemble-based data Assimilation framework (ICEA). Extensive verification of all tested approaches is undertaken using traditional forecast verification measures, including root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE), volumetric bias, joint distribution, rank probability score (RPS), and discrimination and reliability plots. In comparison to the RFC parameters, the DREAM and MACS sets show significant improvement in volumetric bias in flow. Use of assimilation improves hindcasts of higher flows but does not significantly improve performance in the mid flow and low flow categories.

75 FR 1568 - Endangered and Threatened Wildlife and Plants; Proposed Designation of Critical Habitat for...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-12

... designation. The majority of the total future baseline impacts are associated with development projects ($6.4... development projects. The DEA estimates that total potential incremental economic impacts in areas proposed as..., the range in total incremental impacts is due to the range in development forecasts. The lack of...

Medium term municipal solid waste generation prediction by autoregressive integrated moving average

DOE Office of Scientific and Technical Information (OSTI.GOV)

Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.

2014-09-12

Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressivemore » Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.« less

Medium term municipal solid waste generation prediction by autoregressive integrated moving average

NASA Astrophysics Data System (ADS)

Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan

2014-09-01

Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.

Effect of data quality on a hybrid Coulomb/STEP model for earthquake forecasting

NASA Astrophysics Data System (ADS)

Steacy, Sandy; Jimenez, Abigail; Gerstenberger, Matt; Christophersen, Annemarie

2014-05-01

Operational earthquake forecasting is rapidly becoming a 'hot topic' as civil protection authorities seek quantitative information on likely near future earthquake distributions during seismic crises. At present, most of the models in public domain are statistical and use information about past and present seismicity as well as b-value and Omori's law to forecast future rates. A limited number of researchers, however, are developing hybrid models which add spatial constraints from Coulomb stress modeling to existing statistical approaches. Steacy et al. (2013), for instance, recently tested a model that combines Coulomb stress patterns with the STEP (short-term earthquake probability) approach against seismicity observed during the 2010-2012 Canterbury earthquake sequence. They found that the new model performed at least as well as, and often better than, STEP when tested against retrospective data but that STEP was generally better in pseudo-prospective tests that involved data actually available within the first 10 days of each event of interest. They suggested that the major reason for this discrepancy was uncertainty in the slip models and, in particular, in the geometries of the faults involved in each complex major event. Here we test this hypothesis by developing a number of retrospective forecasts for the Landers earthquake using hypothetical slip distributions developed by Steacy et al. (2004) to investigate the sensitivity of Coulomb stress models to fault geometry and earthquake slip. Specifically, we consider slip models based on the NEIC location, the CMT solution, surface rupture, and published inversions and find significant variation in the relative performance of the models depending upon the input data.

Development and Use of the Hydrologic Ensemble Forecast System by the National Weather Service to Support the New York City Water Supply

NASA Astrophysics Data System (ADS)

Shedd, R.; Reed, S. M.; Porter, J. H.

2015-12-01

The National Weather Service (NWS) has been working for several years on the development of the Hydrologic Ensemble Forecast System (HEFS). The objective of HEFS is to provide ensemble river forecasts incorporating the best precipitation and temperature forcings at any specific time horizon. For the current implementation, this includes the Global Ensemble Forecast System (GEFS) and the Climate Forecast System (CFSv2). One of the core partners that has been working with the NWS since the beginning of the development phase of HEFS is the New York City Department of Environmental Protection (NYCDEP) which is responsible for the complex water supply system for New York City. The water supply system involves a network of reservoirs in both the Delaware and Hudson River basins. At the same time that the NWS was developing HEFS, NYCDEP was working on enhancing the operations of their water supply reservoirs through the development of a new Operations Support Tool (OST). OST is designed to guide reservoir system operations to ensure an adequate supply of high-quality drinking water for the city, as well as to meet secondary objectives for reaches downstream of the reservoirs assuming the primary water supply goals can be met. These secondary objectives include fisheries and ecosystem support, enhanced peak flow attenuation beyond that provided natively by the reservoirs, salt front management, and water supply for other cities. Since January 2014, the NWS Northeast and Middle Atlantic River Forecast Centers have provided daily one year forecasts from HEFS to NYCDEP. OST ingests these forecasts, couples them with near-real-time environmental and reservoir system data, and drives models of the water supply system. The input of ensemble forecasts results in an ensemble of model output, from which information on the range and likelihood of possible future system states can be extracted. This type of probabilistic information provides system managers with additional information not available from deterministic forecasts and allows managers to better assess risk, and provides greater context for decision-making than has been available in the past. HEFS has allowed NYCDEP water supply managers to make better decisions on reservoir operations than they likely would have in the past, using only deterministic forecasts.

Research notes : best practices for traffic impact studies.

DOT National Transportation Integrated Search

2006-11-01

Traffic Impact Studies (TISs) are used by the Oregon Department of Transportation (ODOT) and staff of other transportation agencies to forecast future system effects from proposed development projects and to predict the useful life of a transportatio...

Not all past events are equal: biased attention and emerging heuristics in children's past-to-future forecasting.

PubMed

Lagattuta, Kristin Hansen; Sayfan, Liat

2013-01-01

Four- to 10-year-olds and adults (N = 265) responded to eight scenarios presented on an eye tracker. Each trial involved a character who encounters a perpetrator who had previously enacted positive (P), negative (N), or both types of actions toward him or her in varying sequences (NN, PP, PN, and NP). Participants predicted the character's thoughts about the likelihood of future events, emotion type and intensity, and decision to approach or avoid. All ages made more positive forecasts for PP > NP > PN > NN trials, with differentiation by past experience widening with age. Age-related increases in weighting the most recent past event also appeared in eye gaze. Individual differences in biased visual attention correlated with verbal judgments. Findings contribute to research on risk assessment, person perception, and heuristics in judgment and decision making. © 2013 The Authors. Child Development © 2013 Society for Research in Child Development, Inc.

Seasonal Forecast Skill And Teleconnections Over East Africa

NASA Astrophysics Data System (ADS)

MacLeod, D.; Palmer, T.

2017-12-01

Many people living in East Africa are significantly exposed to risks arising from climate variability. The region experiences two rainy seasons and poor performance of either or both of these (such as seen recently in 2016/17) reduces agricultural productivity and threatens food security. In combination with other factors this can lead to famine. By utilizing seasonal climate forecasts, preparatory actions can be taken in order to mitigate the risks arising from such climate variability. As part of the project ForPAc: "Towards forecast-based preparedness action", we are working with humanitarian agencies in Kenya to build such early warning systems on subseasonal-to-seasonal timescales. Here, the seasonal predictability and forecast skill of the two East African rainy seasons will be presented. Results from the new ECMWF operational forecasting system SEAS5 will be shown and compared to the previous System 4. Analysis of a new 110 year long atmosphere-only simulation will also be discussed, demonstrating impacts of atmosphere-ocean coupling as well as putting operational forecast skill in a long-term context. Particular focus will be given to the model representation of teleconnections of seasonal climate with global sea surface temperatures; highlighting sources of forecast error and informing future model development.

Transition of Suomi National Polar-Orbiting Partnership (S-NPP) Data Products for Operational Weather Forecasting Applications

NASA Technical Reports Server (NTRS)

Smith, Matthew R.; Molthan, Andrew L.; Fuell, Kevin K.; Jedlovec, Gary J.

2012-01-01

SPoRT is a team of NASA/NOAA scientists focused on demonstrating the utility of NASA and future NOAA data and derived products on improving short-term weather forecasts. Work collaboratively with a suite of unique products and selected WFOs in an end-to-end transition activity. Stable funding from NASA and NOAA. Recognized by the science community as the "go to" place for transitioning experimental and research data to the operational weather community. Endorsed by NWS ESSD/SSD chiefs. Proven paradigm for transitioning satellite observations and modeling capabilities to operations (R2O). SPoRT s transition of NASA satellite instruments provides unique or higher resolution data products to complement the baseline suite of geostationary data available to forecasters. SPoRT s partnership with NWS WFOs provides them with unique imagery to support disaster response and local forecast challenges. SPoRT has years of proven experience in developing and transitioning research products to the operational weather community. SPoRT has begun work with CONUS and OCONUS WFOs to determine the best products for maximum benefit to forecasters. VIIRS has already proven to be another extremely powerful tool, enhancing forecasters ability to handle difficult forecasting situations.

The MSFC Solar Activity Future Estimation (MSAFE) Model

NASA Technical Reports Server (NTRS)

Suggs, Ron

2017-01-01

The Natural Environments Branch of the Engineering Directorate at Marshall Space Flight Center (MSFC) provides solar cycle forecasts for NASA space flight programs and the aerospace community. These forecasts provide future statistical estimates of sunspot number, solar radio 10.7 cm flux (F10.7), and the geomagnetic planetary index, Ap, for input to various space environment models. For example, many thermosphere density computer models used in spacecraft operations, orbital lifetime analysis, and the planning of future spacecraft missions require as inputs the F10.7 and Ap. The solar forecast is updated each month by executing MSAFE using historical and the latest month's observed solar indices to provide estimates for the balance of the current solar cycle. The forecasted solar indices represent the 13-month smoothed values consisting of a best estimate value stated as a 50 percentile value along with approximate +/- 2 sigma values stated as 95 and 5 percentile statistical values. This presentation will give an overview of the MSAFE model and the forecast for the current solar cycle.

Climate Change and Sea Level Rise: A Challenge to Science and Society

NASA Astrophysics Data System (ADS)

Plag, H.

2009-12-01

Society is challenged by the risk of an anticipated rise of coastal Local Sea Level (LSL) as a consequence of future global warming. Many low-lying and often subsiding and densely populated coastal areas are under risk of increased inundation, with potentially devastating consequences for the global economy, society, and environment. Faced with a trade-off between imposing the very high costs of coastal protection and adaptation upon today's national economies and leaving the costs of potential major disasters to future generations, governments and decision makers are in need of scientific support for the development of mitigation and adaptation strategies for the coastal zone. Low-frequency to secular changes in LSL are the result of many interacting Earth system processes. The complexity of the Earth system makes it difficult to predict Global Sea Level (GSL) rise and, even more so, LSL changes over the next 100 to 200 years. Humans have re-engineered the planet and changed major features of the Earth surface and the atmosphere, thus ruling out extrapolation of past and current changes into the future as a reasonable approach. The risk of rapid changes in ocean circulation and ice sheet mass balance introduces the possibility of unexpected changes. Therefore, science is challenged with understanding and constraining the full range of plausible future LSL trajectories and with providing useful support for informed decisions. In the face of largely unpredictable future sea level changes, monitoring of the relevant processes and development of a forecasting service on realistic time scales is crucial as decision support. Forecasting and "early warning" for LSL rise would have to aim at decadal time scales, giving coastal managers sufficient time to react if the onset of rapid changes would require an immediate response. The social, environmental, and economic risks associated with potentially large and rapid LSL changes are enormous. Therefore, in the light of the current uncertainties and the unpredictable nature of some of the forcing processes for LSL changes, the focus of scientific decision support may have to shift from projections of LSL trajectories on century time scales to the development of models and monitoring systems for a forecasting service on decadal time scales. The requirements for such a LSL forecasting service and the current obstacles will be discussed.

Artificial intelligence based models for stream-flow forecasting: 2000-2015

NASA Astrophysics Data System (ADS)

Yaseen, Zaher Mundher; El-shafie, Ahmed; Jaafar, Othman; Afan, Haitham Abdulmohsin; Sayl, Khamis Naba

2015-11-01

The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.

Agrometeorological models for forecasting the qualitative attributes of "Valência" oranges

NASA Astrophysics Data System (ADS)

Moreto, Victor Brunini; Rolim, Glauco de Souza; Zacarin, Bruno Gustavo; Vanin, Ana Paula; de Souza, Leone Maia; Latado, Rodrigo Rocha

2017-11-01

Forecasting is the act of predicting unknown future events using available data. Estimating, in contrast, uses data to simulate an actual condition. Brazil is the world's largest producer of oranges, and the state of São Paulo is the largest producer in Brazil. The "Valência" orange is among the most common cultivars in the state. We analyzed the influence of monthly meteorological variables during the growth cycle of Valência oranges grafted onto "Rangpur" lime rootstocks (VACR) for São Paulo, and developed monthly agrometeorological models for forecasting the qualitative attributes of VACR in mature orchard. For fruits per box for all months, the best accuracy was of 0.84 % and the minimum forecast range of 4 months. For the relation between °brix and juice acidity (RATIO) the best accuracy was of 0.69 % and the minimum forecast range of 5 months. Minimum, mean and maximum air temperatures, and relative evapotranspiration were the most important variables in the models.

A seasonal hydrologic ensemble prediction system for water resource management

NASA Astrophysics Data System (ADS)

Luo, L.; Wood, E. F.

2006-12-01

A seasonal hydrologic ensemble prediction system, developed for the Ohio River basin, has been improved and expanded to several other regions including the Eastern U.S., Africa and East Asia. The prediction system adopts the traditional Extended Streamflow Prediction (ESP) approach, utilizing the VIC (Variable Infiltration Capacity) hydrological model as the central tool for producing ensemble prediction of soil moisture, snow and streamflow with lead times up to 6-month. VIC is forced by observed meteorology to estimate the hydrological initial condition prior to the forecast, but during the forecast period the atmospheric forcing comes from statistically downscaled, seasonal forecast from dynamic climate models. The seasonal hydrologic ensemble prediction system is currently producing realtime seasonal hydrologic forecast for these regions on a monthly basis. Using hindcasts from a 19-year period (1981-1999), during which seasonal hindcasts from NCEP Climate Forecast System (CFS) and European Union DEMETER project are available, we evaluate the performance of the forecast system over our forecast regions. The evaluation shows that the prediction system using the current forecast approach is able to produce reliable and accurate precipitation, soil moisture and streamflow predictions. The overall skill is much higher then the traditional ESP. In particular, forecasts based on multiple climate model forecast are more skillful than single model-based forecast. This emphasizes the significant need for producing seasonal climate forecast with multiple climate models for hydrologic applications. Forecast from this system is expected to provide very valuable information about future hydrologic states and associated risks for end users, including water resource management and financial sectors.

Integrating Solar Power onto the Electric Grid - Bridging the Gap between Atmospheric Science, Engineering and Economics

NASA Astrophysics Data System (ADS)

Ghonima, M. S.; Yang, H.; Zhong, X.; Ozge, B.; Sahu, D. K.; Kim, C. K.; Babacan, O.; Hanna, R.; Kurtz, B.; Mejia, F. A.; Nguyen, A.; Urquhart, B.; Chow, C. W.; Mathiesen, P.; Bosch, J.; Wang, G.

2015-12-01

One of the main obstacles to high penetrations of solar power is the variable nature of solar power generation. To mitigate variability, grid operators have to schedule additional reliability resources, at considerable expense, to ensure that load requirements are met by generation. Thus despite the cost of solar PV decreasing, the cost of integrating solar power will increase as penetration of solar resources onto the electric grid increases. There are three principal tools currently available to mitigate variability impacts: (i) flexible generation, (ii) storage, either virtual (demand response) or physical devices and (iii) solar forecasting. Storage devices are a powerful tool capable of ensuring smooth power output from renewable resources. However, the high cost of storage is prohibitive and markets are still being designed to leverage their full potential and mitigate their limitation (e.g. empty storage). Solar forecasting provides valuable information on the daily net load profile and upcoming ramps (increasing or decreasing solar power output) thereby providing the grid advance warning to schedule ancillary generation more accurately, or curtail solar power output. In order to develop solar forecasting as a tool that can be utilized by the grid operators we identified two focus areas: (i) develop solar forecast technology and improve solar forecast accuracy and (ii) develop forecasts that can be incorporated within existing grid planning and operation infrastructure. The first issue required atmospheric science and engineering research, while the second required detailed knowledge of energy markets, and power engineering. Motivated by this background we will emphasize area (i) in this talk and provide an overview of recent advancements in solar forecasting especially in two areas: (a) Numerical modeling tools for coastal stratocumulus to improve scheduling in the day-ahead California energy market. (b) Development of a sky imager to provide short term forecasts (0-20 min ahead) to improve optimization and control of equipment on distribution feeders with high penetration of solar. Leveraging such tools that have seen extensive use in the atmospheric sciences supports the development of accurate physics-based solar forecast models. Directions for future research are also provided.

Climate Change Impacts on Worldwide Coffee Production

NASA Astrophysics Data System (ADS)

Foreman, T.; Rising, J. A.

2015-12-01

Coffee (Coffea arabica and Coffea canephora) plays a vital role in many countries' economies, providing necessary income to 25 million members of tropical countries, and supporting a $81 billion industry, making it one of the most valuable commodities in the world. At the same time, coffee is at the center of many issues of sustainability. It is vulnerable to climate change, with disease outbreaks becoming more common and suitable regions beginning to shift. We develop a statistical production model for coffee which incorporates temperature, precipitation, frost, and humidity effects using a new database of worldwide coffee production. We then use this model to project coffee yields and production into the future based on a variety of climate forecasts. This model can then be used together with a market model to forecast the locations of future coffee production as well as future prices, supply, and demand.

Thirty-year solid waste generation forecast for facilities at SRS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1994-07-01

The information supplied by this 30-year solid waste forecast has been compiled as a source document to the Waste Management Environmental Impact Statement (WMEIS). The WMEIS will help to select a sitewide strategic approach to managing present and future Savannah River Site (SRS) waste generated from ongoing operations, environmental restoration (ER) activities, transition from nuclear production to other missions, and decontamination and decommissioning (D&D) programs. The EIS will support project-level decisions on the operation of specific treatment, storage, and disposal facilities within the near term (10 years or less). In addition, the EIS will provide a baseline for analysis ofmore » future waste management activities and a basis for the evaluation of the specific waste management alternatives. This 30-year solid waste forecast will be used as the initial basis for the EIS decision-making process. The Site generates and manages many types and categories of waste. With a few exceptions, waste types are divided into two broad groups-high-level waste and solid waste. High-level waste consists primarily of liquid radioactive waste, which is addressed in a separate forecast and is not discussed further in this document. The waste types discussed in this solid waste forecast are sanitary waste, hazardous waste, low-level mixed waste, low-level radioactive waste, and transuranic waste. As activities at SRS change from primarily production to primarily decontamination and decommissioning and environmental restoration, the volume of each waste s being managed will change significantly. This report acknowledges the changes in Site Missions when developing the 30-year solid waste forecast.« less

Interpersonal Psychotherapy: Past, Present and Future

PubMed Central

Markowitz, John C.; Weissman, Myrna M.

2012-01-01

The authors briefly describe the origins, theory, and development of interpersonal psychotherapy: its roots in clinical outcome research, its spread from major depression to other psychiatric disorders and its increasing dissemination as an empirically validated clinical intervention included in treatment guidelines. They attempt to forecast research, organizational and training issues the growing interpersonal psychotherapy community may face in the future. PMID:22331561

Needed Actions within Defense Acquisitions Based on a Forecast of Future Mobile Information and Communications Technologies Deployed in Austere Environments

DTIC Science & Technology

2013-03-01

Deshmukh , and Vrat (2002) 30 performed an analysis to match forecasting techniques with specific technologies. In this study, the authors found...Technological Forecasting and Social Change, 79, 744-765. Mishra, S., Deshmukh , S., & Vrat, P. (2002). Matching of Technological Forecasting Technique to

Statistical Forecasting of Current and Future Circum-Arctic Ground Temperatures and Active Layer Thickness

NASA Astrophysics Data System (ADS)

Aalto, J.; Karjalainen, O.; Hjort, J.; Luoto, M.

2018-05-01

Mean annual ground temperature (MAGT) and active layer thickness (ALT) are key to understanding the evolution of the ground thermal state across the Arctic under climate change. Here a statistical modeling approach is presented to forecast current and future circum-Arctic MAGT and ALT in relation to climatic and local environmental factors, at spatial scales unreachable with contemporary transient modeling. After deploying an ensemble of multiple statistical techniques, distance-blocked cross validation between observations and predictions suggested excellent and reasonable transferability of the MAGT and ALT models, respectively. The MAGT forecasts indicated currently suitable conditions for permafrost to prevail over an area of 15.1 ± 2.8 × 106 km2. This extent is likely to dramatically contract in the future, as the results showed consistent, but region-specific, changes in ground thermal regime due to climate change. The forecasts provide new opportunities to assess future Arctic changes in ground thermal state and biogeochemical feedback.

Transition, Training, and Assessment of Multispectral Composite Imagery in Support of the NWS Aviation Forecast Mission

NASA Technical Reports Server (NTRS)

Fuell, Kevin; Jedlovec, Gary; Leroy, Anita; Schultz, Lori

2015-01-01

The NASA/Short-term Prediction, Research, and Transition (SPoRT) Program works closely with NOAA/NWS weather forecasters to transition unique satellite data and capabilities into operations in order to assist with nowcasting and short-term forecasting issues. Several multispectral composite imagery (i.e. RGB) products were introduced to users in the early 2000s to support hydrometeorology and aviation challenges as well as incident support. These activities lead to SPoRT collaboration with the GOES-R Proving Ground efforts where instruments such as MODIS (Aqua, Terra) and S-NPP/VIIRS imagers began to be used as near-realtime proxies to future capabilities of the Advanced Baseline Imager (ABI). One of the composite imagery products introduced to users was the Night-time Microphysics RGB, originally developed by EUMETSAT. SPoRT worked to transition this imagery to NWS users, provide region-specific training, and assess the impact of the imagery to aviation forecast needs. This presentation discusses the method used to interact with users to address specific aviation forecast challenges, including training activities undertaken to prepare for a product assessment. Users who assessed the multispectral imagery ranged from southern U.S. inland and coastal NWS weather forecast offices (WFOs), to those in the Rocky Mountain Front Range region and West Coast, as well as highlatitude forecasters of Alaska. These user-based assessments were documented and shared with the satellite community to support product developers and the broad users of new generation satellite data.

In-Depth Analysis of the JACK Model.

DOT National Transportation Integrated Search

2009-04-30

Recently, as part of a comprehensive analysis of budget and funding options, a TxDOT : special task force has examined the agencys current financial forecasting methods and has : developed a model designed to estimate future State Highway Fund rev...

Demand forecasting for automotive sector in Malaysia by system dynamics approach

NASA Astrophysics Data System (ADS)

Zulkepli, Jafri; Fong, Chan Hwa; Abidin, Norhaslinda Zainal

2015-12-01

In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.

Open-source Software for Demand Forecasting of Clinical Laboratory Test Volumes Using Time-series Analysis.

PubMed

Mohammed, Emad A; Naugler, Christopher

2017-01-01

Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical. In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes. This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted. This tool will allow anyone with historic test volume data to model future demand.

Open-source Software for Demand Forecasting of Clinical Laboratory Test Volumes Using Time-series Analysis

PubMed Central

Mohammed, Emad A.; Naugler, Christopher

2017-01-01

Background: Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical. Method: In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes. Results: This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted. Conclusion: This tool will allow anyone with historic test volume data to model future demand. PMID:28400996

Extravehicular Activity (EVA) Technology Development Status and Forecast

NASA Technical Reports Server (NTRS)

Chullen, Cinda; Westheimer, David T.

2010-01-01

Beginning in Fiscal Year (FY) 2011, Extravehicular activity (EVA) technology development became a technology foundational domain under a new program Enabling Technology Development and Demonstration. The goal of the EVA technology effort is to further develop technologies that will be used to demonstrate a robust EVA system that has application for a variety of future missions including microgravity and surface EVA. Overall the objectives will be reduce system mass, reduce consumables and maintenance, increase EVA hardware robustness and life, increase crew member efficiency and autonomy, and enable rapid vehicle egress and ingress. Over the past several years, NASA realized a tremendous increase in EVA system development as part of the Exploration Technology Development Program and the Constellation Program. The evident demand for efficient and reliable EVA technologies, particularly regenerable technologies was apparent under these former programs and will continue to be needed as future mission opportunities arise. The technological need for EVA in space has been realized over the last several decades by the Gemini, Apollo, Skylab, Space Shuttle, and the International Space Station (ISS) programs. EVAs were critical to the success of these programs. Now with the ISS extension to 2028 in conjunction with a current forecasted need of at least eight EVAs per year, the EVA technology life and limited availability of the EMUs will become a critical issue eventually. The current Extravehicular Mobility Unit (EMU) has vastly served EVA demands by performing critical operations to assemble the ISS and provide repairs of satellites such as the Hubble Space Telescope. However, as the life of ISS and the vision for future mission opportunities are realized, a new EVA systems capability could be an option for the future mission applications building off of the technology development over the last several years. Besides ISS, potential mission applications include EVAs for missions to Near Earth Objects (NEO), Phobos, or future surface missions. Surface missions could include either exploration of the Moon or Mars. Providing an EVA capability for these types of missions enables in-space construction of complex vehicles or satellites, hands on exploration of new parts of our solar system, and engages the public through the inspiration of knowing that humans are exploring places that they have never been before. This paper offers insight into what is currently being developed and what the potential opportunities are in the forecast

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

PubMed Central

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

2011-01-01

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

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

PubMed

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

2012-01-01

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

Assessment of Folsom Lake Watershed response to historical and potential future climate scenarios

USGS Publications Warehouse

Carpenter, Theresa M.; Georgakakos, Konstantine P.

2000-01-01

An integrated forecast-control system was designed to allow the profitable use of ensemble forecasts for the operational management of multi-purpose reservoirs. The system ingests large-scale climate model monthly precipitation through the adjustment of the marginal distribution of reservoir-catchment precipitation to reflect occurrence of monthly climate precipitation amounts in the extreme terciles of their distribution. Generation of ensemble reservoir inflow forecasts is then accomplished with due account for atmospheric- forcing and hydrologic- model uncertainties. These ensemble forecasts are ingested by the decision component of the integrated system, which generates non- inferior trade-off surfaces and, given management preferences, estimates of reservoir- management benefits over given periods. In collaboration with the Bureau of Reclamation and the California Nevada River Forecast Center, the integrated system is applied to Folsom Lake in California to evaluate the benefits for flood control, hydroelectric energy production, and low flow augmentation. In addition to retrospective studies involving the historical period 1964-1993, system simulations were performed for the future period 2001-2030, under a control (constant future greenhouse-gas concentrations assumed at the present levels) and a greenhouse-gas- increase (1-% per annum increase assumed) scenario. The present paper presents and validates ensemble 30-day reservoir- inflow forecasts under a variety of situations. Corresponding reservoir management results are presented in Yao and Georgakakos, A., this issue. Principle conclusions of this paper are that the integrated system provides reliable ensemble inflow volume forecasts at the 5-% confidence level for the majority of the deciles of forecast frequency, and that the use of climate model simulations is beneficial mainly during high flow periods. It is also found that, for future periods with potential sharp climatic increases of precipitation amount and to maintain good reliability levels, operational ensemble inflow forecasting should involve atmospheric forcing from appropriate climatic periods.

The future of satellite remote sensing: A worldwide assessment and prediction

NASA Technical Reports Server (NTRS)

Spann, G. W.

1984-01-01

A frame-work in which to assess and predict the future prospects for satellite remote sensing markets is provided. The scope of the analysis is the satellite-related market for data, equipment, and services. It encompasses both domestic and international markets and contains an examination of the various market characteristics by market segment (e.g., Federal Government, State and Local Governments, Academic Organizations, Industrial Companies, and Individuals) and primary applications areas (e.g., Geology, Forestry, Land Resource Management, Agriculture and Cartography). The forecasts are derived from an analysis of both U.S. and foreign market data. The evolution and current status of U.S. and Foreign markets to arrive at market growth rates is evaluated. Circumstances and events which are likely to affect the future market development are examined. A market growth scenario is presented that is consistent with past data sales trends and takes into account the dynamic nature of the future satellite remote sensing market. Several areas of current and future business opportunities available in this market are discussed. Specific worldwide forecasts are presented in three market sectors for the period 1980 to 1990.

Seasonal forecasting of groundwater levels in natural aquifers in the United Kingdom

NASA Astrophysics Data System (ADS)

Mackay, Jonathan; Jackson, Christopher; Pachocka, Magdalena; Brookshaw, Anca; Scaife, Adam

2014-05-01

Groundwater aquifers comprise the world's largest freshwater resource and provide resilience to climate extremes which could become more frequent under future climate changes. Prolonged dry conditions can induce groundwater drought, often characterised by significantly low groundwater levels which may persist for months to years. In contrast, lasting wet conditions can result in anomalously high groundwater levels which result in flooding, potentially at large economic cost. Using computational models to produce groundwater level forecasts allows appropriate management strategies to be considered in advance of extreme events. The majority of groundwater level forecasting studies to date use data-based models, which exploit the long response time of groundwater levels to meteorological drivers and make forecasts based only on the current state of the system. Instead, seasonal meteorological forecasts can be used to drive hydrological models and simulate groundwater levels months into the future. Such approaches have not been used in the past due to a lack of skill in these long-range forecast products. However systems such as the latest version of the Met Office Global Seasonal Forecast System (GloSea5) are now showing increased skill up to a 3-month lead time. We demonstrate the first groundwater level ensemble forecasting system using a multi-member ensemble of hindcasts from GloSea5 between 1996 and 2009 to force 21 simple lumped conceptual groundwater models covering most of the UK's major aquifers. We present the results from this hindcasting study and demonstrate that the system can be used to forecast groundwater levels with some skill up to three months into the future.

The development of the Australian Space Forecast Centre (ASFC)

NASA Astrophysics Data System (ADS)

Wilkinson, Phil; Kennewell, John A.; Cole, David

2018-05-01

The Ionospheric Prediction Service (IPS) was formed in 1947 to provide monthly prediction services for high frequency (HF) radio, in particular to support HF communications with the United Kingdom. It was quickly recognized that to be effective such a service also had to provide advice when ionospheric storms prevented HF communications from taking place. With the advent of the International Geophysical Year (IGY), short-term forecasts were also required for research programmes and the task of supplying the Australian input to these was given to Frank Cook, of the IPS, while Jack Turner, also of the IPS, supervised the generation of ionospheric maps to support high latitude HF communications. These two important IGY activities formed the platform on which all future IPS services would be built. This paper reviews the development of the Australian Space Forecast Centre (ASFC), which arose from these early origins.

7 CFR 1710.300 - General.

Code of Federal Regulations, 2012 CFR

2012-01-01

... forecast. The forecast should be used by the board of directors and the manager to guide the system towards... projected results of future actions planned by the borrower's board of directors; (2) The financial goals... type of large power loads, projections of future borrowings and the associated interest, projected...

7 CFR 1710.300 - General.

Code of Federal Regulations, 2013 CFR

2013-01-01

... forecast. The forecast should be used by the board of directors and the manager to guide the system towards... projected results of future actions planned by the borrower's board of directors; (2) The financial goals... type of large power loads, projections of future borrowings and the associated interest, projected...

Highly accurate prediction of emotions surrounding the attacks of September 11, 2001 over 1-, 2-, and 7-year prediction intervals.

PubMed

Doré, Bruce P; Meksin, Robert; Mather, Mara; Hirst, William; Ochsner, Kevin N

2016-06-01

In the aftermath of a national tragedy, important decisions are predicated on judgments of the emotional significance of the tragedy in the present and future. Research in affective forecasting has largely focused on ways in which people fail to make accurate predictions about the nature and duration of feelings experienced in the aftermath of an event. Here we ask a related but understudied question: can people forecast how they will feel in the future about a tragic event that has already occurred? We found that people were strikingly accurate when predicting how they would feel about the September 11 attacks over 1-, 2-, and 7-year prediction intervals. Although people slightly under- or overestimated their future feelings at times, they nonetheless showed high accuracy in forecasting (a) the overall intensity of their future negative emotion, and (b) the relative degree of different types of negative emotion (i.e., sadness, fear, or anger). Using a path model, we found that the relationship between forecasted and actual future emotion was partially mediated by current emotion and remembered emotion. These results extend theories of affective forecasting by showing that emotional responses to an event of ongoing national significance can be predicted with high accuracy, and by identifying current and remembered feelings as independent sources of this accuracy. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Highly accurate prediction of emotions surrounding the attacks of September 11, 2001 over 1-, 2-, and 7-year prediction intervals

PubMed Central

Doré, B.P.; Meksin, R.; Mather, M.; Hirst, W.; Ochsner, K.N

2016-01-01

In the aftermath of a national tragedy, important decisions are predicated on judgments of the emotional significance of the tragedy in the present and future. Research in affective forecasting has largely focused on ways in which people fail to make accurate predictions about the nature and duration of feelings experienced in the aftermath of an event. Here we ask a related but understudied question: can people forecast how they will feel in the future about a tragic event that has already occurred? We found that people were strikingly accurate when predicting how they would feel about the September 11 attacks over 1-, 2-, and 7-year prediction intervals. Although people slightly under- or overestimated their future feelings at times, they nonetheless showed high accuracy in forecasting 1) the overall intensity of their future negative emotion, and 2) the relative degree of different types of negative emotion (i.e., sadness, fear, or anger). Using a path model, we found that the relationship between forecasted and actual future emotion was partially mediated by current emotion and remembered emotion. These results extend theories of affective forecasting by showing that emotional responses to an event of ongoing national significance can be predicted with high accuracy, and by identifying current and remembered feelings as independent sources of this accuracy. PMID:27100309

Space station evolution: Planning for the future

NASA Technical Reports Server (NTRS)

Diaz, Alphonso V.; Askins, Barbara S.

1987-01-01

The need for permanently manned presence in space has been recognized by the United States and its international partners for many years. The development of this capability was delayed due to the concurrent recognition that reusable earth-to-orbit transportation was also needed and should be developed first. While the decision to go ahead with a permanently manned Space Station was on hold, requirements for the use of the Station were accumulating as ground-based research and the data from unmanned spacecraft sparked the imagination of both scientists and entrepreneurs. Thus, by the time of the Space Station implementation decision in the early 1980's, a variety of disciplines, with a variety of requirements, needed to be accommodated on one Space Station. Additional future requirements could be forecast for advanced missions that were still in the early planning stages. The logical response was the development of a multi-purpose Space Station with the ability to evolve on-orbit to new capabilities as required by user needs and national or international decisions, i.e., to build an evolutionary Space Station. Planning for evolution is conducted in parallel with the design and development of the baseline Space Station. Evolution planning is a strategic management process to facilitate change and protect future decisions. The objective is not to forecast the future, but to understand the future options and the implications of these on today's decisions. The major actions required now are: (1) the incorporation of evolution provisions (hooks and scars) in the baseline Space Station; and (2) the initiation of an evolution advanced development program.

Space station evolution: Planning for the future

NASA Astrophysics Data System (ADS)

Diaz, Alphonso V.; Askins, Barbara S.

1987-06-01

The need for permanently manned presence in space has been recognized by the United States and its international partners for many years. The development of this capability was delayed due to the concurrent recognition that reusable earth-to-orbit transportation was also needed and should be developed first. While the decision to go ahead with a permanently manned Space Station was on hold, requirements for the use of the Station were accumulating as ground-based research and the data from unmanned spacecraft sparked the imagination of both scientists and entrepreneurs. Thus, by the time of the Space Station implementation decision in the early 1980's, a variety of disciplines, with a variety of requirements, needed to be accommodated on one Space Station. Additional future requirements could be forecast for advanced missions that were still in the early planning stages. The logical response was the development of a multi-purpose Space Station with the ability to evolve on-orbit to new capabilities as required by user needs and national or international decisions, i.e., to build an evolutionary Space Station. Planning for evolution is conducted in parallel with the design and development of the baseline Space Station. Evolution planning is a strategic management process to facilitate change and protect future decisions. The objective is not to forecast the future, but to understand the future options and the implications of these on today's decisions. The major actions required now are: (1) the incorporation of evolution provisions (hooks and scars) in the baseline Space Station; and (2) the initiation of an evolution advanced development program.

Survey of air cargo forecasting techniques

NASA Technical Reports Server (NTRS)

Kuhlthan, A. R.; Vermuri, R. S.

1978-01-01

Forecasting techniques currently in use in estimating or predicting the demand for air cargo in various markets are discussed with emphasis on the fundamentals of the different forecasting approaches. References to specific studies are cited when appropriate. The effectiveness of current methods is evaluated and several prospects for future activities or approaches are suggested. Appendices contain summary type analyses of about 50 specific publications on forecasting, and selected bibliographies on air cargo forecasting, air passenger demand forecasting, and general demand and modalsplit modeling.

Stochastic demographic forecasting.

PubMed

Lee, R D

1992-11-01

"This paper describes a particular approach to stochastic population forecasting, which is implemented for the U.S.A. through 2065. Statistical time series methods are combined with demographic models to produce plausible long run forecasts of vital rates, with probability distributions. The resulting mortality forecasts imply gains in future life expectancy that are roughly twice as large as those forecast by the Office of the Social Security Actuary.... Resulting stochastic forecasts of the elderly population, elderly dependency ratios, and payroll tax rates for health, education and pensions are presented." excerpt

Quantifying automobile refinishing VOC air emissions - a methodology with estimates and forecasts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson, S.P.; Rubick, C.

1996-12-31

Automobile refinishing coatings (referred to as paints), paint thinners, reducers, hardeners, catalysts, and cleanup solvents used during their application, contain volatile organic compounds (VOCs) which are precursors to ground level ozone formation. Some of these painting compounds create hazardous air pollutants (HAPs) which are toxic. This paper documents the methodology, data sets, and the results of surveys (conducted in the fall of 1995) used to develop revised per capita emissions factors for estimating and forecasting the VOC air emissions from the area source category of automobile refinishing. Emissions estimates, forecasts, trends, and reasons for these trends are presented. Future emissionsmore » inventory (EI) challenges are addressed in light of data availability and information networks.« less

Developments of the European Flood Awareness System (EFAS)

NASA Astrophysics Data System (ADS)

Thiemig, Vera; Olav Skøien, Jon; Salamon, Peter; Pappenberger, Florian; Wetterhall, Fredrik; Holst, Bo; Asp, Sara-Sophia; Garcia Padilla, Mercedes; Garcia, Rafael J.; Schweim, Christoph; Ziese, Markus

2017-04-01

EFAS (http://www.efas.eu) is an operational system for flood forecasting and early warning for the entire Europe, which is fully operational as part of the Copernicus Emergency Management Service since 2012. The prime aim of EFAS is to gain time for preparedness measures before major flood events - particularly in trans-national river basins - strike. This is achieved by providing complementary, added value information to the national and regional services holding the mandate for flood warning as well as to the ERCC (European Response and Coordination Centre). Using a coherent model for all of Europe forced with a range of deterministic and ensemble weather forecasts, the system can give a probabilistic flood forecast for a medium range lead time (up to 10 days) independent of country borders. The system is under continuous development, and we will present the basic set up, some prominent examples of recent and ongoing developments (such as the rapid impact assessment, seasonal outlook and the extended domain) and the future challenges.

Short-term ensemble streamflow forecasting using operationally-produced single-valued streamflow forecasts - A Hydrologic Model Output Statistics (HMOS) approach

NASA Astrophysics Data System (ADS)

Regonda, Satish Kumar; Seo, Dong-Jun; Lawrence, Bill; Brown, James D.; Demargne, Julie

2013-08-01

We present a statistical procedure for generating short-term ensemble streamflow forecasts from single-valued, or deterministic, streamflow forecasts produced operationally by the U.S. National Weather Service (NWS) River Forecast Centers (RFCs). The resulting ensemble streamflow forecast provides an estimate of the predictive uncertainty associated with the single-valued forecast to support risk-based decision making by the forecasters and by the users of the forecast products, such as emergency managers. Forced by single-valued quantitative precipitation and temperature forecasts (QPF, QTF), the single-valued streamflow forecasts are produced at a 6-h time step nominally out to 5 days into the future. The single-valued streamflow forecasts reflect various run-time modifications, or "manual data assimilation", applied by the human forecasters in an attempt to reduce error from various sources in the end-to-end forecast process. The proposed procedure generates ensemble traces of streamflow from a parsimonious approximation of the conditional multivariate probability distribution of future streamflow given the single-valued streamflow forecast, QPF, and the most recent streamflow observation. For parameter estimation and evaluation, we used a multiyear archive of the single-valued river stage forecast produced operationally by the NWS Arkansas-Red River Basin River Forecast Center (ABRFC) in Tulsa, Oklahoma. As a by-product of parameter estimation, the procedure provides a categorical assessment of the effective lead time of the operational hydrologic forecasts for different QPF and forecast flow conditions. To evaluate the procedure, we carried out hindcasting experiments in dependent and cross-validation modes. The results indicate that the short-term streamflow ensemble hindcasts generated from the procedure are generally reliable within the effective lead time of the single-valued forecasts and well capture the skill of the single-valued forecasts. For smaller basins, however, the effective lead time is significantly reduced by short basin memory and reduced skill in the single-valued QPF.

Categorizing the severity of paralytic shellfish poisoning outbreaks in the Gulf of Maine for forecasting and management

NASA Astrophysics Data System (ADS)

Kleindinst, Judith L.; Anderson, Donald M.; McGillicuddy, Dennis J.; Stumpf, Richard P.; Fisher, Kathleen M.; Couture, Darcie A.; Michael Hickey, J.; Nash, Christopher

2014-05-01

Development of forecasting systems for harmful algal blooms (HABs) has been a long-standing research and management goal. Significant progress has been made in the Gulf of Maine, where seasonal bloom forecasts are now being issued annually using Alexandrium fundyense cyst abundance maps and a population dynamics model developed for that organism. Thus far, these forecasts have used terms such as “significant”, “moderately large” or “moderate” to convey the extent of forecasted paralytic shellfish poisoning (PSP) outbreaks. In this study, historical shellfish harvesting closure data along the coast of the Gulf of Maine were used to derive a series of bloom severity levels that are analogous to those used to define major storms like hurricanes or tornados. Thirty-four years of PSP-related shellfish closure data for Maine, Massachusetts and New Hampshire were collected and mapped to depict the extent of coastline closure in each year. Due to fractal considerations, different methods were explored for measuring length of coastline closed. Ultimately, a simple procedure was developed using arbitrary straight-line segments to represent specific sections of the coastline. This method was consistently applied to each year’s PSP toxicity closure map to calculate the total length of coastline closed. Maps were then clustered together statistically to yield distinct groups of years with similar characteristics. A series of categories or levels was defined (“Level 1: Limited”, “Level 2: Moderate”, and “Level 3: Extensive”) each with an associated range of expected coastline closed, which can now be used instead of vague descriptors in future forecasts. This will provide scientifically consistent and simply defined information to the public as well as resource managers who make decisions on the basis of the forecasts.

Categorizing the severity of paralytic shellfish poisoning outbreaks in the Gulf of Maine for forecasting and management

PubMed Central

Kleindinst, Judith L.; Anderson, Donald M.; McGillicuddy, Dennis J.; Stumpf, Richard P.; Fisher, Kathleen M.; Couture, Darcie A.; Hickey, J. Michael; Nash, Christopher

2014-01-01

Development of forecasting systems for harmful algal blooms (HABs) has been a long-standing research and management goal. Significant progress has been made in the Gulf of Maine, where seasonal bloom forecasts are now being issued annually using Alexandrium fundyense cyst abundance maps and a population dynamics model developed for that organism. Thus far, these forecasts have used terms such as “significant”, “moderately large” or “moderate” to convey the extent of forecasted paralytic shellfish poisoning (PSP) outbreaks. In this study, historical shellfish harvesting closure data along the coast of the Gulf of Maine were used to derive a series of bloom severity levels that are analogous to those used to define major storms like hurricanes or tornados. Thirty-four years of PSP-related shellfish closure data for Maine, Massachusetts and New Hampshire were collected and mapped to depict the extent of coastline closure in each year. Due to fractal considerations, different methods were explored for measuring length of coastline closed. Ultimately, a simple procedure was developed using arbitrary straight-line segments to represent specific sections of the coastline. This method was consistently applied to each year’s PSP toxicity closure map to calculate the total length of coastline closed. Maps were then clustered together statistically to yield distinct groups of years with similar characteristics. A series of categories or levels was defined (“Level 1: Limited”, “Level 2: Moderate”, and “Level 3: Extensive”) each with an associated range of expected coastline closed, which can now be used instead of vague descriptors in future forecasts. This will provide scientifically consistent and simply defined information to the public as well as resource managers who make decisions on the basis of the forecasts. PMID:25076815

Demonstrating the Alaska Ocean Observing System in Prince William Sound

NASA Astrophysics Data System (ADS)

Schoch, G. Carl; McCammon, Molly

2013-07-01

The Alaska Ocean Observing System and the Oil Spill Recovery Institute developed a demonstration project over a 5 year period in Prince William Sound. The primary goal was to develop a quasi-operational system that delivers weather and ocean information in near real time to diverse user communities. This observing system now consists of atmospheric and oceanic sensors, and a new generation of computer models to numerically simulate and forecast weather, waves, and ocean circulation. A state of the art data management system provides access to these products from one internet portal at http://www.aoos.org. The project culminated in a 2009 field experiment that evaluated the observing system and performance of the model forecasts. Observations from terrestrial weather stations and weather buoys validated atmospheric circulation forecasts. Observations from wave gages on weather buoys validated forecasts of significant wave heights and periods. There was an emphasis on validation of surface currents forecasted by the ocean circulation model for oil spill response and search and rescue applications. During the 18 day field experiment a radar array mapped surface currents and drifting buoys were deployed. Hydrographic profiles at fixed stations, and by autonomous vehicles along transects, were made to acquire measurements through the water column. Terrestrial weather stations were the most reliable and least costly to operate, and in situ ocean sensors were more costly and considerably less reliable. The radar surface current mappers were the least reliable and most costly but provided the assimilation and validation data that most improved ocean circulation forecasts. We describe the setting of Prince William Sound and the various observational platforms and forecast models of the observing system, and discuss recommendations for future development.

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

USGS Publications Warehouse

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 investigations. In summary, we show that the AGS audit offers no valid criticism of the USGS conclusion that global warming poses a serious threat to the future welfare of polar bears and that it only serves to distract from reasoned public-policy debate.

7 CFR 1710.208 - RUS criteria for approval of all load forecasts by power supply borrowers and by distribution...

Code of Federal Regulations, 2010 CFR

2010-01-01

... borrower developed an adequate supporting database and analyzed a reasonable range of relevant assumptions and alternative futures; (d) The borrower adopted methods and procedures in general use by the...

Development of a prototype land use model for statewide transportation planning activities.

DOT National Transportation Integrated Search

2011-11-30

Future land use forecasting is an important input to transportation planning modeling. Traditionally, land use is allocated to individual : traffic analysis zones (TAZ) based on variables such as the amount of vacant land, zoning restriction, land us...

Nature: "Water, Water, Everywhere, nor Any Drop to Drink"

ERIC Educational Resources Information Center

Heinhorst, Sabine; Cannon, Gordon

2004-01-01

The difficulties faced by developing countries in obtaining clean water, and its misuse in advanced countries are reported. The new application of zeolites, or molecular synthesis of aluminosilicates in the desalination or purification of water forecasts a brighter future.

Demographic Planning: An Action Approach

ERIC Educational Resources Information Center

Finch, Harold L.; Smith, Joyce

1974-01-01

Community colleges are in a good position to obtain reliable long-term forecasts of future demand. An approach developed at Johnson County Community College in Overland Park, Kansas, has enabled the college to assist other community institutions in their parallel planning efforts. (Author/MLF)

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2012 CFR

2012-01-01

... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate the...

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2014 CFR

2014-01-01

... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate the...

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2011 CFR

2011-01-01

... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate the...

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2013 CFR

2013-01-01

... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate the...

The Impacts of Climate Variations on Military Operations in the Horn of Africa

DTIC Science & Technology

2006-03-01

variability in a region. Climate forecasts are predictions of the future state of the climate , much as we think of weather forecasts but at longer...arrive at accurate characterizations of the future state of the climate . Many of the civilian organizations that generate reanalysis data also

Improvements and Lingering Challenges with Modeling Low-Level Winds Over Complex Terrain during the Wind Forecast Improvement Project 2

NASA Astrophysics Data System (ADS)

Olson, J.; Kenyon, J.; Brown, J. M.; Angevine, W. M.; Marquis, M.; Pichugina, Y. L.; Choukulkar, A.; Bonin, T.; Banta, R. M.; Bianco, L.; Djalalova, I.; McCaffrey, K.; Wilczak, J. M.; Lantz, K. O.; Long, C. N.; Redfern, S.; McCaa, J. R.; Stoelinga, M.; Grimit, E.; Cline, J.; Shaw, W. J.; Lundquist, J. K.; Lundquist, K. A.; Kosovic, B.; Berg, L. K.; Kotamarthi, V. R.; Sharp, J.; Jiménez, P.

2017-12-01

The Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) are NOAA real-time operational hourly updating forecast systems run at 13- and 3-km grid spacing, respectively. Both systems use the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) as the model component of the forecast system. During the second installment of the Wind Forecast Improvement Project (WFIP 2), the RAP/HRRR have been targeted for the improvement of low-level wind forecasts in the complex terrain within the Columbia River Basin (CRB), which requires much finer grid spacing to resolve important terrain peaks in the Cascade Mountains as well as the Columbia River Gorge. Therefore, this project provides a unique opportunity to test and develop the RAP/HRRR physics suite within a very high-resolution nest (Δx = 750 m) over the northwestern US. Special effort is made to incorporate scale-aware aspects into the model physical parameterizations to improve RAP/HRRR wind forecasts for any application at any grid spacing. Many wind profiling and scanning instruments have been deployed in the CRB in support the WFIP 2 field project, which spanned 01 October 2015 to 31 March 2017. During the project, several forecast error modes were identified, such as: (1) too-shallow cold pools during the cool season, which can mix-out more frequently than observed and (2) the low wind speed bias in thermal trough-induced gap flows during the warm season. Development has been focused on the column-based turbulent mixing scheme to improve upon these biases, but investigating the effects of horizontal (and 3D) mixing has also helped improve some of the common forecast failure modes. This presentation will highlight the testing and development of various model components, showing the improvements over original versions for temperature and wind profiles. Examples of case studies and retrospective periods will be presented to illustrate the improvements. We will demonstrate that the improvements made in WFIP 2 will be extendable to other regions, complex or flat terrain. Ongoing and future challenges in RAP/HRRR physics development will be touched upon.

Forecasts of 21st Century Snowpack and Implications for Snowmobile and Snowcoach Use in Yellowstone National Park

PubMed Central

Tercek, Michael; Rodman, Ann

2016-01-01

Climate models project a general decline in western US snowpack throughout the 21st century, but long-term, spatially fine-grained, management-relevant projections of snowpack are not available for Yellowstone National Park. We focus on the implications that future snow declines may have for oversnow vehicle (snowmobile and snowcoach) use because oversnow tourism is critical to the local economy and has been a contentious issue in the park for more than 30 years. Using temperature-indexed snow melt and accumulation equations with temperature and precipitation data from downscaled global climate models, we forecast the number of days that will be suitable for oversnow travel on each Yellowstone road segment during the mid- and late-21st century. The west entrance road was forecast to be the least suitable for oversnow use in the future while the south entrance road was forecast to remain at near historical levels of driveability. The greatest snow losses were forecast for the west entrance road where as little as 29% of the December–March oversnow season was forecast to be driveable by late century. The climatic conditions that allow oversnow vehicle use in Yellowstone are forecast by our methods to deteriorate significantly in the future. At some point it may be prudent to consider plowing the roads that experience the greatest snow losses. PMID:27467778

The Pedagogical Structure of Methods for Thinking about the Future: The Citizens Function in Planning. Working Draft.

ERIC Educational Resources Information Center

Sandow, Stuart A.

The principal problem of this work is to discover, perhaps construct, a pedagogical process for thinking about the future that makes use of the methods of forecasting, but makes use of them to create new attitudes and understandings on the part of institutional planners. The underlying concern is to develop a way for planners to address the future…

The School of the Future in the USSR.

ERIC Educational Resources Information Center

Kostyashkin, E. G.

1980-01-01

Considers a model of education in the last decade of the 20th century devised by the Educational Development Forecasting Laboratory of the USSR Academy of Pedagogical Science. New model schools will feature alternation of work and rest periods, more time in the open air, psycho-physical development, optional studies, and greater opportunities for…

A prospective approach to coastal geography from satellite. [technological forecasting

NASA Technical Reports Server (NTRS)

Munday, J. C., Jr.

1981-01-01

A forecasting protocol termed the "prospective approach' was used to examine probable futures relative to coastal applications of satellite data. Significant variables include the energy situation, the national economy, national Earth satellite programs, and coastal zone research, commercial activity, and regulatory activity. Alternative scenarios for the period until 1986 are presented. Possible response by state/local remote sensing centers include operational applications for users, input to geo-base information systems (GIS), development of decision-making algorithms using GIS data, and long term research programs for coastal management using merged satellite and traditional data.

Forecasting and evaluating patterns of energy development in southwestern Wyoming

USGS Publications Warehouse

Garman, Steven L.

2015-01-01

The effects of future oil and natural gas development in southwestern Wyoming on wildlife populations are topical to conservation of the sagebrush steppe ecosystem. To aid in understanding these potential effects, the U.S. Geological Survey developed an Energy Footprint simulation model that forecasts the amount and pattern of energy development under different assumptions of development rates and well-drilling methods. The simulated disturbance patterns produced by the footprint model are used to assess the potential effects on wildlife habitat and populations. A goal of this modeling effort is to use measures of energy production (number of simulated wells), well-pad and road-surface disturbance, and potential effects on wildlife to identify build-out designs that minimize the physical and ecological footprint of energy development for different levels of energy production and development costs.

Understanding Farmers’ Forecast Use from Their Beliefs, Values, Social Norms, and Perceived Obstacles

NASA Astrophysics Data System (ADS)

Hu, Qi; Pytlik Zillig, Lisa M.; Lynne, Gary D.; Tomkins, Alan J.; Waltman, William J.; Hayes, Michael J.; Hubbard, Kenneth G.; Artikov, Ikrom; Hoffman, Stacey J.; Wilhite, Donald A.

2006-09-01

Although the accuracy of weather and climate forecasts is continuously improving and new information retrieved from climate data is adding to the understanding of climate variation, use of the forecasts and climate information by farmers in farming decisions has changed little. This lack of change may result from knowledge barriers and psychological, social, and economic factors that undermine farmer motivation to use forecasts and climate information. According to the theory of planned behavior (TPB), the motivation to use forecasts may arise from personal attitudes, social norms, and perceived control or ability to use forecasts in specific decisions. These attributes are examined using data from a survey designed around the TPB and conducted among farming communities in the region of eastern Nebraska and the western U.S. Corn Belt. There were three major findings: 1) the utility and value of the forecasts for farming decisions as perceived by farmers are, on average, around 3.0 on a 0 7 scale, indicating much room to improve attitudes toward the forecast value. 2) The use of forecasts by farmers to influence decisions is likely affected by several social groups that can provide “expert viewpoints” on forecast use. 3) A major obstacle, next to forecast accuracy, is the perceived identity and reliability of the forecast makers. Given the rapidly increasing number of forecasts in this growing service business, the ambiguous identity of forecast providers may have left farmers confused and may have prevented them from developing both trust in forecasts and skills to use them. These findings shed light on productive avenues for increasing the influence of forecasts, which may lead to greater farming productivity. In addition, this study establishes a set of reference points that can be used for comparisons with future studies to quantify changes in forecast use and influence.

Forecast of future aviation fuels: The model

NASA Technical Reports Server (NTRS)

Ayati, M. B.; Liu, C. Y.; English, J. M.

1981-01-01

A conceptual models of the commercial air transportation industry is developed which can be used to predict trends in economics, demand, and consumption. The methodology is based on digraph theory, which considers the interaction of variables and propagation of changes. Air transportation economics are treated by examination of major variables, their relationships, historic trends, and calculation of regression coefficients. A description of the modeling technique and a compilation of historic airline industry statistics used to determine interaction coefficients are included. Results of model validations show negligible difference between actual and projected values over the twenty-eight year period of 1959 to 1976. A limited application of the method presents forecasts of air tranportation industry demand, growth, revenue, costs, and fuel consumption to 2020 for two scenarios of future economic growth and energy consumption.

Extravehicular Activity Technology Development Status and Forecast

NASA Technical Reports Server (NTRS)

Chullen, Cinda; Westheimer, David T.

2011-01-01

The goal of NASA s current EVA technology effort is to further develop technologies that will be used to demonstrate a robust EVA system that has application for a variety of future missions including microgravity and surface EVA. Overall the objectives will be to reduce system mass, reduce consumables and maintenance, increase EVA hardware robustness and life, increase crew member efficiency and autonomy, and enable rapid vehicle egress and ingress. Over the past several years, NASA realized a tremendous increase in EVA system development as part of the Exploration Technology Development Program and the Constellation Program. The evident demand for efficient and reliable EVA technologies, particularly regenerable technologies was apparent under these former programs and will continue to be needed as future mission opportunities arise. The technological need for EVA in space has been realized over the last several decades by the Gemini, Apollo, Skylab, Space Shuttle, and the International Space Station (ISS) programs. EVAs were critical to the success of these programs. Now with the ISS extension to 2028 in conjunction with a current forecasted need of at least eight EVAs per year, the EVA hardware life and limited availability of the Extravehicular Mobility Units (EMUs) will eventually become a critical issue. The current EMU has successfully served EVA demands by performing critical operations to assemble the ISS and provide repairs of satellites such as the Hubble Space Telescope. However, as the life of ISS and the vision for future mission opportunities are realized, a new EVA systems capability will be needed and the current architectures and technologies under development offer significant improvements over the current flight systems. In addition to ISS, potential mission applications include EVAs for missions to Near Earth Objects (NEO), Phobos, or future surface missions. Surface missions could include either exploration of the Moon or Mars. Providing an EVA capability for these types of missions enables in-space construction of complex vehicles or satellites, hands on exploration of new parts of our solar system, and engages the public through the inspiration of knowing that humans are exploring places that they have never been before. This paper offers insight into what is currently being developed and what the potential opportunities are in the forecast.

Trends in the predictive performance of raw ensemble weather forecasts

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Scheuerer, Michael; Pappenberger, Florian; Bogner, Konrad; Haiden, Thomas

2015-04-01

Over the last two decades the paradigm in weather forecasting has shifted from being deterministic to probabilistic. Accordingly, numerical weather prediction (NWP) models have been run increasingly as ensemble forecasting systems. The goal of such ensemble forecasts is to approximate the forecast probability distribution by a finite sample of scenarios. Global ensemble forecast systems, like the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble, are prone to probabilistic biases, and are therefore not reliable. They particularly tend to be underdispersive for surface weather parameters. Hence, statistical post-processing is required in order to obtain reliable and sharp forecasts. In this study we apply statistical post-processing to ensemble forecasts of near-surface temperature, 24-hour precipitation totals, and near-surface wind speed from the global ECMWF model. Our main objective is to evaluate the evolution of the difference in skill between the raw ensemble and the post-processed forecasts. The ECMWF ensemble is under continuous development, and hence its forecast skill improves over time. Parts of these improvements may be due to a reduction of probabilistic bias. Thus, we first hypothesize that the gain by post-processing decreases over time. Based on ECMWF forecasts from January 2002 to March 2014 and corresponding observations from globally distributed stations we generate post-processed forecasts by ensemble model output statistics (EMOS) for each station and variable. Parameter estimates are obtained by minimizing the Continuous Ranked Probability Score (CRPS) over rolling training periods that consist of the n days preceding the initialization dates. Given the higher average skill in terms of CRPS of the post-processed forecasts for all three variables, we analyze the evolution of the difference in skill between raw ensemble and EMOS forecasts. The fact that the gap in skill remains almost constant over time, especially for near-surface wind speed, suggests that improvements to the atmospheric model have an effect quite different from what calibration by statistical post-processing is doing. That is, they are increasing potential skill. Thus this study indicates that (a) further model development is important even if one is just interested in point forecasts, and (b) statistical post-processing is important because it will keep adding skill in the foreseeable future.

Development of a hybrid model to predict construction and demolition waste: China as a case study.

PubMed

Song, Yiliao; Wang, Yong; Liu, Feng; Zhang, Yixin

2017-01-01

Construction and demolition waste (C&DW) is currently a worldwide issue, and the situation is the worst in China due to a rapid increase in the construction industry and the short life span of China's buildings. To create an opportunity out of this problem, comprehensive prevention measures and effective management strategies are urgently needed. One major gap in the literature of waste management is a lack of estimations on future C&DW generation. Therefore, this paper presents a forecasting procedure for C&DW in China that can forecast the quantity of each component in such waste. The proposed approach is based on a GM-SVR model that improves the forecasting effectiveness of the gray model (GM), which is achieved by adjusting the residual series by a support vector regression (SVR) method and a transition matrix that aims to estimate the discharge of each component in the C&DW. Through the proposed method, future C&DW volume are listed and analyzed containing their potential components and distribution in different provinces in China. Besides, model testing process provides mathematical evidence to validate the proposed model is an effective way to give future information of C&DW for policy makers. Copyright © 2016 Elsevier Ltd. All rights reserved.

Observation impact studies with the Mercator Ocean analysis and forecasting systems

NASA Astrophysics Data System (ADS)

Remy, E. D.; Le Traon, P. Y.; Lellouche, J. M.; Drevillon, M.; Turpin, V.; Benkiran, M.

2016-02-01

Mercator Ocean produces and delivers in real-time ocean analysis and forecasts on a daily basis. The quality of the analysis highly relies on the availability and quality of the assimilated observations.Tools are developed to estimate the impact of the present network and to help designing the future evolutions of the observing systems in the context of near real time production of ocean analysis and forecasts. OSE and OSSE are the main approaches used in this context. They allow the assessment of the efficiency of a given data set to constrain the ocean model circulation through the data assimilation process. Illustrations will mainly focus on the present and future evolution of the Argo observation network and altimetry constellation, including the potential impact of future SWOT data. Our systems show clear sensitivities to observation array changes, mainly depending on the specified observation error and regional dynamic. Impact on non observed variables can be important and are important to evaluate. Dedicated diagnostics has to be define to measure the improvements bring by each data set. Alternative approaches to OSE and OSSE are also explored: approximate computation of DFS will be presented and discussed. Limitations of each approach will be discussed in the context of real time operation.

Validation of Model Forecasts of the Ambient Solar Wind

NASA Technical Reports Server (NTRS)

Macneice, P. J.; Hesse, M.; Kuznetsova, M. M.; Rastaetter, L.; Taktakishvili, A.

2009-01-01

Independent and automated validation is a vital step in the progression of models from the research community into operational forecasting use. In this paper we describe a program in development at the CCMC to provide just such a comprehensive validation for models of the ambient solar wind in the inner heliosphere. We have built upon previous efforts published in the community, sharpened their definitions, and completed a baseline study. We also provide first results from this program of the comparative performance of the MHD models available at the CCMC against that of the Wang-Sheeley-Arge (WSA) model. An important goal of this effort is to provide a consistent validation to all available models. Clearly exposing the relative strengths and weaknesses of the different models will enable forecasters to craft more reliable ensemble forecasting strategies. Models of the ambient solar wind are developing rapidly as a result of improvements in data supply, numerical techniques, and computing resources. It is anticipated that in the next five to ten years, the MHD based models will supplant semi-empirical potential based models such as the WSA model, as the best available forecast models. We anticipate that this validation effort will track this evolution and so assist policy makers in gauging the value of past and future investment in modeling support.

The Best of Both Worlds: Developing a Hybrid Data System for the ASF DAAC

NASA Astrophysics Data System (ADS)

Arko, S. A.; Buechler, B.; Wolf, V. G.

2017-12-01

The Alaska Satellite Facility (ASF) at the University of Alaska Fairbanks hosts the NASA Distributed Active Archive Center (DAAC) specializing in synthetic aperture radar (SAR). Historically, the ASF DAAC has hosted hardware on-premises and developed DAAC-specific software to operate, manage, and maintain the DAAC data system. In the past year, ASF DAAC has been moving many of the standard DAAC operations into the Amazon Web Services (AWS) cloud. This includes data ingest, basic pre-processing, archiving, and distribution within the AWS environment. While the cloud offers nearly unbounded capacity for expansion and a great host of services, there also can be unexpected and unplanned costs for such. Additionally, these costs can be difficult to forecast even with historic data usage patterns and models for future usage. In an effort to maximize the effectiveness of the DAAC data system, while still managing and accurately forecasting costs, ASF DAAC has developed a hybrid, cloud and on-premises, data system. The goal of this project is to make extensive use of the AWS cloud, and when appropriate, utilize on-premises resources to help constrain costs. This hybrid system attempts to mimic, on premises, a cloud environment using Kubernetes container orchestration in order that software can be run in either location with little change. Combined with hybrid data storage architecture, the new data system makes use of the great capacity of the cloud while maintaining an on-premises options. This presentation will describe the development of the hybrid data system, including the micro-services architecture and design, the container orchestration, and hybrid storage. Additional we will highlight the lessons learned through the development process, cost forecasting for current and future SAR-mission operations, and provide a discussion of the pros and cons of hybrid architectures versus all-cloud deployments. This development effort has led to a system that is capable and flexible for the future while allowing ASF DAAC to continue supporting the SAR community with the highest level of services.

Forecasting the probability of future groundwater levels declining below specified low thresholds in the conterminous U.S.

USGS Publications Warehouse

Dudley, Robert W.; Hodgkins, Glenn A.; Dickinson, Jesse

2017-01-01

We present a logistic regression approach for forecasting the probability of future groundwater levels declining or maintaining below specific groundwater-level thresholds. We tested our approach on 102 groundwater wells in different climatic regions and aquifers of the United States that are part of the U.S. Geological Survey Groundwater Climate Response Network. We evaluated the importance of current groundwater levels, precipitation, streamflow, seasonal variability, Palmer Drought Severity Index, and atmosphere/ocean indices for developing the logistic regression equations. Several diagnostics of model fit were used to evaluate the regression equations, including testing of autocorrelation of residuals, goodness-of-fit metrics, and bootstrap validation testing. The probabilistic predictions were most successful at wells with high persistence (low month-to-month variability) in their groundwater records and at wells where the groundwater level remained below the defined low threshold for sustained periods (generally three months or longer). The model fit was weakest at wells with strong seasonal variability in levels and with shorter duration low-threshold events. We identified challenges in deriving probabilistic-forecasting models and possible approaches for addressing those challenges.

Remote-sensing based approach to forecast habitat quality under climate change scenarios.

PubMed

Requena-Mullor, Juan M; López, Enrique; Castro, Antonio J; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier

2017-01-01

As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071-2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios.

Remote-sensing based approach to forecast habitat quality under climate change scenarios

PubMed Central

Requena-Mullor, Juan M.; López, Enrique; Castro, Antonio J.; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier

2017-01-01

As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071–2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios. PMID:28257501

Forecasting Hospitalization and Emergency Department Visit Rates for Chronic Obstructive Pulmonary Disease. A Time-Series Analysis.

PubMed

Gershon, Andrea; Thiruchelvam, Deva; Moineddin, Rahim; Zhao, Xiu Yan; Hwee, Jeremiah; To, Teresa

2017-06-01

Knowing trends in and forecasting hospitalization and emergency department visit rates for chronic obstructive pulmonary disease (COPD) can enable health care providers, hospitals, and health care decision makers to plan for the future. We conducted a time-series analysis using health care administrative data from the Province of Ontario, Canada, to determine previous trends in acute care hospitalization and emergency department visit rates for COPD and then to forecast future rates. Individuals aged 35 years and older with physician-diagnosed COPD were identified using four universal government health administrative databases and a validated case definition. Monthly COPD hospitalization and emergency department visit rates per 1,000 people with COPD were determined from 2003 to 2014 and then forecasted to 2024 using autoregressive integrated moving average models. Between 2003 and 2014, COPD prevalence increased from 8.9 to 11.1%. During that time, there were 274,951 hospitalizations and 290,482 emergency department visits for COPD. After accounting for seasonality, we found that monthly COPD hospitalization and emergency department visit rates per 1,000 individuals with COPD remained stable. COPD prevalence was forecasted to increase to 12.7% (95% confidence interval [CI], 11.4-14.1) by 2024, whereas monthly COPD hospitalization and emergency department visit rates per 1,000 people with COPD were forecasted to remain stable at 2.7 (95% CI, 1.6-4.4) and 3.7 (95% CI, 2.3-5.6), respectively. Forecasted age- and sex-stratified rates were also stable. COPD hospital and emergency department visit rates per 1,000 people with COPD have been stable for more than a decade and are projected to remain stable in the near future. Given increasing COPD prevalence, this means notably more COPD health service use in the future.

Will the NP workforce grow in the future? New forecasts and implications for healthcare delivery.

PubMed

Auerbach, David I

2012-07-01

The nurse practitioner (NP) workforce has been a focus of considerable policy interest recently, particularly as the Patient Protection and Affordable Care Act may place additional demands on the healthcare professional workforce. The NP workforce has been growing rapidly in recent years, but fluctuation in enrollments in the past decades has resulted in a wide range of forecasts. To forecast the future NP workforce using a novel method that has been applied to the registered nurse and physician workforces and is robust to fluctuating enrollment trends. An age-cohort regression-based model was applied to the current and historical workforce, which was then forecasted to future years assuming stable age effects and a continuation of recent cohort trends. A total of 6798 NPs who were identified as having completed NP training in the National Sample Survey of Registered Nurses between 1992 and 2008. The future workforce is projected to grow to 244,000 in 2025, an increase of 94% from 128,000 in 2008. If NPs are defined more restrictively as those who self-identify their position title as "NP," supply is projected to grow from 86,000 to 198,000 (130%) over this period. The large projected increase in NP supply is higher and more grounded than other forecasts and has several implications: NPs will likely fulfill a substantial amount of future demand for care. Furthermore, as the ratio of NPs to Nurse Practitioners to physicians will surely grow, there could be implications for quality of care and for the configuration of future care delivery systems.

Expert and Knowledge Based Systems.

ERIC Educational Resources Information Center

Demaid, Adrian; Edwards, Lyndon

1987-01-01

Discusses the nature and current state of knowledge-based systems and expert systems. Describes an expert system from the viewpoints of a computer programmer and an applications expert. Addresses concerns related to materials selection and forecasts future developments in the teaching of materials engineering. (ML)

Demand forecasting for automotive sector in Malaysia by system dynamics approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zulkepli, Jafri, E-mail: zhjafri@uum.edu.my; Abidin, Norhaslinda Zainal, E-mail: nhaslinda@uum.edu.my; Fong, Chan Hwa, E-mail: hfchan7623@yahoo.com

In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand frommore » the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.« less

A scoping review of malaria forecasting: past work and future directions

PubMed Central

Zinszer, Kate; Verma, Aman D; Charland, Katia; Brewer, Timothy F; Brownstein, John S; Sun, Zhuoyu; Buckeridge, David L

2012-01-01

Objectives There is a growing body of literature on malaria forecasting methods and the objective of our review is to identify and assess methods, including predictors, used to forecast malaria. Design Scoping review. Two independent reviewers searched information sources, assessed studies for inclusion and extracted data from each study. Information sources Search strategies were developed and the following databases were searched: CAB Abstracts, EMBASE, Global Health, MEDLINE, ProQuest Dissertations & Theses and Web of Science. Key journals and websites were also manually searched. Eligibility criteria for included studies We included studies that forecasted incidence, prevalence or epidemics of malaria over time. A description of the forecasting model and an assessment of the forecast accuracy of the model were requirements for inclusion. Studies were restricted to human populations and to autochthonous transmission settings. Results We identified 29 different studies that met our inclusion criteria for this review. The forecasting approaches included statistical modelling, mathematical modelling and machine learning methods. Climate-related predictors were used consistently in forecasting models, with the most common predictors being rainfall, relative humidity, temperature and the normalised difference vegetation index. Model evaluation was typically based on a reserved portion of data and accuracy was measured in a variety of ways including mean-squared error and correlation coefficients. We could not compare the forecast accuracy of models from the different studies as the evaluation measures differed across the studies. Conclusions Applying different forecasting methods to the same data, exploring the predictive ability of non-environmental variables, including transmission reducing interventions and using common forecast accuracy measures will allow malaria researchers to compare and improve models and methods, which should improve the quality of malaria forecasting. PMID:23180505

A two-stage method of quantitative flood risk analysis for reservoir real-time operation using ensemble-based hydrologic forecasts

NASA Astrophysics Data System (ADS)

Liu, P.

2013-12-01

Quantitative analysis of the risk for reservoir real-time operation is a hard task owing to the difficulty of accurate description of inflow uncertainties. The ensemble-based hydrologic forecasts directly depict the inflows not only the marginal distributions but also their persistence via scenarios. This motivates us to analyze the reservoir real-time operating risk with ensemble-based hydrologic forecasts as inputs. A method is developed by using the forecast horizon point to divide the future time into two stages, the forecast lead-time and the unpredicted time. The risk within the forecast lead-time is computed based on counting the failure number of forecast scenarios, and the risk in the unpredicted time is estimated using reservoir routing with the design floods and the reservoir water levels of forecast horizon point. As a result, a two-stage risk analysis method is set up to quantify the entire flood risks by defining the ratio of the number of scenarios that excessive the critical value to the total number of scenarios. The China's Three Gorges Reservoir (TGR) is selected as a case study, where the parameter and precipitation uncertainties are implemented to produce ensemble-based hydrologic forecasts. The Bayesian inference, Markov Chain Monte Carlo, is used to account for the parameter uncertainty. Two reservoir operation schemes, the real operated and scenario optimization, are evaluated for the flood risks and hydropower profits analysis. With the 2010 flood, it is found that the improvement of the hydrologic forecast accuracy is unnecessary to decrease the reservoir real-time operation risk, and most risks are from the forecast lead-time. It is therefore valuable to decrease the avarice of ensemble-based hydrologic forecasts with less bias for a reservoir operational purpose.

The Norwegian forecasting and warning service for rainfall- and snowmelt-induced landslides

NASA Astrophysics Data System (ADS)

Krøgli, Ingeborg K.; Devoli, Graziella; Colleuille, Hervé; Boje, Søren; Sund, Monica; Engen, Inger Karin

2018-05-01

The Norwegian Water Resources and Energy Directorate (NVE) have run a national flood forecasting and warning service since 1989. In 2009, the directorate was given the responsibility of also initiating a national forecasting service for rainfall-induced landslides. Both services are part of a political effort to improve flood and landslide risk prevention. The Landslide Forecasting and Warning Service was officially launched in 2013 and is developed as a joint initiative across public agencies between NVE, the Norwegian Meteorological Institute (MET), the Norwegian Public Road Administration (NPRA) and the Norwegian Rail Administration (Bane NOR). The main goal of the service is to reduce economic and human losses caused by landslides. The service performs daily a national landslide hazard assessment describing the expected awareness level at a regional level (i.e. for a county and/or group of municipalities). The service is operative 7 days a week throughout the year. Assessments and updates are published at the warning portal http://www.varsom.no/ at least twice a day, for the three coming days. The service delivers continuous updates on the current situation and future development to national and regional stakeholders and to the general public. The service is run in close cooperation with the flood forecasting service. Both services are based on the five pillars: automatic hydrological and meteorological stations, landslide and flood historical database, hydro-meteorological forecasting models, thresholds or return periods, and a trained group of forecasters. The main components of the service are herein described. A recent evaluation, conducted on the 4 years of operation, shows a rate of over 95 % correct daily assessments. In addition positive feedbacks have been received from users through a questionnaire. The capability of the service to forecast landslides by following the hydro-meteorological conditions is illustrated by an example from autumn 2017. The case shows how the landslide service has developed into a well-functioning system providing useful information, effectively and on time.

Development and Implementation of Dynamic Scripts to Support Local Model Verification at National Weather Service Weather Forecast Offices

NASA Technical Reports Server (NTRS)

Zavordsky, Bradley; Case, Jonathan L.; Gotway, John H.; White, Kristopher; Medlin, Jeffrey; Wood, Lance; Radell, Dave

2014-01-01

Local modeling with a customized configuration is conducted at National Weather Service (NWS) Weather Forecast Offices (WFOs) to produce high-resolution numerical forecasts that can better simulate local weather phenomena and complement larger scale global and regional models. The advent of the Environmental Modeling System (EMS), which provides a pre-compiled version of the Weather Research and Forecasting (WRF) model and wrapper Perl scripts, has enabled forecasters to easily configure and execute the WRF model on local workstations. NWS WFOs often use EMS output to help in forecasting highly localized, mesoscale features such as convective initiation, the timing and inland extent of lake effect snow bands, lake and sea breezes, and topographically-modified winds. However, quantitatively evaluating model performance to determine errors and biases still proves to be one of the challenges in running a local model. Developed at the National Center for Atmospheric Research (NCAR), the Model Evaluation Tools (MET) verification software makes performing these types of quantitative analyses easier, but operational forecasters do not generally have time to familiarize themselves with navigating the sometimes complex configurations associated with the MET tools. To assist forecasters in running a subset of MET programs and capabilities, the Short-term Prediction Research and Transition (SPoRT) Center has developed and transitioned a set of dynamic, easily configurable Perl scripts to collaborating NWS WFOs. The objective of these scripts is to provide SPoRT collaborating partners in the NWS with the ability to evaluate the skill of their local EMS model runs in near real time with little prior knowledge of the MET package. The ultimate goal is to make these verification scripts available to the broader NWS community in a future version of the EMS software. This paper provides an overview of the SPoRT MET scripts, instructions for how the scripts are run, and example use cases.

The NRL relocatable ocean/acoustic ensemble forecast system

NASA Astrophysics Data System (ADS)

Rowley, C.; Martin, P.; Cummings, J.; Jacobs, G.; Coelho, E.; Bishop, C.; Hong, X.; Peggion, G.; Fabre, J.

2009-04-01

A globally relocatable regional ocean nowcast/forecast system has been developed to support rapid implementation of new regional forecast domains. The system is in operational use at the Naval Oceanographic Office for a growing number of regional and coastal implementations. The new system is the basis for an ocean acoustic ensemble forecast and adaptive sampling capability. We present an overview of the forecast system and the ocean ensemble and adaptive sampling methods. The forecast system consists of core ocean data analysis and forecast modules, software for domain configuration, surface and boundary condition forcing processing, and job control, and global databases for ocean climatology, bathymetry, tides, and river locations and transports. The analysis component is the Navy Coupled Ocean Data Assimilation (NCODA) system, a 3D multivariate optimum interpolation system that produces simultaneous analyses of temperature, salinity, geopotential, and vector velocity using remotely-sensed SST, SSH, and sea ice concentration, plus in situ observations of temperature, salinity, and currents from ships, buoys, XBTs, CTDs, profiling floats, and autonomous gliders. The forecast component is the Navy Coastal Ocean Model (NCOM). The system supports one-way nesting and multiple assimilation methods. The ensemble system uses the ensemble transform technique with error variance estimates from the NCODA analysis to represent initial condition error. Perturbed surface forcing or an atmospheric ensemble is used to represent errors in surface forcing. The ensemble transform Kalman filter is used to assess the impact of adaptive observations on future analysis and forecast uncertainty for both ocean and acoustic properties.

Interval forecasting of cyber-attacks on industrial control systems

NASA Astrophysics Data System (ADS)

Ivanyo, Y. M.; Krakovsky, Y. M.; Luzgin, A. N.

2018-03-01

At present, cyber-security issues of industrial control systems occupy one of the key niches in a state system of planning and management Functional disruption of these systems via cyber-attacks may lead to emergencies related to loss of life, environmental disasters, major financial and economic damage, or disrupted activities of cities and settlements. There is then an urgent need to develop protection methods against cyber-attacks. This paper studied the results of cyber-attack interval forecasting with a pre-set intensity level of cyber-attacks. Interval forecasting is the forecasting of one interval from two predetermined ones in which a future value of the indicator will be obtained. For this, probability estimates of these events were used. For interval forecasting, a probabilistic neural network with a dynamic updating value of the smoothing parameter was used. A dividing bound of these intervals was determined by a calculation method based on statistical characteristics of the indicator. The number of cyber-attacks per hour that were received through a honeypot from March to September 2013 for the group ‘zeppo-norcal’ was selected as the indicator.

Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level.

PubMed

Marques-Toledo, Cecilia de Almeida; Degener, Carolin Marlen; Vinhal, Livia; Coelho, Giovanini; Meira, Wagner; Codeço, Claudia Torres; Teixeira, Mauro Martins

2017-07-01

Infectious diseases are a leading threat to public health. Accurate and timely monitoring of disease risk and progress can reduce their impact. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Dengue is the fastest growing mosquito-borne viral disease, with an estimated annual incidence of 390 million infections, of which 96 million manifest clinically. Dengue burden is likely to increase in the future owing to trends toward increased urbanization, scarce water supplies and, possibly, environmental change. The epidemiological dynamic of Dengue is complex and difficult to predict, partly due to costly and slow surveillance systems. In this study, we aimed to quantitatively assess the usefulness of data acquired by Twitter for the early detection and monitoring of Dengue epidemics, both at country and city level at a weekly basis. Here, we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast, in comparison with other available web-based data, Google Trends and Wikipedia access logs. Also, we studied the factors that might influence the goodness-of-fit of the model. We built a simple model based on tweets that was able to 'nowcast', i.e. estimate disease numbers in the same week, but also 'forecast' disease in future weeks. At the country level, tweets are strongly associated with Dengue cases, and can estimate present and future Dengue cases until 8 weeks in advance. At city level, tweets are also useful for estimating Dengue activity. Our model can be applied successfully to small and less developed cities, suggesting a robust construction, even though it may be influenced by the incidence of the disease, the activity of Twitter locally, and social factors, including human development index and internet access. Tweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low-cost. Tweets are able to successfully nowcast, i.e. estimate Dengue in the present week, but also forecast, i.e. predict Dengue at until 8 weeks in the future, both at country and city level with high estimation capacity.

Development of a severe local storm prediction system: A 60-day test of a mesoscale primitive equation model

NASA Technical Reports Server (NTRS)

Paine, D. A.; Zack, J. W.; Kaplan, M. L.

1979-01-01

The progress and problems associated with the dynamical forecast system which was developed to predict severe storms are examined. The meteorological problem of severe convective storm forecasting is reviewed. The cascade hypothesis which forms the theoretical core of the nested grid dynamical numerical modelling system is described. The dynamical and numerical structure of the model used during the 1978 test period is presented and a preliminary description of a proposed multigrid system for future experiments and tests is provided. Six cases from the spring of 1978 are discussed to illustrate the model's performance and its problems. Potential solutions to the problems are examined.

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

NASA Astrophysics Data System (ADS)

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 forecast may be useful to emergency managers and non-specialists when confidence or expertise in parametric forecasting may be lacking. The method makes over-tuning impossible, and minimizes the rate of surprises. At the least, this forecast constitutes a useful benchmark for more precisely tuned parametric forecasts.

Ability of matrix models to explain the past and predict the future of plant populations.

USGS Publications Warehouse

McEachern, Kathryn; Crone, Elizabeth E.; Ellis, Martha M.; Morris, William F.; Stanley, Amanda; Bell, Timothy; Bierzychudek, Paulette; Ehrlen, Johan; Kaye, Thomas N.; Knight, Tiffany M.; Lesica, Peter; Oostermeijer, Gerard; Quintana-Ascencio, Pedro F.; Ticktin, Tamara; Valverde, Teresa; Williams, Jennifer I.; Doak, Daniel F.; Ganesan, Rengaian; Thorpe, Andrea S.; Menges, Eric S.

2013-01-01

Uncertainty associated with ecological forecasts has long been recognized, but forecast accuracy is rarely quantified. We evaluated how well data on 82 populations of 20 species of plants spanning 3 continents explained and predicted plant population dynamics. We parameterized stage-based matrix models with demographic data from individually marked plants and determined how well these models forecast population sizes observed at least 5 years into the future. Simple demographic models forecasted population dynamics poorly; only 40% of observed population sizes fell within our forecasts' 95% confidence limits. However, these models explained population dynamics during the years in which data were collected; observed changes in population size during the data-collection period were strongly positively correlated with population growth rate. Thus, these models are at least a sound way to quantify population status. Poor forecasts were not associated with the number of individual plants or years of data. We tested whether vital rates were density dependent and found both positive and negative density dependence. However, density dependence was not associated with forecast error. Forecast error was significantly associated with environmental differences between the data collection and forecast periods. To forecast population fates, more detailed models, such as those that project how environments are likely to change and how these changes will affect population dynamics, may be needed. Such detailed models are not always feasible. Thus, it may be wiser to make risk-averse decisions than to expect precise forecasts from models.

Ability of matrix models to explain the past and predict the future of plant populations.

PubMed

Crone, Elizabeth E; Ellis, Martha M; Morris, William F; Stanley, Amanda; Bell, Timothy; Bierzychudek, Paulette; Ehrlén, Johan; Kaye, Thomas N; Knight, Tiffany M; Lesica, Peter; Oostermeijer, Gerard; Quintana-Ascencio, Pedro F; Ticktin, Tamara; Valverde, Teresa; Williams, Jennifer L; Doak, Daniel F; Ganesan, Rengaian; McEachern, Kathyrn; Thorpe, Andrea S; Menges, Eric S

2013-10-01

Uncertainty associated with ecological forecasts has long been recognized, but forecast accuracy is rarely quantified. We evaluated how well data on 82 populations of 20 species of plants spanning 3 continents explained and predicted plant population dynamics. We parameterized stage-based matrix models with demographic data from individually marked plants and determined how well these models forecast population sizes observed at least 5 years into the future. Simple demographic models forecasted population dynamics poorly; only 40% of observed population sizes fell within our forecasts' 95% confidence limits. However, these models explained population dynamics during the years in which data were collected; observed changes in population size during the data-collection period were strongly positively correlated with population growth rate. Thus, these models are at least a sound way to quantify population status. Poor forecasts were not associated with the number of individual plants or years of data. We tested whether vital rates were density dependent and found both positive and negative density dependence. However, density dependence was not associated with forecast error. Forecast error was significantly associated with environmental differences between the data collection and forecast periods. To forecast population fates, more detailed models, such as those that project how environments are likely to change and how these changes will affect population dynamics, may be needed. Such detailed models are not always feasible. Thus, it may be wiser to make risk-averse decisions than to expect precise forecasts from models. © 2013 Society for Conservation Biology.

2017 Annual Meeting of the American College of Radiology-Moreton Lecture: Forecasting the Futures of Radiology.

PubMed

Bauer, Jeffrey C

2017-12-01

The traditional forces of change in health care are no longer working as they did in the past. Political gridlock has destroyed Washington's ability to create sensible policy for reforming the medical marketplace, creating chaos for providers. Fortunately, chaos creates opportunity. The idea of creating one's future has never been more relevant and necessary. Predicting-the science of extrapolating future values from historical data-is not a valid method for looking ahead when causal relationships that explained change in the past are themselves being redefined (the current situation). Forecasting-the art of estimating probabilities of possibilities-is the appropriate method for anticipating futures when causality is being redefined. With its focus on identifying a range of possibilities, forecasting identifies many different outcomes that are simultaneously possible for radiology. Health care's new climate is being shaped by four defining trends: 1) revolution in biological science that is shifting caregivers' mission from acute care to disease management; 2) proliferation of information and communications technologies that are transforming the way care is delivered; 3) end of economic growth in the medical marketplace that is reshaping demand for care; and 4) political dysfunction that forces caregivers and their business partners to develop successful futures on their own. Radiology 3.0 is nicely aligned with the transformational trends. Staying focused on implementing Radiology 3.0 is supported as the optimal strategy for creating the profession's futures. Diagnostic convergence, establishing a unified diagnostic science with laboratory medicine, is proposed as a complementary initiative. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

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

NASA Technical Reports Server (NTRS)

Rousseaux, Cecile S.; Gregg, Watson W.

2018-01-01

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

Forecasting the future risk of Barmah Forest virus disease under climate change scenarios in Queensland, Australia.

PubMed

Naish, Suchithra; Mengersen, Kerrie; Hu, Wenbiao; Tong, Shilu

2013-01-01

Mosquito-borne diseases are climate sensitive and there has been increasing concern over the impact of climate change on future disease risk. This paper projected the potential future risk of Barmah Forest virus (BFV) disease under climate change scenarios in Queensland, Australia. We obtained data on notified BFV cases, climate (maximum and minimum temperature and rainfall), socio-economic and tidal conditions for current period 2000-2008 for coastal regions in Queensland. Grid-data on future climate projections for 2025, 2050 and 2100 were also obtained. Logistic regression models were built to forecast the otential risk of BFV disease distribution under existing climatic, socio-economic and tidal conditions. The model was applied to estimate the potential geographic distribution of BFV outbreaks under climate change scenarios. The predictive model had good model accuracy, sensitivity and specificity. Maps on potential risk of future BFV disease indicated that disease would vary significantly across coastal regions in Queensland by 2100 due to marked differences in future rainfall and temperature projections. We conclude that the results of this study demonstrate that the future risk of BFV disease would vary across coastal regions in Queensland. These results may be helpful for public health decision making towards developing effective risk management strategies for BFV disease control and prevention programs in Queensland.

Forecast Verification: Identification of small changes in weather forecasting skill

NASA Astrophysics Data System (ADS)

Weatherhead, E. C.; Jensen, T. L.

2017-12-01

Global and regonal weather forecasts have improved over the past seven decades most often because of small, incrmental improvements. The identificaiton and verification of forecast improvement due to proposed small changes in forecasting can be expensive and, if not carried out efficiently, can slow progress in forecasting development. This presentation will look at the skill of commonly used verification techniques and show how the ability to detect improvements can depend on the magnitude of the improvement, the number of runs used to test the improvement, the location on the Earth and the statistical techniques used. For continuous variables, such as temperture, wind and humidity, the skill of a forecast can be directly compared using a pair-wise statistical test that accommodates the natural autocorrelation and magnitude of variability. For discrete variables, such as tornado outbreaks, or icing events, the challenges is to reduce the false alarm rate while improving the rate of correctly identifying th discrete event. For both continuus and discrete verification results, proper statistical approaches can reduce the number of runs needed to identify a small improvement in forecasting skill. Verification within the Next Generation Global Prediction System is an important component to the many small decisions needed to make stat-of-the-art improvements to weather forecasting capabilities. The comparison of multiple skill scores with often conflicting results requires not only appropriate testing, but also scientific judgment to assure that the choices are appropriate not only for improvements in today's forecasting capabilities, but allow improvements that will come in the future.

Improved Weather and Power Forecasts for Energy Operations - the German Research Project EWeLiNE

NASA Astrophysics Data System (ADS)

Lundgren, Kristina; Siefert, Malte; Hagedorn, Renate; Majewski, Detlev

2014-05-01

The German energy system is going through a fundamental change. Based on the energy plans of the German federal government, the share of electrical power production from renewables should increase to 35% by 2020. This means that, in the near future at certain times renewable energies will provide a major part of Germany's power production. Operating a power supply system with a large share of weather-dependent power sources in a secure way requires improved power forecasts. One of the most promising strategies to improve the existing wind power and PV power forecasts is to optimize the underlying weather forecasts and to enhance the collaboration between the meteorology and energy sectors. Deutscher Wetterdienst addresses these challenges in collaboration with Fraunhofer IWES within the research project EWeLiNE. The overarching goal of the project is to improve the wind and PV power forecasts by combining improved power forecast models and optimized weather forecasts. During the project, the numerical weather prediction models COSMO-DE and COSMO-DE-EPS (Ensemble Prediction System) by Deutscher Wetterdienst will be generally optimized towards improved wind power and PV forecasts. For instance, it will be investigated whether the assimilation of new types of data, e.g. power production data, can lead to improved weather forecasts. With regard to the probabilistic forecasts, the focus is on the generation of ensembles and ensemble calibration. One important aspect of the project is to integrate the probabilistic information into decision making processes by developing user-specified products. In this paper we give an overview of the project and present first results.

Forecasting urban water demand: A meta-regression analysis.

PubMed

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.

Amplified Policymaking

ERIC Educational Resources Information Center

Prince, Katherine; Woempner, Carolyn

2010-01-01

This brief examines the policy implications of two drivers of change presented in the "2020 Forecast: Creating the Future of Learning"-- Pattern Recognition and Amplified Organization. These drivers point toward a series of cultural shifts and illuminate how we are developing new ways of organizing, constructing, and managing knowledge.…

The Design and Implementation of Eco-Evolutionary PVA Models: An Integrative Approach Using HexSim

EPA Science Inventory

To persist into the future, species of conservation concern must remain both demographically and genetically viable. Developing mitigation and recovery strategies to ensure species’ viability necessitates the use of forecasting models that can incorporate ecological and/or...

An economic model of the manufacturers' aircraft production and airline earnings potential, volume 3

NASA Technical Reports Server (NTRS)

Kneafsey, J. T.; Hill, R. M.

1978-01-01

A behavioral explanation of the process of technological change in the U. S. aircraft manufacturing and airline industries is presented. The model indicates the principal factors which influence the aircraft (airframe) manufacturers in researching, developing, constructing and promoting new aircraft technology; and the financial requirements which determine the delivery of new aircraft to the domestic trunk airlines. Following specification and calibration of the model, the types and numbers of new aircraft were estimated historically for each airline's fleet. Examples of possible applications of the model to forecasting an individual airline's future fleet also are provided. The functional form of the model is a composite which was derived from several preceding econometric models developed on the foundations of the economics of innovation, acquisition, and technological change and represents an important contribution to the improved understanding of the economic and financial requirements for aircraft selection and production. The model's primary application will be to forecast the future types and numbers of new aircraft required for each domestic airline's fleet.

A national framework for flood forecasting model assessment for use in operations and investment planning over England and Wales

NASA Astrophysics Data System (ADS)

Moore, Robert J.; Wells, Steven C.; Cole, Steven J.

2016-04-01

It has been common for flood forecasting systems to be commissioned at a catchment or regional level in response to local priorities and hydrological conditions, leading to variety in system design and model choice. As systems mature and efficiencies of national management are sought, there can be a drive towards system rationalisation, gaining an overview of model performance and consideration of simplification through model-type convergence. Flood forecasting model assessments, whilst overseen at a national level, may be commissioned and managed at a catchment and regional level, take a variety of forms and be large in number. This presents a challenge when an integrated national assessment is required to guide operational use of flood forecasts and plan future investment in flood forecasting models and supporting hydrometric monitoring. This contribution reports on how a nationally consistent framework for flood forecasting model performance has been developed to embrace many past, ongoing and future assessments for local river systems by engineering consultants across England & Wales. The outcome is a Performance Summary for every site model assessed which, on a single page, contains relevant catchment information for context, a selection of overlain forecast and observed hydrographs and a set of performance statistics with associated displays of novel condensed form. One display provides performance comparison with other models that may exist for the site. The performance statistics include skill scores for forecasting events (flow/level threshold crossings) of differing severity/rarity, indicating their probability and likely timing, which have real value in an operational setting. The local models assessed can be of any type and span rainfall-runoff (conceptual and transfer function) and flow routing (hydrological and hydrodynamic) forms. Also accommodated by the framework is the national G2G (Grid-to-Grid) distributed hydrological model, providing area-wide coverage across the fluvial rivers of England and Wales, which can be assessed at gauged sites. Thus the performance of the national G2G model forecasts can be directly compared with that from the local models. The Performance Summary for each site model is complemented by a national spatial analysis of model performance stratified by model-type, geographical region and forecast lead-time. The map displays provide an extensive evidence-base that can be interrogated, through a Flood Forecasting Model Performance web portal, to reveal fresh insights into comparative performance across locations, lead-times and models. This work was commissioned by the Environment Agency in partnership with Natural Resources Wales and the Flood Forecasting Centre for England and Wales.

A long-term forecast analysis on worldwide land uses.

PubMed

Zhang, Wenjun; Qi, Yanhong; Zhang, Zhiguo

2006-08-01

More and more lands worldwide are being cultivated for food production while forests are disappearing at an unprecedented rate. This paper aims to make a long-term forecast on land uses worldwide and provide the public, researchers, and government officials with a clear profile for land uses in the future. Data of land uses since 1961 were used to fit historical trajectories and make the forecast. The results show that trajectories of land areas can be well fitted with univariate linear regressions. The forecasts of land uses during the coming 25 years were given in detail. Areas of agricultural land, arable land, and permanent pasture land worldwide would increase by 6.6%, 7.2%, and 6.3% respectively in the year 2030 as compared to the current areas. Permanent crops land area all over the world is forecasted to increase 0.64% by 2030. By the year 2030 the areas of forests and woodland, nonarable and nonpermanent land worldwide would decrease by 2.4% and 0.9% against the current areas. All other land area in the world would dramatically decline by 6.4% by the year 2030. Overall the land area related to agriculture would tend to decrease in developed countries, industrialized countries, Europe, and North and Central America. The agriculture related land area would considerably increase in developing countries, least developed countries, low-income countries, Asia, Africa, South America, etc. Developing countries hold larger total land area than developed countries. Dramatic and continuous growth in agricultural land area of developing countries would largely contribute to the expected growth of world agricultural land area in the coming years. Population explosion, food shortage and poverty in the world, especially in developing countries, together caused the excessive cultivation of land for agricultural uses in the past years. Increasing agricultural land area exacerbates the climate changes and degradation of environment. How to limit the growth of human population is a key problem for reducing agricultural land expansion. Development and use of high-yielding and high-quality crop and animal varieties, diversification of human food sources, and technical and financial assistance to developing countries from developed countries, should also be implemented and strengthened in the future in order to slow down or even reverse the increase trend of agricultural land area. Sustainable agriculture is the effective way to stabilize the agricultural land area without food shortage. Through various techniques and measures, sustainable agriculture may meet the food production goals with minimum environmental risk. Public awareness and interest in sustainable agriculture will help realize and ease the increasing stress from agricultural land expansion.

Adapting NEMO for use as the UK operational storm surge forecasting model

NASA Astrophysics Data System (ADS)

Furner, Rachel; Williams, Jane; Horsburgh, Kevin; Saulter, Andrew

2016-04-01

The United Kingdom is an area vulnerable to damage due to storm surges, particularly the East Coast which suffered losses estimated at over £1 billion during the North Sea surge event of the 5th and 6th December 2013. Accurate forecasting of storm surge events for this region is crucial to enable government agencies to assess the risk of overtopping of coastal defences so they can respond appropriately, minimising risk to life and infrastructure. There has been an operational storm surge forecast service for this region since 1978, using a numerical model developed by the National Oceanography Centre (NOC) and run at the UK Met Office. This is also implemented as part of an ensemble prediction system, using perturbed atmospheric forcing to produce an ensemble surge forecast. In order to ensure efficient use of future supercomputer developments and to create synergy with existing operational coastal ocean models the Met Office and NOC have begun a joint project transitioning the storm surge forecast system from the current CS3X code base to a configuration based on the Nucleus for European Modelling of the Ocean (NEMO). This work involves both adapting NEMO to add functionality, such as allowing the drying out of ocean cells and changes allowing NEMO to run efficiently as a two-dimensional, barotropic model. As the ensemble surge forecast system is run with 12 members 4 times a day computational efficiency is of high importance. Upon completion this project will enable interesting scientific comparisons to be made between a NEMO based surge model and the full three-dimensional baroclinic NEMO based models currently run within the Met Office, facilitating assessment of the impact of baroclinic processes, and vertical resolution on sea surface height forecasts. Moving to a NEMO code base will also allow many future developments to be more easily used within the storm surge model due to the wide range of options which currently exist within NEMO or are planned for future NEMO releases, such as data assimilation, and surge-wave coupling. Assessment of tidal performance of the NEMO-surge configuration and comparison to the existing operational CS3X model has been carried out. Evaluation of the models focus on performance relative to the UK Class A tide gauge network, a dataset which was established following the devastating flood of 1953 and which is managed by the British Oceanographic Data Service (BODC) based at NOC. Trials of the NEMO model in tide-only mode have illustrated the importance of having a well specified bathymetry and, for the 7km scaled model, a secondary sensitivity to bed friction coefficient and the specification of the coastline. Preliminary results will also be presented from model runs with atmospheric (wind stress and pressure at mean sea-level) forcing.

The European Drought Observatory (EDO): Current State and Future Directions

NASA Astrophysics Data System (ADS)

Vogt, Jürgen; Sepulcre, Guadalupe; Magni, Diego; Valentini, Luana; Singleton, Andrew; Micale, Fabio; Barbosa, Paulo

2013-04-01

Europe has repeatedly been affected by droughts, resulting in considerable ecological and economic damage and climate change studies indicate a trend towards increasing climate variability most likely resulting in more frequent drought occurrences also in Europe. Against this background, the European Commission's Joint Research Centre (JRC) is developing methods and tools for assessing, monitoring and forecasting droughts in Europe and develops a European Drought Observatory (EDO) to complement and integrate national activities with a European view. At the core of the European Drought Observatory (EDO) is a portal, including a map server, a metadata catalogue, a media-monitor and analysis tools. The map server presents Europe-wide up-to-date information on the occurrence and severity of droughts, which is complemented by more detailed information provided by regional, national and local observatories through OGC compliant web mapping and web coverage services. In addition, time series of historical maps as well as graphs of the temporal evolution of drought indices for individual grid cells and administrative regions in Europe can be retrieved and analysed. Current work is focusing on validating the available products, developing combined indicators, improving the functionalities, extending the linkage to additional national and regional drought information systems and testing options for medium-range probabilistic drought forecasting across Europe. Longer-term goals include the development of long-range drought forecasting products, the analysis of drought hazard and risk, the monitoring of drought impact and the integration of EDO in a global drought information system. The talk will provide an overview on the development and state of EDO, the different products, and the ways to include a wide range of stakeholders (i.e. European, national river basin, and local authorities) in the development of the system as well as an outlook on the future developments.

Developing a Mixed Neural Network Approach to Forecast the Residential Electricity Consumption Based on Sensor Recorded Data

PubMed Central

Bâra, Adela; Stănică, Justina-Lavinia; Coculescu, Cristina

2018-01-01

In this paper, we report a study having as a main goal the obtaining of a method that can provide an accurate forecast of the residential electricity consumption, refining it up to the appliance level, using sensor recorded data, for residential smart homes complexes that use renewable energy sources as a part of their consumed electricity, overcoming the limitations of not having available historical meteorological data and the unwillingness of the contractor to acquire such data periodically in the future accurate short-term forecasts from a specialized institute due to the implied costs. In this purpose, we have developed a mixed artificial neural network (ANN) approach using both non-linear autoregressive with exogenous input (NARX) ANNs and function fitting neural networks (FITNETs). We have used a large dataset containing detailed electricity consumption data recorded by sensors, monitoring a series of individual appliances, while in the NARX case we have also used timestamps datasets as exogenous variables. After having developed and validated the forecasting method, we have compiled it in view of incorporating it into a cloud solution, being delivered to the contractor that can provide it as a service for a monthly fee to both the operators and residential consumers. PMID:29734761

Developing a Mixed Neural Network Approach to Forecast the Residential Electricity Consumption Based on Sensor Recorded Data.

PubMed

Oprea, Simona-Vasilica; Pîrjan, Alexandru; Căruțașu, George; Petroșanu, Dana-Mihaela; Bâra, Adela; Stănică, Justina-Lavinia; Coculescu, Cristina

2018-05-05

In this paper, we report a study having as a main goal the obtaining of a method that can provide an accurate forecast of the residential electricity consumption, refining it up to the appliance level, using sensor recorded data, for residential smart homes complexes that use renewable energy sources as a part of their consumed electricity, overcoming the limitations of not having available historical meteorological data and the unwillingness of the contractor to acquire such data periodically in the future accurate short-term forecasts from a specialized institute due to the implied costs. In this purpose, we have developed a mixed artificial neural network (ANN) approach using both non-linear autoregressive with exogenous input (NARX) ANNs and function fitting neural networks (FITNETs). We have used a large dataset containing detailed electricity consumption data recorded by sensors, monitoring a series of individual appliances, while in the NARX case we have also used timestamps datasets as exogenous variables. After having developed and validated the forecasting method, we have compiled it in view of incorporating it into a cloud solution, being delivered to the contractor that can provide it as a service for a monthly fee to both the operators and residential consumers.

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system

PubMed Central

Jensen, Tue V.; Pinson, Pierre

2017-01-01

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation. PMID:29182600

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system.

PubMed

Jensen, Tue V; Pinson, Pierre

2017-11-28

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation.

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system

NASA Astrophysics Data System (ADS)

Jensen, Tue V.; Pinson, Pierre

2017-11-01

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation.

Putting Educational Forecasts into Perspective: A Guide for Decisionmakers.

ERIC Educational Resources Information Center

Dede, Christopher; Kierstead, Fred

This paper focuses on how educational decision-makers can make use of futures research through a better understanding of forecasters' perspectives. Eight problems in communicating that are significant in contributing to poor usage of forecasts by educational decision-makers are: (1) overuse of jargon, (2) preoccupation with technological…

Fast, Flexible, and Digital: Forecasts for Occupational and Workplace Education.

ERIC Educational Resources Information Center

Ausburn, Lynna J.

2002-01-01

Three Delphi panels of occupational educators (n=16, 9, 12) forecast scenarios for the future of workplace education, which were compared with results of a literature review. Results indicated increasing alignment of practitioners' forecasts for dramatically transformed workplace education with major trends identified in the literature. (Contains…

Forecasting--A Systematic Modeling Methodology. Paper No. 489.

ERIC Educational Resources Information Center

Mabert, Vincent A.; Radcliffe, Robert C.

In an attempt to bridge the gap between academic understanding and practical business use, the Box-Jenkins technique of time series analysis for forecasting future events is presented with a minimum of mathematical notation. The method is presented in three stages: a discussion of traditional forecasting techniques, focusing on traditional…

Faces of the Future: School Counselors as Cultural Mediators

ERIC Educational Resources Information Center

Portman, Tarrell Awe Agahe

2009-01-01

Twenty years ago, futurists examined the changing role of the school counselor and forecasted what the 21st-century school counselor would need to know. This article forecasts the future of school counseling in the next 20 years by focusing on expected diversity of K-12 students. Speculation on student enrollment based on projected trends and…

Technology Forecasting for the Purpose of Predicting Employment Growth

ERIC Educational Resources Information Center

Smith, Cormac

2016-01-01

Throughout history, there has been a great emphasis placed on the ability to predict future events. The value of such prognostication varies between situations and domains, but the objective remains the same. Is it possible to use current or past observations to forecast future events? One specific area in which such insight is sought after is the…

Enhancing the Quantitative Representation of Socioeconomic Conditions in the Shared Socio-economic Pathways (SSPs) using the International Futures Model

NASA Astrophysics Data System (ADS)

Rothman, D. S.; Siraj, A.; Hughes, B.

2013-12-01

The international research community is currently in the process of developing new scenarios for climate change research. One component of these scenarios are the Shared Socio-economic Pathways (SSPs), which describe a set of possible future socioeconomic conditions. These are presented in narrative storylines with associated quantitative drivers. The core quantitative drivers include total population, average GDP per capita, educational attainment, and urbanization at the global, regional, and national levels. At the same time there have been calls, particularly by the IAV community, for the SSPs to include additional quantitative information on other key social factors, such as income inequality, governance, health, and access to key infrastructures, which are discussed in the narratives. The International Futures system (IFs), based at the Pardee Center at the University of Denver, is able to provide forecasts of many of these indicators. IFs cannot use the SSP drivers as exogenous inputs, but we are able to create development pathways that closely reproduce the core quantitative drivers defined by the different SSPs, as well as incorporating assumptions on other key driving factors described in the qualitative narratives. In this paper, we present forecasts for additional quantitative indicators based upon the implementation of the SSP development pathways in IFs. These results will be of value to many researchers.

Univariate time series modeling and an application to future claims amount in SOCSO's invalidity pension scheme

NASA Astrophysics Data System (ADS)

Chek, Mohd Zaki Awang; Ahmad, Abu Bakar; Ridzwan, Ahmad Nur Azam Ahmad; Jelas, Imran Md.; Jamal, Nur Faezah; Ismail, Isma Liana; Zulkifli, Faiz; Noor, Syamsul Ikram Mohd

2012-09-01

The main objective of this study is to forecast the future claims amount of Invalidity Pension Scheme (IPS). All data were derived from SOCSO annual reports from year 1972 - 2010. These claims consist of all claims amount from 7 benefits offered by SOCSO such as Invalidity Pension, Invalidity Grant, Survivors Pension, Constant Attendance Allowance, Rehabilitation, Funeral and Education. Prediction of future claims of Invalidity Pension Scheme will be made using Univariate Forecasting Models to predict the future claims among workforce in Malaysia.

Assessment of an ensemble seasonal streamflow forecasting system for Australia

NASA Astrophysics Data System (ADS)

Bennett, James C.; Wang, Quan J.; Robertson, David E.; Schepen, Andrew; Li, Ming; Michael, Kelvin

2017-11-01

Despite an increasing availability of skilful long-range streamflow forecasts, many water agencies still rely on simple resampled historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skilful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean-land-atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall-runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to resampled historical inflow sequences. FoGSS generally produces skilful forecasts at shorter lead times ( < 4 months), and transits to climatology-like forecasts at longer lead times. Forecasts are generally reliable and unbiased. However, FoGSS does not perform well in very dry catchments (catchments that experience zero flows more than half the time in some months), sometimes producing strongly negative forecast skill and poor reliability. We attempt to improve forecasts through the use of (i) ESP rainfall forcings, (ii) different rainfall-runoff models, and (iii) a Bayesian prior to encourage the error model to return climatology forecasts in months when the rainfall-runoff model performs poorly. Of these, the use of the prior offers the clearest benefit in very dry catchments, where it moderates strongly negative forecast skill and reduces bias in some instances. However, the prior does not remedy poor reliability in very dry catchments. Overall, FoGSS is an attractive alternative to historical inflow sequences in all but the driest catchments. We discuss ways in which forecast reliability in very dry catchments could be improved in future work.

A scoping review of nursing workforce planning and forecasting research.

PubMed

Squires, Allison; Jylhä, Virpi; Jun, Jin; Ensio, Anneli; Kinnunen, Juha

2017-11-01

This study will critically evaluate forecasting models and their content in workforce planning policies for nursing professionals and to highlight the strengths and the weaknesses of existing approaches. Although macro-level nursing workforce issues may not be the first thing that many nurse managers consider in daily operations, the current and impending nursing shortage in many countries makes nursing specific models for workforce forecasting important. A scoping review was conducted using a directed and summative content analysis approach to capture supply and demand analytic methods of nurse workforce planning and forecasting. The literature on nurse workforce forecasting studies published in peer-reviewed journals as well as in grey literature was included in the scoping review. Thirty six studies met the inclusion criteria, with the majority coming from the USA. Forecasting methods were biased towards service utilization analyses and were not consistent across studies. Current methods for nurse workforce forecasting are inconsistent and have not accounted sufficiently for socioeconomic and political factors that can influence workforce projections. Additional studies examining past trends are needed to improve future modelling. Accurate nursing workforce forecasting can help nurse managers, administrators and policy makers to understand the supply and demand of the workforce to prepare and maintain an adequate and competent current and future workforce. © 2017 John Wiley & Sons Ltd.

National Severe Storms Forecast Center

NASA Technical Reports Server (NTRS)

1977-01-01

The principal mission of the National Severe Storms Forecast Center (NSSFC) is to maintain a continuous watch of weather developments that are capable of producing severe local storms, including tornadoes, and to prepare and issue messages designated as either Weather Outlooks or Tornado or Severe Thunderstorm Watches for dissemination to the public and aviation services. In addition to its assigned responsibility at the national level, the NSSFC is involved in a number of programs at the regional and local levels. Subsequent subsections and paragraphs describe the NSSFC, its users, inputs, outputs, interfaces, capabilities, workload, problem areas, and future plans in more detail.

UV Remote Sensing Data Products - Turning Data Into Knowledge

NASA Astrophysics Data System (ADS)

Weiss, M.; Paxton, L.; Schaefer, R. K.; Comberiate, J.; Hsieh, S. W.; Romeo, G.; Wolven, B. C.; Zhang, Y.

2013-12-01

The DMSP/SSUSI instruments have been taking UV images of the upper atmosphere for more than a decade. Each of the SSUSI instruments takes complete global UV images on a daily basis. Although this scientific data is very valuable, it is not actionable information. Perhaps the simplest use of SSUSI data is the assimilation of radiances into the GAIM ionospheric forecast model; even then, the data must be massaged to get it into a GAIM-ingestable form. We describe a development effort funded by the DMSP program and the Air Force Weather Agency to turn the raw data into actionable information in the form of SSUSI environmental data parameters and other derived information. We will describe current nowcasts, forecasts, and other related actionable information (e.g. auroral oval forecasts) that is currently generated by the SSUSI ground processing system for AFWA, and also concepts we have for future tools (e.g., geomagnetic storm alerts, scintillation forecasts, HF radio propagation information, auroral radar clutter) to turn more of the SSUSI dataset into actionable knowledge.

Forecasting Austrian national elections: The Grand Coalition model

PubMed Central

Aichholzer, Julian; Willmann, Johanna

2014-01-01

Forecasting the outcomes of national elections has become established practice in several democracies. In the present paper, we develop an economic voting model for forecasting the future success of the Austrian ‘grand coalition’, i.e., the joint electoral success of the two mainstream parties SPOE and OEVP, at the 2013 Austrian Parliamentary Elections. Our main argument is that the success of both parties is strongly tied to the accomplishments of the Austrian system of corporatism, that is, the Social Partnership (Sozialpartnerschaft), in providing economic prosperity. Using data from Austrian national elections between 1953 and 2008 (n=18), we rely on the following predictors in our forecasting model: (1) unemployment rates, (2) previous incumbency of the two parties, and (3) dealignment over time. We conclude that, in general, the two mainstream parties benefit considerably from low unemployment rates, and are weakened whenever they have previously formed a coalition government. Further, we show that they have gradually been losing a good share of their voter basis over recent decades. PMID:26339109

Spatiotemporal drought forecasting using nonlinear models

NASA Astrophysics Data System (ADS)

Vasiliades, Lampros; Loukas, Athanasios

2010-05-01

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

Development of a satellite-based nowcasting system for surface solar radiation

NASA Astrophysics Data System (ADS)

Limbach, Sebastian; Hungershoefer, Katja; Müller, Richard; Trentmann, Jörg; Asmus, Jörg; Schömer, Elmar; Groß, André

2014-05-01

The goal of the RadNowCast project was the development of a tool-chain for a satellite-based nowcasting of the all sky global and direct surface solar radiation. One important application of such short-term forecasts is the computation of the expected energy yield of photovoltaic systems. This information is of great importance for an efficient balancing of power generation and consumption in large, decentralized power grids. Our nowcasting approach is based on an optical-flow analysis of a series of Meteosat SEVIRI satellite images. For this, we extended and combined several existing software tools and set up a series of benchmarks for determining the optimal forecasting parameters. The first step in our processing-chain is the determination of the cloud albedo from the HRV (High Resolution Visible)-satellite images using a Heliosat-type method. The actual nowcasting is then performed by a commercial software system in two steps: First, vector fields characterizing the movement of the clouds are derived from the cloud albedo data from the previous 15 min to 2 hours. Next, these vector fields are combined with the most recent cloud albedo data in order to extrapolate the cloud albedo in the near future. In the last step of the processing, the Gnu-Magic software is used to calculate the global and direct solar radiation based on the forecasted cloud albedo data. For an evaluation of the strengths and weaknesses of our nowcastig system, we analyzed four different benchmarks, each of which covered different weather conditions. We compared the forecasted data with radiation data derived from the real satellite images of the corresponding time steps. The impact of different parameters on the cloud albedo nowcasting and the surface radiation computation has been analysed. Additionally, we could show that our cloud-albedo-based forecasts outperform forecasts based on the original HRV images. Possible future extension are the incorporation of additional data sources, for example NWC-SAF high resolution wind fields, in order to improve the quality of the atmospheric motion fields, and experiments with custom, optimized software components for the optical-flow estimation and the nowcasting.

Perspective On Income Security and Social Services and An Agenda for Analysis.

DTIC Science & Technology

1981-08-13

economic stability of America and the continued viability of the U.S. social system. This report provides a prespective on many of the major issues, identifies present concerns, forecasts future developments, and briefly discusses GAO’s approach to addressing these issues.

Pathways to the Future: Linking Environmental Scanning to Strategic Management.

ERIC Educational Resources Information Center

Mecca, Thomas V.; Morrison, James L.

1988-01-01

Describes an ED QUEST (Quick Environmental Scanning Technique) workshop demonstrating the links between an environmental scanning/forecasting process and formulation of institutional strategy. Explains ED QUEST's use in identifying and analyzing critical trends and events, and identifying the nature of the organization; developing alternative…

Promoting inclusive water governance and forecasting the structure of water consumption based on compositional data: A case study of Beijing.

PubMed

Wei, Yigang; Wang, Zhichao; Wang, Huiwen; Yao, Tang; Li, Yan

2018-09-01

Water is centrally important for agricultural security, environment, people's livelihoods, and socio-economic development, particularly in the face of extreme climate changes. Due to water shortages in many cities, the conflicts between various stakeholders and sectors over water use and allocation are becoming more common and intense. Effective inclusive governance of water use is critical for relieving water use conflicts. In addition, reliable forecasting of the structure of water usage among different sectors is a basic need for effective water governance planning. Although a large number of studies have attempted to forecast water use, little is known about the forecasted structure and trends of water use in the future. This paper aims to develop a forecasting model for the structure of water usage based on compositional data. Compositional data analysis is an effective approach for investigating the internal structure of a system. A host of data transformation methods and forecasting models were adopted and compared in order to derive the best-performing model. According to mean absolute percent error for compositional data (CoMAPE), a hyperspherical-transformation-based vector autoregression model for compositional data (VAR-DRHT) is the best-performing model. The proportions of the agricultural, industrial, domestic and environmental water will be 6.11%, 5.01%, 37.48% and 51.4% by 2020. Several recommendations for water inclusive development are provided to give a better account for the optimization of the water use structure, alleviation of water shortages, and improving stake holders' wellbeing. Overall, although we focus on groundwater, this study presents a powerful framework broadly applicable to resource management. Copyright © 2018 Elsevier B.V. All rights reserved.

Optimizing Tsunami Forecast Model Accuracy

NASA Astrophysics Data System (ADS)

Whitmore, P.; Nyland, D. L.; Huang, P. Y.

2015-12-01

Recent tsunamis provide a means to determine the accuracy that can be expected of real-time tsunami forecast models. Forecast accuracy using two different tsunami forecast models are compared for seven events since 2006 based on both real-time application and optimized, after-the-fact "forecasts". Lessons learned by comparing the forecast accuracy determined during an event to modified applications of the models after-the-fact provide improved methods for real-time forecasting for future events. Variables such as source definition, data assimilation, and model scaling factors are examined to optimize forecast accuracy. Forecast accuracy is also compared for direct forward modeling based on earthquake source parameters versus accuracy obtained by assimilating sea level data into the forecast model. Results show that including assimilated sea level data into the models increases accuracy by approximately 15% for the events examined.

National Weather Service

Science.gov Websites

Forecast and Warning Services of the National Weather Service Introduction Quantitative precipitation future which is an active area of research currently. 2) Evaluate HPN performance for forecast periods

AWIPS II Application Development, a SPoRT Perspective

NASA Technical Reports Server (NTRS)

Burks, Jason E.; Smith, Matthew; McGrath, Kevin M.

2014-01-01

The National Weather Service (NWS) is deploying its next-generation decision support system, called AWIPS II (Advanced Weather Interactive Processing System II). NASA's Short-term Prediction Research and Transition (SPoRT) Center has developed several software 'plug-ins' to extend the capabilities of AWIPS II. SPoRT aims to continue its mission of improving short-term forecasts by providing NASA and NOAA products on the decision support system used at NWS weather forecast offices (WFOs). These products are not included in the standard Satellite Broadcast Network feed provided to WFOs. SPoRT has had success in providing support to WFOs as they have transitioned to AWIPS II. Specific examples of transitioning SPoRT plug-ins to WFOs with newly deployed AWIPS II systems will be presented. Proving Ground activities (GOES-R and JPSS) will dominate SPoRT's future AWIPS II activities, including tool development as well as enhancements to existing products. In early 2012 SPoRT initiated the Experimental Product Development Team, a group of AWIPS II developers from several institutions supporting NWS forecasters with innovative products. The results of the team's spring and fall 2013 meeting will be presented. Since AWIPS II developers now include employees at WFOs, as well as many other institutions related to weather forecasting, the NWS has dealt with a multitude of software governance issues related to the difficulties of multiple remotely collaborating software developers. This presentation will provide additional examples of Research-to-Operations plugins, as well as an update on how governance issues are being handled in the AWIPS II developer community.

Forecasting Future Sea Ice Conditions: A Lagrangian Approach

DTIC Science & Technology

2015-09-30

1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Forecasting Future Sea Ice Conditions: A Lagrangian ...GCMs participating in IPCC AR5 agree with observed source region patterns from the satellite- derived dataset. 4- Compare Lagrangian ice... Lagrangian sea-ice back trajectories to estimate thermodynamic and dynamic (advection) ice loss. APPROACH We use a Lagrangian trajectory model to

The Past, Present and Future of the Meteorological Phenomena Identification Near the Ground (mPING) Project

NASA Astrophysics Data System (ADS)

Elmore, K. L.

2016-12-01

The Metorological Phenomemna Identification NeartheGround (mPING) project is an example of a crowd-sourced, citizen science effort to gather data of sufficeint quality and quantity needed by new post processing methods that use machine learning. Transportation and infrastructure are particularly sensitive to precipitation type in winter weather. We extract attributes from operational numerical forecast models and use them in a random forest to generate forecast winter precipitation types. We find that random forests applied to forecast soundings are effective at generating skillful forecasts of surface ptype with consideralbly more skill than the current algorithms, especuially for ice pellets and freezing rain. We also find that three very different forecast models yuield similar overall results, showing that random forests are able to extract essentially equivalent information from different forecast models. We also show that the random forest for each model, and each profile type is unique to the particular forecast model and that the random forests developed using a particular model suffer significant degradation when given attributes derived from a different model. This implies that no single algorithm can perform well across all forecast models. Clearly, random forests extract information unavailable to "physically based" methods because the physical information in the models does not appear as we expect. One intersting result is that results from the classic "warm nose" sounding profile are, by far, the most sensitive to the particular forecast model, but this profile is also the one for which random forests are most skillful. Finally, a method for calibrarting probabilties for each different ptype using multinomial logistic regression is shown.

Sensitivity analysis of numerical weather prediction radiative schemes to forecast direct solar radiation over Australia

NASA Astrophysics Data System (ADS)

Mukkavilli, S. K.; Kay, M. J.; Taylor, R.; Prasad, A. A.; Troccoli, A.

2014-12-01

The Australian Solar Energy Forecasting System (ASEFS) project requires forecasting timeframes which range from nowcasting to long-term forecasts (minutes to two years). As concentrating solar power (CSP) plant operators are one of the key stakeholders in the national energy market, research and development enhancements for direct normal irradiance (DNI) forecasts is a major subtask. This project involves comparing different radiative scheme codes to improve day ahead DNI forecasts on the national supercomputing infrastructure running mesoscale simulations on NOAA's Weather Research & Forecast (WRF) model. ASEFS also requires aerosol data fusion for improving accurate representation of spatio-temporally variable atmospheric aerosols to reduce DNI bias error in clear sky conditions over southern Queensland & New South Wales where solar power is vulnerable to uncertainities from frequent aerosol radiative events such as bush fires and desert dust. Initial results from thirteen years of Bureau of Meteorology's (BOM) deseasonalised DNI and MODIS NASA-Terra aerosol optical depth (AOD) anomalies demonstrated strong negative correlations in north and southeast Australia along with strong variability in AOD (~0.03-0.05). Radiative transfer schemes, DNI and AOD anomaly correlations will be discussed for the population and transmission grid centric regions where current and planned CSP plants dispatch electricity to capture peak prices in the market. Aerosol and solar irradiance datasets include satellite and ground based assimilations from the national BOM, regional aerosol researchers and agencies. The presentation will provide an overview of this ASEFS project task on WRF and results to date. The overall goal of this ASEFS subtask is to develop a hybrid numerical weather prediction (NWP) and statistical/machine learning multi-model ensemble strategy that meets future operational requirements of CSP plant operators.

Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit

PubMed Central

Hoover, Stephen; Jackson, Eric V.; Paul, David; Locke, Robert

2016-01-01

Summary Background Accurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations. Objective Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasting models, (ii) explore models with and without patient data characteristics obtained at the time of admission, and (iii) evaluate accuracy of the models compared with the fixed average census approach. Methods We used five years of retrospective daily NICU census data for model development (January 2008 – December 2012, N=1827 observations) and one year of data for validation (January – December 2013, N=365 observations). Best-fitting models of ARIMA and linear regression were applied to various 7-day prediction periods and compared using error statistics. Results The census showed a slightly increasing linear trend. Best fitting models included a non-seasonal model, ARIMA(1,0,0), seasonal ARIMA models, ARIMA(1,0,0)x(1,1,2)7 and ARIMA(2,1,4)x(1,1,2)14, as well as a seasonal linear regression model. Proposed forecasting models resulted on average in 36.49% improvement in forecasting accuracy compared with the fixed average census approach. Conclusions Time series models provide higher prediction accuracy under different census conditions compared with the fixed average census approach. Presented methodology is easily applicable in clinical practice, can be generalized to other care settings, support short- and long-term census forecasting, and inform staff resource planning. PMID:27437040

Improving the Transition of Earth Satellite Observations from Research to Operations

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Lapenta, William M.; Jedlovec, Gary J.

2004-01-01

There are significant gaps between the observations, models, and decision support tools that make use of new data. These challenges include: 1) Decreasing the time to incorporate new satellite data into operational forecast assimilation systems, 2) Blending in-situ and satellite observing systems to produce the most accurate and comprehensive data products and assessments, 3) Accelerating the transition from research to applications through national test beds, field campaigns, and pilot demonstrations, and 4) Developing the partnerships and organizational structures to effectively transition new technology into operations. At the Short-term Prediction Research and Transition (SPORT) Center in Huntsville, Alabama, a NASA-NOAA-University collaboration has been developed to accelerate the infusion of NASA Earth science observations, data assimilation and modeling research into NWS forecast operations and decision-making. The SPoRT Center research focus is to improve forecasts through new observation capability and the regional prediction objectives of the US Weather Research Program dealing with 0-1 day forecast issues such as convective initiation and 24-hr quantitative precipitation forecasting. The near real-time availability of high-resolution experimental products of the atmosphere, land, and ocean from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Advanced Infrared Spectroradiometer (AIRS), and lightning mapping systems provide an opportunity for science and algorithm risk reduction, and for application assessment prior to planned observations from the next generation of operational low Earth orbiting and geostationary Earth orbiting satellites. This paper describes the process for the transition of experimental products into forecast operations, current products undergoing assessment by forecasters, and plans for the future. The SPoRT Web page is at (http://www.ghcc.msfc.nasa.gov/sport).

Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium

NASA Astrophysics Data System (ADS)

Foresti, L.; Reyniers, M.; Seed, A.; Delobbe, L.

2016-01-01

The Short-Term Ensemble Prediction System (STEPS) is implemented in real-time at the Royal Meteorological Institute (RMI) of Belgium. The main idea behind STEPS is to quantify the forecast uncertainty by adding stochastic perturbations to the deterministic Lagrangian extrapolation of radar images. The stochastic perturbations are designed to account for the unpredictable precipitation growth and decay processes and to reproduce the dynamic scaling of precipitation fields, i.e., the observation that large-scale rainfall structures are more persistent and predictable than small-scale convective cells. This paper presents the development, adaptation and verification of the STEPS system for Belgium (STEPS-BE). STEPS-BE provides in real-time 20-member ensemble precipitation nowcasts at 1 km and 5 min resolutions up to 2 h lead time using a 4 C-band radar composite as input. In the context of the PLURISK project, STEPS forecasts were generated to be used as input in sewer system hydraulic models for nowcasting urban inundations in the cities of Ghent and Leuven. Comprehensive forecast verification was performed in order to detect systematic biases over the given urban areas and to analyze the reliability of probabilistic forecasts for a set of case studies in 2013 and 2014. The forecast biases over the cities of Leuven and Ghent were found to be small, which is encouraging for future integration of STEPS nowcasts into the hydraulic models. Probabilistic forecasts of exceeding 0.5 mm h-1 are reliable up to 60-90 min lead time, while the ones of exceeding 5.0 mm h-1 are only reliable up to 30 min. The STEPS ensembles are slightly under-dispersive and represent only 75-90 % of the forecast errors.

Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium

NASA Astrophysics Data System (ADS)

Foresti, L.; Reyniers, M.; Seed, A.; Delobbe, L.

2015-07-01

The Short-Term Ensemble Prediction System (STEPS) is implemented in real-time at the Royal Meteorological Institute (RMI) of Belgium. The main idea behind STEPS is to quantify the forecast uncertainty by adding stochastic perturbations to the deterministic Lagrangian extrapolation of radar images. The stochastic perturbations are designed to account for the unpredictable precipitation growth and decay processes and to reproduce the dynamic scaling of precipitation fields, i.e. the observation that large scale rainfall structures are more persistent and predictable than small scale convective cells. This paper presents the development, adaptation and verification of the system STEPS for Belgium (STEPS-BE). STEPS-BE provides in real-time 20 member ensemble precipitation nowcasts at 1 km and 5 min resolution up to 2 h lead time using a 4 C-band radar composite as input. In the context of the PLURISK project, STEPS forecasts were generated to be used as input in sewer system hydraulic models for nowcasting urban inundations in the cities of Ghent and Leuven. Comprehensive forecast verification was performed in order to detect systematic biases over the given urban areas and to analyze the reliability of probabilistic forecasts for a set of case studies in 2013 and 2014. The forecast biases over the cities of Leuven and Ghent were found to be small, which is encouraging for future integration of STEPS nowcasts into the hydraulic models. Probabilistic forecasts of exceeding 0.5 mm h-1 are reliable up to 60-90 min lead time, while the ones of exceeding 5.0 mm h-1 are only reliable up to 30 min. The STEPS ensembles are slightly under-dispersive and represent only 80-90 % of the forecast errors.

Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit.

PubMed

Capan, Muge; Hoover, Stephen; Jackson, Eric V; Paul, David; Locke, Robert

2016-01-01

Accurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations. Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasting models, (ii) explore models with and without patient data characteristics obtained at the time of admission, and (iii) evaluate accuracy of the models compared with the fixed average census approach. We used five years of retrospective daily NICU census data for model development (January 2008 - December 2012, N=1827 observations) and one year of data for validation (January - December 2013, N=365 observations). Best-fitting models of ARIMA and linear regression were applied to various 7-day prediction periods and compared using error statistics. The census showed a slightly increasing linear trend. Best fitting models included a non-seasonal model, ARIMA(1,0,0), seasonal ARIMA models, ARIMA(1,0,0)x(1,1,2)7 and ARIMA(2,1,4)x(1,1,2)14, as well as a seasonal linear regression model. Proposed forecasting models resulted on average in 36.49% improvement in forecasting accuracy compared with the fixed average census approach. Time series models provide higher prediction accuracy under different census conditions compared with the fixed average census approach. Presented methodology is easily applicable in clinical practice, can be generalized to other care settings, support short- and long-term census forecasting, and inform staff resource planning.

Hydrological Forecasting Practices in Brazil

NASA Astrophysics Data System (ADS)

Fan, Fernando; Paiva, Rodrigo; Collischonn, Walter; Ramos, Maria-Helena

2016-04-01

This work brings a review on current hydrological and flood forecasting practices in Brazil, including the main forecasts applications, the different kinds of techniques that are currently being employed and the institutions involved on forecasts generation. A brief overview of Brazil is provided, including aspects related to its geography, climate, hydrology and flood hazards. A general discussion about the Brazilian practices on hydrological short and medium range forecasting is presented. Detailed examples of some hydrological forecasting systems that are operational or in a research/pre-operational phase using the large scale hydrological model MGB-IPH are also presented. Finally, some suggestions are given about how the forecasting practices in Brazil can be understood nowadays, and what are the perspectives for the future.

Pre-seismic anomalies from optical satellite observations: a review

NASA Astrophysics Data System (ADS)

Jiao, Zhong-Hu; Zhao, Jing; Shan, Xinjian

2018-04-01

Detecting various anomalies using optical satellite data prior to strong earthquakes is key to understanding and forecasting earthquake activities because of its recognition of thermal-radiation-related phenomena in seismic preparation phases. Data from satellite observations serve as a powerful tool in monitoring earthquake preparation areas at a global scale and in a nearly real-time manner. Over the past several decades, many new different data sources have been utilized in this field, and progressive anomaly detection approaches have been developed. This paper reviews the progress and development of pre-seismic anomaly detection technology in this decade. First, precursor parameters, including parameters from the top of the atmosphere, in the atmosphere, and on the Earth's surface, are stated and discussed. Second, different anomaly detection methods, which are used to extract anomalous signals that probably indicate future seismic events, are presented. Finally, certain critical problems with the current research are highlighted, and new developing trends and perspectives for future work are discussed. The development of Earth observation satellites and anomaly detection algorithms can enrich available information sources, provide advanced tools for multilevel earthquake monitoring, and improve short- and medium-term forecasting, which play a large and growing role in pre-seismic anomaly detection research.

Sub-Seasonal Climate Forecast Rodeo

NASA Astrophysics Data System (ADS)

Webb, R. S.; Nowak, K.; Cifelli, R.; Brekke, L. D.

2017-12-01

The Bureau of Reclamation, as the largest water wholesaler and the second largest producer of hydropower in the United States, benefits from skillful forecasts of future water availability. Researchers, water managers from local, regional, and federal agencies, and groups such as the Western States Water Council agree that improved precipitation and temperature forecast information at the sub-seasonal to seasonal (S2S) timescale is an area with significant potential benefit to water management. In response, and recognizing NOAA's leadership in forecasting, Reclamation has partnered with NOAA to develop and implement a real-time S2S forecasting competition. For a year, solvers are submitting forecasts of temperature and precipitation for weeks 3&4 and 5&6 every two weeks on a 1x1 degree grid for the 17 western state domain where Reclamation operates. The competition began on April 18, 2017 and the final real-time forecast is due April 3, 2018. Forecasts are evaluated once observational data become available using spatial anomaly correlation. Scores are posted on a competition leaderboard hosted by the National Integrated Drought Information System (NIDIS). The leaderboard can be accessed at: https://www.drought.gov/drought/sub-seasonal-climate-forecast-rodeo. To be eligible for cash prizes - which total $800,000 - solvers must outperform two benchmark forecasts during the real-time competition as well as in a required 11-year hind-cast. To receive a prize, competitors must grant a non-exclusive license to practice their forecast technique and make it available as open source software. At approximately one quarter complete, there are teams outperforming the benchmarks in three of the four competition categories. With prestige and monetary incentives on the line, it is hoped that the competition will spur innovation of improved S2S forecasts through novel approaches, enhancements to established models, or otherwise. Additionally, the competition aims to raise awareness on the S2S forecast need and the potential benefits- which extend beyond water management - to drought preparedness, public health, and other sectors.

Detection and forecasting of oyster norovirus outbreaks: recent advances and future perspectives.

PubMed

Wang, Jiao; Deng, Zhiqiang

2012-09-01

Norovirus is a highly infectious pathogen that is commonly found in oysters growing in fecally contaminated waters. Norovirus outbreaks can cause the closure of oyster harvesting waters and acute gastroenteritis in humans associated with consumption of contaminated raw oysters. Extensive efforts and progresses have been made in detection and forecasting of oyster norovirus outbreaks over the past decades. The main objective of this paper is to provide a literature review of methods and techniques for detecting and forecasting oyster norovirus outbreaks and thereby to identify the future directions for improving the detection and forecasting of norovirus outbreaks. It is found that (1) norovirus outbreaks display strong seasonality with the outbreak peak occurring commonly in December-March in the U.S. and April-May in the Europe; (2) norovirus outbreaks are affected by multiple environmental factors, including but not limited to precipitation, temperature, solar radiation, wind, and salinity; (3) various modeling approaches may be employed to forecast norovirus outbreaks, including Bayesian models, regression models, Artificial Neural Networks, and process-based models; and (4) diverse techniques are available for near real-time detection of norovirus outbreaks, including multiplex PCR, seminested PCR, real-time PCR, quantitative PCR, and satellite remote sensing. The findings are important to the management of oyster growing waters and to future investigations into norovirus outbreaks. It is recommended that a combined approach of sensor-assisted real time monitoring and modeling-based forecasting should be utilized for an efficient and effective detection and forecasting of norovirus outbreaks caused by consumption of contaminated oysters. Copyright © 2012 Elsevier Ltd. All rights reserved.

Modeling, Simulation, and Forecasting of Subseasonal Variability

NASA Technical Reports Server (NTRS)

Waliser, Duane; Schubert, Siegfried; Kumar, Arun; Weickmann, Klaus; Dole, Randall

2003-01-01

A planning workshop on "Modeling, Simulation and Forecasting of Subseasonal Variability" was held in June 2003. This workshop was the first of a number of meetings planned to follow the NASA-sponsored workshop entitled "Prospects For Improved Forecasts Of Weather And Short-Term Climate Variability On Sub-Seasonal Time Scales" that was held April 2002. The 2002 workshop highlighted a number of key sources of unrealized predictability on subseasonal time scales including tropical heating, soil wetness, the Madden Julian Oscillation (MJO) [a.k.a Intraseasonal Oscillation (ISO)], the Arctic Oscillation (AO) and the Pacific/North American (PNA) pattern. The overarching objective of the 2003 follow-up workshop was to proceed with a number of recommendations made from the 2002 workshop, as well as to set an agenda and collate efforts in the areas of modeling, simulation and forecasting intraseasonal and short-term climate variability. More specifically, the aims of the 2003 workshop were to: 1) develop a baseline of the "state of the art" in subseasonal prediction capabilities, 2) implement a program to carry out experimental subseasonal forecasts, and 3) develop strategies for tapping the above sources of predictability by focusing research, model development, and the development/acquisition of new observations on the subseasonal problem. The workshop was held over two days and was attended by over 80 scientists, modelers, forecasters and agency personnel. The agenda of the workshop focused on issues related to the MJO and tropicalextratropical interactions as they relate to the subseasonal simulation and prediction problem. This included the development of plans for a coordinated set of GCM hindcast experiments to assess current model subseasonal prediction capabilities and shortcomings, an emphasis on developing a strategy to rectify shortcomings associated with tropical intraseasonal variability, namely diabatic processes, and continuing the implementation of an experimental forecast and model development program that focuses on one of the key sources of untapped predictability, namely the MJO. The tangible outcomes of the meeting included: 1) the development of a recommended framework for a set of multi-year ensembles of 45-day hindcasts to be carried out by a number of GCMs so that they can be analyzed in regards to their representations of subseasonal variability, predictability and forecast skill, 2) an assessment of the present status of GCM representations of the MJO and recommendations for future steps to take in order to remedy the remaining shortcomings in these representations, and 3) a final implementation plan for a multi-institute/multi-nation Experimental MJO Prediction Program.

Forecasting the development of nanotechnology with the help of science and technology indicators

NASA Astrophysics Data System (ADS)

Compañó, Ramón; Hullmann, Angela

2002-06-01

Nanotechnology is supposed to become one of the key enabling technologies of the 21st century. Its economic potential is forecast to be a market of several hundred billion Euros in the next decade. Therefore, nanotechnology has attracted the interest of many industry sectors and many companies redirecting internal activities to prepare themselves for this new challenge. At the same time governmental R&D decision makers all over the world are setting up new nanotechnology-specific research programmes aiming at putting their respective countries in a favourable position for the future. The aim of this paper is to use scientific and technological indicators to make predictions on economic development and to compare the situation in different countries.

Evaluating Biodiversity Response toForecasted Land Use Change: A Case Study in the South Platte River Basin, Colorado

EPA Science Inventory

Effects of future land use change on watersheds have important management implications. Seamless, national-scale land-use-change scenarios for developed land were acquired from the U.S. Environmental Protection Agency Integrated Climate and Land Use Scenarios (lCLUS) project and...

Toward 2000. Trends Report II: Elementary-Secondary Projections.

ERIC Educational Resources Information Center

Press, Harold L.

Comprehensive, systematic planning provides the overall direction for education through the development of policies and objectives. An understanding of demographic, social, and economic trends is necessary for educators to make decisions for the future. The 1986 demographic forecasts for the province of Newfoundland are updated in this report,…

Forecasting the Future: Exploring Evidence for Global Climate Change.

ERIC Educational Resources Information Center

California Univ., San Diego, La Jolla. Inst. of Marine Resources.

This curriculum and classroom activity guide considers evidence gathered in answer to questions concerning global environmental change. It describes methods that biologists, chemists, geologists, meteorologists, and physicists use to gather and interpret their findings. The activities and approaches in this guide were developed to meet the skill…

Time series analysis of monthly pulpwood use in the Northeast

Treesearch

James T. Bones

1980-01-01

Time series analysis was used to develop a model that depicts pulpwood use in the Northeast. The model is useful in forecasting future pulpwood requirements (short term) or monitoring pulpwood-use activity in relation to past use patterns. The model predicted a downturn in use during 1980.

The Building of a New Academic Field: The Case of French Didactiques

ERIC Educational Resources Information Center

Caillot, Michel

2007-01-01

In this article, the author attempts to show how French disciplinary "didactiques" were created and have developed. At the beginning, nobody could forecast the future and whether the "didactiques" would one day be recognised by the academic and instructional systems. The French "didactiques" are strongly based on…

Forecasting sex differences in mortality in high income nations: The contribution of smoking

PubMed Central

Pampel, Fred

2011-01-01

To address the question of whether sex differences in mortality will in the future rise, fall, or stay the same, this study uses relative smoking prevalence among males and females to forecast future changes in relative smoking-attributed mortality. Data on 21 high income nations from 1975 to 2000 and a lag between smoking prevalence and mortality allow forecasts up to 2020. Averaged across nations, the results for logged male/female ratios in smoking mortality reveal equalization of the sex differential. However, continued divergence in non-smoking mortality rates would counter convergence in smoking mortality rates and lead to future increases in the female advantage overall, particularly in nations at late stages of the cigarette epidemic (such as the United States and the United Kingdom). PMID:21874120

Forecast Method of Solar Irradiance with Just-In-Time Modeling

NASA Astrophysics Data System (ADS)

Suzuki, Takanobu; Goto, Yusuke; Terazono, Takahiro; Wakao, Shinji; Oozeki, Takashi

PV power output mainly depends on the solar irradiance which is affected by various meteorological factors. So, it is required to predict solar irradiance in the future for the efficient operation of PV systems. In this paper, we develop a novel approach for solar irradiance forecast, in which we introduce to combine the black-box model (JIT Modeling) with the physical model (GPV data). We investigate the predictive accuracy of solar irradiance over wide controlled-area of each electric power company by utilizing the measured data on the 44 observation points throughout Japan offered by JMA and the 64 points around Kanto by NEDO. Finally, we propose the application forecast method of solar irradiance to the point which is difficulty in compiling the database. And we consider the influence of different GPV default time on solar irradiance prediction.

JPSS Proving Ground Activities with NASA's Short-term Prediction Research and Transition (SPoRT) Center

NASA Astrophysics Data System (ADS)

Schultz, L. A.; Smith, M. R.; Fuell, K.; Stano, G. T.; LeRoy, A.; Berndt, E.

2015-12-01

Instruments aboard the Joint Polar Satellite System (JPSS) series of satellites will provide imagery and other data sets relevant to operational weather forecasts. To prepare current and future weather forecasters in application of these data sets, Proving Ground activities have been established that demonstrate future JPSS capabilities through use of similar sensors aboard NASA's Terra and Aqua satellites, and the S-NPP mission. As part of these efforts, NASA's Short-term Prediction Research and Transition (SPoRT) Center in Huntsville, Alabama partners with near real-time providers of S-NPP products (e.g., NASA, UW/CIMSS, UAF/GINA, etc.) to demonstrate future capabilities of JPSS. This includes training materials and product distribution of multi-spectral false color composites of the visible, near-infrared, and infrared bands of MODIS and VIIRS. These are designed to highlight phenomena of interest to help forecasters digest the multispectral data provided by the VIIRS sensor. In addition, forecasters have been trained on the use of the VIIRS day-night band, which provides imagery of moonlit clouds, surface, and lights emitted by human activities. Hyperspectral information from the S-NPP/CrIS instrument provides thermodynamic profiles that aid in the detection of extremely cold air aloft, helping to map specific aviation hazards at high latitudes. Hyperspectral data also support the estimation of ozone concentration, which can highlight the presence of much drier stratospheric air, and map its interaction with mid-latitude or tropical cyclones to improve predictions of their strengthening or decay. Proving Ground activities are reviewed, including training materials and methods that have been provided to forecasters, and forecaster feedback on these products that has been acquired through formal, detailed assessment of their applicability to a given forecast threat or task. Future opportunities for collaborations around the delivery of training are proposed, along with other applications of multispectral data and derived, more quantitative products.

Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.

2010-01-01

The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the loadmore » and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. Currently, uncertainties associated with wind and load forecasts, as well as uncertainties associated with random generator outages and unexpected disconnection of supply lines, are not taken into account in power grid operation. Thus, operators have little means to weigh the likelihood and magnitude of upcoming events of power imbalance. In this project, funded by the U.S. Department of Energy (DOE), a framework has been developed for incorporating uncertainties associated with wind and load forecast errors, unpredicted ramps, and forced generation disconnections into the energy management system (EMS) as well as generation dispatch and commitment applications. A new approach to evaluate the uncertainty ranges for the required generation performance envelope including balancing capacity, ramping capability, and ramp duration has been proposed. The approach includes three stages: forecast and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence levels. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis, incorporating all sources of uncertainties of both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures) nature. A new method called the “flying brick” technique has been developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation algorithm has been developed to validate the accuracy of the confidence intervals.« less

Accounting for groundwater in stream fish thermal habitat responses to climate change

USGS Publications Warehouse

Snyder, Craig D.; Hitt, Nathaniel P.; Young, John A.

2015-01-01

Forecasting climate change effects on aquatic fauna and their habitat requires an understanding of how water temperature responds to changing air temperature (i.e., thermal sensitivity). Previous efforts to forecast climate effects on brook trout habitat have generally assumed uniform air-water temperature relationships over large areas that cannot account for groundwater inputs and other processes that operate at finer spatial scales. We developed regression models that accounted for groundwater influences on thermal sensitivity from measured air-water temperature relationships within forested watersheds in eastern North America (Shenandoah National Park, USA, 78 sites in 9 watersheds). We used these reach-scale models to forecast climate change effects on stream temperature and brook trout thermal habitat, and compared our results to previous forecasts based upon large-scale models. Observed stream temperatures were generally less sensitive to air temperature than previously assumed, and we attribute this to the moderating effect of shallow groundwater inputs. Predicted groundwater temperatures from air-water regression models corresponded well to observed groundwater temperatures elsewhere in the study area. Predictions of brook trout future habitat loss derived from our fine-grained models were far less pessimistic than those from prior models developed at coarser spatial resolutions. However, our models also revealed spatial variation in thermal sensitivity within and among catchments resulting in a patchy distribution of thermally suitable habitat. Habitat fragmentation due to thermal barriers therefore may have an increasingly important role for trout population viability in headwater streams. Our results demonstrate that simple adjustments to air-water temperature regression models can provide a powerful and cost-effective approach for predicting future stream temperatures while accounting for effects of groundwater.

Essays on oil price volatility and irreversible investment

NASA Astrophysics Data System (ADS)

Pastor, Daniel J.

In chapter 1, we provide an extensive and systematic evaluation of the relative forecasting performance of several models for the volatility of daily spot crude oil prices. Empirical research over the past decades has uncovered significant gains in forecasting performance of Markov Switching GARCH models over GARCH models for the volatility of financial assets and crude oil futures. We find that, for spot oil price returns, non-switching models perform better in the short run, whereas switching models tend to do better at longer horizons. In chapter 2, I investigate the impact of volatility on firms' irreversible investment decisions using real options theory. Cost incurred in oil drilling is considered sunk cost, thus irreversible. I collect detailed data on onshore, development oil well drilling on the North Slope of Alaska from 2003 to 2014. Volatility is modeled by constructing GARCH, EGARCH, and GJR-GARCH forecasts based on monthly real oil prices, and realized volatility from 5-minute intraday returns of oil futures prices. Using a duration model, I show that oil price volatility generally has a negative relationship with the hazard rate of drilling an oil well both when aggregating all the fields, and in individual fields.

Study of future world markets for agricultural aircraft

NASA Technical Reports Server (NTRS)

Gobetz, F. W.; Assarabowski, R. J.

1979-01-01

The future world market for US-manufactured agricultural aircraft was studied and the technology needs for foreign markets were identified. Special emphasis was placed on the developing country market, but the developed countries and the communist group were also included in the forecasts. Aircraft needs were projected to the year 2000 by a method which accounted for field size, crop production, treated area, productivity, and attrition of the fleet. A special scenario involving a significant shift toward aerial fertilization was also considered. An operations analysis was conducted to compare the relative application costs of various existing and hypothetical future aircraft. A case study was made of Colombia as an example of a developing country in which aviation is emerging as an important industry.

Probabilistic eruption forecasting at short and long time scales

NASA Astrophysics Data System (ADS)

Marzocchi, Warner; Bebbington, Mark S.

2012-10-01

Any effective volcanic risk mitigation strategy requires a scientific assessment of the future evolution of a volcanic system and its eruptive behavior. Some consider the onus should be on volcanologists to provide simple but emphatic deterministic forecasts. This traditional way of thinking, however, does not deal with the implications of inherent uncertainties, both aleatoric and epistemic, that are inevitably present in observations, monitoring data, and interpretation of any natural system. In contrast to deterministic predictions, probabilistic eruption forecasting attempts to quantify these inherent uncertainties utilizing all available information to the extent that it can be relied upon and is informative. As with many other natural hazards, probabilistic eruption forecasting is becoming established as the primary scientific basis for planning rational risk mitigation actions: at short-term (hours to weeks or months), it allows decision-makers to prioritize actions in a crisis; and at long-term (years to decades), it is the basic component for land use and emergency planning. Probabilistic eruption forecasting consists of estimating the probability of an eruption event and where it sits in a complex multidimensional time-space-magnitude framework. In this review, we discuss the key developments and features of models that have been used to address the problem.

A clustering-based fuzzy wavelet neural network model for short-term load forecasting.

PubMed

Kodogiannis, Vassilis S; Amina, Mahdi; Petrounias, Ilias

2013-10-01

Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.

United States geological survey's reserve-growth models and their implementation

USGS Publications Warehouse

Klett, T.R.

2005-01-01

The USGS has developed several mathematical models to forecast reserve growth of fields both in the United States (U.S.) and the world. The models are based on historical reserve growth patterns of fields in the U.S. The patterns of past reserve growth are extrapolated to forecast future reserve growth. Changes of individual field sizes through time are extremely variable, therefore, the reserve growth models take on a statistical approach whereby volumetric changes for populations of fields are used in the models. Field age serves as a measure of the field-development effort that is applied to promote reserve growth. At the time of the USGS World Petroleum Assessment 2000, a reserve growth model for discovered fields of the world was not available. Reserve growth forecasts, therefore, were made based on a model of historical reserve growth of fields of the U.S. To test the feasibility of such an application, reserve growth forecasts were made of 186 giant oil fields of the world (excluding the U.S. and Canada). In addition, forecasts were made for these giant oil fields subdivided into those located in and outside of Organization of Petroleum Exporting Countries (OPEC). The model provided a reserve-growth forecast that closely matched the actual reserve growth that occurred from 1981 through 1996 for the 186 fields as a whole, as well as for both OPEC and non-OPEC subdivisions, despite the differences in reserves definition among the fields of the U.S. and the rest of the world. ?? 2005 International Association for Mathematical Geology.

Forecasting conditional climate-change using a hybrid approach

USGS Publications Warehouse

Esfahani, Akbar Akbari; Friedel, Michael J.

2014-01-01

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

Cognitive determinants of affective forecasting errors

PubMed Central

Hoerger, Michael; Quirk, Stuart W.; Lucas, Richard E.; Carr, Thomas H.

2011-01-01

Often to the detriment of human decision making, people are prone to an impact bias when making affective forecasts, overestimating the emotional consequences of future events. The cognitive processes underlying the impact bias, and methods for correcting it, have been debated and warrant further exploration. In the present investigation, we examined both individual differences and contextual variables associated with cognitive processing in affective forecasting for an election. Results showed that the perceived importance of the event and working memory capacity were both associated with an increased impact bias for some participants, whereas retrieval interference had no relationship with bias. Additionally, an experimental manipulation effectively reduced biased forecasts, particularly among participants who were most distracted thinking about peripheral life events. These findings have direct theoretical implications for understanding the impact bias, highlight the importance of individual differences in affective forecasting, and have ramifications for future decision making research. The possible functional role of the impact bias is discussed within the context of evolutionary psychology. PMID:21912580

Looking into the crystal ball of our emotional lives: emotion regulation and the overestimation of future guilt and shame.

PubMed

van Dijk, Wilco W; van Dillen, Lotte F; Rotteveel, Mark; Seip, Elise C

2017-04-01

In the present study, we examined the impact of emotion regulation on the intensity bias in guilt and shame. Fifty-two undergraduates either forecasted their emotions and emotion regulation following a guilt- and shame-eliciting situation or reported their actual experienced emotions and employed emotion regulation. Results showed a clear intensity bias, that is, forecasters predicted to experience more guilt and shame than experiencers actually experienced. Furthermore, results showed that forecasters predicted to employ less down-regulating emotion regulation (i.e. less acceptance) and more up-regulating emotion regulation (i.e. more rumination) than experiencers actually employed. Moreover, results showed that the intensity differences between forecasted and experienced guilt and shame could be explained (i.e. were mediated) by the differences between forecasted and actually employed emotion regulation (i.e. acceptance and rumination). These findings provide support for the hypothesis that the intensity bias can-at least in part-be explained by the misprediction of future emotion regulation.

Understanding causality and uncertainty in volcanic observations: An example of forecasting eruptive activity on Soufrière Hills Volcano, Montserrat

NASA Astrophysics Data System (ADS)

Sheldrake, T. E.; Aspinall, W. P.; Odbert, H. M.; Wadge, G.; Sparks, R. S. J.

2017-07-01

Following a cessation in eruptive activity it is important to understand how a volcano will behave in the future and when it may next erupt. Such an assessment can be based on the volcano's long-term pattern of behaviour and insights into its current state via monitoring observations. We present a Bayesian network that integrates these two strands of evidence to forecast future eruptive scenarios using expert elicitation. The Bayesian approach provides a framework to quantify the magmatic causes in terms of volcanic effects (i.e., eruption and unrest). In October 2013, an expert elicitation was performed to populate a Bayesian network designed to help forecast future eruptive (in-)activity at Soufrière Hills Volcano. The Bayesian network was devised to assess the state of the shallow magmatic system, as a means to forecast the future eruptive activity in the context of the long-term behaviour at similar dome-building volcanoes. The findings highlight coherence amongst experts when interpreting the current behaviour of the volcano, but reveal considerable ambiguity when relating this to longer patterns of volcanism at dome-building volcanoes, as a class. By asking questions in terms of magmatic causes, the Bayesian approach highlights the importance of using short-term unrest indicators from monitoring data as evidence in long-term forecasts at volcanoes. Furthermore, it highlights potential biases in the judgements of volcanologists and identifies sources of uncertainty in terms of magmatic causes rather than scenario-based outcomes.

The Nature and Variability of Ensemble Sensitivity Fields that Diagnose Severe Convection

NASA Astrophysics Data System (ADS)

Ancell, B. C.

2017-12-01

Ensemble sensitivity analysis (ESA) is a statistical technique that uses information from an ensemble of forecasts to reveal relationships between chosen forecast metrics and the larger atmospheric state at various forecast times. A number of studies have employed ESA from the perspectives of dynamical interpretation, observation targeting, and ensemble subsetting toward improved probabilistic prediction of high-impact events, mostly at synoptic scales. We tested ESA using convective forecast metrics at the 2016 HWT Spring Forecast Experiment to understand the utility of convective ensemble sensitivity fields in improving forecasts of severe convection and its individual hazards. The main purpose of this evaluation was to understand the temporal coherence and general characteristics of convective sensitivity fields toward future use in improving ensemble predictability within an operational framework.The magnitude and coverage of simulated reflectivity, updraft helicity, and surface wind speed were used as response functions, and the sensitivity of these functions to winds, temperatures, geopotential heights, and dew points at different atmospheric levels and at different forecast times were evaluated on a daily basis throughout the HWT Spring Forecast experiment. These sensitivities were calculated within the Texas Tech real-time ensemble system, which possesses 42 members that run twice daily to 48-hr forecast time. Here we summarize both the findings regarding the nature of the sensitivity fields and the evaluation of the participants that reflects their opinions of the utility of operational ESA. The future direction of ESA for operational use will also be discussed.

Future WGCEP Models and the Need for Earthquake Simulators

NASA Astrophysics Data System (ADS)

Field, E. H.

2008-12-01

The 2008 Working Group on California Earthquake Probabilities (WGCEP) recently released the Uniform California Earthquake Rupture Forecast version 2 (UCERF 2), developed jointly by the USGS, CGS, and SCEC with significant support from the California Earthquake Authority. Although this model embodies several significant improvements over previous WGCEPs, the following are some of the significant shortcomings that we hope to resolve in a future UCERF3: 1) assumptions of fault segmentation and the lack of fault-to-fault ruptures; 2) the lack of an internally consistent methodology for computing time-dependent, elastic-rebound-motivated renewal probabilities; 3) the lack of earthquake clustering/triggering effects; and 4) unwarranted model complexity. It is believed by some that physics-based earthquake simulators will be key to resolving these issues, either as exploratory tools to help guide the present statistical approaches, or as a means to forecast earthquakes directly (although significant challenges remain with respect to the latter).

Modeling and Forecasting Influenza-like Illness (ILI) in Houston, Texas Using Three Surveillance Data Capture Mechanisms.

PubMed

Paul, Susannah; Mgbere, Osaro; Arafat, Raouf; Yang, Biru; Santos, Eunice

2017-01-01

Objective The objective was to forecast and validate prediction estimates of influenza activity in Houston, TX using four years of historical influenza-like illness (ILI) from three surveillance data capture mechanisms. Background Using novel surveillance methods and historical data to estimate future trends of influenza-like illness can lead to early detection of influenza activity increases and decreases. Anticipating surges gives public health professionals more time to prepare and increase prevention efforts. Methods Data was obtained from three surveillance systems, Flu Near You, ILINet, and hospital emergency center (EC) visits, with diverse data capture mechanisms. Autoregressive integrated moving average (ARIMA) models were fitted to data from each source for week 27 of 2012 through week 26 of 2016 and used to forecast influenza-like activity for the subsequent 10 weeks. Estimates were then compared to actual ILI percentages for the same period. Results Forecasted estimates had wide confidence intervals that crossed zero. The forecasted trend direction differed by data source, resulting in lack of consensus about future influenza activity. ILINet forecasted estimates and actual percentages had the least differences. ILINet performed best when forecasting influenza activity in Houston, TX. Conclusion Though the three forecasted estimates did not agree on the trend directions, and thus, were considered imprecise predictors of long-term ILI activity based on existing data, pooling predictions and careful interpretations may be helpful for short term intervention efforts. Further work is needed to improve forecast accuracy considering the promise forecasting holds for seasonal influenza prevention and control, and pandemic preparedness.

Accuracy analysis of TDRSS demand forecasts

NASA Technical Reports Server (NTRS)

Stern, Daniel C.; Levine, Allen J.; Pitt, Karl J.

1994-01-01

This paper reviews Space Network (SN) demand forecasting experience over the past 16 years and describes methods used in the forecasts. The paper focuses on the Single Access (SA) service, the most sought-after resource in the Space Network. Of the ten years of actual demand data available, only the last five years (1989 to 1993) were considered predictive due to the extensive impact of the Challenger accident of 1986. NASA's Space Network provides tracking and communications services to user spacecraft such as the Shuttle and the Hubble Space Telescope. Forecasting the customer requirements is essential to planning network resources and to establishing service commitments to future customers. The lead time to procure Tracking and Data Relay Satellites (TDRS's) requires demand forecasts ten years in the future a planning horizon beyond the funding commitments for missions to be supported. The long range forecasts are shown to have had a bias toward underestimation in the 1991 -1992 period. The trend of underestimation can be expected to be replaced by overestimation for a number of years starting with 1998. At that time demand from new missions slated for launch will be larger than the demand from ongoing missions, making the potential for delay the dominant factor. If the new missions appear as scheduled, the forecasts are likely to be moderately underestimated. The SN commitment to meet the negotiated customer's requirements calls for conservatism in the forecasting. Modification of the forecasting procedure to account for a delay bias is, therefore, not advised. Fine tuning the mission model to more accurately reflect the current actual demand is recommended as it may marginally improve the first year forecasting.

Routine High-Resolution Forecasts/Analyses for the Pacific Disaster Center: User Manual

NASA Technical Reports Server (NTRS)

Roads, John; Han, J.; Chen, S.; Burgan, R.; Fujioka, F.; Stevens, D.; Funayama, D.; Chambers, C.; Bingaman, B.; McCord, C.;

Forecasting the Future Risk of Barmah Forest Virus Disease under Climate Change Scenarios in Queensland, Australia

PubMed Central

Naish, Suchithra; Mengersen, Kerrie; Hu, Wenbiao; Tong, Shilu

2013-01-01

Background Mosquito-borne diseases are climate sensitive and there has been increasing concern over the impact of climate change on future disease risk. This paper projected the potential future risk of Barmah Forest virus (BFV) disease under climate change scenarios in Queensland, Australia. Methods/Principal Findings We obtained data on notified BFV cases, climate (maximum and minimum temperature and rainfall), socio-economic and tidal conditions for current period 2000–2008 for coastal regions in Queensland. Grid-data on future climate projections for 2025, 2050 and 2100 were also obtained. Logistic regression models were built to forecast the otential risk of BFV disease distribution under existing climatic, socio-economic and tidal conditions. The model was applied to estimate the potential geographic distribution of BFV outbreaks under climate change scenarios. The predictive model had good model accuracy, sensitivity and specificity. Maps on potential risk of future BFV disease indicated that disease would vary significantly across coastal regions in Queensland by 2100 due to marked differences in future rainfall and temperature projections. Conclusions/Significance We conclude that the results of this study demonstrate that the future risk of BFV disease would vary across coastal regions in Queensland. These results may be helpful for public health decision making towards developing effective risk management strategies for BFV disease control and prevention programs in Queensland. PMID:23690959

Integrating Fluvial and Oceanic Drivers in Operational Flooding Forecasts for San Francisco Bay

NASA Astrophysics Data System (ADS)

Herdman, Liv; Erikson, Li; Barnard, Patrick; Kim, Jungho; Cifelli, Rob; Johnson, Lynn

2016-04-01

The nine counties that make up the San Francisco Bay area are home to 7.5 million people and these communties are susceptible to flooding along the bay shoreline and inland creeks that drain to the bay. A forecast model that integrates fluvial and oceanic drivers is necessary for predicting flooding in this complex urban environment. The U.S. Geological Survey ( USGS) and National Weather Service (NWS) are developing a state-of-the-art flooding forecast model for the San Francisco Bay area that will predict watershed and ocean-based flooding up to 72 hours in advance of an approaching storm. The model framework for flood forecasts is based on the USGS-developed Coastal Storm Modeling System (CoSMoS) that was applied to San Francisco Bay under the Our Coast Our Future project. For this application, we utilize Delft3D-FM, a hydrodynamic model based on a flexible mesh grid, to calculate water levels that account for tidal forcing, seasonal water level anomalies, surge and in-Bay generated wind waves from the wind and pressure fields of a NWS forecast model, and tributary discharges from the Research Distributed Hydrologic Model (RDHM), developed by the NWS Office of Hydrologic Development. The flooding extent is determined by overlaying the resulting water levels onto a recently completed 2-m digital elevation model of the study area which best resolves the extensive levee and tidal marsh systems in the region. Here we present initial pilot results of hindcast winter storms in January 2010 and December 2012, where the flooding is driven by oceanic and fluvial factors respectively. We also demonstrate the feasibility of predicting flooding on an operational time scale that incorporates both atmospheric and hydrologic forcings.

Forecast of jet engine exhaust emissions for future high altitude commercial aircraft

NASA Technical Reports Server (NTRS)

Grobman, J.; Ingebo, R. D.

1974-01-01

Projected minimum levels of engine exhaust emissions that may be practicably achievable for future commercial aircraft operating at high altitude cruise conditions are presented. The forecasts are based on: (1) current knowledge of emission characteristics of combustors and augmentors; (2) the current status of combustion research in emission reduction technology; (3) predictable trends in combustion systems and operating conditions as required for projected engine designs that are candidates for advanced subsonic or supersonic commercial aircraft. Results are presented for cruise conditions in terms of an emission index, g pollutant/kg fuel. Two sets of engine exhaust emission predictions are presented: the first, based on an independent NASA study and the second, based on the consensus of an ad hoc committee composed of industry, university, and government representatives. The consensus forecasts are in general agreement with the NASA forecasts.

Forecast of jet engine exhaust emissions for future high altitude commercial aircraft

NASA Technical Reports Server (NTRS)

Grobman, J.; Ingebo, R. D.

1974-01-01

Projected minimum levels of engine exhaust emissions that may be practicably achievable for future commercial aircraft operating at high altitude cruise conditions are presented. The forecasts are based on: (1) current knowledge of emission characteristics of combustors and augmentors; (2) the current status of combustion research in emission reduction technology; and (3) predictable trends in combustion systems and operating conditions as required for projected engine designs that are candidates for advanced subsonic or supersonic commercial aircraft. Results are presented for cruise conditions in terms of an emission index, g pollutant/kg fuel. Two sets of engine exhaust emission predictions are presented: the first, based on an independent NASA study and the second, based on the consensus of an ad hoc committee composed of industry, university, and government representatives. The consensus forecasts are in general agreement with the NASA forecasts.

Nambe Pueblo Water Budget and Forecasting model.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brainard, James Robert

2009-10-01

This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Watermore » Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.« less

The Tracking Meteogram, an AWIPS II Tool for Time-Series Analysis

NASA Technical Reports Server (NTRS)

Burks, Jason Eric; Sperow, Ken

2015-01-01

A new tool has been developed for the National Weather Service (NWS) Advanced Weather Interactive Processing System (AWIPS) II through collaboration between NASA's Short-term Prediction Research and Transition (SPoRT) and the NWS Meteorological Development Laboratory (MDL). Referred to as the "Tracking Meteogram", the tool aids NWS forecasters in assessing meteorological parameters associated with moving phenomena. The tool aids forecasters in severe weather situations by providing valuable satellite and radar derived trends such as cloud top cooling rates, radial velocity couplets, reflectivity, and information from ground-based lightning networks. The Tracking Meteogram tool also aids in synoptic and mesoscale analysis by tracking parameters such as the deepening of surface low pressure systems, changes in surface or upper air temperature, and other properties. The tool provides a valuable new functionality and demonstrates the flexibility and extensibility of the NWS AWIPS II architecture. In 2014, the operational impact of the tool was formally evaluated through participation in the NOAA/NWS Operations Proving Ground (OPG), a risk reduction activity to assess performance and operational impact of new forecasting concepts, tools, and applications. Performance of the Tracking Meteogram Tool during the OPG assessment confirmed that it will be a valuable asset to the operational forecasters. This presentation reviews development of the Tracking Meteogram tool, performance and feedback acquired during the OPG activity, and future goals for continued support and extension to other application areas.

First Results of the Regional Earthquake Likelihood Models Experiment

USGS Publications Warehouse

Schorlemmer, D.; Zechar, J.D.; Werner, M.J.; Field, E.H.; Jackson, D.D.; Jordan, T.H.

2010-01-01

The ability to successfully predict the future behavior of a system is a strong indication that the system is well understood. Certainly many details of the earthquake system remain obscure, but several hypotheses related to earthquake occurrence and seismic hazard have been proffered, and predicting earthquake behavior is a worthy goal and demanded by society. Along these lines, one of the primary objectives of the Regional Earthquake Likelihood Models (RELM) working group was to formalize earthquake occurrence hypotheses in the form of prospective earthquake rate forecasts in California. RELM members, working in small research groups, developed more than a dozen 5-year forecasts; they also outlined a performance evaluation method and provided a conceptual description of a Testing Center in which to perform predictability experiments. Subsequently, researchers working within the Collaboratory for the Study of Earthquake Predictability (CSEP) have begun implementing Testing Centers in different locations worldwide, and the RELM predictability experiment-a truly prospective earthquake prediction effort-is underway within the U. S. branch of CSEP. The experiment, designed to compare time-invariant 5-year earthquake rate forecasts, is now approximately halfway to its completion. In this paper, we describe the models under evaluation and present, for the first time, preliminary results of this unique experiment. While these results are preliminary-the forecasts were meant for an application of 5 years-we find interesting results: most of the models are consistent with the observation and one model forecasts the distribution of earthquakes best. We discuss the observed sample of target earthquakes in the context of historical seismicity within the testing region, highlight potential pitfalls of the current tests, and suggest plans for future revisions to experiments such as this one. ?? 2010 The Author(s).

First Results of the Regional Earthquake Likelihood Models Experiment

NASA Astrophysics Data System (ADS)

Schorlemmer, Danijel; Zechar, J. Douglas; Werner, Maximilian J.; Field, Edward H.; Jackson, David D.; Jordan, Thomas H.

2010-08-01

The ability to successfully predict the future behavior of a system is a strong indication that the system is well understood. Certainly many details of the earthquake system remain obscure, but several hypotheses related to earthquake occurrence and seismic hazard have been proffered, and predicting earthquake behavior is a worthy goal and demanded by society. Along these lines, one of the primary objectives of the Regional Earthquake Likelihood Models (RELM) working group was to formalize earthquake occurrence hypotheses in the form of prospective earthquake rate forecasts in California. RELM members, working in small research groups, developed more than a dozen 5-year forecasts; they also outlined a performance evaluation method and provided a conceptual description of a Testing Center in which to perform predictability experiments. Subsequently, researchers working within the Collaboratory for the Study of Earthquake Predictability (CSEP) have begun implementing Testing Centers in different locations worldwide, and the RELM predictability experiment—a truly prospective earthquake prediction effort—is underway within the U.S. branch of CSEP. The experiment, designed to compare time-invariant 5-year earthquake rate forecasts, is now approximately halfway to its completion. In this paper, we describe the models under evaluation and present, for the first time, preliminary results of this unique experiment. While these results are preliminary—the forecasts were meant for an application of 5 years—we find interesting results: most of the models are consistent with the observation and one model forecasts the distribution of earthquakes best. We discuss the observed sample of target earthquakes in the context of historical seismicity within the testing region, highlight potential pitfalls of the current tests, and suggest plans for future revisions to experiments such as this one.

Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions.

PubMed

Kusev, Petko; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Juliusson, Asgeir; Chater, Nick

2018-01-01

When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent-based model-the adaptive anchoring model (ADAM)-to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear-regression time series model. Moreover, ADAM outperformed autoregressive-integrated-moving-average (ARIMA) and exponential-smoothing models, while neither of these models accounts for people's responses on the average estimation task. Copyright © 2017 The Authors. Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society.

Forecasting wetting and drying of post-wildfire soils in response to precipitation: A time series optimization approach

NASA Astrophysics Data System (ADS)

Basak, A.; Kulkarni, C.; Schmidt, K. M.; Mengshoel, O. J.

2015-12-01

Volumetric water content (VWC) in soils is critical for forecasting thresholds for runoff-driven erosion caused by rainfall. Even though theoretical relations (e.g., Richards equation) have been developed to quantify VWC in unsaturated granular soils, site-specific field conditions and hysteresis of suction and VWC in soil preclude their direct use. Although attempts have previously been made to forecast VWC using various time-series models (e.g., autoregressive integrated moving average or ARIMA), these approaches lack hydrologic foundations and perform poorly when used to forecast VWC over time periods longer than 24 hours. In this work, we extend an existing Antecedent Water Index (AWI) based model to express VWC as a function of time and rainfall. AWI models typically overfit data and cannot be used for forecast VWC over long time periods. We developed a new model to overcome this limitation, which accumulates rainfall over a time window and fits a diverse range of wetting and drying curves. Hydraulic redistribution parameters in this model bear resemblance to hydrologic processes driven by gravity and suction. This model reasonably forecasts VWC using only initial VWC values and rainfall forecasts. Experimental VWC data were collected from steep gradient post-wildfire sites in southern California. Rapid landscape change was observed in response to small to moderate rain storms. We formulated a mean-squared error minimization problem over the model parameters and optimized using genetic algorithms. We found that our model fits VWC data for 3 distinct soil textures, each occurring at 3 different depths below the ground surface (5 cm, 15 cm, and 30 cm). Our model successfully forecasts VWC trends, such as drying and wetting rate. To a certain extent, our model achieves spatial and seasonal generalizability. Our accumulative rainfall model is also applicable to continuous predictions, where VWC values are repeatedly used to predict future ones within a 12-hr time frame.

Using volcanic tremor for eruption forecasting at White Island volcano (Whakaari), New Zealand

NASA Astrophysics Data System (ADS)

Chardot, Lauriane; Jolly, Arthur D.; Kennedy, Ben M.; Fournier, Nicolas; Sherburn, Steven

2015-09-01

Eruption forecasting is a challenging task because of the inherent complexity of volcanic systems. Despite remarkable efforts to develop complex models in order to explain volcanic processes prior to eruptions, the material Failure Forecast Method (FFM) is one of the very few techniques that can provide a forecast time for an eruption. However, the method requires testing and automation before being used as a real-time eruption forecasting tool at a volcano. We developed an automatic algorithm to issue forecasts from volcanic tremor increase episodes recorded by Real-time Seismic Amplitude Measurement (RSAM) at one station and optimised this algorithm for the period August 2011-January 2014 which comprises the recent unrest period at White Island volcano (Whakaari), New Zealand. A detailed residual analysis was paramount to select the most appropriate model explaining the RSAM time evolutions. In a hindsight simulation, four out of the five small eruptions reported during this period occurred within a failure window forecast by our optimised algorithm and the probability of an eruption on a day within a failure window was 0.21, which is 37 times higher than the probability of having an eruption on any day during the same period (0.0057). Moreover, the forecasts were issued prior to the eruptions by a few hours which is important from an emergency management point of view. Whereas the RSAM time evolutions preceding these four eruptions have a similar goodness-of-fit with the FFM, their spectral characteristics are different. The duration-amplitude distributions of the precursory tremor episodes support the hypothesis that several processes were likely occurring prior to these eruptions. We propose that slow rock failure and fluid flow processes are plausible candidates for the tremor source of these episodes. This hindsight exercise can be useful for future real-time implementation of the FFM at White Island. A similar methodology could also be tested at other volcanoes even if only a limited network is available.

Forecasting the Revenues of Local Public Health Departments in the Shadows of the ‘Great Recession’

PubMed Central

Reschovsky, Andrew; Zahner, Susan J.

2015-01-01

Context The ability of local health departments (LHD) to provide core public health services depends on a reliable stream of revenue from federal, state, and local governments. This study investigates the impact of the “Great Recession” on major sources of LHD revenues and develops a fiscal forecasting model to predict revenues to LHDs in one state over the period 2012 to 2014. Economic forecasting offers a new financial planning tool for LHD administrators and local government policy-makers. This study represents a novel research application for these econometric methods. Methods Detailed data on revenues by source for each LHD in Wisconsin were taken from annual surveys conducted by the Wisconsin Department of Health Services over an eight year period (2002-2009). A forecasting strategy appropriate for each revenue source was developed resulting in “base case” estimates. An analysis of the sensitivity of these revenue forecasts to a set of alternative fiscal policies by the federal, state, and local governments was carried out. Findings The model forecasts total LHD revenues in 2012 of $170.5 million (in 2010 dollars). By 2014 inflation-adjusted revenues will decline by $8 million, a reduction of 4.7 percent. Because of population growth, per capita real revenues of LHDs are forecast to decline by 6.6 percent between 2012 and 2014. There is a great deal of uncertainty about the future of federal funding in support of local public health. A doubling of the reductions in federal grants scheduled under current law would result in an additional $4.4 million decline in LHD revenues in 2014. Conclusions The impact of the Great Recession continues to haunt LHDs. Multi-year revenue forecasting offers a new financial tool to help LHDs better plan for an environment of declining resources. New revenue sources are needed if sharp drops in public health service delivery are to be avoided. PMID:23531611

Using total precipitable water anomaly as a forecast aid for heavy precipitation events

NASA Astrophysics Data System (ADS)

VandenBoogart, Lance M.

Heavy precipitation events are of interest to weather forecasters, local government officials, and the Department of Defense. These events can cause flooding which endangers lives and property. Military concerns include decreased trafficability for military vehicles, which hinders both war- and peace-time missions. Even in data-rich areas such as the United States, it is difficult to determine when and where a heavy precipitation event will occur. The challenges are compounded in data-denied regions. The hypothesis that total precipitable water anomaly (TPWA) will be positive and increasing preceding heavy precipitation events is tested in order to establish an understanding of TPWA evolution. Results are then used to create a precipitation forecast aid. The operational, 16 km-gridded, 6-hourly TPWA product developed at the Cooperative Institute for Research in the Atmosphere (CIRA) compares a blended TPW product with a TPW climatology to give a percent of normal TPWA value. TPWA evolution is examined for 84 heavy precipitation events which occurred between August 2010 and November 2011. An algorithm which uses various TPWA thresholds derived from the 84 events is then developed and tested using dichotomous contingency table verification statistics to determine the extent to which satellite-based TPWA might be used to aid in forecasting precipitation over mesoscale domains. The hypothesis of positive and increasing TPWA preceding heavy precipitation events is supported by the analysis. Event-average TPWA rises for 36 hours and peaks at 154% of normal at the event time. The average precipitation event detected by the forecast algorithm is not of sufficient magnitude to be termed a "heavy" precipitation event; however, the algorithm adds skill to a climatological precipitation forecast. Probability of detection is low and false alarm ratios are large, thus qualifying the algorithm's current use as an aid rather than a deterministic forecast tool. The algorithm's ability to be easily modified and quickly run gives it potential for future use in precipitation forecasting.

Forecasting the Revenues of Local Public Health Departments in the Shadows of the "Great Recession".

PubMed

Reschovsky, Andrew; Zahner, Susan J

2016-01-01

The ability of local health departments (LHD) to provide core public health services depends on a reliable stream of revenue from federal, state, and local governments. This study investigates the impact of the "Great Recession" on major sources of LHD revenues and develops a fiscal forecasting model to predict revenues to LHDs in one state over the period 2012 to 2014. Economic forecasting offers a new financial planning tool for LHD administrators and local government policy makers. This study represents a novel research application for these econometric methods. Detailed data on revenues by source for each LHD in Wisconsin were taken from annual surveys conducted by the Wisconsin Department of Health Services over an 8-year period (2002-2009). A forecasting strategy appropriate for each revenue source was developed resulting in "base case" estimates. An analysis of the sensitivity of these revenue forecasts to a set of alternative fiscal policies by the federal, state, and local governments was carried out. The model forecasts total LHD revenues in 2012 of $170.5 million (in 2010 dollars). By 2014, inflation-adjusted revenues will decline by $8 million, a reduction of 4.7%. Because of population growth, per capita real revenues of LHDs are forecast to decline by 6.6% between 2012 and 2014. There is a great deal of uncertainty about the future of federal funding in support of local public health. A doubling of the reductions in federal grants scheduled under current law would result in an additional $4.4 million decline in LHD revenues in 2014. The impact of the Great Recession continues to haunt LHDs. Multiyear revenue forecasting offers a new financial tool to help LHDs better plan for an environment of declining resources. New revenue sources are needed if sharp drops in public health service delivery are to be avoided.

Value of Seasonal Fuzzy-based Inflow Prediction in the Jucar River Basin

NASA Astrophysics Data System (ADS)

Pulido-Velazquez, M.; Macian-Sorribes, H.

2016-12-01

The development and application of climate services in Integrated Water Resources Management (IWRM) is said to add important benefits in terms of water use efficiency due to an increase ability to foresee future water availability. A method to evaluate the economic impact of these services is presented, based on the use of hydroeconomic modelling techniques (hydroeconomic simulation) to compare the net benefits from water use in the system with and without the inflow forecasting. The Jucar River Basin (Spain) has been used as case study. Operating rules currently applied in the basin were assessed using fuzzy rule-based (FRB) systems via a co-development process involving the system operators. These operating rules use as input variable the hydrological inflows in several sub-basins, which need to be foreseen by the system operators. The inflow forecasting mechanism to preview water availability in the irrigation season (May-September) relied on fuzzy regression in which future inflows were foreseen based on past inflows and rainfall in the basin. This approach was compared with the current use of the two past year inflows for projecting the future inflow. For each irrigation season, the previewed inflows were determined using both methods and their impact on the system operation assessed through a hydroeconomic DSS. Results show that the implementation of the fuzzy inflow forecasting system offers higher economic returns. Another advantage of the fuzzy approach regards to the uncertainty treatment using fuzzy numbers, which allow us to estimate the uncertainty range of the expected benefits. Consequently, we can use the fuzzy approach to estimate the uncertainty associated with both the prediction and the associated benefits.

Set-up and validation of a Delft-FEWS based coastal hazard forecasting system

NASA Astrophysics Data System (ADS)

Valchev, Nikolay; Eftimova, Petya; Andreeva, Nataliya

2017-04-01

European coasts are increasingly threatened by hazards related to low-probability and high-impact hydro-meteorological events. Uncertainties in hazard prediction and capabilities to cope with their impact lie in both future storm pattern and increasing coastal development. Therefore, adaptation to future conditions requires a re-evaluation of coastal disaster risk reduction (DRR) strategies and introduction of a more efficient mix of prevention, mitigation and preparedness measures. The latter presumes that development of tools, which can manage the complex process of merging data and models and generate products on the current and expected hydro-and morpho-dynamic states of the coasts, such as forecasting system of flooding and erosion hazards at vulnerable coastal locations (hotspots), is of vital importance. Output of such system can be of an utmost value for coastal stakeholders and the entire coastal community. In response to these challenges, Delft-FEWS provides a state-of-the-art framework for implementation of such system with vast capabilities to trigger the early warning process. In addition, this framework is highly customizable to the specific requirements of any individual coastal hotspot. Since its release many Delft-FEWS based forecasting system related to inland flooding have been developed. However, limited number of coastal applications was implemented. In this paper, a set-up of Delft-FEWS based forecasting system for Varna Bay (Bulgaria) and a coastal hotspot, which includes a sandy beach and port infrastructure, is presented. It is implemented in the frame of RISC-KIT project (Resilience-Increasing Strategies for Coasts - toolKIT). The system output generated in hindcast mode is validated with available observations of surge levels, wave and morphodynamic parameters for a sequence of three short-duration and relatively weak storm events occurred during February 4-12, 2015. Generally, the models' performance is considered as very good and results obtained - quite promising for reliable prediction of both boundary conditions and coastal hazard and gives a good basis for estimation of onshore impact.

Data sensitivity in a hybrid STEP/Coulomb model for aftershock forecasting

NASA Astrophysics Data System (ADS)

Steacy, S.; Jimenez Lloret, A.; Gerstenberger, M.

2014-12-01

Operational earthquake forecasting is rapidly becoming a 'hot topic' as civil protection authorities seek quantitative information on likely near future earthquake distributions during seismic crises. At present, most of the models in public domain are statistical and use information about past and present seismicity as well as b-value and Omori's law to forecast future rates. A limited number of researchers, however, are developing hybrid models which add spatial constraints from Coulomb stress modeling to existing statistical approaches. Steacy et al. (2013), for instance, recently tested a model that combines Coulomb stress patterns with the STEP (short-term earthquake probability) approach against seismicity observed during the 2010-2012 Canterbury earthquake sequence. They found that the new model performed at least as well as, and often better than, STEP when tested against retrospective data but that STEP was generally better in pseudo-prospective tests that involved data actually available within the first 10 days of each event of interest. They suggested that the major reason for this discrepancy was uncertainty in the slip models and, in particular, in the geometries of the faults involved in each complex major event. Here we test this hypothesis by developing a number of retrospective forecasts for the Landers earthquake using hypothetical slip distributions developed by Steacy et al. (2004) to investigate the sensitivity of Coulomb stress models to fault geometry and earthquake slip, and we also examine how the choice of receiver plane geometry affects the results. We find that the results are strongly sensitive to the slip models and moderately sensitive to the choice of receiver orientation. We further find that comparison of the stress fields (resulting from the slip models) with the location of events in the learning period provides advance information on whether or not a particular hybrid model will perform better than STEP.

Development and Application of Advanced Weather Prediction Technologies for the Wind Energy Industry (Invited)

NASA Astrophysics Data System (ADS)

Mahoney, W. P.; Wiener, G.; Liu, Y.; Myers, W.; Johnson, D.

2010-12-01

Wind energy decision makers are required to make critical judgments on a daily basis with regard to energy generation, distribution, demand, storage, and integration. Accurate knowledge of the present and future state of the atmosphere is vital in making these decisions. As wind energy portfolios expand, this forecast problem is taking on new urgency because wind forecast inaccuracies frequently lead to substantial economic losses and constrain the national expansion of renewable energy. Improved weather prediction and precise spatial analysis of small-scale weather events are crucial for renewable energy management. In early 2009, the National Center for Atmospheric Research (NCAR) began a collaborative project with Xcel Energy Services, Inc. to perform research and develop technologies to improve Xcel Energy's ability to increase the amount of wind energy in their generation portfolio. The agreement and scope of work was designed to provide highly detailed, localized wind energy forecasts to enable Xcel Energy to more efficiently integrate electricity generated from wind into the power grid. The wind prediction technologies are designed to help Xcel Energy operators make critical decisions about powering down traditional coal and natural gas-powered plants when sufficient wind energy is predicted. The wind prediction technologies have been designed to cover Xcel Energy wind resources spanning a region from Wisconsin to New Mexico. The goal of the project is not only to improve Xcel Energy’s wind energy prediction capabilities, but also to make technological advancements in wind and wind energy prediction, expand our knowledge of boundary layer meteorology, and share the results across the renewable energy industry. To generate wind energy forecasts, NCAR is incorporating observations of current atmospheric conditions from a variety of sources including satellites, aircraft, weather radars, ground-based weather stations, wind profilers, and even wind sensors on individual wind turbines. The information is utilized by several technologies including: a) the Weather Research and Forecasting (WRF) model, which generates finely detailed simulations of future atmospheric conditions, b) the Real-Time Four-Dimensional Data Assimilation System (RTFDDA), which performs continuous data assimilation providing the WRF model with continuous updates of the initial atmospheric state, 3) the Dynamic Integrated Forecast System (DICast®), which statistically optimizes the forecasts using all predictors, and 4) a suite of wind-to-power algorithms that convert wind speed to power for a wide range of wind farms with varying real-time data availability capabilities. In addition to these core wind energy prediction capabilities, NCAR implemented a high-resolution (10 km grid increment) 30-member ensemble RTFDDA prediction system that provides information on the expected range of wind power over a 72-hour forecast period covering Xcel Energy’s service areas. This talk will include descriptions of these capabilities and report on several topics including initial results of next-day forecasts and nowcasts of wind energy ramp events, influence of local observations on forecast skill, and overall lessons learned to date.

Implementing drought early warning systems: policy lessons and future needs

NASA Astrophysics Data System (ADS)

Iglesias, Ana; Werner, Micha; Maia, Rodrigo; Garrote, Luis; Nyabeze, Washington

2014-05-01

Drought forecasting and Warning provides the potential of reducing impacts to society due to drought events. The implementation of effective drought forecasting and warning, however, requires not only science to support reliable forecasting, but also adequate policy and societal response. Here we propose a protocol to develop drought forecasting and early warning based in the international cooperation of African and European institutions in the DEWFORA project (EC, 7th Framework Programme). The protocol includes four major phases that address the scientific knowledge and the social capacity to use the knowledge: (a) What is the science available? Evaluating how signs of impending drought can be detected and predicted, defining risk levels, and analysing of the signs of drought in an integrated vulnerability approach. (b) What are the societal capacities? In this the institutional framework that enables policy development is evaluated. The protocol gathers information on vulnerability and pending hazard in advance so that early warnings can be declared at sufficient lead time and drought mitigation planning can be implemented at an early stage. (c) How can science be translated into policy? Linking science indicators into the actions/interventions that society needs to implement, and evaluating how policy is implemented. Key limitations to planning for drought are the social capacities to implement early warning systems. Vulnerability assessment contributes to identify these limitations and therefore provides crucial information to policy development. Based on the assessment of vulnerability we suggest thresholds for management actions to respond to drought forecasts and link predictive indicators to relevant potential mitigation strategies. Vulnerability assessment is crucial to identify relief, coping and management responses that contribute to a more resilient society. (d) How can society benefit from the forecast? Evaluating how information is provided to potentially affected groups, and how mitigation strategies can be taken in response. This paper presents an outline of the protocol that was developed in the DEWFORA project, outlining the complementary roles of science, policy and societal uptake in effective drought forecasting and warning. A consensus on the need to emphasise the social component of early warning was reached when testing the DEWFORA early warning system protocol among experts from 18 countries.

A robust method to forecast volcanic ash clouds

USGS Publications Warehouse

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 efficient means to assess all of the hazards associated with these ash clouds.

Estimating the budget impact of orphan drugs in Sweden and France 2013–2020

PubMed Central

2014-01-01

Background The growth in expenditure on orphan medicinal products (OMP) across Europe has been identified as a concern. Estimates of future expenditure in Europe have suggested that OMPs could account for a significant proportion of total pharmaceutical expenditure in some countries, but few of these forecasts have been well validated. This analysis aims to establish a robust forecast of the future budget impact of OMPs on the healthcare systems in Sweden and France. Methods A dynamic forecasting model was created to estimate the budget impact of OMPs in Sweden and France between 2013 and 2020. The model used historical data on OMP designation and approval rates to predict the number of new OMPs coming to the market. Average OMP sales were estimated for each year post-launch by regression analysis of historical sales data. Total forecast sales were compared with expected sales of all pharmaceuticals in each country to quantify the relative budget impact. Results The model predicts that by 2020, 152 OMPs will have marketing authorization in Europe. The base case OMP budget impacts are forecast to grow from 2.7% in Sweden and 3.2% in France of total drug expenditure in 2013 to 4.1% in Sweden and 4.9% in France by 2020. The principal driver of expenditure growth is the number of new OMPs obtaining OMP designation. This is tempered by the slowing success rate for new approvals and the loss of intellectual property protection on existing orphan medicines. Given the forward-looking nature of the analysis, uncertainty exists around model parameters and sensitivity analysis found peak year budget impact varying between 2% and 11%. Conclusion The budget impact of OMPs in Sweden and France is likely to remain sustainable over time and a relatively small proportion of total pharmaceutical expenditure. This forecast could be affected by changes in the success rate for OMP approvals, average cost of OMPs, and the type of companies developing OMPs. PMID:24524281

Estimating the budget impact of orphan drugs in Sweden and France 2013-2020.

PubMed

Hutchings, Adam; Schey, Carina; Dutton, Richard; Achana, Felix; Antonov, Karolina

2014-02-13

The growth in expenditure on orphan medicinal products (OMP) across Europe has been identified as a concern. Estimates of future expenditure in Europe have suggested that OMPs could account for a significant proportion of total pharmaceutical expenditure in some countries, but few of these forecasts have been well validated. This analysis aims to establish a robust forecast of the future budget impact of OMPs on the healthcare systems in Sweden and France. A dynamic forecasting model was created to estimate the budget impact of OMPs in Sweden and France between 2013 and 2020. The model used historical data on OMP designation and approval rates to predict the number of new OMPs coming to the market. Average OMP sales were estimated for each year post-launch by regression analysis of historical sales data. Total forecast sales were compared with expected sales of all pharmaceuticals in each country to quantify the relative budget impact. The model predicts that by 2020, 152 OMPs will have marketing authorization in Europe. The base case OMP budget impacts are forecast to grow from 2.7% in Sweden and 3.2% in France of total drug expenditure in 2013 to 4.1% in Sweden and 4.9% in France by 2020. The principal driver of expenditure growth is the number of new OMPs obtaining OMP designation. This is tempered by the slowing success rate for new approvals and the loss of intellectual property protection on existing orphan medicines. Given the forward-looking nature of the analysis, uncertainty exists around model parameters and sensitivity analysis found peak year budget impact varying between 2% and 11%. The budget impact of OMPs in Sweden and France is likely to remain sustainable over time and a relatively small proportion of total pharmaceutical expenditure. This forecast could be affected by changes in the success rate for OMP approvals, average cost of OMPs, and the type of companies developing OMPs.

Financial options methodology for analyzing investments in new technology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wenning, B.D.

1994-12-31

The evaluation of investments in longer term research and development in emerging technologies, because of the nature of such subjects, must address inherent uncertainties. Most notably, future cash flow forecasts include substantial uncertainties. Conventional present value methodology, when applied to emerging technologies severely penalizes cash flow forecasts, and strategic investment opportunities are at risk of being neglected. Use of options valuation methodology adapted from the financial arena has been introduced as having applicability in such technology evaluations. Indeed, characteristics of superconducting magnetic energy storage technology suggest that it is a candidate for the use of options methodology when investment decisionsmore » are being contemplated.« less

Financial options methodology for analyzing investments in new technology

NASA Technical Reports Server (NTRS)

Wenning, B. D.

1995-01-01

The evaluation of investments in longer term research and development in emerging technologies, because of the nature of such subjects, must address inherent uncertainties. Most notably, future cash flow forecasts include substantial uncertainties. Conventional present value methodology, when applied to emerging technologies severely penalizes cash flow forecasts, and strategic investment opportunities are at risk of being neglected. Use of options evaluation methodology adapted from the financial arena has been introduced as having applicability in such technology evaluations. Indeed, characteristics of superconducting magnetic energy storage technology suggest that it is a candidate for the use of options methodology when investment decisions are being contemplated.

Uncertainty based modeling of rainfall-runoff: Combined differential evolution adaptive Metropolis (DREAM) and K-means clustering

NASA Astrophysics Data System (ADS)

Zahmatkesh, Zahra; Karamouz, Mohammad; Nazif, Sara

2015-09-01

Simulation of rainfall-runoff process in urban areas is of great importance considering the consequences and damages of extreme runoff events and floods. The first issue in flood hazard analysis is rainfall simulation. Large scale climate signals have been proved to be effective in rainfall simulation and prediction. In this study, an integrated scheme is developed for rainfall-runoff modeling considering different sources of uncertainty. This scheme includes three main steps of rainfall forecasting, rainfall-runoff simulation and future runoff prediction. In the first step, data driven models are developed and used to forecast rainfall using large scale climate signals as rainfall predictors. Due to high effect of different sources of uncertainty on the output of hydrologic models, in the second step uncertainty associated with input data, model parameters and model structure is incorporated in rainfall-runoff modeling and simulation. Three rainfall-runoff simulation models are developed for consideration of model conceptual (structural) uncertainty in real time runoff forecasting. To analyze the uncertainty of the model structure, streamflows generated by alternative rainfall-runoff models are combined, through developing a weighting method based on K-means clustering. Model parameters and input uncertainty are investigated using an adaptive Markov Chain Monte Carlo method. Finally, calibrated rainfall-runoff models are driven using the forecasted rainfall to predict future runoff for the watershed. The proposed scheme is employed in the case study of the Bronx River watershed, New York City. Results of uncertainty analysis of rainfall-runoff modeling reveal that simultaneous estimation of model parameters and input uncertainty significantly changes the probability distribution of the model parameters. It is also observed that by combining the outputs of the hydrological models using the proposed clustering scheme, the accuracy of runoff simulation in the watershed is remarkably improved up to 50% in comparison to the simulations by the individual models. Results indicate that the developed methodology not only provides reliable tools for rainfall and runoff modeling, but also adequate time for incorporating required mitigation measures in dealing with potentially extreme runoff events and flood hazard. Results of this study can be used in identification of the main factors affecting flood hazard analysis.

CEREF: A hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system

NASA Astrophysics Data System (ADS)

Zhang, Hongbo; Singh, Vijay P.; Wang, Bin; Yu, Yinghao

2016-09-01

Hydrological forecasting is complicated by flow regime alterations in a coupled socio-hydrologic system, encountering increasingly non-stationary, nonlinear and irregular changes, which make decision support difficult for future water resources management. Currently, many hybrid data-driven models, based on the decomposition-prediction-reconstruction principle, have been developed to improve the ability to make predictions of annual streamflow. However, there exist many problems that require further investigation, the chief among which is the direction of trend components decomposed from annual streamflow series and is always difficult to ascertain. In this paper, a hybrid data-driven model was proposed to capture this issue, which combined empirical mode decomposition (EMD), radial basis function neural networks (RBFNN), and external forces (EF) variable, also called the CEREF model. The hybrid model employed EMD for decomposition and RBFNN for intrinsic mode function (IMF) forecasting, and determined future trend component directions by regression with EF as basin water demand representing the social component in the socio-hydrologic system. The Wuding River basin was considered for the case study, and two standard statistical measures, root mean squared error (RMSE) and mean absolute error (MAE), were used to evaluate the performance of CEREF model and compare with other models: the autoregressive (AR), RBFNN and EMD-RBFNN. Results indicated that the CEREF model had lower RMSE and MAE statistics, 42.8% and 7.6%, respectively, than did other models, and provided a superior alternative for forecasting annual runoff in the Wuding River basin. Moreover, the CEREF model can enlarge the effective intervals of streamflow forecasting compared to the EMD-RBFNN model by introducing the water demand planned by the government department to improve long-term prediction accuracy. In addition, we considered the high-frequency component, a frequent subject of concern in EMD-based forecasting, and results showed that removing high-frequency component is an effective measure to improve forecasting precision and is suggested for use with the CEREF model for better performance. Finally, the study concluded that the CEREF model can be used to forecast non-stationary annual streamflow change as a co-evolution of hydrologic and social systems with better accuracy. Also, the modification about removing high-frequency can further improve the performance of the CEREF model. It should be noted that the CEREF model is beneficial for data-driven hydrologic forecasting in complex socio-hydrologic systems, and as a simple data-driven socio-hydrologic forecasting model, deserves more attention.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wei, Max; Smith, Sarah J.; Sohn, Michael D.

Fuel cells are both a longstanding and emerging technology for stationary and transportation applications, and their future use will likely be critical for the deep decarbonization of global energy systems. As we look into future applications, a key challenge for policy-makers and technology market forecasters who seek to track and/or accelerate their market adoption is the ability to forecast market costs of the fuel cells as technology innovations are incorporated into market products. Specifically, there is a need to estimate technology learning rates, which are rates of cost reduction versus production volume. Unfortunately, no literature exists for forecasting future learningmore » rates for fuel cells. In this paper, we look retrospectively to estimate learning rates for two fuel cell deployment programs: (1) the micro-combined heat and power (CHP) program in Japan, and (2) the Self-Generation Incentive Program (SGIP) in California. These two examples have a relatively broad set of historical market data and thus provide an informative and international comparison of distinct fuel cell technologies and government deployment programs. We develop a generalized procedure for disaggregating experience-curve cost-reductions in order to disaggregate the Japanese fuel cell micro-CHP market into its constituent components, and we derive and present a range of learning rates that may explain observed market trends. Finally, we explore the differences in the technology development ecosystem and market conditions that may have contributed to the observed differences in cost reduction and draw policy observations for the market adoption of future fuel cell technologies. The scientific and policy contributions of this paper are the first comparative experience curve analysis of past fuel cell technologies in two distinct markets, and the first quantitative comparison of a detailed cost model of fuel cell systems with actual market data. The resulting approach is applicable to analyzing other fuel cell markets and other energy-related technologies, and highlights the data needed for cost modeling and quantitative assessment of key cost reduction components.« less

EpiCaster: An Integrated Web Application For Situation Assessment and Forecasting of Global Epidemics

PubMed Central

Deodhar, Suruchi; Bisset, Keith; Chen, Jiangzhuo; Barrett, Chris; Wilson, Mandy; Marathe, Madhav

2016-01-01

Public health decision makers need access to high resolution situation assessment tools for understanding the extent of various epidemics in different regions of the world. In addition, they need insights into the future course of epidemics by way of forecasts. Such forecasts are essential for planning the allocation of limited resources and for implementing several policy-level and behavioral intervention strategies. The need for such forecasting systems became evident in the wake of the recent Ebola outbreak in West Africa. We have developed EpiCaster, an integrated Web application for situation assessment and forecasting of various epidemics, such as Flu and Ebola, that are prevalent in different regions of the world. Using EpiCaster, users can assess the magnitude and severity of different epidemics at highly resolved spatio-temporal levels. EpiCaster provides time-varying heat maps and graphical plots to view trends in the disease dynamics. EpiCaster also allows users to visualize data gathered through surveillance mechanisms, such as Google Flu Trends (GFT) and the World Health Organization (WHO). The forecasts provided by EpiCaster are generated using different epidemiological models, and the users can select the models through the interface to filter the corresponding forecasts. EpiCaster also allows the users to study epidemic propagation in the presence of a number of intervention strategies specific to certain diseases. Here we describe the modeling techniques, methodologies and computational infrastructure that EpiCaster relies on to support large-scale predictive analytics for situation assessment and forecasting of global epidemics. PMID:27796009

A new forecast presentation tool for offshore contractors

NASA Astrophysics Data System (ADS)

Jørgensen, M.

2009-09-01

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

Comparative Studies of Prediction Strategies for Solar X-ray Time Series

NASA Astrophysics Data System (ADS)

Muranushi, T.; Hattori, T.; Jin, Q.; Hishinuma, T.; Tominaga, M.; Nakagawa, K.; Fujiwara, Y.; Nakamura, T.; Sakaue, T.; Takahashi, T.; Seki, D.; Namekata, K.; Tei, A.; Ban, M.; Kawamura, A. D.; Hada-Muranushi, Y.; Asai, A.; Nemoto, S.; Shibata, K.

2016-12-01

Crucial virtues for operational space weather forecast are real-timeforecast ability, forecast precision and customizability to userneeds. The recent development of deep-learning makes it veryattractive to space weather, because (1) it learns gradually incomingdata, (2) it exhibits superior accuracy over conventional algorithmsin many fields, and (3) it makes the customization of the forecasteasier because it accepts raw images.However, the best deep-learning applications are only attainable bycareful human designers that understands both the mechanism of deeplearning and the application field. Therefore, we need to foster youngresearchers to enter the field of machine-learning aided forecast. So,we have held a seminar every Monday with undergraduate and graduatestudents from May to August 2016.We will review the current status of space weather science and theautomated real-time space weather forecast engine UFCORIN. Then, weintroduce the deep-learning space weather forecast environments wehave set up using Python and Chainer on students' laptop computers.We have started from simple image classification neural network, thenimplemented space-weather neural network that predicts future X-rayflux of the Sun based on the past X-ray lightcurve and magnetic fieldline-of-sight images.In order to perform each forecast faster, we have focused on simplelightcurve-to-lightcurve forecast, and performed comparative surveysby changing following parameters: The size and topology of the neural network Batchsize Neural network hyperparameters such as learning rates to optimize the preduction accuracy, and time for prediction.We have found how to design compact, fast but accurate neural networkto perform forecast. Our forecasters can perform predictionexperiment for four-year timespan in a few minutes, and achieveslog-scale errors of the order of 1. Our studies is ongoing, and inour talk we will review our progress till December.

Development of RGB Composite Imagery for Operational Weather Forecasting Applications

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Fuell, Kevin K.; Oswald, Hayden, K; Knaff, John A.

2012-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center, in collaboration with the Cooperative Institute for Research in the Atmosphere (CIRA), is providing red-green-blue (RGB) color composite imagery to several of NOAA s National Centers and National Weather Service forecast offices as a demonstration of future capabilities of the Advanced Baseline Imager (ABI) to be implemented aboard GOES-R. Forecasters rely upon geostationary satellite imagery to monitor conditions over their regions of responsibility. Since the ABI will provide nearly three times as many channels as the current GOES imager, the volume of data available for analysis will increase. RGB composite imagery can aid in the compression of large data volumes by combining information from multiple channels or paired channel differences into single products that communicate more information than provided by a single channel image. A standard suite of RGB imagery has been developed by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), based upon the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The SEVIRI instrument currently provides visible and infrared wavelengths comparable to the future GOES-R ABI. In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the NASA Terra and Aqua satellites can be used to demonstrate future capabilities of GOES-R. This presentation will demonstrate an overview of the products currently disseminated to SPoRT partners within the GOES-R Proving Ground, and other National Weather Service forecast offices, along with examples of their application. For example, CIRA has used the channels of the current GOES sounder to produce an "air mass" RGB originally designed for SEVIRI. This provides hourly imagery over CONUS for looping applications while demonstrating capabilities similar to the future ABI instrument. SPoRT has developed similar "air mass" RGB imagery from MODIS, and through a case study example, synoptic-scale features evident in single-channel water vapor imagery are shown in the context of the air mass product. Other products, such as the "nighttime microphysics" RGB, are useful in the detection of low clouds and fog. Nighttime microphysics products from MODIS offer some advantages over single-channel or spectral difference techniques and will be discussed in the context of a case study. Finally, other RGB products from SEVIRI are being demonstrated as precursors to GOES-R within the GOES-R Proving Ground. Examples of "natural color" and "dust" imagery will be shown with relevant applications.

Current Usage and Future Prospects of Multispectral (RGB) Satellite Imagery in Support of NWS Forecast Offices and National Centers

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Fuell, Kevin K.; Knaff, John; Lee, Thomas

2012-01-01

Current and future satellite sensors provide remotely sensed quantities from a variety of wavelengths ranging from the visible to the passive microwave, from both geostationary and low-Earth orbits. The NASA Short-term Prediction Research and Transition (SPoRT) Center has a long history of providing multispectral imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA s Terra and Aqua satellites in support of NWS forecast office activities. Products from MODIS have recently been extended to include a broader suite of multispectral imagery similar to those developed by EUMETSAT, based upon the spectral channel s available from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard METEOSAT-9. This broader suite includes products that discriminate between air mass types associated with synoptic-scale features, assists in the identification of dust, and improves upon paired channel difference detection of fog and low cloud events. Similarly, researchers at NOAA/NESDIS and CIRA have developed air mass discrimination capabilities using channels available from the current GOES Sounders. Other applications of multispectral composites include combinations of high and low frequency, horizontal and vertically polarized passive microwave brightness temperatures to discriminate tropical cyclone structures and other synoptic-scale features. Many of these capabilities have been transitioned for evaluation and operational use at NWS Weather Forecast Offices and National Centers through collaborations with SPoRT and CIRA. Future instruments will continue the availability of these products and also expand upon current capabilities. The Advanced Baseline Imager (ABI) on GOES-R will improve the spectral, spatial, and temporal resolution of our current geostationary capabilities, and the recent launch of the Suomi National Polar-Orbiting Partnership (S-NPP) carries instruments such as the Visible Infrared Imager Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrIS), and the Advanced Technology Microwave Sounder (ATMS), which have unrivaled spectral and spatial resolution, as precursors to the JPSS era (i.e., the next generation of polar orbiting satellites). At the same time, new image manipulation and display capabilities are available within AWIPS II, the next generation of the NWS forecaster decision support system. This presentation will present a review of SPoRT, CIRA, and NRL collaborations regarding multispectral satellite imagery and articulate an integrated and collaborative path forward with Raytheon AWIPS II development staff for integrating current and future capabilities that support new satellite instrumentation and the AWIPS II decision support system.

Easy to retrieve but hard to believe: metacognitive discounting of the unpleasantly possible.

PubMed

O'Brien, Ed

2013-06-01

People who recall or forecast many pleasant moments should perceive themselves as happier in the past or future than people who generate few such moments; the same principle should apply to generating unpleasant moments and perceiving unhappiness. Five studies suggest that this is not always true. Rather, people's metacognitive experience of ease of thought retrieval ("fluency") can affect perceived well-being over time beyond actual thought content. The easier it is to recall positive past experiences, the happier people think they were at the time; likewise, the easier it is to recall negative past experiences, the unhappier people think they were. But this is not the case for predicting the future. Although people who easily generate positive forecasts predict more future happiness, people who easily generate negative forecasts do not infer future unhappiness. Given pervasive tendencies to underestimate the likelihood of experiencing negative events, people apparently discount hard-to-believe metacognitive feelings (e.g., easily imagined unpleasant futures). Paradoxically, people's well-being may be maximized when they contemplate some bad moments or just a few good moments.

The Latest Forecast.

ERIC Educational Resources Information Center

Laurence, David

2002-01-01

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

Forecasting the VCR: A Retrospective Assessment of Media Trade Press and Academic Forecasts of Its Impact on Broadcasting.

ERIC Educational Resources Information Center

Napoli, Philip M.

Retrospective technology assessment (RTA) is the use of historical research to assess current and future technology issues. This paper uses the introduction of the videocassette recorder (VCR) as an RTA case study, focusing on the broadcasting and advertising trade presses and their forecasts of the VCR's potential impact on broadcasting. Trade…

Space weather forecasting: Past, Present, Future

NASA Astrophysics Data System (ADS)

Lanzerotti, L. J.

2012-12-01

There have been revolutionary advances in electrical technologies over the last 160 years. The historical record demonstrates that space weather processes have often provided surprises in the implementation and operation of many of these technologies. The historical record also demonstrates that as the complexity of systems increase, including their interconnectedness and interoperability, they can become more susceptible to space weather effects. An engineering goal, beginning during the decades following the 1859 Carrington event, has been to attempt to forecast solar-produced disturbances that could affect technical systems, be they long grounded conductor-based or radio-based or required for exploration, or the increasingly complex systems immersed in the space environment itself. Forecasting of space weather events involves both frontier measurements and models to address engineering requirements, and industrial and governmental policies that encourage and permit creativity and entrepreneurship. While analogies of space weather forecasting to terrestrial weather forecasting are frequently made, and while many of the analogies are valid, there are also important differences. This presentation will provide some historical perspectives on the forecast problem, a personal assessment of current status of several areas including important policy issues, and a look into the not-too-distant future.

Perspectives on model forecasts of the 2014-2015 Ebola epidemic in West Africa: lessons and the way forward.

PubMed

Chowell, Gerardo; Viboud, Cécile; Simonsen, Lone; Merler, Stefano; Vespignani, Alessandro

2017-03-01

The unprecedented impact and modeling efforts associated with the 2014-2015 Ebola epidemic in West Africa provides a unique opportunity to document the performances and caveats of forecasting approaches used in near-real time for generating evidence and to guide policy. A number of international academic groups have developed and parameterized mathematical models of disease spread to forecast the trajectory of the outbreak. These modeling efforts often relied on limited epidemiological data to derive key transmission and severity parameters, which are needed to calibrate mechanistic models. Here, we provide a perspective on some of the challenges and lessons drawn from these efforts, focusing on (1) data availability and accuracy of early forecasts; (2) the ability of different models to capture the profile of early growth dynamics in local outbreaks and the importance of reactive behavior changes and case clustering; (3) challenges in forecasting the long-term epidemic impact very early in the outbreak; and (4) ways to move forward. We conclude that rapid availability of aggregated population-level data and detailed information on a subset of transmission chains is crucial to characterize transmission patterns, while ensemble-forecasting approaches could limit the uncertainty of any individual model. We believe that coordinated forecasting efforts, combined with rapid dissemination of disease predictions and underlying epidemiological data in shared online platforms, will be critical in optimizing the response to current and future infectious disease emergencies.

Predictability of Bristol Bay, Alaska, sockeye salmon returns one to four years in the future

USGS Publications Warehouse

Adkison, Milo D.; Peterson, R.M.

2000-01-01

Historically, forecast error for returns of sockeye salmon Oncorhynchus nerka to Bristol Bay, Alaska, has been large. Using cross-validation forecast error as our criterion, we selected forecast models for each of the nine principal Bristol Bay drainages. Competing forecast models included stock-recruitment relationships, environmental variables, prior returns of siblings, or combinations of these predictors. For most stocks, we found prior returns of siblings to be the best single predictor of returns; however, forecast accuracy was low even when multiple predictors were considered. For a typical drainage, an 80% confidence interval ranged from one half to double the point forecast. These confidence intervals appeared to be appropriately wide.

The Evolvement of Automobile Steering System Based on TRIZ

NASA Astrophysics Data System (ADS)

Zhao, Xinjun; Zhang, Shuang

Products and techniques pass through a process of birth, growth, maturity, death and quit the stage like biological evolution process. The developments of products and techniques conform to some evolvement rules. If people know and hold these rules, they can design new kind of products and forecast the develop trends of the products. Thereby, enterprises can grasp the future technique directions of products, and make product and technique innovation. Below, based on TRIZ theory, the mechanism evolvement, the function evolvement and the appearance evolvement of automobile steering system had been analyzed and put forward some new ideas about future automobile steering system.

A PILOT CENTER FOR EDUCATIONAL POLICY RESEARCH. FINAL REPORT--PART I.

ERIC Educational Resources Information Center

ADELSON, MARVIN; AND OTHERS

THE PILOT CENTER FOR EDUCATIONAL POLICY RESEARCH, OPERATED BY THE SYSTEM DEVELOPMENT CORPORATION FROM JUNE 1, 1967, THROUGH FEBRUARY 29, 1968, HAD THREE OBJECTIVES--(1) TO INVESTIGATE, ANALYZE, AND EXPERIMENT WITH METHODS, PROCEDURES, AND TOOLS FOR STUDYING THE FUTURE AS IT COULD AFFECT EDUCATION IN THE UNITED STATES, (2) TO FORECAST POSSIBLE…

Hawaii energy strategy report, October 1995

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

This is a report on the Hawaii Energy Strategy Program. The topics of the report include the a description of the program including an overview, objectives, policy statement and purpose and objectives; energy strategy policy development; energy strategy projects; current energy situation; modeling Hawaii`s energy future; energy forecasts; reducing energy demand; scenario assessment, and recommendations.

Focus on the Future: Environmental Scanning at Columbia Basin College.

ERIC Educational Resources Information Center

Knutzen, Judi, Comp.

As the first step of the process of developing a strategic plan, the Institutional Research Director at Columbia Basin College (Washington) was asked to perform an environmental scan. Environmental scanning is a careful and continuous process of tracking and analyzing trends relevant to an institution's goals. It involves making forecasts of…

Potential Improvements in Space Weather Forecasting using New Products Developed for the Upcoming DSCOVR Solar Wind Mission

NASA Astrophysics Data System (ADS)

Cash, M. D.; Biesecker, D. A.; Reinard, A. A.

2013-05-01

The Deep Space Climate Observatory (DSCOVR) mission, which is scheduled for launch in late 2014, will provide real-time solar wind thermal plasma and magnetic measurements to ensure continuous monitoring for space weather forecasting. DSCOVR will be located at the L1 Lagrangian point and will include a Faraday cup to measure the proton and alpha components of the solar wind and a triaxial fluxgate magnetometer to measure the magnetic field in three dimensions. The real-time data provided by DSCOVR will be used to generate space weather applications and products that have been demonstrated to be highly accurate and provide actionable information for customers. We present several future space weather products currently under evaluation for development. New potential space weather products for use with DSCOVR real-time data include: automated shock detection, more accurate L1 to Earth delay time, automatic solar wind regime identification, and prediction of rotations in solar wind Bz within magnetic clouds. Additional ideas from the community on future space weather products are encouraged.

Forecasting European Droughts using the North American Multi-Model Ensemble (NMME)

NASA Astrophysics Data System (ADS)

Thober, Stephan; Kumar, Rohini; Samaniego, Luis; Sheffield, Justin; Schäfer, David; Mai, Juliane

2015-04-01

Soil moisture droughts have the potential to diminish crop yields causing economic damage or even threatening the livelihood of societies. State-of-the-art drought forecasting systems incorporate seasonal meteorological forecasts to estimate future drought conditions. Meteorological forecasting skill (in particular that of precipitation), however, is limited to a few weeks because of the chaotic behaviour of the atmosphere. One of the most important challenges in drought forecasting is to understand how the uncertainty in the atmospheric forcings (e.g., precipitation and temperature) is further propagated into hydrologic variables such as soil moisture. The North American Multi-Model Ensemble (NMME) provides the latest collection of a multi-institutional seasonal forecasting ensemble for precipitation and temperature. In this study, we analyse the skill of NMME forecasts for predicting European drought events. The monthly NMME forecasts are downscaled to daily values to force the mesoscale hydrological model (mHM). The mHM soil moisture forecasts obtained with the forcings of the dynamical models are then compared against those obtained with the Ensemble Streamflow Prediction (ESP) approach. ESP recombines historical meteorological forcings to create a new ensemble forecast. Both forecasts are compared against reference soil moisture conditions obtained using observation based meteorological forcings. The study is conducted for the period from 1982 to 2009 and covers a large part of the Pan-European domain (10°W to 40°E and 35°N to 55°N). Results indicate that NMME forecasts are better at predicting the reference soil moisture variability as compared to ESP. For example, NMME explains 50% of the variability in contrast to only 31% by ESP at a six-month lead time. The Equitable Threat Skill Score (ETS), which combines the hit and false alarm rates, is analysed for drought events using a 0.2 threshold of a soil moisture percentile index. On average, the NMME based ensemble forecasts have consistently higher skill than the ESP based ones (ETS of 13% as compared to 5% at a six-month lead time). Additionally, the ETS ensemble spread of NMME forecasts is considerably narrower than that of ESP; the lower boundary of the NMME ensemble spread coincides most of the time with the ensemble median of ESP. Among the NMME models, NCEP-CFSv2 outperforms the other models in terms of ETS most of the time. Removing the three worst performing models does not deteriorate the ensemble performance (neither in skill nor in spread), but would substantially reduce the computational resources required in an operational forecasting system. For major European drought events (e.g., 1990, 1992, 2003, and 2007), NMME forecasts tend to underestimate area under drought and drought magnitude during times of drought development. During drought recovery, this underestimation is weaker for area under drought or even reversed into an overestimation for drought magnitude. This indicates that the NMME models are too wet during drought development and too dry during drought recovery. In summary, soil moisture drought forecasts by NMME are more skillful than those of an ESP based approach. However, they still show systematic biases in reproducing the observed drought dynamics during drought development and recovery.

Forecasting fluid milk and cheese demands for the next decade.

PubMed

Schmit, T M; Kaiser, H M

2006-12-01

Predictions of future market demands and farm prices for dairy products are important determinants in developing marketing strategies and farm-production planning decisions. The objective of this report was to use current aggregate forecast data, combined with existing econometric models of demand and supply, to forecast retail demands for fluid milk and cheese and the supply and price of farm milk over the next decade. In doing so, we can investigate whether projections of population and consumer food-spending patterns will extend or alter current consumption trends and examine the implications of future generic advertising strategies for dairy products. To conduct the forecast simulations and appropriately allocate the farm milk supply to various uses, we used a partial equilibrium model of the US domestic dairy sector that segmented the industry into retail, wholesale, and farm markets. Model simulation results indicated that declines in retail per capita demand would persist but at a reduced rate from years past and that retail per capita demand for cheese would continue to grow and strengthen over the next decade. These predictions rely on expected changes in the size of populations of various ages, races, and ethnicities and on existing patterns of spending on food at home and away from home. The combined effect of these forecasted changes in demand levels was reflected in annualized growth in the total farm-milk supply that was similar to growth realized during the past few years. Although we expect nominal farm milk prices to increase over the next decade, we expect real prices (relative to assumed growth in feed costs) to remain relatively stable and show no increase until the end of the forecast period. Supplemental industry model simulations also suggested that net losses in producer revenues would result if only nominal levels of generic advertising spending were maintained in forthcoming years. In fact, if real generic advertising expenditures are increased relative to 2005 levels, returns to the investment in generic advertising can be improved. Specifically, each additional real dollar invested in generic advertising for fluid milk and cheese products over the forecast period would result in an additional 5.61 dollars in producer revenues.

A comparison of the domestic satellite communications forecast to the year 2000

NASA Technical Reports Server (NTRS)

Poley, W. A.; Lekan, J. F.; Salzman, J. A.; Stevenson, S. M.

1983-01-01

The methodologies and results of three NASA-sponsored market demand assessment studies are presented and compared. Forecasts of future satellite addressable traffic (both trunking and customer premises services) were developed for the three main service categories of voice, data and video and subcategories thereof for the benchmark years of 1980, 1990 and 2000. The contractor results are presented on a service by service basis in two formats: equivalent 36 MHz transponders and basic transmission units (voice: half-voice circuits, data: megabits per second and video: video channels). It is shown that while considerable differences exist at the service category level, the overall forecasts by the two contractors are quite similar. ITT estimates the total potential satellite market to be 3594 transponders in the year 2000 with data service comprising 54 percent of this total. The WU outlook for the same time period is 2779 transponders with voice services accounting for 66 percent of the total.

Real-time anomaly detection for very short-term load forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Jian; Hong, Tao; Yue, Meng

Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonlymore » used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Lastly, a general anomaly detection framework is proposed for the future research.« less

Real-time anomaly detection for very short-term load forecasting

DOE PAGES

Luo, Jian; Hong, Tao; Yue, Meng

2018-01-06

Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonlymore » used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Lastly, a general anomaly detection framework is proposed for the future research.« less

Method for assigning sites to projected generic nuclear power plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Holter, G.M.; Purcell, W.L.; Shutz, M.E.

1986-07-01

Pacific Northwest Laboratory developed a method for forecasting potential locations and startup sequences of nuclear power plants that will be required in the future but have not yet been specifically identified by electric utilities. Use of the method results in numerical ratings for potential nuclear power plant sites located in each of the 10 federal energy regions. The rating for each potential site is obtained from numerical factors assigned to each of 5 primary siting characteristics: (1) cooling water availability, (2) site land area, (3) power transmission land area, (4) proximity to metropolitan areas, and (5) utility plans for themore » site. The sequence of plant startups in each federal energy region is obtained by use of the numerical ratings and the forecasts of generic nuclear power plant startups obtained from the EIA Middle Case electricity forecast. Sites are assigned to generic plants in chronological order according to startup date.« less

Climate, weather, space weather: model development in an operational context

NASA Astrophysics Data System (ADS)

Folini, Doris

2018-05-01

Aspects of operational modeling for climate, weather, and space weather forecasts are contrasted, with a particular focus on the somewhat conflicting demands of "operational stability" versus "dynamic development" of the involved models. Some common key elements are identified, indicating potential for fruitful exchange across communities. Operational model development is compelling, driven by factors that broadly fall into four categories: model skill, basic physics, advances in computer architecture, and new aspects to be covered, from costumer needs over physics to observational data. Evaluation of model skill as part of the operational chain goes beyond an automated skill score. Permanent interaction between "pure research" and "operational forecast" people is beneficial to both sides. This includes joint model development projects, although ultimate responsibility for the operational code remains with the forecast provider. The pace of model development reflects operational lead times. The points are illustrated with selected examples, many of which reflect the author's background and personal contacts, notably with the Swiss Weather Service and the Max Planck Institute for Meteorology, Hamburg, Germany. In view of current and future challenges, large collaborations covering a range of expertise are a must - within and across climate, weather, and space weather. To profit from and cope with the rapid progress of computer architectures, supercompute centers must form part of the team.

Financial Risk Reduction and Management of Water Reservoirs Using Forecasts: A Case for Pernambuco, Brazil

NASA Astrophysics Data System (ADS)

Kumar, I.; Josset, L.; e Silva, E. C.; Possas, J. M. C.; Asfora, M. C.; Lall, U.

2017-12-01

The financial health and sustainability, ensuring adequate supply, and adapting to climate are fundamental challenges faced by water managers. These challenges are worsened in semi-arid regions with socio-economic pressures, seasonal supply of water, and projected increase in intensity and frequency of droughts. Over time, probabilistic rainfall forecasts are improving and for water managers, it could be key in addressing the above challenges. Using forecasts can also help make informed decisions about future infrastructure. The study proposes a model to minimize cost of water supply (including cost of deficit) given ensemble forecasts. The model can be applied to seasonal to annual ensemble forecasts, to determine the least cost solution. The objective of the model is to evaluate the resiliency and cost associated to supplying water. A case study is conducted in one of the largest reservoirs (Jucazinho) in Pernambuco state, Brazil, and four other reservoirs, which provide water to nineteen municipalities in the Jucazinho system. The state has been in drought since 2011, and the Jucazinho reservoir, has been empty since January 2017. The importance of climate adaptation along with risk management and financial sustainability are important to the state as it is extremely vulnerable to droughts, and has seasonal streamflow. The objectives of the case study are first, to check if streamflow forecasts help reduce future supply costs by comparing k-nearest neighbor ensemble forecasts with a fixed release policy. Second, to determine the value of future infrastructure, a new source of supply from Rio São Francisco, considered to mitigate drought conditions. The study concludes that using forecasts improve the supply and financial sustainability of water, by reducing cost of failure. It also concludes that additional infrastructure can help reduce the risks of failure significantly, but does not guarantee supply during prolonged droughts like the one experienced currently.

Anti-EGFR Agents: Current Status, Forecasts and Future Directions.

PubMed

Kwapiszewski, Radoslaw; Pawlak, Sebastian D; Adamkiewicz, Karolina

2016-12-01

The epidermal growth factor receptor (EGFR) is one of the most important and attractive targets for specific anticancer therapies. It is a robust regulator of pathways involved in cancer pathogenesis and progression. Thus far, clinical trials have demonstrated the benefits of monoclonal antibodies and synthetic tyrosine kinase inhibitors in targeting this receptor; however, novel strategies are still being developed. This article reviews the current state of efforts in targeting the EGFR in cancer therapy. Following a brief characterization of EGFR, we will present a complete list of anti-EGFR agents that are already approved, and available in clinical practice. Aside from the indications, we will present the sales forecasts and expiry dates of product patents for the selected agents. Finally, we discuss the novel anti-EGFR strategies that are currently in preclinical development.

The social function of technology assessment

NASA Technical Reports Server (NTRS)

Huddle, F. P.

1972-01-01

The problem of preserving the uneasy balance between a dynamic society and the equilibrium of man-environment society is discussed. Four sets of activities involved in technology assessment are considered: (1) Technology forecasting is necessary to warn of future dangers and opportunities, for effective timing, and to identify tradeoffs and alternatives. But forecasting is also chancy at best. (2) Social indicators need to be developed for the characterization of social status and measurement of social progress, as well as a better understanding of social needs. (3) With respect to technology assessment, the conflict between profitable directions of innovations and socially desirable directions is described, and a systematic way is needed to determine in advance what is technologically feasible to meet social needs. (4) National goals with respect to scientific and technological developments are also required.

Visualizing and Integrating AFSCN Utilization into a Common Operational Picture

NASA Astrophysics Data System (ADS)

Hays, B.; Carlile, A.; Mitchell, T.

The Department of Defense (DoD) and the 50th Space Network Operations Group Studies and Analysis branch (50th SCS/SCXI), located at Schriever AFB Colorado, face the unique challenge of forecasting the expected near term and future utilization of the Air Force Satellite Control Network (AFSCN). The forecasting timeframe covers the planned load from the current date to ten years out. The various satellite missions, satellite requirements, orbital regions, and ground architecture dynamics provide the model inputs and constraints that are used in generating the forecasted load. The AFSCN is the largest network the Air Force uses to control satellites worldwide. Each day, network personnel perform over 500 scheduled events-from satellite maneuvers to critical data downloads. The Forecasting Objective is to provide leadership with the insights necessary to manage the network today and tomorrow. For both today's needs and future needs, SCXI develops AFSCN utilization forecasts to optimize the ground system's coverage and capacity to meet user satellite requirements. SCXI also performs satellite program specific studies to determine network support feasibility. STK and STK Scheduler form the core of the tools used by SCXI. To establish this tool suite, we had to evaluate, evolve, and validate both the COTS products and our own developed code and processes. This began with calibrating the network model to emulate the real life scheduling environment of the AFSCN. Multiple STK Scheduler optimizing (de-confliction) algorithms, including Multi-Pass, Sequential, Random, and Neural, were evaluated and adjusted to determine applicability to the model and the accuracy of the prediction. Additionally, the scheduling Figure of Merit (FOM), which permits custom weighting of various parameters, was analyzed and tested to achieve the most accurate real life result. With the inherent capabilities of STK and the ability to wrap and automate output, SCXI is now able to visually communicate satellite loads in a manner never seen before in AFSCN management meetings. Scenarios such as regional antenna load stress, satellite missed opportunities, and the overall network "big picture" can be visually displayed in 3D versus the textual and line graph methods used for many years. This is the first step towards an integrated space awareness picture with an operational focus. SCXI is working on taking the visual forecast concept farther and begin fusing multiple sources of data to build a 50 SW Common Operating Picture (COP). The vision is to integrate more effective orbital determination processes, resource outages, current and forecasted satellite mission requirements, and future architectural changes into a real-time visual status to enable quick and responsive decisions. This COP would be utilized in a Wing Operations Center to provide up to the minute network status on where satellites are, which ground resources are in contact with them, and what resources are down. The ability to quickly absorb and process this data will enhance decision analysis and save valuable time in both day to day operations and wartime scenarios.

Past, Current, and Future Incidence Rates and Burden of Metastatic Prostate Cancer in the United States.

PubMed

Kelly, Scott P; Anderson, William F; Rosenberg, Philip S; Cook, Michael B

2017-11-18

Metastatic prostate cancer (PCA) remains a highly lethal malignancy in the USA. As prostate-specific antigen testing declines nationally, detailed assessment of current age- and race-specific incidence trends and quantitative forecasts are needed. To evaluate the current trends of metastatic PCA by age and race, and forecast the number of new cases (annual burden) and future trends. We derived incidence data for men aged ≥45 yr who were diagnosed with metastatic PCA from the population-based Surveillance, Epidemiology, and End Results registries. We examined the current trends of metastatic PCA from 2004 to 2014, and forecast the annual burden and incidence rates by age and race for 2015-2025, using age-period-cohort models and population projections. We also examined alternative forecasts (2012-2025) using trends prior to the revised screening guidelines issued in 2012. Metastatic PCA, steadily declining from 2004 to 2007 by 1.45%/yr, began to increase by 0.58%/yr after 2008, which accelerated to 2.74%/yr following the 2012 United States Preventive Services Task Force recommendations-a pattern that was magnified among men aged ≤69 yr and white men. Forecasts project the incidence to increase by 1.03%/yr through 2025, with men aged 45-54 yr (2.29%/yr) and 55-69 yr (1.53%/yr) increasing more rapidly. Meanwhile, the annual burden is expected to increase 42% by 2025. Our forecasts estimated an additional 15 891 metastatic cases from 2015 to 2025 compared with alternative forecasts using trends prior to 2012. The recent uptick in metastatic PCA rates has resulted in forecasts that project increasing rates through 2025, particularly among men aged ≤69 yr. Moreover, racial disparities are expected to persist and the annual burden will increase considerably. The impact of the prior and current PCA screening recommendations on metastatic PCA rates requires continued examination. In this report, we assessed how the incidence of metastatic prostate cancer has changed over recent years, and forecast future incidence trends and the number of new cases expected each year. We found that the incidence of metastatic prostate cancer has been increasing more rapidly since 2012, resulting in a rise in both future incidence and the number of new cases by 2025. Future incidence rates and the number of new cases were reduced in alternative forecasts using data prior to the 2012 United States Preventive Services Task Force (USPSTF) recommendations against prostate-specific antigen (PSA) testing for prostate cancer. There is a need for additional research that examines whether national declines in PSA testing contributed to increases in rates of metastatic disease. The incidence of metastatic disease in black men is still expected to occur at considerably higher rates compared with that in white men. Published by Elsevier B.V.

2017 One‐year seismic‐hazard forecast for the central and eastern United States from induced and natural earthquakes

USGS Publications Warehouse

Petersen, Mark D.; Mueller, Charles; Moschetti, Morgan P.; Hoover, Susan M.; Shumway, Allison; McNamara, Daniel E.; Williams, Robert; Llenos, Andrea L.; Ellsworth, William L.; Rubinstein, Justin L.; McGarr, Arthur F.; Rukstales, Kenneth S.

2017-01-01

We produce a one‐year 2017 seismic‐hazard forecast for the central and eastern United States from induced and natural earthquakes that updates the 2016 one‐year forecast; this map is intended to provide information to the public and to facilitate the development of induced seismicity forecasting models, methods, and data. The 2017 hazard model applies the same methodology and input logic tree as the 2016 forecast, but with an updated earthquake catalog. We also evaluate the 2016 seismic‐hazard forecast to improve future assessments. The 2016 forecast indicated high seismic hazard (greater than 1% probability of potentially damaging ground shaking in one year) in five focus areas: Oklahoma–Kansas, the Raton basin (Colorado/New Mexico border), north Texas, north Arkansas, and the New Madrid Seismic Zone. During 2016, several damaging induced earthquakes occurred in Oklahoma within the highest hazard region of the 2016 forecast; all of the 21 moment magnitude (M) ≥4 and 3 M≥5 earthquakes occurred within the highest hazard area in the 2016 forecast. Outside the Oklahoma–Kansas focus area, two earthquakes with M≥4 occurred near Trinidad, Colorado (in the Raton basin focus area), but no earthquakes with M≥2.7 were observed in the north Texas or north Arkansas focus areas. Several observations of damaging ground‐shaking levels were also recorded in the highest hazard region of Oklahoma. The 2017 forecasted seismic rates are lower in regions of induced activity due to lower rates of earthquakes in 2016 compared with 2015, which may be related to decreased wastewater injection caused by regulatory actions or by a decrease in unconventional oil and gas production. Nevertheless, the 2017 forecasted hazard is still significantly elevated in Oklahoma compared to the hazard calculated from seismicity before 2009.

Using ensembles in water management: forecasting dry and wet episodes

NASA Astrophysics Data System (ADS)

van het Schip-Haverkamp, Tessa; van den Berg, Wim; van de Beek, Remco

2015-04-01

Extreme weather situations as droughts and extensive precipitation are becoming more frequent, which makes it more important to obtain accurate weather forecasts for the short and long term. Ensembles can provide a solution in terms of scenario forecasts. MeteoGroup uses ensembles in a new forecasting technique which presents a number of weather scenarios for a dynamical water management project, called Water-Rijk, in which water storage and water retention plays a large role. The Water-Rijk is part of Park Lingezegen, which is located between Arnhem and Nijmegen in the Netherlands. In collaboration with the University of Wageningen, Alterra and Eijkelkamp a forecasting system is developed for this area which can provide water boards with a number of weather and hydrology scenarios in order to assist in the decision whether or not water retention or water storage is necessary in the near future. In order to make a forecast for drought and extensive precipitation, the difference 'precipitation- evaporation' is used as a measurement of drought in the weather forecasts. In case of an upcoming drought this difference will take larger negative values. In case of a wet episode, this difference will be positive. The Makkink potential evaporation is used which gives the most accurate potential evaporation values during the summer, when evaporation plays an important role in the availability of surface water. Scenarios are determined by reducing the large number of forecasts in the ensemble to a number of averaged members with each its own likelihood of occurrence. For the Water-Rijk project 5 scenario forecasts are calculated: extreme dry, dry, normal, wet and extreme wet. These scenarios are constructed for two forecasting periods, each using its own ensemble technique: up to 48 hours ahead and up to 15 days ahead. The 48-hour forecast uses an ensemble constructed from forecasts of multiple high-resolution regional models: UKMO's Euro4 model,the ECMWF model, WRF and Hirlam. Using multiple model runs and additional post processing, an ensemble can be created from non-ensemble models. The 15-day forecast uses the ECMWF Ensemble Prediction System forecast from which scenarios can be deduced directly. A combination of the ensembles from the two forecasting periods is used in order to have the highest possible resolution of the forecast for the first 48 hours followed by the lower resolution long term forecast.

[A preliminary study on dental-manpower forecasting model of Miyun County in Beijing].

PubMed

Huang, H; Wang, H; Yang, S

1999-01-01

To explore the dental-manpower forecasting model of Chinese rural region and provide references for Chinese dental-manpower researches. Chose rural Miyun County in Beijing as a sample, according to the need-based and demand-weighted forecasting method, a protocol WHO-CH model and corresponding JWG-6-M package developed by authors were used to calculate the present and future need and demand of dental-manpower in Miyun County. Further predications were also calculated on the effects of four modeling parameters to the demand of dental manpower. The present need and demand of oral care personnel for Miyun were 114.5 and 29.1 respectively. At present, Miyun has 43 oral care providers who can satisfy the demand but not the need. The change of oral health demand had a major effect on the forecast of the manpower. Dental-manpower planning should consider the need as a prime factor but must be modified by the demand. It was suggested that corresponding factors of oral care personnel need to be discussed further.

Interval forecasting of cyberattack intensity on informatization objects of industry using probability cluster model

NASA Astrophysics Data System (ADS)

Krakovsky, Y. M.; Luzgin, A. N.; Mikhailova, E. A.

2018-05-01

At present, cyber-security issues associated with the informatization objects of industry occupy one of the key niches in the state management system. As a result of functional disruption of these systems via cyberattacks, an emergency may arise related to loss of life, environmental disasters, major financial and economic damage, or disrupted activities of cities and settlements. When cyberattacks occur with high intensity, in these conditions there is the need to develop protection against them, based on machine learning methods. This paper examines interval forecasting and presents results with a pre-set intensity level. The interval forecasting is carried out based on a probabilistic cluster model. This method involves forecasting of one of the two predetermined intervals in which a future value of the indicator will be located; probability estimates are used for this purpose. A dividing bound of these intervals is determined by a calculation method based on statistical characteristics of the indicator. Source data are used that includes a number of hourly cyberattacks using a honeypot from March to September 2013.

Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jacobs, John M.; Rhodes, M.; Brown, C. W.

The aim is to construct statistical models to predict the presence, abundance and potential virulence of Vibrio vulnificus in surface waters. A variety of statistical techniques were used in concert to identify water quality parameters associated with V. vulnificus presence, abundance and virulence markers in the interest of developing strong predictive models for use in regional oceanographic modeling systems. A suite of models are provided to represent the best model fit and alternatives using environmental variables that allow them to be put to immediate use in current ecological forecasting efforts. Conclusions: Environmental parameters such as temperature, salinity and turbidity aremore » capable of accurately predicting abundance and distribution of V. vulnificus in Chesapeake Bay. Forcing these empirical models with output from ocean modeling systems allows for spatially explicit forecasts for up to 48 h in the future. This study uses one of the largest data sets compiled to model Vibrio in an estuary, enhances our understanding of environmental correlates with abundance, distribution and presence of potentially virulent strains and offers a method to forecast these pathogens that may be replicated in other regions.« less

Mesoscale data assimilation for a local severe rainfall event with the NHM-LETKF system

NASA Astrophysics Data System (ADS)

Kunii, M.

2013-12-01

This study aims to improve forecasts of local severe weather events through data assimilation and ensemble forecasting approaches. Here, the local ensemble transform Kalman filter (LETKF) is implemented with the Japan Meteorological Agency's nonhydrostatic model (NHM). The newly developed NHM-LETKF contains an adaptive inflation scheme and a spatial covariance localization scheme with physical distance. One-way nested analysis in which a finer-resolution LETKF is conducted by using the outputs of an outer model also becomes feasible. These new contents should enhance the potential of the LETKF for convective scale events. The NHM-LETKF is applied to a local severe rainfall event in Japan in 2012. Comparison of the root mean square errors between the model first guess and analysis reveals that the system assimilates observations appropriately. Analysis ensemble spreads indicate a significant increase around the time torrential rainfall occurred, which would imply an increase in the uncertainty of environmental fields. Forecasts initialized with LETKF analyses successfully capture intense rainfalls, suggesting that the system can work effectively for local severe weather. Investigation of probabilistic forecasts by ensemble forecasting indicates that this could become a reliable data source for decision making in the future. A one-way nested data assimilation scheme is also tested. The experiment results demonstrate that assimilation with a finer-resolution model provides an advantage in the quantitative precipitation forecasting of local severe weather conditions.

An investigation of the role of current and future remote sensing data systems in numerical meteorology

NASA Technical Reports Server (NTRS)

Diak, George R.; Smith, William L.

1992-01-01

A flexible system for performing observing system simulation experiments which made contributions to meteorology across all elements of the observing system simulation experiment (OSSE) components was developed. Future work will seek better understanding of the links between satellite-measured radiation and radiative transfer in the clear, cloudy and precipitating atmosphere and investigate how that understanding might be applied to improve the depiction of the initial state and the treatment of physical processes in forecast models of the atmosphere.

Forecasting distributions of an aquatic invasive species (Nitellopsis obtusa) under future climate scenarios

PubMed Central

Varela, Sara; Larkin, Daniel J.; Phelps, Nicholas B. D.

2017-01-01

Starry stonewort (Nitellopsis obtusa) is an alga that has emerged as an aquatic invasive species of concern in the United States. Where established, starry stonewort can interfere with recreational uses of water bodies and potentially have ecological impacts. Incipient invasion of starry stonewort in Minnesota provides an opportunity to predict future expansion in order to target early detection and strategic management. We used ecological niche models to identify suitable areas for starry stonewort in Minnesota based on global occurrence records and present-day and future climate conditions. We assessed sensitivity of forecasts to different parameters, using four emission scenarios (i.e., RCP 2.6, RCP 4.5, RCP 6, and RCP 8.5) from five future climate models (i.e., CCSM, GISS, IPSL, MIROC, and MRI). From our niche model analyses, we found that (i) occurrences from the entire range, instead of occurrences restricted to the invaded range, provide more informed models; (ii) default settings in Maxent did not provide the best model; (iii) the model calibration area and its background samples impact model performance; (iv) model projections to future climate conditions should be restricted to analogous environments; and (v) forecasts in future climate conditions should include different future climate models and model calibration areas to better capture uncertainty in forecasts. Under present climate, the most suitable areas for starry stonewort are predicted to be found in central and southeastern Minnesota. In the future, suitable areas for starry stonewort are predicted to shift in geographic range under some future climate models and to shrink under others, with most permutations indicating a net decrease of the species’ suitable range. Our suitability maps can serve to design short-term plans for surveillance and education, while future climate models suggest a plausible reduction of starry stonewort spread in the long-term if the trends in climate warming remain. PMID:28704433

Forecasting distributions of an aquatic invasive species (Nitellopsis obtusa) under future climate scenarios.

PubMed

Romero-Alvarez, Daniel; Escobar, Luis E; Varela, Sara; Larkin, Daniel J; Phelps, Nicholas B D

2017-01-01

Starry stonewort (Nitellopsis obtusa) is an alga that has emerged as an aquatic invasive species of concern in the United States. Where established, starry stonewort can interfere with recreational uses of water bodies and potentially have ecological impacts. Incipient invasion of starry stonewort in Minnesota provides an opportunity to predict future expansion in order to target early detection and strategic management. We used ecological niche models to identify suitable areas for starry stonewort in Minnesota based on global occurrence records and present-day and future climate conditions. We assessed sensitivity of forecasts to different parameters, using four emission scenarios (i.e., RCP 2.6, RCP 4.5, RCP 6, and RCP 8.5) from five future climate models (i.e., CCSM, GISS, IPSL, MIROC, and MRI). From our niche model analyses, we found that (i) occurrences from the entire range, instead of occurrences restricted to the invaded range, provide more informed models; (ii) default settings in Maxent did not provide the best model; (iii) the model calibration area and its background samples impact model performance; (iv) model projections to future climate conditions should be restricted to analogous environments; and (v) forecasts in future climate conditions should include different future climate models and model calibration areas to better capture uncertainty in forecasts. Under present climate, the most suitable areas for starry stonewort are predicted to be found in central and southeastern Minnesota. In the future, suitable areas for starry stonewort are predicted to shift in geographic range under some future climate models and to shrink under others, with most permutations indicating a net decrease of the species' suitable range. Our suitability maps can serve to design short-term plans for surveillance and education, while future climate models suggest a plausible reduction of starry stonewort spread in the long-term if the trends in climate warming remain.

Evaluation of NASA SPoRT's Pseudo-Geostationary Lightning Mapper Products in the 2011 Spring Program

NASA Technical Reports Server (NTRS)

Stano, Geoffrey T.; Carcione, Brian; Siewert, Christopher; Kuhlman, Kristin M.

2012-01-01

NASA's Short-term Prediction Research and Transition (SPoRT) program is a contributing partner with the GOES-R Proving Ground (PG) preparing forecasters to understand and utilize the unique products that will be available in the GOES-R era. This presentation emphasizes SPoRT s actions to prepare the end user community for the Geostationary Lightning Mapper (GLM). This preparation is a collaborative effort with SPoRT's National Weather Service partners, the National Severe Storms Laboratory (NSSL), and the Hazardous Weather Testbed s Spring Program. SPoRT continues to use its effective paradigm of matching capabilities to forecast problems through collaborations with our end users and working with the developers at NSSL to create effective evaluations and visualizations. Furthermore, SPoRT continues to develop software plug-ins so that these products will be available to forecasters in their own decision support system, AWIPS and eventually AWIPS II. In 2009, the SPoRT program developed the original pseudo geostationary lightning mapper (PGLM) flash extent product to demonstrate what forecasters may see with GLM. The PGLM replaced the previous GLM product and serves as a stepping-stone until the AWG s official GLM proxy is ready. The PGLM algorithm is simple and can be applied to any ground-based total lightning network. For 2011, the PGLM used observations from four ground-based networks (North Alabama, Kennedy Space Center, Oklahoma, and Washington D.C.). While the PGLM is not a true proxy product, it is intended as a tool to train forecasters about total lightning as well as foster discussions on product visualizations and incorporating GLM-resolution data into forecast operations. The PGLM has been used in 2010 and 2011 and is likely to remain the primary lightning training tool for the GOES-R program for the near future. This presentation will emphasize the feedback received during the 2011 Spring Program. This will discuss several topics. Based on feedback from the 2010 Spring Program, SPoRT created two variant PGLM products, which NSSL produced locally and provided in real-time within AWIPS for 2011. The first is the flash initiation density (FID) product, which creates a gridded display showing the number of flashes that originated in each 8 8 km grid box. The second product is the maximum flash density (MFD). This shows the highest PGLM value for each grid point over a specific period of time, ranging from 30 to 120 minutes. In addition to the evaluation of these two new products, the evaluation of the PGLM itself will be covered. The presentation will conclude with forecaster feedback for additional improvements requested for future evaluations, such as within the 2012 Spring Program.

Fishing for Novel Approaches to Ecosystem Service Forecasts

EPA Science Inventory

The ecosystem service concept provides a powerful framework for conserving species and the environments they depend upon. Describing current distributions of ecosystem services and forecasting their future distributions have therefore become central objectives in many conservati...

The MSFC Solar Activity Future Estimation (MSAFE) Model

NASA Technical Reports Server (NTRS)

Suggs, Ron

2017-01-01

The MSAFE model provides forecasts for the solar indices SSN, F10.7, and Ap. These solar indices are used as inputs to space environment models used in orbital spacecraft operations and space mission analysis. Forecasts from the MSAFE model are provided on the MSFC Natural Environments Branch's solar web page and are updated as new monthly observations become available. The MSAFE prediction routine employs a statistical technique that calculates deviations of past solar cycles from the mean cycle and performs a regression analysis to calculate the deviation from the mean cycle of the solar index at the next future time interval. The forecasts are initiated for a given cycle after about 8 to 9 monthly observations from the start of the cycle are collected. A forecast made at the beginning of cycle 24 using the MSAFE program captured the cycle fairly well with some difficulty in discerning the double peak that occurred at solar cycle maximum.

Assessing and forecasting population health: integrating knowledge and beliefs in a comprehensive framework.

PubMed

Van Meijgaard, Jeroen; Fielding, Jonathan E; Kominski, Gerald F

2009-01-01

A comprehensive population health-forecasting model has the potential to interject new and valuable information about the future health status of the population based on current conditions, socioeconomic and demographic trends, and potential changes in policies and programs. Our Health Forecasting Model uses a continuous-time microsimulation framework to simulate individuals' lifetime histories by using birth, risk exposures, disease incidence, and death rates to mark changes in the state of the individual. The model generates a reference forecast of future health in California, including details on physical activity, obesity, coronary heart disease, all-cause mortality, and medical expenditures. We use the model to answer specific research questions, inform debate on important policy issues in public health, support community advocacy, and provide analysis on the long-term impact of proposed changes in policies and programs, thus informing stakeholders at all levels and supporting decisions that can improve the health of populations.

Towards a better knowledge of flash flood forecasting at the Three Gorges Region: Progress over the past decade and challenges ahead

NASA Astrophysics Data System (ADS)

Li, Zhe; Yang, Dawen; Yang, Hanbo; Wu, Tianjiao; Xu, Jijun; Gao, Bing; Xu, Tao

2015-04-01

The study area, the Three Gorges Region (TGR), plays a critical role in predicting the floods drained into the Three Gorges Reservoir, as reported local floods often exceed 10000m3/s during rainstorm events and trigger fast as well as significant impacts on the Three Gorges Reservoir's regulation. Meanwhile, it is one of typical mountainous areas in China, which is located in the transition zone between two monsoon systems: the East Asian monsoon and the South Asian (Indian) monsoon. This climatic feature, combined with local irregular terrains, has shaped complicated rainfall-runoff regimes in this focal region. However, due to the lack of high-resolution hydrometeorological data and physically-based hydrologic modeling framework, there was little knowledge about rainfall variability and flood pattern in this historically ungauged region, which posed great uncertainties to flash flood forecasting in the past. The present study summarize latest progresses of regional flash floods monitoring and prediction, including installation of a ground-based Hydrometeorological Observation Network (TGR-HMON), application of a regional geomorphology-based hydrological model (TGR-GBHM), development of an integrated forecasting and modeling system (TGR-INFORMS), and evaluation of quantitative precipitation estimations (QPE) and quantitative precipitation forecasting (QPF) products in TGR flash flood forecasting. With these continuing efforts to improve the forecasting performance of flash floods in TGR, we have addressed several critical issues: (1) Current observation network is still insufficient to capture localized rainstorms, and weather radar provides valuable information to forecast flash floods induced by localized rainstorms, although current radar QPE products can be improved substantially in future; (2) Long-term evaluation shows that the geomorphology-based distributed hydrologic model (GBHM) is able to simulate flash flooding processes reasonably, while model performance will decline at hourly scale with larger uncertainties. However, model comparison suggests that this physically-based distributed model (GBHM), compared with a traditional lumped model (Xin'anjiang model), shows more robust performance and larger transferability for prediction in those ungauged basins in TGR; (3) Operational test of our integrated forecasting system (TRG-INFORMS) shows that it works reasonably to simulate the flood routing in Three Gorges reservoir, indicating the accuracy of simulation of total floods generated at region scale; (4) Current operational QPF is too coarse to provide valuable information even for flood forecasting of whole TGR, thus, downscaling and high-resolution QPF are necessary to unravel the potentials of weather forecasting. Finally, according to these results, we also discuss about some possible solutions with high priority for future advanced forecasting scheme of local flash floods in TGR.

Forecasting Trends in Disability in a Super-Aging Society: Adapting the Future Elderly Model to Japan.

PubMed

Chen, Brian K; Jalal, Hawre; Hashimoto, Hideki; Suen, Sze-Chuan; Eggleston, Karen; Hurley, Michael; Schoemaker, Lena; Bhattacharya, Jay

2016-12-01

Japan has experienced pronounced population aging, and now has the highest proportion of elderly adults in the world. Yet few projections of Japan's future demography go beyond estimating population by age and sex to forecast the complex evolution of the health and functioning of the future elderly. This study estimates a new state-transition microsimulation model - the Japanese Future Elderly Model (FEM) - for Japan. We use the model to forecast disability and health for Japan's future elderly. Our simulation suggests that by 2040, over 27 percent of Japan's elderly will exhibit 3 or more limitations in IADLs and social functioning; almost one in 4 will experience difficulties with 3 or more ADLs; and approximately one in 5 will suffer limitations in cognitive or intellectual functioning. Since the majority of the increase in disability arises from the aging of the Japanese population, prevention efforts that reduce age-specific morbidity can help reduce the burden of disability but may have only a limited impact on reducing the overall prevalence of disability among Japanese elderly. While both age and morbidity contribute to a predicted increase in disability burden among elderly Japanese in the future, our simulation results suggest that the impact of population aging exceeds the effect of age-specific morbidity on increasing disability in Japan's future.

Assessing Space Weather Applications and Understanding: IMF Bz at L1

NASA Astrophysics Data System (ADS)

Riley, P.; Savani, N.; Mays, M. L.; Austin, H. J.

2017-12-01

The CCMC - International (CCMC-I) is designed as a self-organizing informal forum for facilitating novel global initiatives on space weather research, development, forecasting and education. Here we capitalize on CCMC'AGUs experience in providing highly utilized web-based services, leadership and trusted relationships with space weather model developers. One of the CCMC-I initiatives is the International Forum for Space Weather Capabilities Assessment. As part of this initiative, within the solar and heliosphere domain, we focus our community discussion on forecasting the magnetic structure of interplanetary CMEs and the ambient solar wind. During the International CCMC-LWS Working Meeting in April 2017 the group instigated open communication to agree upon a standardized process by which all current and future models can be compared under an unbiased test. In this poster, we present our initial findings how we expect different models will move forward with validating and forecasting the magnetic vectors of the solar wind at L1. We also present a new IMF Bz Score-board which will be used to assist in the transitioning of research models into more operational settings.

Bridging the Gap Between Research and Operations in the National Weather Service: The Huntsville Model

NASA Technical Reports Server (NTRS)

Darden, C.; Carroll, B.; Lapenta, W.; Jedlovec, G.; Goodman, S.; Bradshaw, T.; Gordon, J.; Arnold, James E. (Technical Monitor)

2002-01-01

The National Weather Service Office (WFO) in Huntsville, Alabama (HUN) is slated to begin full-time operations in early 2003. With the opening of the Huntsville WFO, a unique opportunity has arisen for close and productive collaboration with scientists at NASA Marshall Space Flight Center (MSFC) and the University of Alabama Huntsville (UAH). As a part of the collaboration effort, NASA has developed the Short-term Prediction Research and Transition (SPoRT) Center. The mission of the SPoRT center is to incorporate NASA earth science technology and research into the NWS operational environment. Emphasis will be on improving mesoscale and short-term forecasting in the first 24 hours of the forecast period. As part of the collaboration effort, the NWS and NASA will develop an implementation and evaluation plan to streamline the integration of the latest technologies and techniques into the operational forecasting environment. The desire of WFO HUN, NASA, and UAH is to provide a model for future collaborative activities between research and operational communities across the country.

Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools.

PubMed

Seto, Karen C; Güneralp, Burak; Hutyra, Lucy R

2012-10-02

Urban land-cover change threatens biodiversity and affects ecosystem productivity through loss of habitat, biomass, and carbon storage. However, despite projections that world urban populations will increase to nearly 5 billion by 2030, little is known about future locations, magnitudes, and rates of urban expansion. Here we develop spatially explicit probabilistic forecasts of global urban land-cover change and explore the direct impacts on biodiversity hotspots and tropical carbon biomass. If current trends in population density continue and all areas with high probabilities of urban expansion undergo change, then by 2030, urban land cover will increase by 1.2 million km(2), nearly tripling the global urban land area circa 2000. This increase would result in considerable loss of habitats in key biodiversity hotspots, with the highest rates of forecasted urban growth to take place in regions that were relatively undisturbed by urban development in 2000: the Eastern Afromontane, the Guinean Forests of West Africa, and the Western Ghats and Sri Lanka hotspots. Within the pan-tropics, loss in vegetation biomass from areas with high probability of urban expansion is estimated to be 1.38 PgC (0.05 PgC yr(-1)), equal to ∼5% of emissions from tropical deforestation and land-use change. Although urbanization is often considered a local issue, the aggregate global impacts of projected urban expansion will require significant policy changes to affect future growth trajectories to minimize global biodiversity and vegetation carbon losses.

Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools

PubMed Central

Seto, Karen C.; Güneralp, Burak; Hutyra, Lucy R.

2012-01-01

Urban land-cover change threatens biodiversity and affects ecosystem productivity through loss of habitat, biomass, and carbon storage. However, despite projections that world urban populations will increase to nearly 5 billion by 2030, little is known about future locations, magnitudes, and rates of urban expansion. Here we develop spatially explicit probabilistic forecasts of global urban land-cover change and explore the direct impacts on biodiversity hotspots and tropical carbon biomass. If current trends in population density continue and all areas with high probabilities of urban expansion undergo change, then by 2030, urban land cover will increase by 1.2 million km2, nearly tripling the global urban land area circa 2000. This increase would result in considerable loss of habitats in key biodiversity hotspots, with the highest rates of forecasted urban growth to take place in regions that were relatively undisturbed by urban development in 2000: the Eastern Afromontane, the Guinean Forests of West Africa, and the Western Ghats and Sri Lanka hotspots. Within the pan-tropics, loss in vegetation biomass from areas with high probability of urban expansion is estimated to be 1.38 PgC (0.05 PgC yr−1), equal to ∼5% of emissions from tropical deforestation and land-use change. Although urbanization is often considered a local issue, the aggregate global impacts of projected urban expansion will require significant policy changes to affect future growth trajectories to minimize global biodiversity and vegetation carbon losses. PMID:22988086

Earth Observations and the Role of UAVs: A Capabilities Assessment. Version 1.1

NASA Technical Reports Server (NTRS)

Cox, Timothy H.; Somers, Ivan; Fratello, David J.

2006-01-01

This document provides an assessment of the civil UAV missions and technologies and is intended to parallel the Office of the Secretary of Defense UAV Roadmap. The intent of this document is four-fold: 1. Determine and document desired future missions of Earth observation UAVs based on user-defined needs 2. Determine and document the technologies necessary to support those missions 3. Discuss the present state of the platform capabilities and required technologies, identifying those in progress, those planned, and those for which no current plans exist 4. Provide the foundations for development of a comprehensive civil UAV roadmap to complement the Department of Defense (DoD) effort (http://www.acq.osd.mil/uas/). Two aspects of the President's Management Agenda (refer to the document located at: www.whitehouse.gov/omb/budget/fy2002/mgmt.pdf ) are supported by this undertaking. First, it is one that will engage multiple Agencies in the effort as stakeholders and benefactors of the systems. In that sense, the market will be driven by the user requirements and applications. The second aspect is one of supporting economic development in the commercial sector. Market forecasts for the civil use of UAVs have indicated an infant market stage at present with a sustained forecasted growth. There is some difficulty in quantifying the value of the market since the typical estimate excludes system components other than the aerial platforms. Section 2.4 addresses the civil UAV market forecast and lists several independent forecasts. One conclusion that can be drawn from these forecasts is that all show a sustained growth for the duration of each long-term forecast.

Anvil Forecast Tool in the Advanced Weather Interactive Processing System (AWIPS)

NASA Technical Reports Server (NTRS)

Barrett, Joe H., III; Hood, Doris

2009-01-01

Launch Weather Officers (LWOs) from the 45th Weather Squadron (45 WS) and forecasters from the National Weather Service (NWS) Spaceflight Meteorology Group (SMG) have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violating the Lightning Launch Commit Criteria (LLCC) (Krider et al. 2006; Space Shuttle Flight Rules (FR), NASA/JSC 2004)). As a result, the Applied Meteorology Unit (AMU) developed a tool that creates an anvil threat corridor graphic that can be overlaid on satellite imagery using the Meteorological Interactive Data Display System (MIDDS, Short and Wheeler, 2002). The tool helps forecasters estimate the locations of thunderstorm anvils at one, two, and three hours into the future. It has been used extensively in launch and landing operations by both the 45 WS and SMG. The Advanced Weather Interactive Processing System (AWIPS) is now used along with MIDDS for weather analysis and display at SMG. In Phase I of this task, SMG tasked the AMU to transition the tool from MIDDS to AWIPS (Barrett et aI., 2007). For Phase II, SMG requested the AMU make the Anvil Forecast Tool in AWIPS more configurable by creating the capability to read model gridded data from user-defined model files instead of hard-coded files. An NWS local AWIPS application called AGRID was used to accomplish this. In addition, SMG needed to be able to define the pressure levels for the model data, instead of hard-coding the bottom level as 300 mb and the top level as 150 mb. This paper describes the initial development of the Anvil Forecast Tool for MIDDS, followed by the migration of the tool to AWIPS in Phase I. It then gives a detailed presentation of the Phase II improvements to the AWIPS tool.

Development of Parallel Code for the Alaska Tsunami Forecast Model

NASA Astrophysics Data System (ADS)

Bahng, B.; Knight, W. R.; Whitmore, P.

2014-12-01

The Alaska Tsunami Forecast Model (ATFM) is a numerical model used to forecast propagation and inundation of tsunamis generated by earthquakes and other means in both the Pacific and Atlantic Oceans. At the U.S. National Tsunami Warning Center (NTWC), the model is mainly used in a pre-computed fashion. That is, results for hundreds of hypothetical events are computed before alerts, and are accessed and calibrated with observations during tsunamis to immediately produce forecasts. ATFM uses the non-linear, depth-averaged, shallow-water equations of motion with multiply nested grids in two-way communications between domains of each parent-child pair as waves get closer to coastal waters. Even with the pre-computation the task becomes non-trivial as sub-grid resolution gets finer. Currently, the finest resolution Digital Elevation Models (DEM) used by ATFM are 1/3 arc-seconds. With a serial code, large or multiple areas of very high resolution can produce run-times that are unrealistic even in a pre-computed approach. One way to increase the model performance is code parallelization used in conjunction with a multi-processor computing environment. NTWC developers have undertaken an ATFM code-parallelization effort to streamline the creation of the pre-computed database of results with the long term aim of tsunami forecasts from source to high resolution shoreline grids in real time. Parallelization will also permit timely regeneration of the forecast model database with new DEMs; and, will make possible future inclusion of new physics such as the non-hydrostatic treatment of tsunami propagation. The purpose of our presentation is to elaborate on the parallelization approach and to show the compute speed increase on various multi-processor systems.

A novel single-parameter approach for forecasting algal blooms.

PubMed

Xiao, Xi; He, Junyu; Huang, Haomin; Miller, Todd R; Christakos, George; Reichwaldt, Elke S; Ghadouani, Anas; Lin, Shengpan; Xu, Xinhua; Shi, Jiyan

2017-01-01

Harmful algal blooms frequently occur globally, and forecasting could constitute an essential proactive strategy for bloom control. To decrease the cost of aquatic environmental monitoring and increase the accuracy of bloom forecasting, a novel single-parameter approach combining wavelet analysis with artificial neural networks (WNN) was developed and verified based on daily online monitoring datasets of algal density in the Siling Reservoir, China and Lake Winnebago, U.S.A. Firstly, a detailed modeling process was illustrated using the forecasting of cyanobacterial cell density in the Chinese reservoir as an example. Three WNN models occupying various prediction time intervals were optimized through model training using an early stopped training approach. All models performed well in fitting historical data and predicting the dynamics of cyanobacterial cell density, with the best model predicting cyanobacteria density one-day ahead (r = 0.986 and mean absolute error = 0.103 × 10 4  cells mL -1 ). Secondly, the potential of this novel approach was further confirmed by the precise predictions of algal biomass dynamics measured as chl a in both study sites, demonstrating its high performance in forecasting algal blooms, including cyanobacteria as well as other blooming species. Thirdly, the WNN model was compared to current algal forecasting methods (i.e. artificial neural networks, autoregressive integrated moving average model), and was found to be more accurate. In addition, the application of this novel single-parameter approach is cost effective as it requires only a buoy-mounted fluorescent probe, which is merely a fraction (∼15%) of the cost of a typical auto-monitoring system. As such, the newly developed approach presents a promising and cost-effective tool for the future prediction and management of harmful algal blooms. Copyright © 2016 Elsevier Ltd. All rights reserved.

Decision Theory: Individual Biases and Their Effect on Forecasting in an Organization.

DTIC Science & Technology

1983-12-01

has not bien a great deal written about how these biases effact decisicns in an organizational environment . The purpcse of -:his thesis is tc examine...and prospers while using fallible information to infer the stateb of his uncer- tain environment and to pr.dict future events. Experiments that have...chapters deal with data from two separate crganizati-ons in two different environments . The Judgmental processes of forecasTing future organizational

Time Relevance of Convective Weather Forecast for Air Traffic Automation

NASA Technical Reports Server (NTRS)

Chan, William N.

2006-01-01

The Federal Aviation Administration (FAA) is handling nearly 120,000 flights a day through its Air Traffic Management (ATM) system and air traffic congestion is expected to increse substantially over the next 20 years. Weather-induced impacts to throughput and efficiency are the leading cause of flight delays accounting for 70% of all delays with convective weather accounting for 60% of all weather related delays. To support the Next Generation Air Traffic System goal of operating at 3X current capacity in the NAS, ATC decision support tools are being developed to create advisories to assist controllers in all weather constraints. Initial development of these decision support tools did not integrate information regarding weather constraints such as thunderstorms and relied on an additional system to provide that information. Future Decision Support Tools should move towards an integrated system where weather constraints are factored into the advisory of a Decision Support Tool (DST). Several groups such at NASA-Ames, Lincoln Laboratories, and MITRE are integrating convective weather data with DSTs. A survey of current convective weather forecast and observation data show they span a wide range of temporal and spatial resolutions. Short range convective observations can be obtained every 5 mins with longer range forecasts out to several days updated every 6 hrs. Today, the short range forecasts of less than 2 hours have a temporal resolution of 5 mins. Beyond 2 hours, forecasts have much lower temporal. resolution of typically 1 hour. Spatial resolutions vary from 1km for short range to 40km for longer range forecasts. Improving the accuracy of long range convective forecasts is a major challenge. A report published by the National Research Council states improvements for convective forecasts for the 2 to 6 hour time frame will only be achieved for a limited set of convective phenomena in the next 5 to 10 years. Improved longer range forecasts will be probabilistic as opposed to the deterministic shorter range forecasts. Despite the known low level of confidence with respect to long range convective forecasts, these data are still useful to a DST routing algorithm. It is better to develop an aircraft route using the best information available than no information. The temporally coarse long range forecast data needs to be interpolated to be useful to a DST. A DST uses aircraft trajectory predictions that need to be evaluated for impacts by convective storms. Each time-step of a trajectory prediction n&s to be checked against weather data. For the case of coarse temporal data, there needs to be a method fill in weather data where there is none. Simply using the coarse weather data without any interpolation can result in DST routes that are impacted by regions of strong convection. Increasing the temporal resolution of these data can be achieved but result in a large dataset that may prove to be an operational challenge in transmission and loading by a DST. Currently, it takes about 7mins retrieve a 7mb RUC2 forecast file from NOAA at NASA-Ames Research Center. A prototype NCWF6 1 hour forecast is about 3mb in size. A Six hour NCWFG forecast with a 1hr forecast time-step will be about l8mb (6 x 3mb). A 6 hour NCWF6 forecast with a l5min forecast time-step will be about 7mb (24 x 3mb). Based on the time it takes to retrieve a 7mb RUC2 forecast, it will take approximately 70mins to retrieve a 6 hour NCWF forecast with 15min time steps. Until those issues are addressed, there is a need to develop an algorithm that interpolates between these temporally coarse long range forecasts. This paper describes a method of how to use low temporal resolution probabilistic weather forecasts in a DST. The beginning of this paper is a description of some convective weather forecast and observation products followed by an example of how weather data are used by a DST. The subsequent sections will describe probabilistic forecasts followed by a descrtion of a method to use low temporal resolution probabilistic weather forecasts by providing a relevance value to these data outside of their valid times.

Motivated prediction of future feelings: effects of negative mood and mood orientation on affective forecasts.

PubMed

Buehler, Roger; McFarland, Cathy; Spyropoulos, Vassili; Lam, Kent C H

2007-09-01

This article examines the role of motivational factors in affective forecasting. The primary hypothesis was that people predict positive emotional reactions to future events when they are motivated to enhance their current feelings. Three experiments manipulated participants' moods (negative vs. neutral) and orientation toward their moods (reflective vs. ruminative) and then assessed the positivity of their affective predictions for future events. As hypothesized, when participants adopted a reflective orientation, and thus should have been motivated to engage in mood-regulation processes, they predicted more positive feelings in the negative than in the neutral mood condition. This pattern of mood-incongruent affective prediction was not exhibited when participants adopted a ruminative orientation. Additionally, within the negative mood condition, generating affective forecasts had a more positive emotional impact on reflectors than on ruminators. The findings suggest that affective predictions are sometimes driven by mood-regulatory motives.

Past speculations of the future: a review of the methods used for forecasting emerging health technologies

PubMed Central

Doos, Lucy; Packer, Claire; Ward, Derek; Simpson, Sue; Stevens, Andrew

2016-01-01

Objectives Forecasting can support rational decision-making around the introduction and use of emerging health technologies and prevent investment in technologies that have limited long-term potential. However, forecasting methods need to be credible. We performed a systematic search to identify the methods used in forecasting studies to predict future health technologies within a 3–20-year timeframe. Identification and retrospective assessment of such methods potentially offer a route to more reliable prediction. Design Systematic search of the literature to identify studies reported on methods of forecasting in healthcare. Participants People are not needed in this study. Data sources The authors searched MEDLINE, EMBASE, PsychINFO and grey literature sources, and included articles published in English that reported their methods and a list of identified technologies. Main outcome measure Studies reporting methods used to predict future health technologies within a 3–20-year timeframe with an identified list of individual healthcare technologies. Commercially sponsored reviews, long-term futurology studies (with over 20-year timeframes) and speculative editorials were excluded. Results 15 studies met our inclusion criteria. Our results showed that the majority of studies (13/15) consulted experts either alone or in combination with other methods such as literature searching. Only 2 studies used more complex forecasting tools such as scenario building. Conclusions The methodological fundamentals of formal 3–20-year prediction are consistent but vary in details. Further research needs to be conducted to ascertain if the predictions made were accurate and whether accuracy varies by the methods used or by the types of technologies identified. PMID:26966060

How is the weather? Forecasting inpatient glycemic control

PubMed Central

Saulnier, George E; Castro, Janna C; Cook, Curtiss B; Thompson, Bithika M

2017-01-01

Aim: Apply methods of damped trend analysis to forecast inpatient glycemic control. Method: Observed and calculated point-of-care blood glucose data trends were determined over 62 weeks. Mean absolute percent error was used to calculate differences between observed and forecasted values. Comparisons were drawn between model results and linear regression forecasting. Results: The forecasted mean glucose trends observed during the first 24 and 48 weeks of projections compared favorably to the results provided by linear regression forecasting. However, in some scenarios, the damped trend method changed inferences compared with linear regression. In all scenarios, mean absolute percent error values remained below the 10% accepted by demand industries. Conclusion: Results indicate that forecasting methods historically applied within demand industries can project future inpatient glycemic control. Additional study is needed to determine if forecasting is useful in the analyses of other glucometric parameters and, if so, how to apply the techniques to quality improvement. PMID:29134125

Forecasting the absolute and relative shortage of physicians in Japan using a system dynamics model approach

PubMed Central

2013-01-01

Background In Japan, a shortage of physicians, who serve a key role in healthcare provision, has been pointed out as a major medical issue. The healthcare workforce policy planner should consider future dynamic changes in physician numbers. The purpose of this study was to propose a physician supply forecasting methodology by applying system dynamics modeling to estimate future absolute and relative numbers of physicians. Method We constructed a forecasting model using a system dynamics approach. Forecasting the number of physician was performed for all clinical physician and OB/GYN specialists. Moreover, we conducted evaluation of sufficiency for the number of physicians and sensitivity analysis. Result & conclusion As a result, it was forecast that the number of physicians would increase during 2008–2030 and the shortage would resolve at 2026 for all clinical physicians. However, the shortage would not resolve for the period covered. This suggests a need for measures for reconsidering the allocation system of new entry physicians to resolve maldistribution between medical departments, in addition, for increasing the overall number of clinical physicians. PMID:23981198

Long-term flow forecasts based on climate and hydrologic modeling: Uruguay River basin

NASA Astrophysics Data System (ADS)

Tucci, Carlos Eduardo Morelli; Clarke, Robin Thomas; Collischonn, Walter; da Silva Dias, Pedro Leite; de Oliveira, Gilvan Sampaio

2003-07-01

This paper describes a procedure for predicting seasonal flow in the Rio Uruguay drainage basin (area 75,000 km2, lying in Brazilian territory), using sequences of future daily rainfall given by the global climate model (GCM) of the Brazilian agency for climate prediction (Centro de Previsão de Tempo e Clima, or CPTEC). Sequences of future daily rainfall given by this model were used as input to a rainfall-runoff model appropriate for large drainage basins. Forecasts of flow in the Rio Uruguay were made for the period 1995-2001 of the full record, which began in 1940. Analysis showed that GCM forecasts underestimated rainfall over almost all the basin, particularly in winter, although interannual variability in regional rainfall was reproduced relatively well. A statistical procedure was used to correct for the underestimation of rainfall. When the corrected rainfall sequences were transformed to flow by the hydrologic model, forecasts of flow in the Rio Uruguay basin were better than forecasts based on historic mean or median flows by 37% for monthly flows and by 54% for 3-monthly flows.

Forecasting the absolute and relative shortage of physicians in Japan using a system dynamics model approach.

PubMed

Ishikawa, Tomoki; Ohba, Hisateru; Yokooka, Yuki; Nakamura, Kozo; Ogasawara, Katsuhiko

2013-08-27

In Japan, a shortage of physicians, who serve a key role in healthcare provision, has been pointed out as a major medical issue. The healthcare workforce policy planner should consider future dynamic changes in physician numbers. The purpose of this study was to propose a physician supply forecasting methodology by applying system dynamics modeling to estimate future absolute and relative numbers of physicians. We constructed a forecasting model using a system dynamics approach. Forecasting the number of physician was performed for all clinical physician and OB/GYN specialists. Moreover, we conducted evaluation of sufficiency for the number of physicians and sensitivity analysis. As a result, it was forecast that the number of physicians would increase during 2008-2030 and the shortage would resolve at 2026 for all clinical physicians. However, the shortage would not resolve for the period covered. This suggests a need for measures for reconsidering the allocation system of new entry physicians to resolve maldistribution between medical departments, in addition, for increasing the overall number of clinical physicians.

Proceedings of the First National Workshop on the Global Weather Experiment: Current Achievements and Future Directions, volume 2, part 1

NASA Technical Reports Server (NTRS)

1985-01-01

Topics covered include: data systems and quality; analysis and assimilation techniques; impacts on forecasts; tropical forecasts; analysis intercomparisons; improvements in predictability; and heat sources and sinks.

Using a safety forecast model to calculate future safety metrics.

DOT National Transportation Integrated Search

2017-05-01

This research sought to identify a process to improve long-range planning prioritization by using forecasted : safety metrics in place of the existing Utah Department of Transportation Safety Indexa metric based on historical : crash data. The res...

Forecasting the duration of volcanic eruptions: an empirical probabilistic model

NASA Astrophysics Data System (ADS)

Gunn, L. S.; Blake, S.; Jones, M. C.; Rymer, H.

2014-01-01

The ability to forecast future volcanic eruption durations would greatly benefit emergency response planning prior to and during a volcanic crises. This paper introduces a probabilistic model to forecast the duration of future and on-going eruptions. The model fits theoretical distributions to observed duration data and relies on past eruptions being a good indicator of future activity. A dataset of historical Mt. Etna flank eruptions is presented and used to demonstrate the model. The data have been compiled through critical examination of existing literature along with careful consideration of uncertainties on reported eruption start and end dates between the years 1300 AD and 2010. Data following 1600 is considered to be reliable and free of reporting biases. The distribution of eruption duration between the years 1600 and 1669 is found to be statistically different from that following it and the forecasting model is run on two datasets of Mt. Etna flank eruption durations: 1600-2010 and 1670-2010. Each dataset is modelled using a log-logistic distribution with parameter values found by maximum likelihood estimation. Survivor function statistics are applied to the model distributions to forecast (a) the probability of an eruption exceeding a given duration, (b) the probability of an eruption that has already lasted a particular number of days exceeding a given total duration and (c) the duration with a given probability of being exceeded. Results show that excluding the 1600-1670 data has little effect on the forecasting model result, especially where short durations are involved. By assigning the terms `likely' and `unlikely' to probabilities of 66 % or more and 33 % or less, respectively, the forecasting model based on the 1600-2010 dataset indicates that a future flank eruption on Mt. Etna would be likely to exceed 20 days (± 7 days) but unlikely to exceed 86 days (± 29 days). This approach can easily be adapted for use on other highly active, well-documented volcanoes or for different duration data such as the duration of explosive episodes or the duration of repose periods between eruptions.

Emotional Intelligence: A Theoretical Framework for Individual Differences in Affective Forecasting

PubMed Central

Hoerger, Michael; Chapman, Benjamin P.; Epstein, Ronald M.; Duberstein, Paul R.

2011-01-01

Only recently have researchers begun to examine individual differences in affective forecasting. The present investigation was designed to make a theoretical contribution to this emerging literature by examining the role of emotional intelligence in affective forecasting. Emotional intelligence was hypothesized to be associated with affective forecasting accuracy, memory for emotional reactions, and subsequent improvement on an affective forecasting task involving emotionally-evocative pictures. Results from two studies (N = 511) supported our hypotheses. Emotional intelligence was associated with accuracy in predicting, encoding, and consolidating emotional reactions. Furthermore, emotional intelligence was associated with greater improvement on a second affective forecasting task, with the relationship explained by basic memory processes. Implications for future research on basic and applied decision making are discussed. PMID:22251053

Performance of univariate forecasting on seasonal diseases: the case of tuberculosis.

PubMed

Permanasari, Adhistya Erna; Rambli, Dayang Rohaya Awang; Dominic, P Dhanapal Durai

2011-01-01

The annual disease incident worldwide is desirable to be predicted for taking appropriate policy to prevent disease outbreak. This chapter considers the performance of different forecasting method to predict the future number of disease incidence, especially for seasonal disease. Six forecasting methods, namely linear regression, moving average, decomposition, Holt-Winter's, ARIMA, and artificial neural network (ANN), were used for disease forecasting on tuberculosis monthly data. The model derived met the requirement of time series with seasonality pattern and downward trend. The forecasting performance was compared using similar error measure in the base of the last 5 years forecast result. The findings indicate that ARIMA model was the most appropriate model since it obtained the less relatively error than the other model.

Emotional intelligence: a theoretical framework for individual differences in affective forecasting.

PubMed

Hoerger, Michael; Chapman, Benjamin P; Epstein, Ronald M; Duberstein, Paul R

2012-08-01

Only recently have researchers begun to examine individual differences in affective forecasting. The present investigation was designed to make a theoretical contribution to this emerging literature by examining the role of emotional intelligence in affective forecasting. Emotional intelligence was hypothesized to be associated with affective forecasting accuracy, memory for emotional reactions, and subsequent improvement on an affective forecasting task involving emotionally evocative pictures. Results from two studies (N = 511) supported our hypotheses. Emotional intelligence was associated with accuracy in predicting, encoding, and consolidating emotional reactions. Furthermore, emotional intelligence was associated with greater improvement on a second affective forecasting task, with the relationship explained by basic memory processes. Implications for future research on basic and applied decision making are discussed.

Real time soil moisture forecasts for irrigation management: the Pre.G.I. project

NASA Astrophysics Data System (ADS)

Ceppi, A.; Ravazzani, G.; Mancini, M.; Salerno, R.

2012-04-01

In recent years frequent periods of water scarcity have enhanced the need to use water more carefully. Future climate change scenarios, combined with limited water resources require better irrigation management and planning for farmers' water cooperatives. This has occurred also in areas traditionally rich of water as Lombardy Region, in the North of Italy. In this study we show the development and implementation of a real-time drought forecasting system with a soil moisture hydrological alert, in particular we describe preliminary results of the Pre.G.I. Project, an Italian acronym that stands for "Hydro-Meteorological forecast for irrigation management", funded by Lombardy Region. The project develops a support decision system based on an ensemble weather prediction in the medium-long range (up to 30 days) with hydrological simulation of water balance to forecast the soil water content in every parcel over the Consorzio Muzza basin, in order to use the irrigation water in a wiser and thriftier way. The studied area covers 74,000 ha in the middle of the Po Valley, near Lodi city. The hydrological ensemble forecasts are based on 20 meteorological members of a modified version of the non-hydrostatic WRF model, with multiple nesting to scale to the region of interest. Different physical schemes are also used to take into account a larger variability; these data are provided by Epson Meteo Centre. The hydrological model used to generate the soil moisture and water table simulations is the rainfall-runoff distributed FEST-WB model, developed at Politecnico di Milano. The analysis shows the system reliability based on most significant case-studies occurred in the recent years.

The Role of Secondary Frontal Waves in Causing Missed or False Alarm Flood Forecasts During Landfalling Atmospheric Rivers

NASA Astrophysics Data System (ADS)

Martin, A.; Ralph, F. M.; Lavers, D. A.; Kalansky, J.; Kawzenuk, B.

2015-12-01

The previous ten years has seen an explosion in research devoted to the Atmospheric River (AR) phenomena, features of the midlatitude circulation responsible for large horizontal water vapor transport. Upon landfall, ARs can be associated with 30-50% of annual precipitation in some regions, while also causing the largest flooding events in places such as coastal California. Little discussed is the role secondary frontal waves play in modulating precipitation during a landfalling AR. Secondary frontal waves develop along an existing cold front in response to baroclinic frontogenesis, often coinciding with a strong upper-tropospheric jet. If the secondary wave develops along a front associated with a landfalling AR, the resulting precipitation may be much greater or much less than originally forecasted - especially in regions where orographic uplift of horizontally transported water vapor is responsible for a large portion of precipitation. In this study, we present several cases of secondary frontal waves that have occurred in conjunction with a landfalling AR on the US West Coast. We put the impact of these cases in historical perspective using quantitative precipitation forecasts, satellite data, reanalyses, and estimates of damage related to flooding. We also discuss the dynamical mechanisms behind secondary frontal wave development and relate these mechanisms to the high spatiotemporal variability in precipitation observed during ARs with secondary frontal waves. Finally, we demonstrate that even at lead times less than 24 hours, current quantitative precipitation forecasting methods have difficulty accurately predicting the rainfall in the area near the secondary wave landfall, in some cases leading to missed or false alarm flood warnings, and suggest methods which may improve quantitative precipitation forecasts for this type of system in the future.

Operational Monitoring and Forecasting in Regional Seas: the Aegean Sea example

NASA Astrophysics Data System (ADS)

Nittis, K.; Perivoliotis, L.; Zervakis, V.; Papadopoulos, A.; Tziavos, C.

2003-04-01

The increasing economic activities in the coastal zone and the associated pressure on the marine environment have raised the interest on monitoring systems able to provide supporting information for its effective management and protection. Such an integrated monitoring, forecasting and information system is being developed during the past years in the Aegean Sea. Its main component is the POSEIDON network that provides real-time data for meteorological and surface oceanographic parameters (waves, currents, hydrological and biochemical data) from 11 fixed oceanographic buoys. The numerical forecasting system is composed by an ETA atmospheric model, a WAM wave model and a POM hydrodynamic model that provide every day 72 hours forecasts. The system is operational since May 2000 and its products are published through Internet while a sub-set is also available through cellular telephony. New type of observing platforms will be available in the near future through a number of EU funded research projects. The Mediterranean Moored Multi-sensor Array (M3A) that was developed for the needs of the Mediterranean Forecasting System and was tested during 2000-2001 will be operational in 2004 during the MFSTEP project. The M3A system incorporates sensors for optical and chemical measurements (Oxygen, Turbidity, Chlorophyll-a, Nutrients and PAR) in the euphotic zone (0-100m) together with sensors for physical parameters (Temperature, Salinity, Current speed and direction) at the 0-500m layer. A Ferry-Box system will also operate during 2004 in the southern Aegean Sea, providing surface data for physical and bio-chemical properties. The ongoing modeling efforts include coupling with larger scale circulation models of the Mediterranean, high-resolution downscaling to coastal areas of the Aegean Sea and development of multi-variate data assimilation methods.

Earthquake Forecasting Methodology Catalogue - A collection and comparison of the state-of-the-art in earthquake forecasting and prediction methodologies

NASA Astrophysics Data System (ADS)

Schaefer, Andreas; Daniell, James; Wenzel, Friedemann

2015-04-01

Earthquake forecasting and prediction has been one of the key struggles of modern geosciences for the last few decades. A large number of approaches for various time periods have been developed for different locations around the world. A categorization and review of more than 20 of new and old methods was undertaken to develop a state-of-the-art catalogue in forecasting algorithms and methodologies. The different methods have been categorised into time-independent, time-dependent and hybrid methods, from which the last group represents methods where additional data than just historical earthquake statistics have been used. It is necessary to categorize in such a way between pure statistical approaches where historical earthquake data represents the only direct data source and also between algorithms which incorporate further information e.g. spatial data of fault distributions or which incorporate physical models like static triggering to indicate future earthquakes. Furthermore, the location of application has been taken into account to identify methods which can be applied e.g. in active tectonic regions like California or in less active continental regions. In general, most of the methods cover well-known high-seismicity regions like Italy, Japan or California. Many more elements have been reviewed, including the application of established theories and methods e.g. for the determination of the completeness magnitude or whether the modified Omori law was used or not. Target temporal scales are identified as well as the publication history. All these different aspects have been reviewed and catalogued to provide an easy-to-use tool for the development of earthquake forecasting algorithms and to get an overview in the state-of-the-art.

An Optimization of Inventory Demand Forecasting in University Healthcare Centre

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

Parcel-scale urban coastal flood mapping: Leveraging the multi-scale CoSMoS model for coastal flood forecasting

NASA Astrophysics Data System (ADS)

Gallien, T.; Barnard, P. L.; Sanders, B. F.

2011-12-01

California coastal sea levels are projected to rise 1-1.4 meters in the next century and evidence suggests mean tidal range, and consequently, mean high water (MHW) is increasing along portions of Southern California Bight. Furthermore, emerging research indicates wind stress patterns associated with the Pacific Decadal Oscillation (PDO) have suppressed sea level rise rates along the West Coast since 1980, and a reversal in this pattern would result in the resumption of regional sea level rise rates equivalent to or exceeding global mean sea level rise rates, thereby enhancing coastal flooding. Newport Beach is a highly developed, densely populated lowland along the Southern California coast currently subject to episodic flooding from coincident high tides and waves, and the frequency and intensity of flooding is expected to increase with projected future sea levels. Adaptation to elevated sea levels will require flood mapping and forecasting tools that are sensitive to the dominant factors affecting flooding including extreme high tides, waves and flood control infrastructure. Considerable effort has been focused on the development of nowcast and forecast systems including Scripps Institute of Oceanography's Coastal Data Information Program (CDIP) and the USGS Multi-hazard model, the Southern California Coastal Storm Modeling System (CoSMoS). However, fine scale local embayment dynamics and overtopping flows are needed to map unsteady flooding effects in coastal lowlands protected by dunes, levees and seawalls. Here, a recently developed two dimensional Godunov non-linear shallow water solver is coupled to water level and wave forecasts from the CoSMoS model to investigate the roles of tides, waves, sea level changes and flood control infrastructure in accurate flood mapping and forecasting. The results of this study highlight the important roles of topographic data, embayment hydrodynamics, water level uncertainties and critical flood processes required for meaningful prediction of sea level rise impacts and coastal flood forecasting.

Multivariate time series modeling of short-term system scale irrigation demand

NASA Astrophysics Data System (ADS)

Perera, Kushan C.; Western, Andrew W.; George, Biju; Nawarathna, Bandara

2015-12-01

Travel time limits the ability of irrigation system operators to react to short-term irrigation demand fluctuations that result from variations in weather, including very hot periods and rainfall events, as well as the various other pressures and opportunities that farmers face. Short-term system-wide irrigation demand forecasts can assist in system operation. Here we developed a multivariate time series (ARMAX) model to forecast irrigation demands with respect to aggregated service points flows (IDCGi, ASP) and off take regulator flows (IDCGi, OTR) based across 5 command areas, which included area covered under four irrigation channels and the study area. These command area specific ARMAX models forecast 1-5 days ahead daily IDCGi, ASP and IDCGi, OTR using the real time flow data recorded at the service points and the uppermost regulators and observed meteorological data collected from automatic weather stations. The model efficiency and the predictive performance were quantified using the root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE), anomaly correlation coefficient (ACC) and mean square skill score (MSSS). During the evaluation period, NSE for IDCGi, ASP and IDCGi, OTR across 5 command areas were ranged 0.98-0.78. These models were capable of generating skillful forecasts (MSSS ⩾ 0.5 and ACC ⩾ 0.6) of IDCGi, ASP and IDCGi, OTR for all 5 lead days and IDCGi, ASP and IDCGi, OTR forecasts were better than using the long term monthly mean irrigation demand. Overall these predictive performance from the ARMAX time series models were higher than almost all the previous studies we are aware. Further, IDCGi, ASP and IDCGi, OTR forecasts have improved the operators' ability to react for near future irrigation demand fluctuations as the developed ARMAX time series models were self-adaptive to reflect the short-term changes in the irrigation demand with respect to various pressures and opportunities that farmers' face, such as changing water policy, continued development of water markets, drought and changing technology.

Probabilistic accounting of uncertainty in forecasts of species distributions under climate change

USGS Publications Warehouse

Wenger, Seth J.; Som, Nicholas A.; Dauwalter, Daniel C.; Isaak, Daniel J.; Neville, Helen M.; Luce, Charles H.; Dunham, Jason B.; Young, Michael K.; Fausch, Kurt D.; Rieman, Bruce E.

2013-01-01

Forecasts of species distributions under future climates are inherently uncertain, but there have been few attempts to describe this uncertainty comprehensively in a probabilistic manner. We developed a Monte Carlo approach that accounts for uncertainty within generalized linear regression models (parameter uncertainty and residual error), uncertainty among competing models (model uncertainty), and uncertainty in future climate conditions (climate uncertainty) to produce site-specific frequency distributions of occurrence probabilities across a species’ range. We illustrated the method by forecasting suitable habitat for bull trout (Salvelinus confluentus) in the Interior Columbia River Basin, USA, under recent and projected 2040s and 2080s climate conditions. The 95% interval of total suitable habitat under recent conditions was estimated at 30.1–42.5 thousand km; this was predicted to decline to 0.5–7.9 thousand km by the 2080s. Projections for the 2080s showed that the great majority of stream segments would be unsuitable with high certainty, regardless of the climate data set or bull trout model employed. The largest contributor to uncertainty in total suitable habitat was climate uncertainty, followed by parameter uncertainty and model uncertainty. Our approach makes it possible to calculate a full distribution of possible outcomes for a species, and permits ready graphical display of uncertainty for individual locations and of total habitat.

Swainson's Thrushes do not show strong wind selectivity prior to crossing the Gulf of Mexico.

PubMed

Bolus, Rachel T; Diehl, Robert H; Moore, Frank R; Deppe, Jill L; Ward, Michael P; Smolinsky, Jaclyn; Zenzal, Theodore J

2017-10-27

During long-distance fall migrations, nocturnally migrating Swainson's Thrushes often stop on the northern Gulf of Mexico coast before flying across the Gulf. To minimize energetic costs, trans-Gulf migrants should stop over when they encounter crosswinds or headwinds, and depart with supportive tailwinds. However, time constrained migrants should be less selective, balancing costs of headwinds with benefits of continuing their migrations. To test the hypotheses that birds select supportive winds and that selectivity is mediated by seasonal time constraints, we examined whether local winds affected Swainson's Thrushes' arrival and departure at Ft. Morgan, Alabama, USA at annual, seasonal, and nightly time scales. Additionally, migrants could benefit from forecasting future wind conditions, crossing on nights when winds are consistently supportive across the Gulf, thereby avoiding the potentially lethal consequences of depleting their energetic reserves over water. To test whether birds forecast, we developed a movement model, calculated to what extent departure winds were predictive of future Gulf winds, and tested whether birds responded to predictability. Swainson's Thrushes were only slightly selective and did not appear to forecast. By following the simple rule of avoiding only the strongest headwinds at departure, Swainson's Thrushes could survive the 1500 km flight between Alabama and Veracruz, Mexico.

Forecasting Occurrences of Activities.

PubMed

Minor, Bryan; Cook, Diane J

2017-07-01

While activity recognition has been shown to be valuable for pervasive computing applications, less work has focused on techniques for forecasting the future occurrence of activities. We present an activity forecasting method to predict the time that will elapse until a target activity occurs. This method generates an activity forecast using a regression tree classifier and offers an advantage over sequence prediction methods in that it can predict expected time until an activity occurs. We evaluate this algorithm on real-world smart home datasets and provide evidence that our proposed approach is most effective at predicting activity timings.

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

PubMed Central

2016-01-01

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

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

PubMed

Chan Phooi M'ng, Jacinta; Mehralizadeh, Mohammadali

2016-01-01

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

A Photo Storm Report Mobile Application, Processing/Distribution System, and AWIPS-II Display Concept

NASA Astrophysics Data System (ADS)

Longmore, S. P.; Bikos, D.; Szoke, E.; Miller, S. D.; Brummer, R.; Lindsey, D. T.; Hillger, D.

2014-12-01

The increasing use of mobile phones equipped with digital cameras and the ability to post images and information to the Internet in real-time has significantly improved the ability to report events almost instantaneously. In the context of severe weather reports, a representative digital image conveys significantly more information than a simple text or phone relayed report to a weather forecaster issuing severe weather warnings. It also allows the forecaster to reasonably discern the validity and quality of a storm report. Posting geo-located, time stamped storm report photographs utilizing a mobile phone application to NWS social media weather forecast office pages has generated recent positive feedback from forecasters. Building upon this feedback, this discussion advances the concept, development, and implementation of a formalized Photo Storm Report (PSR) mobile application, processing and distribution system and Advanced Weather Interactive Processing System II (AWIPS-II) plug-in display software.The PSR system would be composed of three core components: i) a mobile phone application, ii) a processing and distribution software and hardware system, and iii) AWIPS-II data, exchange and visualization plug-in software. i) The mobile phone application would allow web-registered users to send geo-location, view direction, and time stamped PSRs along with severe weather type and comments to the processing and distribution servers. ii) The servers would receive PSRs, convert images and information to NWS network bandwidth manageable sizes in an AWIPS-II data format, distribute them on the NWS data communications network, and archive the original PSRs for possible future research datasets. iii) The AWIPS-II data and exchange plug-ins would archive PSRs, and the visualization plug-in would display PSR locations, times and directions by hour, similar to surface observations. Hovering on individual PSRs would reveal photo thumbnails and clicking on them would display the full resolution photograph.Here, we present initial NWS forecaster feedback received from social media posted PSRs, motivating the possible advantages of PSRs within AWIPS-II, the details of developing and implementing a PSR system, and possible future applications beyond severe weather reports and AWIPS-II.

Neither Feast nor Famine: Summary of the Second Twenty Year Forecast Project.

ERIC Educational Resources Information Center

Enzer, Selwyn; And Others

1977-01-01

The key question to ask in determining whether a solution will be found to the world food problem is whether people will learn to effectively manage the food/population balance. Predictions concerning the world food situation should be made on the basis of these factors: (1) possible future changes involving technological development, political…

The NASA Short-Term Prediction Research and Transition (SPoRT) Center: Opportunities for Collaboration in the Great Lakes Region

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.

2010-01-01

The presentation slides include: The SPoRT Center, History and Future of SPoRT, Great Lakes Applications, Great Lakes Forecasting Issues, Applications to the WRF-EMS, Precipitation Science, Lake Effect Precipitation, Sensitivity to Microphysics, Exploring New Schemes, Opportunities for Collaboration, and SPoRT Research and Development.

Hawaii energy strategy: Executive summary, October 1995

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

This is an executive summary to a report on the Hawaii Energy Strategy Program. The topics of the report include the a description of the program including an overview, objectives, policy statement and purpose and objectives; energy strategy policy development; energy strategy projects; current energy situation; modeling Hawaii`s energy future; energy forecasts; reducing energy demand; scenario assessment, and recommendations.

Rate of recovery from perturbations as a means to forecast future stability of living systems.

PubMed

Ghadami, Amin; Gourgou, Eleni; Epureanu, Bogdan I

2018-06-18

Anticipating critical transitions in complex ecological and living systems is an important need because it is often difficult to restore a system to its pre-transition state once the transition occurs. Recent studies demonstrate that several indicators based on changes in ecological time series can indicate that the system is approaching an impending transition. An exciting question is, however, whether we can predict more characteristics of the future system stability using measurements taken away from the transition. We address this question by introducing a model-less forecasting method to forecast catastrophic transition of an experimental ecological system. The experiment is based on the dynamics of a yeast population, which is known to exhibit a catastrophic transition as the environment deteriorates. By measuring the system's response to perturbations prior to transition, we forecast the distance to the upcoming transition, the type of the transition (i.e., catastrophic/non-catastrophic) and the future equilibrium points within a range near the transition. Experimental results suggest a strong potential for practical applicability of this approach for ecological systems which are at risk of catastrophic transitions, where there is a pressing need for information about upcoming thresholds.

Toward Sub-seasonal to Seasonal Arctic Sea Ice Forecasting Using the Regional Arctic System Model (RASM)

NASA Astrophysics Data System (ADS)

Kamal, S.; Maslowski, W.; Roberts, A.; Osinski, R.; Cassano, J. J.; Seefeldt, M. W.

2017-12-01

The Regional Arctic system model has been developed and used to advance the current state of Arctic modeling and increase the skill of sea ice forecast. RASM is a fully coupled, limited-area model that includes the atmosphere, ocean, sea ice, land hydrology and runoff routing components and the flux coupler to exchange information among them. Boundary conditions are derived from NCEP Climate Forecasting System Reanalyses (CFSR) or Era Iterim (ERA-I) for hindcast simulations or from NCEP Coupled Forecast System Model version 2 (CFSv2) for seasonal forecasts. We have used RASM to produce sea ice forecasts for September 2016 and 2017, in contribution to the Sea Ice Outlook (SIO) of the Sea Ice Prediction Network (SIPN). Each year, we produced three SIOs for the September minimum, initialized on June 1, July 1 and August 1. In 2016, predictions used a simple linear regression model to correct for systematic biases and included the mean September sea ice extent, the daily minimum and the week of the minimum. In 2017, we produced a 12-member ensemble on June 1 and July 1, and 28-member ensemble August 1. The predictions of September 2017 included the pan-Arctic and regional Alaskan sea ice extent, daily and monthly mean pan-Arctic maps of sea ice probability, concentration and thickness. No bias correction was applied to the 2017 forecasts. Finally, we will also discuss future plans for RASM forecasts, which include increased resolution for model components, ecosystem predictions with marine biogeochemistry extensions (mBGC) to the ocean and sea ice components, and feasibility of optional boundary conditions using the Navy Global Environmental Model (NAVGEM).

Short-term load forecasting of power system

NASA Astrophysics Data System (ADS)

Xu, Xiaobin

2017-05-01

In order to ensure the scientific nature of optimization about power system, it is necessary to improve the load forecasting accuracy. Power system load forecasting is based on accurate statistical data and survey data, starting from the history and current situation of electricity consumption, with a scientific method to predict the future development trend of power load and change the law of science. Short-term load forecasting is the basis of power system operation and analysis, which is of great significance to unit combination, economic dispatch and safety check. Therefore, the load forecasting of the power system is explained in detail in this paper. First, we use the data from 2012 to 2014 to establish the partial least squares model to regression analysis the relationship between daily maximum load, daily minimum load, daily average load and each meteorological factor, and select the highest peak by observing the regression coefficient histogram Day maximum temperature, daily minimum temperature and daily average temperature as the meteorological factors to improve the accuracy of load forecasting indicators. Secondly, in the case of uncertain climate impact, we use the time series model to predict the load data for 2015, respectively, the 2009-2014 load data were sorted out, through the previous six years of the data to forecast the data for this time in 2015. The criterion for the accuracy of the prediction is the average of the standard deviations for the prediction results and average load for the previous six years. Finally, considering the climate effect, we use the BP neural network model to predict the data in 2015, and optimize the forecast results on the basis of the time series model.

Florida Model Information eXchange System (MIXS).

DOT National Transportation Integrated Search

2013-08-01

Transportation planning largely relies on travel demand forecasting, which estimates the number and type of vehicles that will use a roadway at some point in the future. Forecasting estimates are made by computer models that use a wide variety of dat...

CMB constraints on running non-Gaussianity

NASA Astrophysics Data System (ADS)

Oppizzi, F.; Liguori, M.; Renzi, A.; Arroja, F.; Bartolo, N.

2018-05-01

We develop a complete set of tools for CMB forecasting, simulation and estimation of primordial running bispectra, arising from a variety of curvaton and single-field (DBI) models of Inflation. We validate our pipeline using mock CMB running non-Gaussianity realizations and test it on real data by obtaining experimental constraints on the fNL running spectral index, nNG, using WMAP 9-year data. Our final bounds (68% C.L.) read ‑0.6< nNG<1.4}, ‑0.3< nNG<1.2, ‑1.1

Technology requirements for communication satellites in the 1980's

NASA Technical Reports Server (NTRS)

Burtt, J. E.; Moe, C. R.; Elms, R. V.; Delateur, L. A.; Sedlacek, W. C.; Younger, G. G.

1973-01-01

The key technology requirements are defined for meeting the forecasted demands for communication satellite services in the 1985 to 1995 time frame. Evaluation is made of needs for services and technical and functional requirements for providing services. The future growth capabilities of the terrestrial telephone network, cable television, and satellite networks are forecasted. The impact of spacecraft technology and booster performance and costs upon communication satellite costs are analyzed. Systems analysis techniques are used to determine functional requirements and the sensitivities of technology improvements for reducing the costs of meeting requirements. Recommended development plans and funding levels are presented, as well as the possible cost saving for communications satellites in the post 1985 era.

Satellite temperature monitoring and prediction system

NASA Technical Reports Server (NTRS)

Barnett, U. R.; Martsolf, J. D.; Crosby, F. L.

1980-01-01

The paper describes the Florida Satellite Freeze Forecast System (SFFS) in its current state. All data collection options have been demonstrated, and data collected over a three year period have been stored for future analysis. Presently, specific minimum temperature forecasts are issued routinely from November through March. The procedures for issuing these forecast are discussed. The automated data acquisition and processing system is described, and the physical and statistical models employed are examined.

Ensemble averaging and stacking of ARIMA and GSTAR model for rainfall forecasting

NASA Astrophysics Data System (ADS)

Anggraeni, D.; Kurnia, I. F.; Hadi, A. F.

2018-04-01

Unpredictable rainfall changes can affect human activities, such as in agriculture, aviation, shipping which depend on weather forecasts. Therefore, we need forecasting tools with high accuracy in predicting the rainfall in the future. This research focus on local forcasting of the rainfall at Jember in 2005 until 2016, from 77 rainfall stations. The rainfall here was not only related to the occurrence of the previous of its stations, but also related to others, it’s called the spatial effect. The aim of this research is to apply the GSTAR model, to determine whether there are some correlations of spatial effect between one to another stations. The GSTAR model is an expansion of the space-time model that combines the time-related effects, the locations (stations) in a time series effects, and also the location it self. The GSTAR model will also be compared to the ARIMA model that completely ignores the independent variables. The forcested value of the ARIMA and of the GSTAR models then being combined using the ensemble forecasting technique. The averaging and stacking method of ensemble forecasting method here provide us the best model with higher acuracy model that has the smaller RMSE (Root Mean Square Error) value. Finally, with the best model we can offer a better local rainfall forecasting in Jember for the future.

Jet aircraft emissions during cruise: Present and future

NASA Technical Reports Server (NTRS)

Grobman, J. S.

1975-01-01

Forecasts of engine exhaust emissions that may be practicably achievable for future commercial aircraft operating at high altitude cruise conditions are compared to cruise emission for present day aircraft. The forecasts are based on: (1) knowledge of emission characteristics of combustors and augmentors; (2) combustion research in emission reduction technology, and (3) trends in projected engine designs for advanced subsonic or supersonic commercial aircraft. Recent progress that was made in the evolution of emissions reduction technology is discussed.

Assessing skill of a global bimonthly streamflow ensemble prediction system

NASA Astrophysics Data System (ADS)

van Dijk, A. I.; Peña-Arancibia, J.; Sheffield, J.; Wood, E. F.

2011-12-01

Ideally, a seasonal streamflow forecasting system might be conceived of as a system that ingests skillful climate forecasts from general circulation models and propagates these through thoroughly calibrated hydrological models that are initialised using hydrometric observations. In practice, there are practical problems with each of these aspects. Instead, we analysed whether a comparatively simple hydrological model-based Ensemble Prediction System (EPS) can provide global bimonthly streamflow forecasts with some skill and if so, under what circumstances the greatest skill may be expected. The system tested produces ensemble forecasts for each of six annual bimonthly periods based on the previous 30 years of global daily gridded 1° resolution climate variables and an initialised global hydrological model. To incorporate some of the skill derived from ocean conditions, a post-EPS analog method was used to sample from the ensemble based on El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO) index values observed prior to the forecast. Forecasts skill was assessed through a hind-casting experiment for the period 1979-2008. Potential skill was calculated with reference to a model run with the actual forcing for the forecast period (the 'perfect' model) and was compared to actual forecast skill calculated for each of the six forecast times for an average 411 Australian and 51 pan-tropical catchments. Significant potential skill in bimonthly forecasts was largely limited to northern regions during the snow melt period, seasonally wet tropical regions at the transition of wet to dry season, and the Indonesian region where rainfall is well correlated to ENSO. The actual skill was approximately 34-50% of the potential skill. We attribute this primarily to limitations in the model structure, parameterisation and global forcing data. Use of better climate forecasts and remote sensing observations of initial catchment conditions should help to increase actual skill in future. Future work also could address the potential skill gain from using weather and climate forecasts and from a calibrated and/or alternative hydrological model or model ensemble. The approach and data might be useful as a benchmark for joint seasonal forecasting experiments planned under GEWEX.

Hurricane track forecast cones from fluctuations

PubMed Central

Meuel, T.; Prado, G.; Seychelles, F.; Bessafi, M.; Kellay, H.

2012-01-01

Trajectories of tropical cyclones may show large deviations from predicted tracks leading to uncertainty as to their landfall location for example. Prediction schemes usually render this uncertainty by showing track forecast cones representing the most probable region for the location of a cyclone during a period of time. By using the statistical properties of these deviations, we propose a simple method to predict possible corridors for the future trajectory of a cyclone. Examples of this scheme are implemented for hurricane Ike and hurricane Jimena. The corridors include the future trajectory up to at least 50 h before landfall. The cones proposed here shed new light on known track forecast cones as they link them directly to the statistics of these deviations. PMID:22701776

A Pilot Tsunami Inundation Forecast System for Australia

NASA Astrophysics Data System (ADS)

Allen, Stewart C. R.; Greenslade, Diana J. M.

2016-12-01

The Joint Australian Tsunami Warning Centre (JATWC) provides a tsunami warning service for Australia. Warnings are currently issued according to a technique that does not include explicit modelling at the coastline, including any potential coastal inundation. This paper investigates the feasibility of developing and implementing tsunami inundation modelling as part of the JATWC warning system. An inundation model was developed for a site in Southeast Australia, on the basis of the availability of bathymetric and topographic data and observations of past tsunamis. The model was forced using data from T2, the operational deep-water tsunami scenario database currently used for generating warnings. The model was evaluated not only for its accuracy but also for its computational speed, particularly with respect to operational applications. Limitations of the proposed forecast processes in the Australian context and areas requiring future improvement are discussed.

A study on the utilization of advanced composites in commercial aircraft wing structure

NASA Technical Reports Server (NTRS)

Watts, D. J.

1978-01-01

A study was conducted to define the technology and data needed to support the introduction of advanced composite materials in the wing structure of future production aircraft. The study accomplished the following: (1) definition of acceptance factors, (2) identification of technology issues, (3) evaluation of six candidate wing structures, (4) evaluation of five program options, (5) definition of a composite wing technology development plan, (6) identification of full-scale tests, (7) estimation of program costs for the total development plan, (8) forecast of future utilization of composites in commercial transport aircraft and (9) identification of critical technologies for timely program planning.

Ecosystem Model Skill Assessment. Yes We Can!

PubMed

Olsen, Erik; Fay, Gavin; Gaichas, Sarah; Gamble, Robert; Lucey, Sean; Link, Jason S

2016-01-01

Accelerated changes to global ecosystems call for holistic and integrated analyses of past, present and future states under various pressures to adequately understand current and projected future system states. Ecosystem models can inform management of human activities in a complex and changing environment, but are these models reliable? Ensuring that models are reliable for addressing management questions requires evaluating their skill in representing real-world processes and dynamics. Skill has been evaluated for just a limited set of some biophysical models. A range of skill assessment methods have been reviewed but skill assessment of full marine ecosystem models has not yet been attempted. We assessed the skill of the Northeast U.S. (NEUS) Atlantis marine ecosystem model by comparing 10-year model forecasts with observed data. Model forecast performance was compared to that obtained from a 40-year hindcast. Multiple metrics (average absolute error, root mean squared error, modeling efficiency, and Spearman rank correlation), and a suite of time-series (species biomass, fisheries landings, and ecosystem indicators) were used to adequately measure model skill. Overall, the NEUS model performed above average and thus better than expected for the key species that had been the focus of the model tuning. Model forecast skill was comparable to the hindcast skill, showing that model performance does not degenerate in a 10-year forecast mode, an important characteristic for an end-to-end ecosystem model to be useful for strategic management purposes. We identify best-practice approaches for end-to-end ecosystem model skill assessment that would improve both operational use of other ecosystem models and future model development. We show that it is possible to not only assess the skill of a complicated marine ecosystem model, but that it is necessary do so to instill confidence in model results and encourage their use for strategic management. Our methods are applicable to any type of predictive model, and should be considered for use in fields outside ecology (e.g. economics, climate change, and risk assessment).

Aggregate Auto Travel Forecasting : State of the Art and Suggestions for Future Research

DOT National Transportation Integrated Search

1976-12-01

The report reviews existing forecasting models of auto vehicle miles of travel (VMT), and presents evidence that such models incorrectly omit time cost and spatial form variables. The omission of these variables biases parameter estimates in existing...

An evolving-requirements technology assessment process for advanced propulsion concepts

NASA Astrophysics Data System (ADS)

McClure, Erin Kathleen

The following dissertation investigates the development of a methodology suitable for the evaluation of advanced propulsion concepts. At early stages of development, both the future performance of these concepts and their requirements are highly uncertain, making it difficult to forecast their future value. Developing advanced propulsion concepts requires a huge investment of resources. The methodology was developed to enhance the decision-makers understanding of the concepts, so that they could mitigate the risks associated with developing such concepts. A systematic methodology to identify potential advanced propulsion concepts and assess their robustness is necessary to reduce the risk of developing advanced propulsion concepts. Existing advanced design methodologies have evaluated the robustness of technologies or concepts to variations in requirements, but they are not suitable to evaluate a large number of dissimilar concepts. Variations in requirements have been shown to impact the development of advanced propulsion concepts, and any method designed to evaluate these concepts must incorporate the possible variations of the requirements into the assessment. In order to do so, a methodology was formulated to be capable of accounting for two aspects of the problem. First, it had to systemically identify a probabilistic distribution for the future requirements. Such a distribution would allow decision-makers to quantify the uncertainty introduced by variations in requirements. Second, the methodology must be able to assess the robustness of the propulsion concepts as a function of that distribution. This dissertation describes in depth these enabling elements and proceeds to synthesize them into a new method, the Evolving Requirements Technology Assessment (ERTA). As a proof of concept, the ERTA method was used to evaluate and compare advanced propulsion systems that will be capable of powering a hurricane tracking, High Altitude, Long Endurance (HALE) unmanned aerial vehicle (UAV). The use of the ERTA methodology to assess HALE UAV propulsion concepts demonstrated that potential variations in requirements do significantly impact the assessment and selection of propulsion concepts. The proof of concept also demonstrated that traditional forecasting techniques, such as the cross impact analysis, could be used to forecast the requirements for advanced propulsion concepts probabilistically. "Fitness", a measure of relative goodness, was used to evaluate the concepts. Finally, stochastic optimizations were used to evaluate the propulsion concepts across the range of requirement sets that were considered.

Forecasting vegetation greenness with satellite and climate data

USGS Publications Warehouse

Ji, Lei; Peters, Albert J.

2004-01-01

A new and unique vegetation greenness forecast (VGF) model was designed to predict future vegetation conditions to three months through the use of current and historical climate data and satellite imagery. The VGF model is implemented through a seasonality-adjusted autoregressive distributed-lag function, based on our finding that the normalized difference vegetation index is highly correlated with lagged precipitation and temperature. Accurate forecasts were obtained from the VGF model in Nebraska grassland and cropland. The regression R2 values range from 0.97-0.80 for 2-12 week forecasts, with higher R2 associated with a shorter prediction. An important application would be to produce real-time forecasts of greenness images.

Future Research in Health Information Technology: A Review.

PubMed

Hemmat, Morteza; Ayatollahi, Haleh; Maleki, Mohammad Reza; Saghafi, Fatemeh

2017-01-01

Currently, information technology is considered an important tool to improve healthcare services. To adopt the right technologies, policy makers should have adequate information about present and future advances. This study aimed to review and compare studies with a focus on the future of health information technology. This review study was completed in 2015. The databases used were Scopus, Web of Science, ProQuest, Ovid Medline, and PubMed. Keyword searches were used to identify papers and materials published between 2000 and 2015. Initially, 407 papers were obtained, and they were reduced to 11 papers at the final stage. The selected papers were described and compared in terms of the country of origin, objective, methodology, and time horizon. The papers were divided into two groups: those forecasting the future of health information technology (seven papers) and those providing health information technology foresight (four papers). The results showed that papers related to forecasting the future of health information technology were mostly a literature review, and the time horizon was up to 10 years in most of these studies. In the health information technology foresight group, most of the studies used a combination of techniques, such as scenario building and Delphi methods, and had long-term objectives. To make the most of an investment and to improve planning and successful implementation of health information technology, a strategic plan for the future needs to be set. To achieve this aim, methods such as forecasting the future of health information technology and offering health information technology foresight can be applied. The forecasting method is used when the objectives are not very large, and the foresight approach is recommended when large-scale objectives are set to be achieved. In the field of health information technology, the results of foresight studies can help to establish realistic long-term expectations of the future of health information technology.

Future Research in Health Information Technology: A Review

PubMed Central

Hemmat, Morteza; Ayatollahi, Haleh; Maleki, Mohammad Reza; Saghafi, Fatemeh

2017-01-01

Introduction Currently, information technology is considered an important tool to improve healthcare services. To adopt the right technologies, policy makers should have adequate information about present and future advances. This study aimed to review and compare studies with a focus on the future of health information technology. Method This review study was completed in 2015. The databases used were Scopus, Web of Science, ProQuest, Ovid Medline, and PubMed. Keyword searches were used to identify papers and materials published between 2000 and 2015. Initially, 407 papers were obtained, and they were reduced to 11 papers at the final stage. The selected papers were described and compared in terms of the country of origin, objective, methodology, and time horizon. Results The papers were divided into two groups: those forecasting the future of health information technology (seven papers) and those providing health information technology foresight (four papers). The results showed that papers related to forecasting the future of health information technology were mostly a literature review, and the time horizon was up to 10 years in most of these studies. In the health information technology foresight group, most of the studies used a combination of techniques, such as scenario building and Delphi methods, and had long-term objectives. Conclusion To make the most of an investment and to improve planning and successful implementation of health information technology, a strategic plan for the future needs to be set. To achieve this aim, methods such as forecasting the future of health information technology and offering health information technology foresight can be applied. The forecasting method is used when the objectives are not very large, and the foresight approach is recommended when large-scale objectives are set to be achieved. In the field of health information technology, the results of foresight studies can help to establish realistic long-term expectations of the future of health information technology. PMID:28566991

An investigation of the role of current and future remote sensing data systems in numerical meteorology

NASA Technical Reports Server (NTRS)

Diak, George R.; Smith, William L.

1993-01-01

The goals of this research endeavor have been to develop a flexible and relatively complete framework for the investigation of current and future satellite data sources in numerical meteorology. In order to realistically model how satellite information might be used for these purposes, it is necessary that Observing System Simulation Experiments (OSSEs) be as complete as possible. It is therefore desirable that these experiments simulate in entirety the sequence of steps involved in bringing satellite information from the radiance level through product retrieval to a realistic analysis and forecast sequence. In this project we have worked to make this sequence realistic by synthesizing raw satellite data from surrogate atmospheres, deriving satellite products from these data and subsequently producing analyses and forecasts using the retrieved products. The accomplishments made in 1991 are presented. The emphasis was on examining atmospheric soundings and microphysical products which we expect to produce with the launch of the Advanced Microwave Sounding Unit (AMSU), slated for flight in mid 1994.

Magnetohydrodynamic modelling of solar disturbances in the interplanetary medium

NASA Astrophysics Data System (ADS)

Dryer, M.

1985-12-01

A scientifically constructed series of interplanetary magnetohydrodynamic models is made that comprise the foundations for a composite solar terrestrial environment model. These models, unique in the field of solar wind physics, include both 2-1/2D as well as 3D time dependent codes that will lead to future operational status. We have also developed a geomagnetic storm forecasting strategy, referred to as the Solar Terrestrial Environment Model (STEM/2000), whereby these models would be appended in modular fashion to solar, magnetosphere, ionosphere, thermosphere, and neutral atmosphere models. We stress that these models, while still not appropriate at this date for operational use, outline a strategy or blueprint for the future. This strategy, if implemented in its essential features, offers a high probability for technology transfer from theory to operational testing within, approximately, a decade. It would ensure that real time observations would be used to drive physically based models that outputs of which would be used by space environment forecasters.

Taking Risks for the Future of Space Weather Forecasting, Research, and Operations

NASA Astrophysics Data System (ADS)

Jaynes, A. N.; Baker, D. N.; Kanekal, S. G.; Li, X.; Turner, D. L.

2017-12-01

Taking Risks for the Future of Space Weather Forecasting, Research, and Operations The need for highly improved space weather modeling and monitoring is quickly becoming imperative as our society depends ever more on the sensitive technology that builds and connects our world. Instead of relying primarily on tried and true concepts, academic institutions and funding agencies alike should be focusing on truly new and innovative ways to solve this pressing problem. In this exciting time, where student-led groups can launch CubeSats for under a million dollars and companies like SpaceX are actively reducing the cost-cap of access to space, the space physics community should be pushing the boundaries of what is possible to enhance our understanding of the space environment. Taking great risks in instrumentation, mission concepts, operational development, collaborations, and scientific research is the best way to move our field forward to where it needs to be for the betterment of science and society.

Past speculations of the future: a review of the methods used for forecasting emerging health technologies.

PubMed

Doos, Lucy; Packer, Claire; Ward, Derek; Simpson, Sue; Stevens, Andrew

2016-03-10

Forecasting can support rational decision-making around the introduction and use of emerging health technologies and prevent investment in technologies that have limited long-term potential. However, forecasting methods need to be credible. We performed a systematic search to identify the methods used in forecasting studies to predict future health technologies within a 3-20-year timeframe. Identification and retrospective assessment of such methods potentially offer a route to more reliable prediction. Systematic search of the literature to identify studies reported on methods of forecasting in healthcare. People are not needed in this study. The authors searched MEDLINE, EMBASE, PsychINFO and grey literature sources, and included articles published in English that reported their methods and a list of identified technologies. Studies reporting methods used to predict future health technologies within a 3-20-year timeframe with an identified list of individual healthcare technologies. Commercially sponsored reviews, long-term futurology studies (with over 20-year timeframes) and speculative editorials were excluded. 15 studies met our inclusion criteria. Our results showed that the majority of studies (13/15) consulted experts either alone or in combination with other methods such as literature searching. Only 2 studies used more complex forecasting tools such as scenario building. The methodological fundamentals of formal 3-20-year prediction are consistent but vary in details. Further research needs to be conducted to ascertain if the predictions made were accurate and whether accuracy varies by the methods used or by the types of technologies identified. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Recent Progress of Solar Weather Forecasting at Naoc

NASA Astrophysics Data System (ADS)

He, Han; Wang, Huaning; Du, Zhanle; Zhang, Liyun; Huang, Xin; Yan, Yan; Fan, Yuliang; Zhu, Xiaoshuai; Guo, Xiaobo; Dai, Xinghua

The history of solar weather forecasting services at National Astronomical Observatories, Chinese Academy of Sciences (NAOC) can be traced back to 1960s. Nowadays, NAOC is the headquarters of the Regional Warning Center of China (RWC-China), which is one of the members of the International Space Environment Service (ISES). NAOC is responsible for exchanging data, information and space weather forecasts of RWC-China with other RWCs. The solar weather forecasting services at NAOC cover short-term prediction (within two or three days), medium-term prediction (within several weeks), and long-term prediction (in time scale of solar cycle) of solar activities. Most efforts of the short-term prediction research are concentrated on the solar eruptive phenomena, such as flares, coronal mass ejections (CMEs) and solar proton events, which are the key driving sources of strong space weather disturbances. Based on the high quality observation data of the latest space-based and ground-based solar telescopes and with the help of artificial intelligence techniques, new numerical models with quantitative analyses and physical consideration are being developed for the predictions of solar eruptive events. The 3-D computer simulation technology is being introduced for the operational solar weather service platform to visualize the monitoring of solar activities, the running of the prediction models, as well as the presenting of the forecasting results. A new generation operational solar weather monitoring and forecasting system is expected to be constructed in the near future at NAOC.

Forecasting land cover change impacts on drinking water treatment costs in Minneapolis, Minnesota

NASA Astrophysics Data System (ADS)

Woznicki, S. A.; Wickham, J.

2017-12-01

Source protection is a critical aspect of drinking water treatment. The benefits of protecting source water quality in reducing drinking water treatment costs are clear. However, forecasting the impacts of environmental change on source water quality and its potential to influence future treatment processes is lacking. The drinking water treatment plant in Minneapolis, MN has recognized that land cover change threatens water quality in their source watershed, the Upper Mississippi River Basin (UMRB). Over 1,000 km2 of forests, wetlands, and grasslands in the UMRB were lost to agriculture from 2008-2013. This trend, coupled with a projected population increase of one million people in Minnesota by 2030, concerns drinking water treatment plant operators in Minneapolis with respect to meeting future demand for clean water in the UMRB. The objective of this study is to relate land cover change (forest and wetland loss, agricultural expansion, urbanization) to changes in treatment costs for the Minneapolis, MN drinking water utility. To do this, we first developed a framework to determine the relationship between land cover change and water quality in the context of recent historical changes and projected future changes in land cover. Next we coupled a watershed model, the Soil and Water Assessment Tool (SWAT) to projections of land cover change from the FOREcasting SCEnarios of Land-use Change (FORE-SCE) model for the mid-21st century. Using historical Minneapolis drinking water treatment data (chemical usage and costs), source water quality in the UMRB was linked to changes in treatment requirements as a function of projected future land cover change. These analyses will quantify the value of natural landscapes in protecting drinking water quality and future treatment processes requirements. In addition, our study provides the Minneapolis drinking water utility with information critical to their planning and capital improvement process.

Probabilistic short-term forecasting of eruption rate at Kīlauea Volcano using a physics-based model

NASA Astrophysics Data System (ADS)

Anderson, K. R.

2016-12-01

Deterministic models of volcanic eruptions yield predictions of future activity conditioned on uncertainty in the current state of the system. Physics-based eruption models are well-suited for deterministic forecasting as they can relate magma physics with a wide range of observations. Yet, physics-based eruption forecasting is strongly limited by an inadequate understanding of volcanic systems, and the need for eruption models to be computationally tractable. At Kīlauea Volcano, Hawaii, episodic depressurization-pressurization cycles of the magma system generate correlated, quasi-exponential variations in ground deformation and surface height of the active summit lava lake. Deflations are associated with reductions in eruption rate, or even brief eruptive pauses, and thus partly control lava flow advance rates and associated hazard. Because of the relatively well-understood nature of Kīlauea's shallow magma plumbing system, and because more than 600 of these events have been recorded to date, they offer a unique opportunity to refine a physics-based effusive eruption forecasting approach and apply it to lava eruption rates over short (hours to days) time periods. A simple physical model of the volcano ascribes observed data to temporary reductions in magma supply to an elastic reservoir filled with compressible magma. This model can be used to predict the evolution of an ongoing event, but because the mechanism that triggers events is unknown, event durations are modeled stochastically from previous observations. A Bayesian approach incorporates diverse data sets and prior information to simultaneously estimate uncertain model parameters and future states of the system. Forecasts take the form of probability distributions for eruption rate or cumulative erupted volume at some future time. Results demonstrate the significant uncertainties that still remain even for short-term eruption forecasting at a well-monitored volcano - but also the value of a physics-based, mixed deterministic-probabilistic eruption forecasting approach in reducing and quantifying these uncertainties.

Forecasting Trends in Disability in a Super-Aging Society: Adapting the Future Elderly Model to Japan

PubMed Central

Chen, Brian K.; Jalal, Hawre; Hashimoto, Hideki; Suen, Sze-chuan; Eggleston, Karen; Hurley, Michael; Schoemaker, Lena; Bhattacharya, Jay

2016-01-01

Japan has experienced pronounced population aging, and now has the highest proportion of elderly adults in the world. Yet few projections of Japan’s future demography go beyond estimating population by age and sex to forecast the complex evolution of the health and functioning of the future elderly. This study estimates a new state-transition microsimulation model – the Japanese Future Elderly Model (FEM) – for Japan. We use the model to forecast disability and health for Japan’s future elderly. Our simulation suggests that by 2040, over 27 percent of Japan’s elderly will exhibit 3 or more limitations in IADLs and social functioning; almost one in 4 will experience difficulties with 3 or more ADLs; and approximately one in 5 will suffer limitations in cognitive or intellectual functioning. Since the majority of the increase in disability arises from the aging of the Japanese population, prevention efforts that reduce age-specific morbidity can help reduce the burden of disability but may have only a limited impact on reducing the overall prevalence of disability among Japanese elderly. While both age and morbidity contribute to a predicted increase in disability burden among elderly Japanese in the future, our simulation results suggest that the impact of population aging exceeds the effect of age-specific morbidity on increasing disability in Japan’s future. PMID:28580275

Air quality early-warning system for cities in China

NASA Astrophysics Data System (ADS)

Xu, Yunzhen; Yang, Wendong; Wang, Jianzhou

2017-01-01

Air pollution has become a serious issue in many developing countries, especially in China, and could generate adverse effects on human beings. Air quality early-warning systems play an increasingly significant role in regulatory plans that reduce and control emissions of air pollutants and inform the public in advance when harmful air pollution is foreseen. However, building a robust early-warning system that will improve the ability of early-warning is not only a challenge but also a critical issue for the entire society. Relevant research is still poor in China and cannot always satisfy the growing requirements of regulatory planning, despite the issue's significance. Therefore, in this paper, a hybrid air quality early-warning system was successfully developed, composed of forecasting and evaluation. First, a hybrid forecasting model was proposed as an important part of this system based on the theory of "decomposition and ensemble" and combined with the advanced data processing technique, support vector machine, the latest bio-inspired optimization algorithm and the leave-one-out strategy for deciding weights. Afterwards, to intensify the research, fuzzy evaluation was performed, which also plays an indispensable role in the early-warning system. The forecasting model and fuzzy evaluation approaches are complementary. Case studies using daily air pollution concentrations of six air pollutants from three cities in China (i.e., Taiyuan, Harbin and Chongqing) are used as examples to evaluate the efficiency and effectiveness of the developed air quality early-warning system. Experimental results demonstrate that both the accuracy and the effectiveness of the developed system are greatly superior for air quality early warning. Furthermore, the application of forecasting and evaluation enables the informative and effective quantification of future air quality, offering a significant advantage, and can be employed to develop rapid air quality early-warning systems.

Road safety forecasts in five European countries using structural time series models.

PubMed

Antoniou, Constantinos; Papadimitriou, Eleonora; Yannis, George

2014-01-01

Modeling road safety development is a complex task and needs to consider both the quantifiable impact of specific parameters as well as the underlying trends that cannot always be measured or observed. The objective of this research is to apply structural time series models for obtaining reliable medium- to long-term forecasts of road traffic fatality risk using data from 5 countries with different characteristics from all over Europe (Cyprus, Greece, Hungary, Norway, and Switzerland). Two structural time series models are considered: (1) the local linear trend model and the (2) latent risk time series model. Furthermore, a structured decision tree for the selection of the applicable model for each situation (developed within the Road Safety Data, Collection, Transfer and Analysis [DaCoTA] research project, cofunded by the European Commission) is outlined. First, the fatality and exposure data that are used for the development of the models are presented and explored. Then, the modeling process is presented, including the model selection process, introduction of intervention variables, and development of mobility scenarios. The forecasts using the developed models appear to be realistic and within acceptable confidence intervals. The proposed methodology is proved to be very efficient for handling different cases of data availability and quality, providing an appropriate alternative from the family of structural time series models in each country. A concluding section providing perspectives and directions for future research is presented.

Accuracy and artifact: reexamining the intensity bias in affective forecasting.

PubMed

Levine, Linda J; Lench, Heather C; Kaplan, Robin L; Safer, Martin A

2012-10-01

Research on affective forecasting shows that people have a robust tendency to overestimate the intensity of future emotion. We hypothesized that (a) people can accurately predict the intensity of their feelings about events and (b) a procedural artifact contributes to people's tendency to overestimate the intensity of their feelings in general. People may misinterpret the forecasting question as asking how they will feel about a focal event, but they are later asked to report their feelings in general without reference to that event. In the current investigation, participants predicted and reported both their feelings in general and their feelings about an election outcome (Study 1) and an exam grade (Study 3). We also assessed how participants interpreted forecasting questions (Studies 2 and 4) and conducted a meta-analysis of affective forecasting research (Study 5). The results showed that participants accurately predicted the intensity of their feelings about events. They overestimated only when asked to predict how they would feel in general and later report their feelings without reference to the focal event. Most participants, however, misinterpreted requests to predict their feelings in general as asking how they would feel when they were thinking about the focal event. Clarifying the meaning of the forecasting question significantly reduced overestimation. These findings reveal that people have more sophisticated self-knowledge than is commonly portrayed in the affective forecasting literature. Overestimation of future emotion is partly due to a procedure in which people predict one thing but are later asked to report another.

The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region

NASA Astrophysics Data System (ADS)

Song, Yiliao; Qin, Shanshan; Qu, Jiansheng; Liu, Feng

2015-10-01

The issue of air quality regarding PM pollution levels in China is a focus of public attention. To address that issue, to date, a series of studies is in progress, including PM monitoring programs, PM source apportionment, and the enactment of new ambient air quality index standards. However, related research concerning computer modeling for PM future trends estimation is rare, despite its significance to forecasting and early warning systems. Thereby, a study regarding deterministic and interval forecasts of PM is performed. In this study, data on hourly and 12 h-averaged air pollutants are applied to forecast PM concentrations within the Yangtze River Delta (YRD) region of China. The characteristics of PM emissions have been primarily examined and analyzed using different distribution functions. To improve the distribution fitting that is crucial for estimating PM levels, an artificial intelligence algorithm is incorporated to select the optimal parameters. Following that step, an ANF model is used to conduct deterministic forecasts of PM. With the identified distributions and deterministic forecasts, different levels of PM intervals are estimated. The results indicate that the lognormal or gamma distributions are highly representative of the recorded PM data with a goodness-of-fit R2 of approximately 0.998. Furthermore, the results of the evaluation metrics (MSE, MAPE and CP, AW) also show high accuracy within the deterministic and interval forecasts of PM, indicating that this method enables the informative and effective quantification of future PM trends.

Waste to Watts and Water: Enabling Self-Contained Facilities Using Microbial Fuel Cells

DTIC Science & Technology

2009-03-01

will require in future facilities is the ability to operate apart from the infrastructure net- work and line of communications (LOC) in a clean and ef...in future technologies, observes that “forecasters are im- prisoned by their times.”33 Humans tend to look at today’s crisis and project it into the...2030. In 2007 the United States Department of Energy (DOE) forecast international power demand to double by 2030.34 Today’s energy crisis is well

Implementation of the Short-Term Ensemble Prediction System (STEPS) in Belgium and verification of case studies

NASA Astrophysics Data System (ADS)

Foresti, Loris; Reyniers, Maarten; Delobbe, Laurent

2014-05-01

The Short-Term Ensemble Prediction System (STEPS) is a probabilistic precipitation nowcasting scheme developed at the Australian Bureau of Meteorology in collaboration with the UK Met Office. In order to account for the multiscaling nature of rainfall structures, the radar field is decomposed into an 8 levels multiplicative cascade using a Fast Fourier Transform. The cascade is advected using the velocity field estimated with optical flow and evolves stochastically according to a hierarchy of auto-regressive processes. This allows reproducing the empirical observation that the rate of temporal evolution of the small scales is faster than the large scales. The uncertainty in radar rainfall measurement and the unknown future development of the velocity field are also considered by stochastic modelling in order to reflect their typical spatial and temporal variability. Recently, a 4 years national research program has been initiated by the University of Leuven, the Royal Meteorological Institute (RMI) of Belgium and 3 other partners: PLURISK ("forecasting and management of extreme rainfall induced risks in the urban environment"). The project deals with the nowcasting of rainfall and subsequent urban inundations, as well as socio-economic risk quantification, communication, warning and prevention. At the urban scale it is widely recognized that the uncertainty of hydrological and hydraulic models is largely driven by the input rainfall estimation and forecast uncertainty. In support to the PLURISK project the RMI aims at integrating STEPS in the current operational deterministic precipitation nowcasting system INCA-BE (Integrated Nowcasting through Comprehensive Analysis). This contribution will illustrate examples of STEPS ensemble and probabilistic nowcasts for a few selected case studies of stratiform and convective rain in Belgium. The paper focuses on the development of STEPS products for potential hydrological users and a preliminary verification of the nowcasts, especially to analyze the spatial distribution of forecast errors. The analysis of nowcast biases reveals the locations where the convective initiation, rainfall growth and decay processes significantly reduce the forecast accuracy, but also points out the need for improving the radar-based quantitative precipitation estimation product that is used both to generate and verify the nowcasts. The collection of fields of verification statistics is implemented using an online update strategy, which potentially enables the system to learn from forecast errors as the archive of nowcasts grows. The study of the spatial or temporal distribution of nowcast errors is a key step to convey to the users an overall estimation of the nowcast accuracy and to drive future model developments.

The climate of the Taimyr Peninsula in the Holocene and a Forecast of Climatic Changes in the Arctic

NASA Astrophysics Data System (ADS)

Ukraintseva, V.

2009-04-01

Based on the data of the spore-pollen and radiocarbon methods during our research of a peat bog in the south-eastern part of the Taimyr Peninsula we discovered for the first time the natural dynamics of the climate for this region during the period of the last 10 500 years [2, 3] and made a long-term forecast of climatic changes both for the Taimyr Peninsula and for other Arctic regions. By the quantitative characteristics of the climate and their dynamics in time, reconstructed for the basin of the Fomich River (71 ° 42 ' North, 108 ° 03 ' East) and for the Taimyr Peninsula on the whole, we have established two climatic types: tundra (10500 ±140 years BP- 7040 ± 60 years BP) and forest (5720± 60 years BP - 500 ± 60 years BP to the present time). In the first half of the Holocene the climate there was rather stable; only 7530 years ago a sharp cooling took place; the second half of the Holocene, beginning with 5720 years ago, is characterized by alternating fluctuations in the climate [3]. Taking only the palaeoclimatic reconstructions as a basis, we can talk about a trend of climatic changes in the future. However comparing the Sun activity` forecast, expressed in Wolf units (Max W), made by V.N. Kupetsky [1], with the climatic characteristics, which we have reconstructed, we could then make a more precise forecast of climatic changes for the Taimyr Peninsula and the Russian part of the Arctic (Table). The above forecast lets us make the following basically important conclusions: (1) the climate`s warming, which is currently being observed on the Earth (the 23rd cycle of the Sun`s activity) will last till 2011; (2) during the following two cycles (24th and 25th) the Sun`s activity will decrease to 100-110 Wolf units, which will cause a cooling of the climate on the Earth; (3) in the following, the 26th cycle, the Sun`s activity will increase up to 130 Wolf units, which will cause a warming of the climate; (4) in the 27th cycle (2037-2048) the Sun`s activity will decrease to 100 Wolf units, causing a cooling on the Earth again. Thus, the forecast of climatic changes in the Arctic, which we have worked up and based on the Sun-Earth connections, is an objective natural reality. The climate fluctuations in the Arctic, which we have identified for the last 12-10 thousand years, will continue in the forthcoming 50-100 years. Consequently, only the synthesis of solar-telescopic, palaeoclimatic and modern meteorological data allows making a valid long-term global forecast of climatic changes and of the Earth`s landscape in the future. Regional and local forecasts developed on the basis of a global forecast will be then of the primary value. Since the solar-telescopic data are alpha and omega for forecast constructions, their publication in the open press is an absolute necessity. This would enable scientists to make realistic forecasts of climatic changes for specific districts and regions of the Earth in the future. The contemporary scientific knowledge level does not show us any other way yet. Bibliography: 1. Kupetsky, V.N. Landscapes of freezing seas. Dissertation for the Degree of Doctor of Geographical Science. Saint-Petersburg State University, 1998 ( Russian). 2. Ukraintseva, V.V. Use of the index of similarity for the assessment of fossil spore-pollen spectra // Modern Problems of Paleofloristics, Paleophytogeography and Phytostratigraphy. Transaction of the International Paleobotanical Conference. Moscow, May 17-18, 2005. Vol.1. - Moscow: GEOS, 2005. P. 314 - 318. 3. Ukraintseva, V.V. On the new method of reconstruction of climates of the past on the basis of the spore-pollen analysis method data// SOCIETY. ENVIRONMENT. DEVELOPMENT. 2008. No.3. P.142-154 (Russian). 4. Ukraintseva V.V., Pospelov I.N. Reconstruction of Climates of the Past and a Forecast: a New Method in Principal// The Holocene, 2008 (in press).

Stated choice for transportation demand management models : using a disaggregate truth set to study predictive validity

DOT National Transportation Integrated Search

1997-01-01

Discrete choice models have expanded the ability of transportation planners to forecast future trends. Where new services or policies are proposed, the stated-choice approach can provide an objective basis for forecasts. Stated-choice models are subj...

The Gods of the Copybook Headings: A Caution to Forecasters.

ERIC Educational Resources Information Center

Martino, Joseph P.

1984-01-01

Technological forecasters often fail to recognize the whole range of changing circumstances that may affect their predictions. Drawing on his own broad experience, the author offers basic reminders to those who set out to gauge the direction of future changes in technology. (IM)

Project 1990: Educational Planning at the Metropolitan Level.

ERIC Educational Resources Information Center

Swanson, Austin D.; Lamitie, Robert E.

This paper describes a project designed to provide educational decisionmakers with projections of and forecasts about future metropolitan conditions and problems, and information about the implications of alternative ways of solving metropolitan problems. Project components included (1) population and economic projections and forecasts, (2)…

Novel Modeling Tools for Propagating Climate Change Variability and Uncertainty into Hydrodynamic Forecasts

EPA Science Inventory

Understanding impacts of climate change on hydrodynamic processes and ecosystem response within the Great Lakes is an important and challenging task. Variability in future climate conditions, uncertainty in rainfall-runoff model forecasts, the potential for land use change, and t...

The MSFC Solar Activity Future Estimation (MSAFE) Model

NASA Technical Reports Server (NTRS)

Suggs, Ronnie J.

2017-01-01

The MSAFE model provides forecasts for the solar indices SSN, F10.7, and Ap. These solar indices are used as inputs to many space environment models used in orbital spacecraft operations and space mission analysis. Forecasts from the MSAFE model are provided on the MSFC Natural Environments Branch's solar webpage and are updated as new monthly observations come available. The MSAFE prediction routine employs a statistical technique that calculates deviations of past solar cycles from the mean cycle and performs a regression analysis to predict the deviation from the mean cycle of the solar index at the next future time interval. The prediction algorithm is applied recursively to produce monthly smoothed solar index values for the remaining of the cycle. The forecasts are initiated for a given cycle after about 8 to 12 months of observations are collected. A forecast made at the beginning of cycle 24 using the MSAFE program captured the cycle fairly well with some difficulty in discerning the double peak that occurred at solar cycle maximum.

Understanding land use change impacts on microclimate using Weather Research and Forecasting (WRF) model

NASA Astrophysics Data System (ADS)

Li, Xia; Mitra, Chandana; Dong, Li; Yang, Qichun

2018-02-01

To explore potential climatic consequences of land cover change in the Kolkata Metropolitan Development area, we projected microclimate conditions in this area using the Weather Research and Forecasting (WRF) model driven by future land use scenarios. Specifically, we considered two land conversion scenarios including an urbanization scenario that all the wetlands and croplands would be converted to built-up areas, and an irrigation expansion scenario in which all wetlands and dry croplands would be replaced by irrigated croplands. Results indicated that land use and land cover (LULC) change would dramatically increase regional temperature in this area under the urbanization scenario, but expanded irrigation tended to have a cooling effect. In the urbanization scenario, precipitation center tended to move eastward and lead to increased rainfall in eastern parts of this region. Increased irrigation stimulated rainfall in central and eastern areas but reduced rainfall in southwestern and northwestern parts of the study area. This study also demonstrated that urbanization significantly reduced latent heat fluxes and albedo of land surface; while increased sensible heat flux changes following urbanization suggested that developed land surfaces mainly acted as heat sources. In this study, climate change projection not only predicts future spatiotemporal patterns of multiple climate factors, but also provides valuable insights into policy making related to land use management, water resource management, and agriculture management to adapt and mitigate future climate changes in this populous region.

Future Impacts of Hydroelectric Power Development on Methylmercury Exposures of Canadian Indigenous Communities.

PubMed

Calder, Ryan S D; Schartup, Amina T; Li, Miling; Valberg, Amelia P; Balcom, Prentiss H; Sunderland, Elsie M

2016-12-06

Developing Canadian hydroelectric resources is a key component of North American plans for meeting future energy demands. Microbial production of the bioaccumulative neurotoxin methylmercury (MeHg) is stimulated in newly flooded soils by degradation of labile organic carbon and associated changes in geochemical conditions. We find all 22 Canadian hydroelectric facilities being considered for near-term development are located within 100 km of indigenous communities. For a facility in Labrador, Canada (Muskrat Falls) with planned completion in 2017, we probabilistically modeled peak MeHg enrichment relative to measured baseline conditions in the river to be impounded, downstream estuary, locally harvested fish, birds and seals, and three Inuit communities. Results show a projected 10-fold increase in riverine MeHg levels and a 2.6-fold increase in estuarine surface waters. MeHg concentrations in locally caught species increase 1.3 to 10-fold depending on time spent foraging in different environments. Mean Inuit MeHg exposure is forecasted to double following flooding and over half of the women of childbearing age and young children in the most northern community are projected to exceed the U.S. EPA's reference dose. Equal or greater aqueous MeHg concentrations relative to Muskrat Falls are forecasted for 11 sites across Canada, suggesting the need for mitigation measures prior to flooding.

Atlas : A library for numerical weather prediction and climate modelling

NASA Astrophysics Data System (ADS)

Deconinck, Willem; Bauer, Peter; Diamantakis, Michail; Hamrud, Mats; Kühnlein, Christian; Maciel, Pedro; Mengaldo, Gianmarco; Quintino, Tiago; Raoult, Baudouin; Smolarkiewicz, Piotr K.; Wedi, Nils P.

2017-11-01

The algorithms underlying numerical weather prediction (NWP) and climate models that have been developed in the past few decades face an increasing challenge caused by the paradigm shift imposed by hardware vendors towards more energy-efficient devices. In order to provide a sustainable path to exascale High Performance Computing (HPC), applications become increasingly restricted by energy consumption. As a result, the emerging diverse and complex hardware solutions have a large impact on the programming models traditionally used in NWP software, triggering a rethink of design choices for future massively parallel software frameworks. In this paper, we present Atlas, a new software library that is currently being developed at the European Centre for Medium-Range Weather Forecasts (ECMWF), with the scope of handling data structures required for NWP applications in a flexible and massively parallel way. Atlas provides a versatile framework for the future development of efficient NWP and climate applications on emerging HPC architectures. The applications range from full Earth system models, to specific tools required for post-processing weather forecast products. The Atlas library thus constitutes a step towards affordable exascale high-performance simulations by providing the necessary abstractions that facilitate the application in heterogeneous HPC environments by promoting the co-design of NWP algorithms with the underlying hardware.

Coal resources, reserves and peak coal production in the United States

USGS Publications Warehouse

Milici, Robert C.; Flores, Romeo M.; Stricker, Gary D.

2013-01-01

In spite of its large endowment of coal resources, recent studies have indicated that United States coal production is destined to reach a maximum and begin an irreversible decline sometime during the middle of the current century. However, studies and assessments illustrating coal reserve data essential for making accurate forecasts of United States coal production have not been compiled on a national basis. As a result, there is a great deal of uncertainty in the accuracy of the production forecasts. A very large percentage of the coal mined in the United States comes from a few large-scale mines (mega-mines) in the Powder River Basin of Wyoming and Montana. Reported reserves at these mines do not account for future potential reserves or for future development of technology that may make coal classified currently as resources into reserves in the future. In order to maintain United States coal production at or near current levels for an extended period of time, existing mines will eventually have to increase their recoverable reserves and/or new large-scale mines will have to be opened elsewhere. Accordingly, in order to facilitate energy planning for the United States, this paper suggests that probabilistic assessments of the remaining coal reserves in the country would improve long range forecasts of coal production. As it is in United States coal assessment projects currently being conducted, a major priority of probabilistic assessments would be to identify the numbers and sizes of remaining large blocks of coal capable of supporting large-scale mining operations for extended periods of time and to conduct economic evaluations of those resources.

Quercus pollen season dynamics in the Iberian peninsula: response to meteorological parameters and possible consequences of climate change.

PubMed

Garcia-Mozo, Herminia; Galan, Carmen; Jato, Victoria; Belmonte, Jordina; de la Guardia, Consuelo; Fernandez, Delia; Gutierrez, Montserrat; Aira, M; Roure, Joan; Ruiz, Luis; Trigo, Mar; Dominguez-Vilches, Eugenio

2006-01-01

The main characteristics of the Quercus pollination season were studied in 14 different localities of the Iberian Peninsula from 1992-2004. Results show that Quercus flowering season has tended to start earlier in recent years, probably due to the increased temperatures in the pre-flowering period, detected at study sites over the second half of the 20th century. A Growing Degree Days forecasting model was used, together with future meteorological data forecast using the Regional Climate Model developed by the Hadley Meteorological Centre, in order to determine the expected advance in the start of Quercus pollination in future years. At each study site, airborne pollen curves presented a similar pattern in all study years, with different peaks over the season attributable in many cases to the presence of several species. High pollen concentrations were recorded, particularly at Mediterranean sites. This study also proposes forecasting models to predict both daily pollen values and annual pollen emission. All models were externally validated using data for 2001 and 2004, with acceptable results. Finally, the impact of the highly-likely climate change on Iberian Quercus pollen concentration values was studied by applying RCM meteorological data for different future years, 2025, 2050, 2075 and 2099. Results indicate that under a doubled CO(2) scenario at the end of the 21st century Quercus pollination season could start on average one month earlier and airborne pollen concentrations will increase by 50 % with respect to current levels, with higher values in Mediterranean inland areas.

Latent Patient Cluster Discovery for Robust Future Forecasting and New-Patient Generalization

PubMed Central

Masino, Aaron J.

2016-01-01

Commonly referred to as predictive modeling, the use of machine learning and statistical methods to improve healthcare outcomes has recently gained traction in biomedical informatics research. Given the vast opportunities enabled by large Electronic Health Records (EHR) data and powerful resources for conducting predictive modeling, we argue that it is yet crucial to first carefully examine the prediction task and then choose predictive methods accordingly. Specifically, we argue that there are at least three distinct prediction tasks that are often conflated in biomedical research: 1) data imputation, where a model fills in the missing values in a dataset, 2) future forecasting, where a model projects the development of a medical condition for a known patient based on existing observations, and 3) new-patient generalization, where a model transfers the knowledge learned from previously observed patients to newly encountered ones. Importantly, the latter two tasks—future forecasting and new-patient generalizations—tend to be more difficult than data imputation as they require predictions to be made on potentially out-of-sample data (i.e., data following a different predictable pattern from what has been learned by the model). Using hearing loss progression as an example, we investigate three regression models and show that the modeling of latent clusters is a robust method for addressing the more challenging prediction scenarios. Overall, our findings suggest that there exist significant differences between various kinds of prediction tasks and that it is important to evaluate the merits of a predictive model relative to the specific purpose of a prediction task. PMID:27636203

Latent Patient Cluster Discovery for Robust Future Forecasting and New-Patient Generalization.

PubMed

Qian, Ting; Masino, Aaron J

2016-01-01

Commonly referred to as predictive modeling, the use of machine learning and statistical methods to improve healthcare outcomes has recently gained traction in biomedical informatics research. Given the vast opportunities enabled by large Electronic Health Records (EHR) data and powerful resources for conducting predictive modeling, we argue that it is yet crucial to first carefully examine the prediction task and then choose predictive methods accordingly. Specifically, we argue that there are at least three distinct prediction tasks that are often conflated in biomedical research: 1) data imputation, where a model fills in the missing values in a dataset, 2) future forecasting, where a model projects the development of a medical condition for a known patient based on existing observations, and 3) new-patient generalization, where a model transfers the knowledge learned from previously observed patients to newly encountered ones. Importantly, the latter two tasks-future forecasting and new-patient generalizations-tend to be more difficult than data imputation as they require predictions to be made on potentially out-of-sample data (i.e., data following a different predictable pattern from what has been learned by the model). Using hearing loss progression as an example, we investigate three regression models and show that the modeling of latent clusters is a robust method for addressing the more challenging prediction scenarios. Overall, our findings suggest that there exist significant differences between various kinds of prediction tasks and that it is important to evaluate the merits of a predictive model relative to the specific purpose of a prediction task.

Challenges and potential solutions for European coastal ocean modelling

NASA Astrophysics Data System (ADS)

She, Jun; Stanev, Emil

2017-04-01

Coastal operational oceanography is a science and technological platform to integrate and transform the outcomes in marine monitoring, new knowledge generation and innovative technologies into operational information products and services in the coastal ocean. It has been identified as one of the four research priorities by EuroGOOS (She et al. 2016). Coastal modelling plays a central role in such an integration and transformation. A next generation coastal ocean forecasting system should have following features: i) being able to fully exploit benefits from future observations, ii) generate meaningful products in finer scales e.g., sub-mesoscale and in estuary-coast-sea continuum, iii) efficient parallel computing and model grid structure, iv) provide high quality forecasts as forcing to NWP and coastal climate models, v) resolving correctly inter-basin and inter-sub-basin water exchange, vi) resolving synoptic variability and predictability in marine ecosystems, e.g., for algae bloom, vi) being able to address critical and relevant issues in coastal applications, e.g., marine spatial planning, maritime safety, marine pollution protection, disaster prevention, offshore wind energy, climate change adaptation and mitigation, ICZM (integrated coastal zone management), the WFD (Water Framework Directive), and the MSFD (Marine Strategy Framework Directive), especially on habitat, eutrophication, and hydrographic condition descriptors. This presentation will address above challenges, identify limits of current models and propose correspondent research needed. The proposed roadmap will address an integrated monitoring-modelling approach and developing Unified European Coastal Ocean Models. In the coming years, a few new developments in European Sea observations can expected, e.g., more near real time delivering on profile observations made by research vessels, more shallow water Argo floats and bio-Argo floats deployed, much more high resolution sea level data from SWOT and on-going altimetry missions, contributing to resolving (sub-)mesoscale eddies, more currents measurements from ADCPs and HF radars, geostationary data for suspended sediment and diurnal observations from satellite SST products. These developments will make it possible to generate new knowledge and build up new capacities for modelling and forecasting systems, e.g., improved currents forecast, improved water skin temperature and surface winds forecast, improved modelling and forecast of (sub) mesoscale activities and drift forecast, new forecast capabilities on SPM (Suspended Particle Matter) and algae bloom. There will be much more in-situ and satellite data available for assimilation. The assimilation of sea level, chl-a, ferrybox and profile observations will greatly improves the ocean-ice-ecosystem forecast quality.

STAPOL: A Simulation of the Impact of Policy, Values, and Technological and Societal Developments upon the Quality of Life.

ERIC Educational Resources Information Center

Little, Dennis; Feller, Richard

The Institute for the Future has been conducting research in technological and societal forecasting, social indicators, value change, and simulation gaming. This paper describes an effort to bring together parts of that research into a simulation game ("State Policy," or STAPOL) for analysis of the impact of government policy, social values, and…

Developing a planning model to estimate future cash flows.

PubMed

Barenbaum, L; Monahan, T F

1988-03-01

Financial managers are discovering that net income and other traditional measures of cash flow may not provide them with the flexibility needed for comprehensive internal planning and control. By using a discretionary cash flow model, financial managers have a forecasting tool that can help them measure anticipated cash flows, and make better decisions concerning financing alternatives, capital expansion, and performance appraisal.

NASA Lewis Research Center Futuring Workshop

NASA Technical Reports Server (NTRS)

Boroush, Mark; Stover, John; Thomas, Charles

1987-01-01

On October 21 and 22, 1986, the Futures Group ran a two-day Futuring Workshop on the premises of NASA Lewis Research Center. The workshop had four main goals: to acquaint participants with the general history of technology forecasting; to familiarize participants with the range of forecasting methodologies; to acquaint participants with the range of applicability, strengths, and limitations of each method; and to offer participants some hands-on experience by working through both judgmental and quantitative case studies. Among the topics addressed during this workshop were: information sources; judgmental techniques; quantitative techniques; merger of judgment with quantitative measurement; data collection methods; and dealing with uncertainty.

Beyond Bounded Solutions

ERIC Educational Resources Information Center

Enzer, Selwyn

1977-01-01

Futures research offers new tools for forecasting and for designing alternative intervention strategies. Interactive cross-impact modeling is presented as a useful method for identifying future events. (Author/MV)

Forecasting future needs and optimal allocation of medical residency positions: the Emilia-Romagna Region case study.

PubMed

Senese, Francesca; Tubertini, Paolo; Mazzocchetti, Angelina; Lodi, Andrea; Ruozi, Corrado; Grilli, Roberto

2015-01-30

Italian regional health authorities annually negotiate the number of residency grants to be financed by the National government and the number and mix of supplementary grants to be funded by the regional budget. This study provides regional decision-makers with a requirement model to forecast the future demand of specialists at the regional level. We have developed a system dynamics (SD) model that projects the evolution of the supply of medical specialists and three demand scenarios across the planning horizon (2030). Demand scenarios account for different drivers: demography, service utilization rates (ambulatory care and hospital discharges) and hospital beds. Based on the SD outputs (occupational and training gaps), a mixed integer programming (MIP) model computes potentially effective assignments of medical specialization grants for each year of the projection. To simulate the allocation of grants, we have compared how regional and national grants can be managed in order to reduce future gaps with respect to current training patterns. The allocation of 25 supplementary grants per year does not appear as effective in reducing expected occupational gaps as the re-modulation of all regional training vacancies.

Modeling AWSoM CMEs with EEGGL: A New Approach for Space Weather Forecasting

NASA Astrophysics Data System (ADS)

Jin, M.; Manchester, W.; van der Holst, B.; Sokolov, I.; Toth, G.; Vourlidas, A.; de Koning, C. A.; Gombosi, T. I.

2015-12-01

The major source of destructive space weather is coronal mass ejections (CMEs). However, our understanding of CMEs and their propagation in the heliosphere is limited by the insufficient observations. Therefore, the development of first-principals numerical models plays a vital role in both theoretical investigation and providing space weather forecasts. Here, we present results of the simulation of CME propagation from the Sun to 1AU by combining the analytical Gibson & Low (GL) flux rope model with the state-of-art solar wind model AWSoM. We also provide an approach for transferring this research model to a space weather forecasting tool by demonstrating how the free parameters of the GL flux rope can be prescribed based on remote observations via the new Eruptive Event Generator by Gibson-Low (EEGGL) toolkit. This capability allows us to predict the long-term evolution of the CME in interplanetary space. We perform proof-of-concept case studies to show the capability of the model to capture physical processes that determine CME evolution while also reproducing many observed features both in the corona and at 1 AU. We discuss the potential and limitations of this model as a future space weather forecasting tool.

A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia.

PubMed

Aboagye-Sarfo, Patrick; Mai, Qun; Sanfilippo, Frank M; Preen, David B; Stewart, Louise M; Fatovich, Daniel M

2015-10-01

To develop multivariate vector-ARMA (VARMA) forecast models for predicting emergency department (ED) demand in Western Australia (WA) and compare them to the benchmark univariate autoregressive moving average (ARMA) and Winters' models. Seven-year monthly WA state-wide public hospital ED presentation data from 2006/07 to 2012/13 were modelled. Graphical and VARMA modelling methods were used for descriptive analysis and model fitting. The VARMA models were compared to the benchmark univariate ARMA and Winters' models to determine their accuracy to predict ED demand. The best models were evaluated by using error correction methods for accuracy. Descriptive analysis of all the dependent variables showed an increasing pattern of ED use with seasonal trends over time. The VARMA models provided a more precise and accurate forecast with smaller confidence intervals and better measures of accuracy in predicting ED demand in WA than the ARMA and Winters' method. VARMA models are a reliable forecasting method to predict ED demand for strategic planning and resource allocation. While the ARMA models are a closely competing alternative, they under-estimated future ED demand. Copyright © 2015 Elsevier Inc. All rights reserved.

New Measurements and Modeling Capability to Improve Real-time Forecast of Cascadia Tsunamis along U.S. West Coast

NASA Astrophysics Data System (ADS)

Wei, Y.; Titov, V. V.; Bernard, E. N.; Spillane, M. C.

2014-12-01

The tragedies of 2004 Sumatra and 2011 Tohoku tsunamis exposed the limits of our knowledge in preparing for devastating tsunamis, especially in the near field. The 1,100-km coastline of the Pacific coast of North America has tectonic and geological settings similar to Sumatra and Japan. The geological records unambiguously show that the Cascadia fault had caused devastating tsunamis in the past and this geological process will cause tsunamis in the future. Existing observational instruments along the Cascadia Subduction Zone are capable of providing tsunami data within minutes of tsunami generation. However, this strategy requires separation of the tsunami signals from the overwhelming high-frequency seismic waves produced during a strong earthquake- a real technical challenge for existing operational tsunami observational network. A new-generation of nano-resolution pressure sensors can provide high temporal resolution of the earthquake and tsunami signals without loosing precision. The nano-resolution pressure sensor offers a state-of the-science ability to separate earthquake vibrations and other oceanic noise from tsunami waveforms, paving the way for accurate, early warnings of local tsunamis. This breakthrough underwater technology has been tested and verified for a couple of micro-tsunami events (Paros et al., 2011). Real-time forecast of Cascadia tsunamis is becoming a possibility with the development of nano-tsunameter technology. The present study provides an investigation on optimizing the placement of these new sensors so that the forecast time can be shortened.. The presentation will cover the optimization of an observational array to quickly detect and forecast a tsunami generated by a strong Cascadia earthquake, including short and long rupture scenarios. Lessons learned from the 2011 Tohoku tsunami will be examined to demonstrate how we can improve the local forecast using the new technology We expect this study to provide useful guideline for future siting and deployment of the new-generation tsunameters. Driven by the new technology, we demonstrate scenarios of real-time forecast of Cascadia tsunami impact along the Pacific Northwest, as well as in the Puget Sound.

Time series analysis of reference crop evapotranspiration using soft computing techniques for Ganjam District, Odisha, India

NASA Astrophysics Data System (ADS)

Patra, S. R.

2017-12-01

Evapotranspiration (ET0) influences water resources and it is considered as a vital process in aridic hydrologic frameworks. It is one of the most important measure in finding the drought condition. Therefore, time series forecasting of evapotranspiration is very important in order to help the decision makers and water system mangers build up proper systems to sustain and manage water resources. Time series considers that -history repeats itself, hence by analysing the past values, better choices, or forecasts, can be carried out for the future. Ten years of ET0 data was used as a part of this study to make sure a satisfactory forecast of monthly values. In this study, three models: (ARIMA) mathematical model, artificial neural network model, support vector machine model are presented. These three models are used for forecasting monthly reference crop evapotranspiration based on ten years of past historical records (1991-2001) of measured evaporation at Ganjam region, Odisha, India without considering the climate data. The developed models will allow water resource managers to predict up to 12 months, making these predictions very useful to optimize the resources needed for effective water resources management. In this study multistep-ahead prediction is performed which is more complex and troublesome than onestep ahead. Our investigation proposed that nonlinear relationships may exist among the monthly indices, so that the ARIMA model might not be able to effectively extract the full relationship hidden in the historical data. Support vector machines are potentially helpful time series forecasting strategies on account of their strong nonlinear mapping capability and resistance to complexity in forecasting data. SVMs have great learning capability in time series modelling compared to ANN. For instance, the SVMs execute the structural risk minimization principle, which allows in better generalization as compared to neural networks that use the empirical risk minimization principle. The reliability of these computational models was analysed in light of simulation results and it was found out that SVM model produces better results among the three. The future research should be routed to extend the validation data set and to check the validity of our results on different areas with hybrid intelligence techniques.

US computer research networks: Current and future

NASA Technical Reports Server (NTRS)

Kratochvil, D.; Sood, D.; Verostko, A.

1989-01-01

During the last decade, NASA LeRC's Communication Program has conducted a series of telecommunications forecasting studies to project trends and requirements and to identify critical telecommunications technologies that must be developed to meet future requirements. The Government Networks Division of Contel Federal Systems has assisted NASA in these studies, and the current study builds upon these earlier efforts. The current major thrust of the NASA Communications Program is aimed at developing the high risk, advanced, communications satellite and terminal technologies required to significantly increase the capacity of future communications systems. Also, major new technological, economic, and social-political events and trends are now shaping the communications industry of the future. Therefore, a re-examination of future telecommunications needs and requirements is necessary to enable NASA to make management decisions in its Communications Program and to ensure the proper technologies and systems are addressed. This study, through a series of Task Orders, is helping NASA define the likely communication service needs and requirements of the future and thereby ensuring that the most appropriate technology developments are pursued.

An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index

NASA Astrophysics Data System (ADS)

Ali, Mumtaz; Deo, Ravinesh C.; Downs, Nathan J.; Maraseni, Tek

2018-07-01

Forecasting drought by means of the World Meteorological Organization-approved Standardized Precipitation Index (SPI) is considered to be a fundamental task to support socio-economic initiatives and effectively mitigating the climate-risk. This study aims to develop a robust drought modelling strategy to forecast multi-scalar SPI in drought-rich regions of Pakistan where statistically significant lagged combinations of antecedent SPI are used to forecast future SPI. With ensemble-Adaptive Neuro Fuzzy Inference System ('ensemble-ANFIS') executed via a 10-fold cross-validation procedure, a model is constructed by randomly partitioned input-target data. Resulting in 10-member ensemble-ANFIS outputs, judged by mean square error and correlation coefficient in the training period, the optimal forecasts are attained by the averaged simulations, and the model is benchmarked with M5 Model Tree and Minimax Probability Machine Regression (MPMR). The results show the proposed ensemble-ANFIS model's preciseness was notably better (in terms of the root mean square and mean absolute error including the Willmott's, Nash-Sutcliffe and Legates McCabe's index) for the 6- and 12- month compared to the 3-month forecasts as verified by the largest error proportions that registered in smallest error band. Applying 10-member simulations, ensemble-ANFIS model was validated for its ability to forecast severity (S), duration (D) and intensity (I) of drought (including the error bound). This enabled uncertainty between multi-models to be rationalized more efficiently, leading to a reduction in forecast error caused by stochasticity in drought behaviours. Through cross-validations at diverse sites, a geographic signature in modelled uncertainties was also calculated. Considering the superiority of ensemble-ANFIS approach and its ability to generate uncertainty-based information, the study advocates the versatility of a multi-model approach for drought-risk forecasting and its prime importance for estimating drought properties over confidence intervals to generate better information for strategic decision-making.

Parametric Cost Modeling of Space Missions Using the Develop New Projects (DMP) Implementation Process

NASA Technical Reports Server (NTRS)

Rosenberg, Leigh; Hihn, Jairus; Roust, Kevin; Warfield, Keith

2000-01-01

This paper presents an overview of a parametric cost model that has been built at JPL to estimate costs of future, deep space, robotic science missions. Due to the recent dramatic changes in JPL business practices brought about by an internal reengineering effort known as develop new products (DNP), high-level historic cost data is no longer considered analogous to future missions. Therefore, the historic data is of little value in forecasting costs for projects developed using the DNP process. This has lead to the development of an approach for obtaining expert opinion and also for combining actual data with expert opinion to provide a cost database for future missions. In addition, the DNP cost model has a maximum of objective cost drivers which reduces the likelihood of model input error. Version 2 is now under development which expands the model capabilities, links it more tightly with key design technical parameters, and is grounded in more rigorous statistical techniques. The challenges faced in building this model will be discussed, as well as it's background, development approach, status, validation, and future plans.

A Real-time Irrigation Forecasting System in Jiefangzha Irrigation District, China

NASA Astrophysics Data System (ADS)

Cong, Z.

2015-12-01

In order to improve the irrigation efficiency, we need to know when and how much to irrigate in real time. If we know the soil moisture content at this time, we can forecast the soil moisture content in the next days based on the rainfall forecasting and the crop evapotranspiration forecasting. Then the irrigation should be considered when the forecasting soil moisture content reaches to a threshold. Jiefangzha Irrigation District, a part of Hetao Irrigation District, is located in Inner Mongolia, China. The irrigated area of this irrigation district is about 140,000 ha mainly planting wheat, maize and sunflower. The annual precipitation is below 200mm, so the irrigation is necessary and the irrigation water comes from the Yellow river. We set up 10 sites with 4 TDR sensors at each site (20cm, 40cm, 60cm and 80cm depth) to monitor the soil moisture content. The weather forecasting data are downloaded from the website of European Centre for Medium-Range Weather Forecasts (ECMWF). The reference evapotranspiration is estimated based on FAO-Blaney-Criddle equation with only the air temperature from ECMWF. Then the crop water requirement is forecasted by the crop coefficient multiplying the reference evapotranspiration. Finally, the soil moisture content is forecasted based on soil water balance with the initial condition is set as the monitoring soil moisture content. When the soil moisture content reaches to a threshold, the irrigation warning will be announced. The irrigation mount can be estimated through three ways: (1) making the soil moisture content be equal to the field capacity; (2) making the soil moisture saturated; or (3) according to the irrigation quota. The forecasting period is 10 days. The system is developed according to B2C model with Java language. All the databases and the data analysis are carried out in the server. The customers can log in the website with their own username and password then get the information about the irrigation forecasting and other information about the irrigation. This system can be expanded in other irrigation districts. In future, it is even possible to upgrade the system for the mobile user.

Seasonal scale water deficit forecasting in Africa and the Middle East using NASA's Land Information System (LIS)

NASA Astrophysics Data System (ADS)

Peters-Lidard, C. D.; Arsenault, K. R.; Shukla, S.; Getirana, A.; McNally, A.; Koster, R. D.; Zaitchik, B. F.; Badr, H. S.; Roningen, J. M.; Kumar, S.; Funk, C. C.

2017-12-01

A seamless and effective water deficit monitoring and early warning system is critical for assessing food security in Africa and the Middle East. In this presentation, we report on the ongoing development and validation of a seasonal scale water deficit forecasting system based on NASA's Land Information System (LIS) and seasonal climate forecasts. First, our presentation will focus on the implementation and validation of drought and water availability monitoring products in the region. Next, it will focus on evaluating drought and water availability forecasts. Finally, details will be provided of our ongoing collaboration with end-user partners in the region (e.g., USAID's Famine Early Warning Systems Network, FEWS NET), on formulating meaningful early warning indicators, effective communication and seamless dissemination of the products through NASA's web-services. The water deficit forecasting system thus far incorporates NASA GMAO's Catchment and the Noah Multi-Physics (MP) LSMs. In addition, the LSMs' surface and subsurface runoff are routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics. To establish a climatology from 1981-2015, the two LSMs are driven by NASA/GMAO's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS and UCSB Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily rainfall dataset. Comparison of the models' energy and hydrological budgets with independent observations suggests that major droughts are well-reflected in the climatology. The system uses seasonal climate forecasts from NASA's GEOS-5 (the Goddard Earth Observing System Model-5) and NCEP's Climate Forecast System-2, and it produces forecasts of soil moisture, ET and streamflow out to 6 months in the future. Forecasts of those variables are formulated in terms of indicators to provide forecasts of drought and water availability in the region. Current work suggests that for the Blue Nile basin, (1) the combination of GEOS-5 and CFSv2 is equivalent in skill to the full North American Multimodel Ensemble (NMME); and (2) the seasonal water deficit forecasting system skill for both soil moisture and streamflow anomalies is greater than the standard Ensemble Streamflow Prediction (ESP) approach.